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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/common.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/common.proto', package='nvidia.clara.platform', syntax='proto3', serialized_options=_b('\n\031com.nvidia.clara.platformZ\004apis\252\002\032Nvidia.Clara.Platform.Grpc'), serialized_pb=_b('\n\"nvidia/clara/platform/common.proto\x12\x15nvidia.clara.platform\"\x1b\n\nIdentifier\x12\r\n\x05value\x18\x01 \x01(\t\"E\n\x07Version\x12\r\n\x05major\x18\x01 \x01(\x05\x12\r\n\x05minor\x18\x02 \x01(\x05\x12\r\n\x05patch\x18\x03 \x01(\x05\x12\r\n\x05label\x18\x04 \x01(\t\"X\n\rRequestHeader\x12\x33\n\x0b\x61pi_version\x18\x01 \x01(\x0b\x32\x1e.nvidia.clara.platform.Version\x12\x12\n\nuser_agent\x18\x02 \x01(\t\"0\n\x0eResponseHeader\x12\x0c\n\x04\x63ode\x18\x01 \x01(\x11\x12\x10\n\x08messages\x18\x02 \x03(\t\"\x1a\n\tTimestamp\x12\r\n\x05value\x18\x01 \x01(\x12\x42>\n\x19\x63om.nvidia.clara.platformZ\x04\x61pis\xaa\x02\x1aNvidia.Clara.Platform.Grpcb\x06proto3') ) _IDENTIFIER = _descriptor.Descriptor( name='Identifier', full_name='nvidia.clara.platform.Identifier', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.Identifier.value', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=61, serialized_end=88, ) _VERSION = _descriptor.Descriptor( name='Version', full_name='nvidia.clara.platform.Version', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='major', full_name='nvidia.clara.platform.Version.major', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='minor', full_name='nvidia.clara.platform.Version.minor', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='patch', full_name='nvidia.clara.platform.Version.patch', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='label', full_name='nvidia.clara.platform.Version.label', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=90, serialized_end=159, ) _REQUESTHEADER = _descriptor.Descriptor( name='RequestHeader', full_name='nvidia.clara.platform.RequestHeader', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='api_version', full_name='nvidia.clara.platform.RequestHeader.api_version', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='user_agent', full_name='nvidia.clara.platform.RequestHeader.user_agent', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=161, serialized_end=249, ) _RESPONSEHEADER = _descriptor.Descriptor( name='ResponseHeader', full_name='nvidia.clara.platform.ResponseHeader', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='code', full_name='nvidia.clara.platform.ResponseHeader.code', index=0, number=1, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='messages', full_name='nvidia.clara.platform.ResponseHeader.messages', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=251, serialized_end=299, ) _TIMESTAMP = _descriptor.Descriptor( name='Timestamp', full_name='nvidia.clara.platform.Timestamp', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.Timestamp.value', index=0, number=1, type=18, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=301, serialized_end=327, ) _REQUESTHEADER.fields_by_name['api_version'].message_type = _VERSION DESCRIPTOR.message_types_by_name['Identifier'] = _IDENTIFIER DESCRIPTOR.message_types_by_name['Version'] = _VERSION DESCRIPTOR.message_types_by_name['RequestHeader'] = _REQUESTHEADER DESCRIPTOR.message_types_by_name['ResponseHeader'] = _RESPONSEHEADER DESCRIPTOR.message_types_by_name['Timestamp'] = _TIMESTAMP _sym_db.RegisterFileDescriptor(DESCRIPTOR) Identifier = _reflection.GeneratedProtocolMessageType('Identifier', (_message.Message,), dict( DESCRIPTOR = _IDENTIFIER, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.Identifier) )) _sym_db.RegisterMessage(Identifier) Version = _reflection.GeneratedProtocolMessageType('Version', (_message.Message,), dict( DESCRIPTOR = _VERSION, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.Version) )) _sym_db.RegisterMessage(Version) RequestHeader = _reflection.GeneratedProtocolMessageType('RequestHeader', (_message.Message,), dict( DESCRIPTOR = _REQUESTHEADER, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.RequestHeader) )) _sym_db.RegisterMessage(RequestHeader) ResponseHeader = _reflection.GeneratedProtocolMessageType('ResponseHeader', (_message.Message,), dict( DESCRIPTOR = _RESPONSEHEADER, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ResponseHeader) )) _sym_db.RegisterMessage(ResponseHeader) Timestamp = _reflection.GeneratedProtocolMessageType('Timestamp', (_message.Message,), dict( DESCRIPTOR = _TIMESTAMP, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.Timestamp) )) _sym_db.RegisterMessage(Timestamp) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/common_pb2.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/pipelines.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from nvidia_clara.grpc import common_pb2 as nvidia_dot_clara_dot_platform_dot_common__pb2 from nvidia_clara.grpc.common_pb2 import * DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/pipelines.proto', package='nvidia.clara.platform', syntax='proto3', serialized_options=_b('\n\031com.nvidia.clara.platformZ\004apis\252\002\032Nvidia.Clara.Platform.Grpc'), serialized_pb=_b('\n%nvidia/clara/platform/pipelines.proto\x12\x15nvidia.clara.platform\x1a\"nvidia/clara/platform/common.proto\"7\n\x16PipelineDefinitionFile\x12\x0c\n\x04path\x18\x01 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, dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,], public_dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,]) _PIPELINEDEFINITIONFILE = _descriptor.Descriptor( name='PipelineDefinitionFile', full_name='nvidia.clara.platform.PipelineDefinitionFile', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='path', full_name='nvidia.clara.platform.PipelineDefinitionFile.path', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='content', full_name='nvidia.clara.platform.PipelineDefinitionFile.content', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=100, serialized_end=155, ) _PIPELINESADDMETADATAREQUEST_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=383, serialized_end=430, ) _PIPELINESADDMETADATAREQUEST = _descriptor.Descriptor( name='PipelinesAddMetadataRequest', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.PipelinesAddMetadataRequest.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESADDMETADATAREQUEST_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=158, serialized_end=430, ) _PIPELINESADDMETADATARESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=383, serialized_end=430, ) _PIPELINESADDMETADATARESPONSE = _descriptor.Descriptor( name='PipelinesAddMetadataResponse', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.PipelinesAddMetadataResponse.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESADDMETADATARESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=433, serialized_end=708, ) _PIPELINESCREATEREQUEST_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.PipelinesCreateRequest.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.PipelinesCreateRequest.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.PipelinesCreateRequest.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=383, serialized_end=430, ) _PIPELINESCREATEREQUEST = _descriptor.Descriptor( name='PipelinesCreateRequest', full_name='nvidia.clara.platform.PipelinesCreateRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesCreateRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='definition', full_name='nvidia.clara.platform.PipelinesCreateRequest.definition', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesCreateRequest.pipeline_id', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.PipelinesCreateRequest.metadata', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESCREATEREQUEST_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=711, serialized_end=1040, ) _PIPELINESCREATERESPONSE = _descriptor.Descriptor( name='PipelinesCreateResponse', full_name='nvidia.clara.platform.PipelinesCreateResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesCreateResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesCreateResponse.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1043, serialized_end=1179, ) _PIPELINESDETAILSREQUEST = _descriptor.Descriptor( name='PipelinesDetailsRequest', full_name='nvidia.clara.platform.PipelinesDetailsRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesDetailsRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesDetailsRequest.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1182, serialized_end=1317, ) _PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE = _descriptor.Descriptor( name='PipelineDagNode', full_name='nvidia.clara.platform.PipelinesDetailsResponse.PipelineDagNode', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.PipelinesDetailsResponse.PipelineDagNode.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='next', full_name='nvidia.clara.platform.PipelinesDetailsResponse.PipelineDagNode.next', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1699, serialized_end=1809, ) _PIPELINESDETAILSRESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.PipelinesDetailsResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.PipelinesDetailsResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.PipelinesDetailsResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=383, serialized_end=430, ) _PIPELINESDETAILSRESPONSE = _descriptor.Descriptor( name='PipelinesDetailsResponse', full_name='nvidia.clara.platform.PipelinesDetailsResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesDetailsResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesDetailsResponse.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.PipelinesDetailsResponse.name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='definition', full_name='nvidia.clara.platform.PipelinesDetailsResponse.definition', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='dag', full_name='nvidia.clara.platform.PipelinesDetailsResponse.dag', index=4, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.PipelinesDetailsResponse.metadata', index=5, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE, _PIPELINESDETAILSRESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1320, serialized_end=1858, ) _PIPELINESLISTREQUEST = _descriptor.Descriptor( name='PipelinesListRequest', full_name='nvidia.clara.platform.PipelinesListRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesListRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1860, serialized_end=1936, ) _PIPELINESLISTRESPONSE_PIPELINEDETAILS_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=383, serialized_end=430, ) _PIPELINESLISTRESPONSE_PIPELINEDETAILS = _descriptor.Descriptor( name='PipelineDetails', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails.pipeline_id', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.PipelinesListResponse.PipelineDetails.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESLISTRESPONSE_PIPELINEDETAILS_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2099, serialized_end=2329, ) _PIPELINESLISTRESPONSE = _descriptor.Descriptor( name='PipelinesListResponse', full_name='nvidia.clara.platform.PipelinesListResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesListResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='details', full_name='nvidia.clara.platform.PipelinesListResponse.details', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESLISTRESPONSE_PIPELINEDETAILS, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1939, serialized_end=2329, ) _PIPELINESREMOVEMETADATAREQUEST = _descriptor.Descriptor( name='PipelinesRemoveMetadataRequest', full_name='nvidia.clara.platform.PipelinesRemoveMetadataRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesRemoveMetadataRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesRemoveMetadataRequest.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='keys', full_name='nvidia.clara.platform.PipelinesRemoveMetadataRequest.keys', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2332, serialized_end=2488, ) _PIPELINESREMOVEMETADATARESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=383, serialized_end=430, ) _PIPELINESREMOVEMETADATARESPONSE = _descriptor.Descriptor( name='PipelinesRemoveMetadataResponse', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.PipelinesRemoveMetadataResponse.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_PIPELINESREMOVEMETADATARESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2491, serialized_end=2772, ) _PIPELINESREMOVEREQUEST = _descriptor.Descriptor( name='PipelinesRemoveRequest', full_name='nvidia.clara.platform.PipelinesRemoveRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesRemoveRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesRemoveRequest.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2775, serialized_end=2909, ) _PIPELINESREMOVERESPONSE = _descriptor.Descriptor( name='PipelinesRemoveResponse', full_name='nvidia.clara.platform.PipelinesRemoveResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesRemoveResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2911, serialized_end=2991, ) _PIPELINESUPDATEREQUEST = _descriptor.Descriptor( name='PipelinesUpdateRequest', full_name='nvidia.clara.platform.PipelinesUpdateRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesUpdateRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.PipelinesUpdateRequest.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='definition', full_name='nvidia.clara.platform.PipelinesUpdateRequest.definition', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2994, serialized_end=3195, ) _PIPELINESUPDATERESPONSE = _descriptor.Descriptor( name='PipelinesUpdateResponse', full_name='nvidia.clara.platform.PipelinesUpdateResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.PipelinesUpdateResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3197, serialized_end=3277, ) _PIPELINESADDMETADATAREQUEST_METADATAENTRY.containing_type = _PIPELINESADDMETADATAREQUEST _PIPELINESADDMETADATAREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESADDMETADATAREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESADDMETADATAREQUEST.fields_by_name['metadata'].message_type = _PIPELINESADDMETADATAREQUEST_METADATAENTRY _PIPELINESADDMETADATARESPONSE_METADATAENTRY.containing_type = _PIPELINESADDMETADATARESPONSE _PIPELINESADDMETADATARESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _PIPELINESADDMETADATARESPONSE.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESADDMETADATARESPONSE.fields_by_name['metadata'].message_type = _PIPELINESADDMETADATARESPONSE_METADATAENTRY _PIPELINESCREATEREQUEST_METADATAENTRY.containing_type = _PIPELINESCREATEREQUEST _PIPELINESCREATEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESCREATEREQUEST.fields_by_name['definition'].message_type = _PIPELINEDEFINITIONFILE _PIPELINESCREATEREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESCREATEREQUEST.fields_by_name['metadata'].message_type = _PIPELINESCREATEREQUEST_METADATAENTRY _PIPELINESCREATERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _PIPELINESCREATERESPONSE.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESDETAILSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESDETAILSREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE.fields_by_name['next'].message_type = _PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE _PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE.containing_type = _PIPELINESDETAILSRESPONSE _PIPELINESDETAILSRESPONSE_METADATAENTRY.containing_type = _PIPELINESDETAILSRESPONSE _PIPELINESDETAILSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _PIPELINESDETAILSRESPONSE.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESDETAILSRESPONSE.fields_by_name['definition'].message_type = _PIPELINEDEFINITIONFILE _PIPELINESDETAILSRESPONSE.fields_by_name['dag'].message_type = _PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE _PIPELINESDETAILSRESPONSE.fields_by_name['metadata'].message_type = _PIPELINESDETAILSRESPONSE_METADATAENTRY _PIPELINESLISTREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESLISTRESPONSE_PIPELINEDETAILS_METADATAENTRY.containing_type = _PIPELINESLISTRESPONSE_PIPELINEDETAILS _PIPELINESLISTRESPONSE_PIPELINEDETAILS.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESLISTRESPONSE_PIPELINEDETAILS.fields_by_name['metadata'].message_type = _PIPELINESLISTRESPONSE_PIPELINEDETAILS_METADATAENTRY _PIPELINESLISTRESPONSE_PIPELINEDETAILS.containing_type = _PIPELINESLISTRESPONSE _PIPELINESLISTRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _PIPELINESLISTRESPONSE.fields_by_name['details'].message_type = _PIPELINESLISTRESPONSE_PIPELINEDETAILS _PIPELINESREMOVEMETADATAREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESREMOVEMETADATAREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESREMOVEMETADATARESPONSE_METADATAENTRY.containing_type = _PIPELINESREMOVEMETADATARESPONSE _PIPELINESREMOVEMETADATARESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _PIPELINESREMOVEMETADATARESPONSE.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESREMOVEMETADATARESPONSE.fields_by_name['metadata'].message_type = _PIPELINESREMOVEMETADATARESPONSE_METADATAENTRY _PIPELINESREMOVEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESREMOVEREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESREMOVERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _PIPELINESUPDATEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _PIPELINESUPDATEREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _PIPELINESUPDATEREQUEST.fields_by_name['definition'].message_type = _PIPELINEDEFINITIONFILE _PIPELINESUPDATERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER DESCRIPTOR.message_types_by_name['PipelineDefinitionFile'] = _PIPELINEDEFINITIONFILE DESCRIPTOR.message_types_by_name['PipelinesAddMetadataRequest'] = _PIPELINESADDMETADATAREQUEST DESCRIPTOR.message_types_by_name['PipelinesAddMetadataResponse'] = _PIPELINESADDMETADATARESPONSE DESCRIPTOR.message_types_by_name['PipelinesCreateRequest'] = _PIPELINESCREATEREQUEST DESCRIPTOR.message_types_by_name['PipelinesCreateResponse'] = _PIPELINESCREATERESPONSE DESCRIPTOR.message_types_by_name['PipelinesDetailsRequest'] = _PIPELINESDETAILSREQUEST DESCRIPTOR.message_types_by_name['PipelinesDetailsResponse'] = _PIPELINESDETAILSRESPONSE DESCRIPTOR.message_types_by_name['PipelinesListRequest'] = _PIPELINESLISTREQUEST DESCRIPTOR.message_types_by_name['PipelinesListResponse'] = _PIPELINESLISTRESPONSE DESCRIPTOR.message_types_by_name['PipelinesRemoveMetadataRequest'] = _PIPELINESREMOVEMETADATAREQUEST DESCRIPTOR.message_types_by_name['PipelinesRemoveMetadataResponse'] = _PIPELINESREMOVEMETADATARESPONSE DESCRIPTOR.message_types_by_name['PipelinesRemoveRequest'] = _PIPELINESREMOVEREQUEST DESCRIPTOR.message_types_by_name['PipelinesRemoveResponse'] = _PIPELINESREMOVERESPONSE DESCRIPTOR.message_types_by_name['PipelinesUpdateRequest'] = _PIPELINESUPDATEREQUEST DESCRIPTOR.message_types_by_name['PipelinesUpdateResponse'] = _PIPELINESUPDATERESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) PipelineDefinitionFile = _reflection.GeneratedProtocolMessageType('PipelineDefinitionFile', (_message.Message,), dict( DESCRIPTOR = _PIPELINEDEFINITIONFILE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelineDefinitionFile) )) _sym_db.RegisterMessage(PipelineDefinitionFile) PipelinesAddMetadataRequest = _reflection.GeneratedProtocolMessageType('PipelinesAddMetadataRequest', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _PIPELINESADDMETADATAREQUEST_METADATAENTRY, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesAddMetadataRequest.MetadataEntry) )) , DESCRIPTOR = _PIPELINESADDMETADATAREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesAddMetadataRequest) )) _sym_db.RegisterMessage(PipelinesAddMetadataRequest) _sym_db.RegisterMessage(PipelinesAddMetadataRequest.MetadataEntry) PipelinesAddMetadataResponse = _reflection.GeneratedProtocolMessageType('PipelinesAddMetadataResponse', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _PIPELINESADDMETADATARESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesAddMetadataResponse.MetadataEntry) )) , DESCRIPTOR = _PIPELINESADDMETADATARESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesAddMetadataResponse) )) _sym_db.RegisterMessage(PipelinesAddMetadataResponse) _sym_db.RegisterMessage(PipelinesAddMetadataResponse.MetadataEntry) PipelinesCreateRequest = _reflection.GeneratedProtocolMessageType('PipelinesCreateRequest', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _PIPELINESCREATEREQUEST_METADATAENTRY, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesCreateRequest.MetadataEntry) )) , DESCRIPTOR = _PIPELINESCREATEREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesCreateRequest) )) _sym_db.RegisterMessage(PipelinesCreateRequest) _sym_db.RegisterMessage(PipelinesCreateRequest.MetadataEntry) PipelinesCreateResponse = _reflection.GeneratedProtocolMessageType('PipelinesCreateResponse', (_message.Message,), dict( DESCRIPTOR = _PIPELINESCREATERESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesCreateResponse) )) _sym_db.RegisterMessage(PipelinesCreateResponse) PipelinesDetailsRequest = _reflection.GeneratedProtocolMessageType('PipelinesDetailsRequest', (_message.Message,), dict( DESCRIPTOR = _PIPELINESDETAILSREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesDetailsRequest) )) _sym_db.RegisterMessage(PipelinesDetailsRequest) PipelinesDetailsResponse = _reflection.GeneratedProtocolMessageType('PipelinesDetailsResponse', (_message.Message,), dict( PipelineDagNode = _reflection.GeneratedProtocolMessageType('PipelineDagNode', (_message.Message,), dict( DESCRIPTOR = _PIPELINESDETAILSRESPONSE_PIPELINEDAGNODE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesDetailsResponse.PipelineDagNode) )) , MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _PIPELINESDETAILSRESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesDetailsResponse.MetadataEntry) )) , DESCRIPTOR = _PIPELINESDETAILSRESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesDetailsResponse) )) _sym_db.RegisterMessage(PipelinesDetailsResponse) _sym_db.RegisterMessage(PipelinesDetailsResponse.PipelineDagNode) _sym_db.RegisterMessage(PipelinesDetailsResponse.MetadataEntry) PipelinesListRequest = _reflection.GeneratedProtocolMessageType('PipelinesListRequest', (_message.Message,), dict( DESCRIPTOR = _PIPELINESLISTREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesListRequest) )) _sym_db.RegisterMessage(PipelinesListRequest) PipelinesListResponse = _reflection.GeneratedProtocolMessageType('PipelinesListResponse', (_message.Message,), dict( PipelineDetails = _reflection.GeneratedProtocolMessageType('PipelineDetails', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _PIPELINESLISTRESPONSE_PIPELINEDETAILS_METADATAENTRY, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesListResponse.PipelineDetails.MetadataEntry) )) , DESCRIPTOR = _PIPELINESLISTRESPONSE_PIPELINEDETAILS, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesListResponse.PipelineDetails) )) , DESCRIPTOR = _PIPELINESLISTRESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesListResponse) )) _sym_db.RegisterMessage(PipelinesListResponse) _sym_db.RegisterMessage(PipelinesListResponse.PipelineDetails) _sym_db.RegisterMessage(PipelinesListResponse.PipelineDetails.MetadataEntry) PipelinesRemoveMetadataRequest = _reflection.GeneratedProtocolMessageType('PipelinesRemoveMetadataRequest', (_message.Message,), dict( DESCRIPTOR = _PIPELINESREMOVEMETADATAREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesRemoveMetadataRequest) )) _sym_db.RegisterMessage(PipelinesRemoveMetadataRequest) PipelinesRemoveMetadataResponse = _reflection.GeneratedProtocolMessageType('PipelinesRemoveMetadataResponse', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _PIPELINESREMOVEMETADATARESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesRemoveMetadataResponse.MetadataEntry) )) , DESCRIPTOR = _PIPELINESREMOVEMETADATARESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesRemoveMetadataResponse) )) _sym_db.RegisterMessage(PipelinesRemoveMetadataResponse) _sym_db.RegisterMessage(PipelinesRemoveMetadataResponse.MetadataEntry) PipelinesRemoveRequest = _reflection.GeneratedProtocolMessageType('PipelinesRemoveRequest', (_message.Message,), dict( DESCRIPTOR = _PIPELINESREMOVEREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesRemoveRequest) )) _sym_db.RegisterMessage(PipelinesRemoveRequest) PipelinesRemoveResponse = _reflection.GeneratedProtocolMessageType('PipelinesRemoveResponse', (_message.Message,), dict( DESCRIPTOR = _PIPELINESREMOVERESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesRemoveResponse) )) _sym_db.RegisterMessage(PipelinesRemoveResponse) PipelinesUpdateRequest = _reflection.GeneratedProtocolMessageType('PipelinesUpdateRequest', (_message.Message,), dict( DESCRIPTOR = _PIPELINESUPDATEREQUEST, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesUpdateRequest) )) _sym_db.RegisterMessage(PipelinesUpdateRequest) PipelinesUpdateResponse = _reflection.GeneratedProtocolMessageType('PipelinesUpdateResponse', (_message.Message,), dict( DESCRIPTOR = _PIPELINESUPDATERESPONSE, __module__ = 'nvidia.clara.platform.pipelines_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.PipelinesUpdateResponse) )) _sym_db.RegisterMessage(PipelinesUpdateResponse) DESCRIPTOR._options = None _PIPELINESADDMETADATAREQUEST_METADATAENTRY._options = None _PIPELINESADDMETADATARESPONSE_METADATAENTRY._options = None _PIPELINESCREATEREQUEST_METADATAENTRY._options = None _PIPELINESDETAILSRESPONSE_METADATAENTRY._options = None _PIPELINESLISTRESPONSE_PIPELINEDETAILS_METADATAENTRY._options = None _PIPELINESREMOVEMETADATARESPONSE_METADATAENTRY._options = None _PIPELINES = _descriptor.ServiceDescriptor( name='Pipelines', full_name='nvidia.clara.platform.Pipelines', file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=3280, serialized_end=4070, methods=[ _descriptor.MethodDescriptor( name='AddMetadata', full_name='nvidia.clara.platform.Pipelines.AddMetadata', index=0, containing_service=None, input_type=_PIPELINESADDMETADATAREQUEST, output_type=_PIPELINESADDMETADATARESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Create', full_name='nvidia.clara.platform.Pipelines.Create', index=1, containing_service=None, input_type=_PIPELINESCREATEREQUEST, output_type=_PIPELINESCREATERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Details', full_name='nvidia.clara.platform.Pipelines.Details', index=2, containing_service=None, input_type=_PIPELINESDETAILSREQUEST, output_type=_PIPELINESDETAILSRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='List', full_name='nvidia.clara.platform.Pipelines.List', index=3, containing_service=None, input_type=_PIPELINESLISTREQUEST, output_type=_PIPELINESLISTRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Remove', full_name='nvidia.clara.platform.Pipelines.Remove', index=4, containing_service=None, input_type=_PIPELINESREMOVEREQUEST, output_type=_PIPELINESREMOVERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='RemoveMetadata', full_name='nvidia.clara.platform.Pipelines.RemoveMetadata', index=5, containing_service=None, input_type=_PIPELINESREMOVEMETADATAREQUEST, output_type=_PIPELINESREMOVEMETADATARESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Update', full_name='nvidia.clara.platform.Pipelines.Update', index=6, containing_service=None, input_type=_PIPELINESUPDATEREQUEST, output_type=_PIPELINESUPDATERESPONSE, serialized_options=None, ), ]) _sym_db.RegisterServiceDescriptor(_PIPELINES) DESCRIPTOR.services_by_name['Pipelines'] = _PIPELINES # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/pipelines_pb2.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/node-monitor/metrics.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from nvidia_clara.grpc import common_pb2 as nvidia_dot_clara_dot_platform_dot_common__pb2 from nvidia_clara.grpc.common_pb2 import * DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/node-monitor/metrics.proto', package='nvidia.clara.platform.node_monitor', syntax='proto3', serialized_options=_b('\n%com.nvidia.clara.platform.nodemonitorZ\004apis\252\002&Nvidia.Clara.Platform.NodeMonitor.Grpc'), serialized_pb=_b('\n0nvidia/clara/platform/node-monitor/metrics.proto\x12\"nvidia.clara.platform.node_monitor\x1a\"nvidia/clara/platform/common.proto\"\xbb\x02\n\nGpuDetails\x12\x11\n\tdevice_id\x18\x01 \x01(\x05\x12G\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x39.nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics\x12\x33\n\ttimestamp\x18\x03 \x01(\x0b\x32 .nvidia.clara.platform.Timestamp\x1a\x9b\x01\n\nGpuMetrics\x12\x1a\n\x12memory_utilization\x18\x01 \x01(\x02\x12\x17\n\x0fgpu_utilization\x18\x02 \x01(\x02\x12\x12\n\nfree_bar_1\x18\x03 \x01(\x03\x12\x12\n\nused_bar_1\x18\x04 \x01(\x03\x12\x17\n\x0f\x66ree_gpu_memory\x18\x05 \x01(\x03\x12\x17\n\x0fused_gpu_memory\x18\x06 \x01(\x03\"P\n\x18MonitorGpuMetricsRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\"\x97\x01\n\x19MonitorGpuMetricsResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x43\n\x0bgpu_details\x18\x02 \x03(\x0b\x32..nvidia.clara.platform.node_monitor.GpuDetails2\x97\x01\n\x07Monitor\x12\x8b\x01\n\nGpuMetrics\x12<.nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest\x1a=.nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse0\x01\x42V\n%com.nvidia.clara.platform.nodemonitorZ\x04\x61pis\xaa\x02&Nvidia.Clara.Platform.NodeMonitor.GrpcP\x00\x62\x06proto3') , dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,], public_dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,]) _GPUDETAILS_GPUMETRICS = _descriptor.Descriptor( name='GpuMetrics', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='memory_utilization', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.memory_utilization', index=0, number=1, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='gpu_utilization', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.gpu_utilization', index=1, number=2, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='free_bar_1', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.free_bar_1', index=2, number=3, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='used_bar_1', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.used_bar_1', index=3, number=4, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='free_gpu_memory', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.free_gpu_memory', index=4, number=5, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='used_gpu_memory', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.used_gpu_memory', index=5, number=6, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=285, serialized_end=440, ) _GPUDETAILS = _descriptor.Descriptor( name='GpuDetails', full_name='nvidia.clara.platform.node_monitor.GpuDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='device_id', full_name='nvidia.clara.platform.node_monitor.GpuDetails.device_id', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='data', full_name='nvidia.clara.platform.node_monitor.GpuDetails.data', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp', full_name='nvidia.clara.platform.node_monitor.GpuDetails.timestamp', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_GPUDETAILS_GPUMETRICS, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=125, serialized_end=440, ) _MONITORGPUMETRICSREQUEST = _descriptor.Descriptor( name='MonitorGpuMetricsRequest', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=442, serialized_end=522, ) _MONITORGPUMETRICSRESPONSE = _descriptor.Descriptor( name='MonitorGpuMetricsResponse', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='gpu_details', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse.gpu_details', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=525, serialized_end=676, ) _GPUDETAILS_GPUMETRICS.containing_type = _GPUDETAILS _GPUDETAILS.fields_by_name['data'].message_type = _GPUDETAILS_GPUMETRICS _GPUDETAILS.fields_by_name['timestamp'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _MONITORGPUMETRICSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MONITORGPUMETRICSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MONITORGPUMETRICSRESPONSE.fields_by_name['gpu_details'].message_type = _GPUDETAILS DESCRIPTOR.message_types_by_name['GpuDetails'] = _GPUDETAILS DESCRIPTOR.message_types_by_name['MonitorGpuMetricsRequest'] = _MONITORGPUMETRICSREQUEST DESCRIPTOR.message_types_by_name['MonitorGpuMetricsResponse'] = _MONITORGPUMETRICSRESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) GpuDetails = _reflection.GeneratedProtocolMessageType('GpuDetails', (_message.Message,), dict( GpuMetrics = _reflection.GeneratedProtocolMessageType('GpuMetrics', (_message.Message,), dict( DESCRIPTOR = _GPUDETAILS_GPUMETRICS, __module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics) )) , DESCRIPTOR = _GPUDETAILS, __module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.GpuDetails) )) _sym_db.RegisterMessage(GpuDetails) _sym_db.RegisterMessage(GpuDetails.GpuMetrics) MonitorGpuMetricsRequest = _reflection.GeneratedProtocolMessageType('MonitorGpuMetricsRequest', (_message.Message,), dict( DESCRIPTOR = _MONITORGPUMETRICSREQUEST, __module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest) )) _sym_db.RegisterMessage(MonitorGpuMetricsRequest) MonitorGpuMetricsResponse = _reflection.GeneratedProtocolMessageType('MonitorGpuMetricsResponse', (_message.Message,), dict( DESCRIPTOR = _MONITORGPUMETRICSRESPONSE, __module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse) )) _sym_db.RegisterMessage(MonitorGpuMetricsResponse) DESCRIPTOR._options = None _MONITOR = _descriptor.ServiceDescriptor( name='Monitor', full_name='nvidia.clara.platform.node_monitor.Monitor', file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=679, serialized_end=830, methods=[ _descriptor.MethodDescriptor( name='GpuMetrics', full_name='nvidia.clara.platform.node_monitor.Monitor.GpuMetrics', index=0, containing_service=None, input_type=_MONITORGPUMETRICSREQUEST, output_type=_MONITORGPUMETRICSRESPONSE, serialized_options=None, ), ]) _sym_db.RegisterServiceDescriptor(_MONITOR) DESCRIPTOR.services_by_name['Monitor'] = _MONITOR # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/metrics_pb2.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc from nvidia_clara.grpc import clara_pb2 as nvidia_dot_clara_dot_platform_dot_clara__pb2 class ClaraStub(object): # missing associated documentation comment in .proto file pass def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.Stop = channel.unary_unary( '/nvidia.clara.platform.Clara/Stop', request_serializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraStopRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraStopResponse.FromString, ) self.Utilization = channel.unary_stream( '/nvidia.clara.platform.Clara/Utilization', request_serializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraUtilizationRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraUtilizationResponse.FromString, ) self.Version = channel.unary_unary( '/nvidia.clara.platform.Clara/Version', request_serializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraVersionRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraVersionResponse.FromString, ) class ClaraServicer(object): # missing associated documentation comment in .proto file pass def Stop(self, request, context): """Requests the termination of Clara Platform Server and associated resource cleanup. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Utilization(self, request, context): """Requests utilization data for all Clara Platform managed GPUs. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Version(self, request, context): """Requests version information from Clara Platform Server. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_ClaraServicer_to_server(servicer, server): rpc_method_handlers = { 'Stop': grpc.unary_unary_rpc_method_handler( servicer.Stop, request_deserializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraStopRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraStopResponse.SerializeToString, ), 'Utilization': grpc.unary_stream_rpc_method_handler( servicer.Utilization, request_deserializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraUtilizationRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraUtilizationResponse.SerializeToString, ), 'Version': grpc.unary_unary_rpc_method_handler( servicer.Version, request_deserializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraVersionRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_clara__pb2.ClaraVersionResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'nvidia.clara.platform.Clara', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,))
clara-platform-python-client-main
nvidia_clara/grpc/clara_pb2_grpc.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
clara-platform-python-client-main
nvidia_clara/grpc/__init__.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/models.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from nvidia_clara.grpc import common_pb2 as nvidia_dot_clara_dot_platform_dot_common__pb2 from nvidia_clara.grpc.common_pb2 import * DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/models.proto', package='nvidia.clara.platform', syntax='proto3', serialized_options=_b('\n\031com.nvidia.clara.platformZ\004apis\252\002\032Nvidia.Clara.Platform.Grpc'), serialized_pb=_b('\n\"nvidia/clara/platform/models.proto\x12\x15nvidia.clara.platform\x1a\"nvidia/clara/platform/common.proto\"\x81\x01\n\x13ModelCatalogDetails\x12\x35\n\ncatalog_id\x18\x01 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\x12\x33\n\x06models\x18\x02 \x03(\x0b\x32#.nvidia.clara.platform.ModelDetails\"\xf7\x01\n\x0cModelDetails\x12\x33\n\x08model_id\x18\x01 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\x12\x0c\n\x04name\x18\x02 \x01(\t\x12.\n\x04type\x18\x04 \x01(\x0e\x32 .nvidia.clara.platform.ModelType\x12\x43\n\x08metadata\x18\x08 \x03(\x0b\x32\x31.nvidia.clara.platform.ModelDetails.MetadataEntry\x1a/\n\rMetadataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\x87\x02\n\x18ModelsAddMetadataRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\x33\n\x08model_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\x12O\n\x08metadata\x18\x03 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\x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\"\x8d\x01\n\x1cModelsCreateInstanceResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x36\n\x0binstance_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x89\x01\n\x1aModelsDeleteCatalogRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\x35\n\ncatalog_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x8b\x01\n\x1bModelsDeleteCatalogResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x35\n\ncatalog_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x8b\x01\n\x1bModelsDeleteInstanceRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\x36\n\x0binstance_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x8d\x01\n\x1cModelsDeleteInstanceResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x36\n\x0binstance_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x85\x01\n\x18ModelsDeleteModelRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\x33\n\x08model_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x87\x01\n\x19ModelsDeleteModelResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x33\n\x08model_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x87\x01\n\x1aModelsDownloadModelRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\x33\n\x08model_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"\x98\x01\n\x1bModelsDownloadModelResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x34\n\x07\x64\x65tails\x18\x02 \x01(\x0b\x32#.nvidia.clara.platform.ModelDetails\x12\x0c\n\x04\x64\x61ta\x18\x04 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, dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,], public_dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,]) _MODELTYPE = _descriptor.EnumDescriptor( name='ModelType', full_name='nvidia.clara.platform.ModelType', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='MODEL_TYPE_UNKNOWN', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MODEL_TYPE_TENSOR_FLOW', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MODEL_TYPE_TENSOR_RT', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MODEL_TYPE_PY_TORCH', index=3, number=3, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=5347, serialized_end=5461, ) _sym_db.RegisterEnumDescriptor(_MODELTYPE) ModelType = enum_type_wrapper.EnumTypeWrapper(_MODELTYPE) MODEL_TYPE_UNKNOWN = 0 MODEL_TYPE_TENSOR_FLOW = 1 MODEL_TYPE_TENSOR_RT = 2 MODEL_TYPE_PY_TORCH = 3 _MODELCATALOGDETAILS = _descriptor.Descriptor( name='ModelCatalogDetails', full_name='nvidia.clara.platform.ModelCatalogDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelCatalogDetails.catalog_id', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='models', full_name='nvidia.clara.platform.ModelCatalogDetails.models', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=98, serialized_end=227, ) _MODELDETAILS_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.ModelDetails.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.ModelDetails.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.ModelDetails.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=430, serialized_end=477, ) _MODELDETAILS = _descriptor.Descriptor( name='ModelDetails', full_name='nvidia.clara.platform.ModelDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelDetails.model_id', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.ModelDetails.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='type', full_name='nvidia.clara.platform.ModelDetails.type', index=2, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.ModelDetails.metadata', index=3, number=8, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_MODELDETAILS_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=230, serialized_end=477, ) _MODELSADDMETADATAREQUEST_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.ModelsAddMetadataRequest.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.ModelsAddMetadataRequest.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.ModelsAddMetadataRequest.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=430, serialized_end=477, ) _MODELSADDMETADATAREQUEST = _descriptor.Descriptor( name='ModelsAddMetadataRequest', full_name='nvidia.clara.platform.ModelsAddMetadataRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsAddMetadataRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsAddMetadataRequest.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.ModelsAddMetadataRequest.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_MODELSADDMETADATAREQUEST_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=480, serialized_end=743, ) _MODELSADDMETADATARESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.ModelsAddMetadataResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.ModelsAddMetadataResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.ModelsAddMetadataResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=430, serialized_end=477, ) _MODELSADDMETADATARESPONSE = _descriptor.Descriptor( name='ModelsAddMetadataResponse', full_name='nvidia.clara.platform.ModelsAddMetadataResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsAddMetadataResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsAddMetadataResponse.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.ModelsAddMetadataResponse.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_MODELSADDMETADATARESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=746, serialized_end=1012, ) _MODELSCREATECATALOGREQUEST = _descriptor.Descriptor( name='ModelsCreateCatalogRequest', full_name='nvidia.clara.platform.ModelsCreateCatalogRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsCreateCatalogRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1014, serialized_end=1096, ) _MODELSCREATECATALOGRESPONSE = _descriptor.Descriptor( name='ModelsCreateCatalogResponse', full_name='nvidia.clara.platform.ModelsCreateCatalogResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsCreateCatalogResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsCreateCatalogResponse.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1099, serialized_end=1238, ) _MODELSCREATEINSTANCEREQUEST = _descriptor.Descriptor( name='ModelsCreateInstanceRequest', full_name='nvidia.clara.platform.ModelsCreateInstanceRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsCreateInstanceRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1240, serialized_end=1323, ) _MODELSCREATEINSTANCERESPONSE = _descriptor.Descriptor( name='ModelsCreateInstanceResponse', full_name='nvidia.clara.platform.ModelsCreateInstanceResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsCreateInstanceResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsCreateInstanceResponse.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1326, serialized_end=1467, ) _MODELSDELETECATALOGREQUEST = _descriptor.Descriptor( name='ModelsDeleteCatalogRequest', full_name='nvidia.clara.platform.ModelsDeleteCatalogRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDeleteCatalogRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsDeleteCatalogRequest.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1470, serialized_end=1607, ) _MODELSDELETECATALOGRESPONSE = _descriptor.Descriptor( name='ModelsDeleteCatalogResponse', full_name='nvidia.clara.platform.ModelsDeleteCatalogResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDeleteCatalogResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsDeleteCatalogResponse.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1610, serialized_end=1749, ) _MODELSDELETEINSTANCEREQUEST = _descriptor.Descriptor( name='ModelsDeleteInstanceRequest', full_name='nvidia.clara.platform.ModelsDeleteInstanceRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDeleteInstanceRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsDeleteInstanceRequest.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1752, serialized_end=1891, ) _MODELSDELETEINSTANCERESPONSE = _descriptor.Descriptor( name='ModelsDeleteInstanceResponse', full_name='nvidia.clara.platform.ModelsDeleteInstanceResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDeleteInstanceResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsDeleteInstanceResponse.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1894, serialized_end=2035, ) _MODELSDELETEMODELREQUEST = _descriptor.Descriptor( name='ModelsDeleteModelRequest', full_name='nvidia.clara.platform.ModelsDeleteModelRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDeleteModelRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsDeleteModelRequest.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2038, serialized_end=2171, ) _MODELSDELETEMODELRESPONSE = _descriptor.Descriptor( name='ModelsDeleteModelResponse', full_name='nvidia.clara.platform.ModelsDeleteModelResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDeleteModelResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsDeleteModelResponse.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2174, serialized_end=2309, ) _MODELSDOWNLOADMODELREQUEST = _descriptor.Descriptor( name='ModelsDownloadModelRequest', full_name='nvidia.clara.platform.ModelsDownloadModelRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDownloadModelRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsDownloadModelRequest.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2312, serialized_end=2447, ) _MODELSDOWNLOADMODELRESPONSE = _descriptor.Descriptor( name='ModelsDownloadModelResponse', full_name='nvidia.clara.platform.ModelsDownloadModelResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsDownloadModelResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='details', full_name='nvidia.clara.platform.ModelsDownloadModelResponse.details', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='data', full_name='nvidia.clara.platform.ModelsDownloadModelResponse.data', index=2, number=4, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2450, serialized_end=2602, ) _MODELSLISTCATALOGSREQUEST = _descriptor.Descriptor( name='ModelsListCatalogsRequest', full_name='nvidia.clara.platform.ModelsListCatalogsRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsListCatalogsRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2604, serialized_end=2685, ) _MODELSLISTCATALOGSRESPONSE = _descriptor.Descriptor( name='ModelsListCatalogsResponse', full_name='nvidia.clara.platform.ModelsListCatalogsResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsListCatalogsResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalogs', full_name='nvidia.clara.platform.ModelsListCatalogsResponse.catalogs', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2688, serialized_end=2833, ) _MODELSLISTINSTANCESREQUEST = _descriptor.Descriptor( name='ModelsListInstancesRequest', full_name='nvidia.clara.platform.ModelsListInstancesRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsListInstancesRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2835, serialized_end=2917, ) _MODELSLISTINSTANCESRESPONSE = _descriptor.Descriptor( name='ModelsListInstancesResponse', full_name='nvidia.clara.platform.ModelsListInstancesResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsListInstancesResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instances', full_name='nvidia.clara.platform.ModelsListInstancesResponse.instances', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2920, serialized_end=3067, ) _MODELSLISTMODELSREQUEST = _descriptor.Descriptor( name='ModelsListModelsRequest', full_name='nvidia.clara.platform.ModelsListModelsRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsListModelsRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3069, serialized_end=3148, ) _MODELSLISTMODELSRESPONSE = _descriptor.Descriptor( name='ModelsListModelsResponse', full_name='nvidia.clara.platform.ModelsListModelsResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsListModelsResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='models', full_name='nvidia.clara.platform.ModelsListModelsResponse.models', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3151, serialized_end=3285, ) _MODELSREADCATALOGREQUEST = _descriptor.Descriptor( name='ModelsReadCatalogRequest', full_name='nvidia.clara.platform.ModelsReadCatalogRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsReadCatalogRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsReadCatalogRequest.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3288, serialized_end=3423, ) _MODELSREADCATALOGRESPONSE = _descriptor.Descriptor( name='ModelsReadCatalogResponse', full_name='nvidia.clara.platform.ModelsReadCatalogResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsReadCatalogResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsReadCatalogResponse.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='models', full_name='nvidia.clara.platform.ModelsReadCatalogResponse.models', index=2, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3426, serialized_end=3616, ) _MODELSREADINSTANCEREQUEST = _descriptor.Descriptor( name='ModelsReadInstanceRequest', full_name='nvidia.clara.platform.ModelsReadInstanceRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsReadInstanceRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsReadInstanceRequest.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3619, serialized_end=3756, ) _MODELSREADINSTANCERESPONSE = _descriptor.Descriptor( name='ModelsReadInstanceResponse', full_name='nvidia.clara.platform.ModelsReadInstanceResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsReadInstanceResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsReadInstanceResponse.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='models', full_name='nvidia.clara.platform.ModelsReadInstanceResponse.models', index=2, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3759, serialized_end=3951, ) _MODELSREMOVEMETADATAREQUEST = _descriptor.Descriptor( name='ModelsRemoveMetadataRequest', full_name='nvidia.clara.platform.ModelsRemoveMetadataRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsRemoveMetadataRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsRemoveMetadataRequest.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='keys', full_name='nvidia.clara.platform.ModelsRemoveMetadataRequest.keys', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3954, serialized_end=4104, ) _MODELSREMOVEMETADATARESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=430, serialized_end=477, ) _MODELSREMOVEMETADATARESPONSE = _descriptor.Descriptor( name='ModelsRemoveMetadataResponse', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_id', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse.model_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.ModelsRemoveMetadataResponse.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_MODELSREMOVEMETADATARESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4107, serialized_end=4379, ) _MODELSUPDATECATALOGREQUEST = _descriptor.Descriptor( name='ModelsUpdateCatalogRequest', full_name='nvidia.clara.platform.ModelsUpdateCatalogRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsUpdateCatalogRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsUpdateCatalogRequest.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_ids', full_name='nvidia.clara.platform.ModelsUpdateCatalogRequest.model_ids', index=2, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4382, serialized_end=4573, ) _MODELSUPDATECATALOGRESPONSE = _descriptor.Descriptor( name='ModelsUpdateCatalogResponse', full_name='nvidia.clara.platform.ModelsUpdateCatalogResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsUpdateCatalogResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='catalog_id', full_name='nvidia.clara.platform.ModelsUpdateCatalogResponse.catalog_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4576, serialized_end=4715, ) _MODELSUPDATEINSTANCEREQUEST = _descriptor.Descriptor( name='ModelsUpdateInstanceRequest', full_name='nvidia.clara.platform.ModelsUpdateInstanceRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsUpdateInstanceRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsUpdateInstanceRequest.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='model_ids', full_name='nvidia.clara.platform.ModelsUpdateInstanceRequest.model_ids', index=2, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4718, serialized_end=4911, ) _MODELSUPDATEINSTANCERESPONSE = _descriptor.Descriptor( name='ModelsUpdateInstanceResponse', full_name='nvidia.clara.platform.ModelsUpdateInstanceResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsUpdateInstanceResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='instance_id', full_name='nvidia.clara.platform.ModelsUpdateInstanceResponse.instance_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4914, serialized_end=5055, ) _MODELSUPLOADMODELREQUEST = _descriptor.Descriptor( name='ModelsUploadModelRequest', full_name='nvidia.clara.platform.ModelsUploadModelRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsUploadModelRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='details', full_name='nvidia.clara.platform.ModelsUploadModelRequest.details', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='data', full_name='nvidia.clara.platform.ModelsUploadModelRequest.data', index=2, number=4, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=5058, serialized_end=5206, ) _MODELSUPLOADMODELRESPONSE = _descriptor.Descriptor( name='ModelsUploadModelResponse', full_name='nvidia.clara.platform.ModelsUploadModelResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ModelsUploadModelResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='details', full_name='nvidia.clara.platform.ModelsUploadModelResponse.details', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=5209, serialized_end=5345, ) _MODELCATALOGDETAILS.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELCATALOGDETAILS.fields_by_name['models'].message_type = _MODELDETAILS _MODELDETAILS_METADATAENTRY.containing_type = _MODELDETAILS _MODELDETAILS.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELDETAILS.fields_by_name['type'].enum_type = _MODELTYPE _MODELDETAILS.fields_by_name['metadata'].message_type = _MODELDETAILS_METADATAENTRY _MODELSADDMETADATAREQUEST_METADATAENTRY.containing_type = _MODELSADDMETADATAREQUEST _MODELSADDMETADATAREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSADDMETADATAREQUEST.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSADDMETADATAREQUEST.fields_by_name['metadata'].message_type = _MODELSADDMETADATAREQUEST_METADATAENTRY _MODELSADDMETADATARESPONSE_METADATAENTRY.containing_type = _MODELSADDMETADATARESPONSE _MODELSADDMETADATARESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSADDMETADATARESPONSE.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSADDMETADATARESPONSE.fields_by_name['metadata'].message_type = _MODELSADDMETADATARESPONSE_METADATAENTRY _MODELSCREATECATALOGREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSCREATECATALOGRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSCREATECATALOGRESPONSE.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSCREATEINSTANCEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSCREATEINSTANCERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSCREATEINSTANCERESPONSE.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDELETECATALOGREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSDELETECATALOGREQUEST.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDELETECATALOGRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSDELETECATALOGRESPONSE.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDELETEINSTANCEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSDELETEINSTANCEREQUEST.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDELETEINSTANCERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSDELETEINSTANCERESPONSE.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDELETEMODELREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSDELETEMODELREQUEST.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDELETEMODELRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSDELETEMODELRESPONSE.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDOWNLOADMODELREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSDOWNLOADMODELREQUEST.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSDOWNLOADMODELRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSDOWNLOADMODELRESPONSE.fields_by_name['details'].message_type = _MODELDETAILS _MODELSLISTCATALOGSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSLISTCATALOGSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSLISTCATALOGSRESPONSE.fields_by_name['catalogs'].message_type = _MODELCATALOGDETAILS _MODELSLISTINSTANCESREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSLISTINSTANCESRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSLISTINSTANCESRESPONSE.fields_by_name['instances'].message_type = _MODELCATALOGDETAILS _MODELSLISTMODELSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSLISTMODELSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSLISTMODELSRESPONSE.fields_by_name['models'].message_type = _MODELDETAILS _MODELSREADCATALOGREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSREADCATALOGREQUEST.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSREADCATALOGRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSREADCATALOGRESPONSE.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSREADCATALOGRESPONSE.fields_by_name['models'].message_type = _MODELDETAILS _MODELSREADINSTANCEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSREADINSTANCEREQUEST.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSREADINSTANCERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSREADINSTANCERESPONSE.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSREADINSTANCERESPONSE.fields_by_name['models'].message_type = _MODELDETAILS _MODELSREMOVEMETADATAREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSREMOVEMETADATAREQUEST.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSREMOVEMETADATARESPONSE_METADATAENTRY.containing_type = _MODELSREMOVEMETADATARESPONSE _MODELSREMOVEMETADATARESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSREMOVEMETADATARESPONSE.fields_by_name['model_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSREMOVEMETADATARESPONSE.fields_by_name['metadata'].message_type = _MODELSREMOVEMETADATARESPONSE_METADATAENTRY _MODELSUPDATECATALOGREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSUPDATECATALOGREQUEST.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSUPDATECATALOGREQUEST.fields_by_name['model_ids'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSUPDATECATALOGRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSUPDATECATALOGRESPONSE.fields_by_name['catalog_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSUPDATEINSTANCEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSUPDATEINSTANCEREQUEST.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSUPDATEINSTANCEREQUEST.fields_by_name['model_ids'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSUPDATEINSTANCERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSUPDATEINSTANCERESPONSE.fields_by_name['instance_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _MODELSUPLOADMODELREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _MODELSUPLOADMODELREQUEST.fields_by_name['details'].message_type = _MODELDETAILS _MODELSUPLOADMODELRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _MODELSUPLOADMODELRESPONSE.fields_by_name['details'].message_type = _MODELDETAILS DESCRIPTOR.message_types_by_name['ModelCatalogDetails'] = _MODELCATALOGDETAILS DESCRIPTOR.message_types_by_name['ModelDetails'] = _MODELDETAILS DESCRIPTOR.message_types_by_name['ModelsAddMetadataRequest'] = _MODELSADDMETADATAREQUEST DESCRIPTOR.message_types_by_name['ModelsAddMetadataResponse'] = _MODELSADDMETADATARESPONSE DESCRIPTOR.message_types_by_name['ModelsCreateCatalogRequest'] = _MODELSCREATECATALOGREQUEST DESCRIPTOR.message_types_by_name['ModelsCreateCatalogResponse'] = _MODELSCREATECATALOGRESPONSE DESCRIPTOR.message_types_by_name['ModelsCreateInstanceRequest'] = _MODELSCREATEINSTANCEREQUEST DESCRIPTOR.message_types_by_name['ModelsCreateInstanceResponse'] = _MODELSCREATEINSTANCERESPONSE DESCRIPTOR.message_types_by_name['ModelsDeleteCatalogRequest'] = _MODELSDELETECATALOGREQUEST DESCRIPTOR.message_types_by_name['ModelsDeleteCatalogResponse'] = _MODELSDELETECATALOGRESPONSE DESCRIPTOR.message_types_by_name['ModelsDeleteInstanceRequest'] = _MODELSDELETEINSTANCEREQUEST DESCRIPTOR.message_types_by_name['ModelsDeleteInstanceResponse'] = _MODELSDELETEINSTANCERESPONSE DESCRIPTOR.message_types_by_name['ModelsDeleteModelRequest'] = _MODELSDELETEMODELREQUEST DESCRIPTOR.message_types_by_name['ModelsDeleteModelResponse'] = _MODELSDELETEMODELRESPONSE DESCRIPTOR.message_types_by_name['ModelsDownloadModelRequest'] = _MODELSDOWNLOADMODELREQUEST DESCRIPTOR.message_types_by_name['ModelsDownloadModelResponse'] = _MODELSDOWNLOADMODELRESPONSE DESCRIPTOR.message_types_by_name['ModelsListCatalogsRequest'] = _MODELSLISTCATALOGSREQUEST DESCRIPTOR.message_types_by_name['ModelsListCatalogsResponse'] = _MODELSLISTCATALOGSRESPONSE DESCRIPTOR.message_types_by_name['ModelsListInstancesRequest'] = _MODELSLISTINSTANCESREQUEST DESCRIPTOR.message_types_by_name['ModelsListInstancesResponse'] = _MODELSLISTINSTANCESRESPONSE DESCRIPTOR.message_types_by_name['ModelsListModelsRequest'] = _MODELSLISTMODELSREQUEST DESCRIPTOR.message_types_by_name['ModelsListModelsResponse'] = _MODELSLISTMODELSRESPONSE DESCRIPTOR.message_types_by_name['ModelsReadCatalogRequest'] = _MODELSREADCATALOGREQUEST DESCRIPTOR.message_types_by_name['ModelsReadCatalogResponse'] = _MODELSREADCATALOGRESPONSE DESCRIPTOR.message_types_by_name['ModelsReadInstanceRequest'] = _MODELSREADINSTANCEREQUEST DESCRIPTOR.message_types_by_name['ModelsReadInstanceResponse'] = _MODELSREADINSTANCERESPONSE DESCRIPTOR.message_types_by_name['ModelsRemoveMetadataRequest'] = _MODELSREMOVEMETADATAREQUEST DESCRIPTOR.message_types_by_name['ModelsRemoveMetadataResponse'] = _MODELSREMOVEMETADATARESPONSE DESCRIPTOR.message_types_by_name['ModelsUpdateCatalogRequest'] = _MODELSUPDATECATALOGREQUEST DESCRIPTOR.message_types_by_name['ModelsUpdateCatalogResponse'] = _MODELSUPDATECATALOGRESPONSE DESCRIPTOR.message_types_by_name['ModelsUpdateInstanceRequest'] = _MODELSUPDATEINSTANCEREQUEST DESCRIPTOR.message_types_by_name['ModelsUpdateInstanceResponse'] = _MODELSUPDATEINSTANCERESPONSE DESCRIPTOR.message_types_by_name['ModelsUploadModelRequest'] = _MODELSUPLOADMODELREQUEST DESCRIPTOR.message_types_by_name['ModelsUploadModelResponse'] = _MODELSUPLOADMODELRESPONSE DESCRIPTOR.enum_types_by_name['ModelType'] = _MODELTYPE _sym_db.RegisterFileDescriptor(DESCRIPTOR) ModelCatalogDetails = _reflection.GeneratedProtocolMessageType('ModelCatalogDetails', (_message.Message,), dict( DESCRIPTOR = _MODELCATALOGDETAILS, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelCatalogDetails) )) _sym_db.RegisterMessage(ModelCatalogDetails) ModelDetails = _reflection.GeneratedProtocolMessageType('ModelDetails', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _MODELDETAILS_METADATAENTRY, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelDetails.MetadataEntry) )) , DESCRIPTOR = _MODELDETAILS, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelDetails) )) _sym_db.RegisterMessage(ModelDetails) _sym_db.RegisterMessage(ModelDetails.MetadataEntry) ModelsAddMetadataRequest = _reflection.GeneratedProtocolMessageType('ModelsAddMetadataRequest', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _MODELSADDMETADATAREQUEST_METADATAENTRY, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsAddMetadataRequest.MetadataEntry) )) , DESCRIPTOR = _MODELSADDMETADATAREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsAddMetadataRequest) )) _sym_db.RegisterMessage(ModelsAddMetadataRequest) _sym_db.RegisterMessage(ModelsAddMetadataRequest.MetadataEntry) ModelsAddMetadataResponse = _reflection.GeneratedProtocolMessageType('ModelsAddMetadataResponse', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _MODELSADDMETADATARESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsAddMetadataResponse.MetadataEntry) )) , DESCRIPTOR = _MODELSADDMETADATARESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsAddMetadataResponse) )) _sym_db.RegisterMessage(ModelsAddMetadataResponse) _sym_db.RegisterMessage(ModelsAddMetadataResponse.MetadataEntry) ModelsCreateCatalogRequest = _reflection.GeneratedProtocolMessageType('ModelsCreateCatalogRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSCREATECATALOGREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsCreateCatalogRequest) )) _sym_db.RegisterMessage(ModelsCreateCatalogRequest) ModelsCreateCatalogResponse = _reflection.GeneratedProtocolMessageType('ModelsCreateCatalogResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSCREATECATALOGRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsCreateCatalogResponse) )) _sym_db.RegisterMessage(ModelsCreateCatalogResponse) ModelsCreateInstanceRequest = _reflection.GeneratedProtocolMessageType('ModelsCreateInstanceRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSCREATEINSTANCEREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsCreateInstanceRequest) )) _sym_db.RegisterMessage(ModelsCreateInstanceRequest) ModelsCreateInstanceResponse = _reflection.GeneratedProtocolMessageType('ModelsCreateInstanceResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSCREATEINSTANCERESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsCreateInstanceResponse) )) _sym_db.RegisterMessage(ModelsCreateInstanceResponse) ModelsDeleteCatalogRequest = _reflection.GeneratedProtocolMessageType('ModelsDeleteCatalogRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSDELETECATALOGREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDeleteCatalogRequest) )) _sym_db.RegisterMessage(ModelsDeleteCatalogRequest) ModelsDeleteCatalogResponse = _reflection.GeneratedProtocolMessageType('ModelsDeleteCatalogResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSDELETECATALOGRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDeleteCatalogResponse) )) _sym_db.RegisterMessage(ModelsDeleteCatalogResponse) ModelsDeleteInstanceRequest = _reflection.GeneratedProtocolMessageType('ModelsDeleteInstanceRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSDELETEINSTANCEREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDeleteInstanceRequest) )) _sym_db.RegisterMessage(ModelsDeleteInstanceRequest) ModelsDeleteInstanceResponse = _reflection.GeneratedProtocolMessageType('ModelsDeleteInstanceResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSDELETEINSTANCERESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDeleteInstanceResponse) )) _sym_db.RegisterMessage(ModelsDeleteInstanceResponse) ModelsDeleteModelRequest = _reflection.GeneratedProtocolMessageType('ModelsDeleteModelRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSDELETEMODELREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDeleteModelRequest) )) _sym_db.RegisterMessage(ModelsDeleteModelRequest) ModelsDeleteModelResponse = _reflection.GeneratedProtocolMessageType('ModelsDeleteModelResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSDELETEMODELRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDeleteModelResponse) )) _sym_db.RegisterMessage(ModelsDeleteModelResponse) ModelsDownloadModelRequest = _reflection.GeneratedProtocolMessageType('ModelsDownloadModelRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSDOWNLOADMODELREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDownloadModelRequest) )) _sym_db.RegisterMessage(ModelsDownloadModelRequest) ModelsDownloadModelResponse = _reflection.GeneratedProtocolMessageType('ModelsDownloadModelResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSDOWNLOADMODELRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsDownloadModelResponse) )) _sym_db.RegisterMessage(ModelsDownloadModelResponse) ModelsListCatalogsRequest = _reflection.GeneratedProtocolMessageType('ModelsListCatalogsRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSLISTCATALOGSREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsListCatalogsRequest) )) _sym_db.RegisterMessage(ModelsListCatalogsRequest) ModelsListCatalogsResponse = _reflection.GeneratedProtocolMessageType('ModelsListCatalogsResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSLISTCATALOGSRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsListCatalogsResponse) )) _sym_db.RegisterMessage(ModelsListCatalogsResponse) ModelsListInstancesRequest = _reflection.GeneratedProtocolMessageType('ModelsListInstancesRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSLISTINSTANCESREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsListInstancesRequest) )) _sym_db.RegisterMessage(ModelsListInstancesRequest) ModelsListInstancesResponse = _reflection.GeneratedProtocolMessageType('ModelsListInstancesResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSLISTINSTANCESRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsListInstancesResponse) )) _sym_db.RegisterMessage(ModelsListInstancesResponse) ModelsListModelsRequest = _reflection.GeneratedProtocolMessageType('ModelsListModelsRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSLISTMODELSREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsListModelsRequest) )) _sym_db.RegisterMessage(ModelsListModelsRequest) ModelsListModelsResponse = _reflection.GeneratedProtocolMessageType('ModelsListModelsResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSLISTMODELSRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsListModelsResponse) )) _sym_db.RegisterMessage(ModelsListModelsResponse) ModelsReadCatalogRequest = _reflection.GeneratedProtocolMessageType('ModelsReadCatalogRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSREADCATALOGREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsReadCatalogRequest) )) _sym_db.RegisterMessage(ModelsReadCatalogRequest) ModelsReadCatalogResponse = _reflection.GeneratedProtocolMessageType('ModelsReadCatalogResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSREADCATALOGRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsReadCatalogResponse) )) _sym_db.RegisterMessage(ModelsReadCatalogResponse) ModelsReadInstanceRequest = _reflection.GeneratedProtocolMessageType('ModelsReadInstanceRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSREADINSTANCEREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsReadInstanceRequest) )) _sym_db.RegisterMessage(ModelsReadInstanceRequest) ModelsReadInstanceResponse = _reflection.GeneratedProtocolMessageType('ModelsReadInstanceResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSREADINSTANCERESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsReadInstanceResponse) )) _sym_db.RegisterMessage(ModelsReadInstanceResponse) ModelsRemoveMetadataRequest = _reflection.GeneratedProtocolMessageType('ModelsRemoveMetadataRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSREMOVEMETADATAREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsRemoveMetadataRequest) )) _sym_db.RegisterMessage(ModelsRemoveMetadataRequest) ModelsRemoveMetadataResponse = _reflection.GeneratedProtocolMessageType('ModelsRemoveMetadataResponse', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _MODELSREMOVEMETADATARESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsRemoveMetadataResponse.MetadataEntry) )) , DESCRIPTOR = _MODELSREMOVEMETADATARESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsRemoveMetadataResponse) )) _sym_db.RegisterMessage(ModelsRemoveMetadataResponse) _sym_db.RegisterMessage(ModelsRemoveMetadataResponse.MetadataEntry) ModelsUpdateCatalogRequest = _reflection.GeneratedProtocolMessageType('ModelsUpdateCatalogRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSUPDATECATALOGREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsUpdateCatalogRequest) )) _sym_db.RegisterMessage(ModelsUpdateCatalogRequest) ModelsUpdateCatalogResponse = _reflection.GeneratedProtocolMessageType('ModelsUpdateCatalogResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSUPDATECATALOGRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsUpdateCatalogResponse) )) _sym_db.RegisterMessage(ModelsUpdateCatalogResponse) ModelsUpdateInstanceRequest = _reflection.GeneratedProtocolMessageType('ModelsUpdateInstanceRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSUPDATEINSTANCEREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsUpdateInstanceRequest) )) _sym_db.RegisterMessage(ModelsUpdateInstanceRequest) ModelsUpdateInstanceResponse = _reflection.GeneratedProtocolMessageType('ModelsUpdateInstanceResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSUPDATEINSTANCERESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsUpdateInstanceResponse) )) _sym_db.RegisterMessage(ModelsUpdateInstanceResponse) ModelsUploadModelRequest = _reflection.GeneratedProtocolMessageType('ModelsUploadModelRequest', (_message.Message,), dict( DESCRIPTOR = _MODELSUPLOADMODELREQUEST, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsUploadModelRequest) )) _sym_db.RegisterMessage(ModelsUploadModelRequest) ModelsUploadModelResponse = _reflection.GeneratedProtocolMessageType('ModelsUploadModelResponse', (_message.Message,), dict( DESCRIPTOR = _MODELSUPLOADMODELRESPONSE, __module__ = 'nvidia.clara.platform.models_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ModelsUploadModelResponse) )) _sym_db.RegisterMessage(ModelsUploadModelResponse) DESCRIPTOR._options = None _MODELDETAILS_METADATAENTRY._options = None _MODELSADDMETADATAREQUEST_METADATAENTRY._options = None _MODELSADDMETADATARESPONSE_METADATAENTRY._options = None _MODELSREMOVEMETADATARESPONSE_METADATAENTRY._options = None _MODELS = _descriptor.ServiceDescriptor( name='Models', full_name='nvidia.clara.platform.Models', file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=5464, serialized_end=7383, methods=[ _descriptor.MethodDescriptor( name='AddMetadata', full_name='nvidia.clara.platform.Models.AddMetadata', index=0, containing_service=None, input_type=_MODELSADDMETADATAREQUEST, output_type=_MODELSADDMETADATARESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='CreateCatalog', full_name='nvidia.clara.platform.Models.CreateCatalog', index=1, containing_service=None, input_type=_MODELSCREATECATALOGREQUEST, output_type=_MODELSCREATECATALOGRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='CreateInstance', full_name='nvidia.clara.platform.Models.CreateInstance', index=2, containing_service=None, input_type=_MODELSCREATEINSTANCEREQUEST, output_type=_MODELSCREATEINSTANCERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='DeleteCatalog', full_name='nvidia.clara.platform.Models.DeleteCatalog', index=3, containing_service=None, input_type=_MODELSDELETECATALOGREQUEST, output_type=_MODELSDELETECATALOGRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='DeleteInstance', full_name='nvidia.clara.platform.Models.DeleteInstance', index=4, containing_service=None, input_type=_MODELSDELETEINSTANCEREQUEST, output_type=_MODELSDELETEINSTANCERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='DeleteModel', full_name='nvidia.clara.platform.Models.DeleteModel', index=5, containing_service=None, input_type=_MODELSDELETEMODELREQUEST, output_type=_MODELSDELETEMODELRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='DownloadModel', full_name='nvidia.clara.platform.Models.DownloadModel', index=6, containing_service=None, input_type=_MODELSDOWNLOADMODELREQUEST, output_type=_MODELSDOWNLOADMODELRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='ListCatalogs', full_name='nvidia.clara.platform.Models.ListCatalogs', index=7, containing_service=None, input_type=_MODELSLISTCATALOGSREQUEST, output_type=_MODELSLISTCATALOGSRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='ListInstances', full_name='nvidia.clara.platform.Models.ListInstances', index=8, containing_service=None, input_type=_MODELSLISTINSTANCESREQUEST, output_type=_MODELSLISTINSTANCESRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='ListModels', full_name='nvidia.clara.platform.Models.ListModels', index=9, containing_service=None, input_type=_MODELSLISTMODELSREQUEST, output_type=_MODELSLISTMODELSRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='ReadCatalog', full_name='nvidia.clara.platform.Models.ReadCatalog', index=10, containing_service=None, input_type=_MODELSREADCATALOGREQUEST, output_type=_MODELSREADCATALOGRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='ReadInstance', full_name='nvidia.clara.platform.Models.ReadInstance', index=11, containing_service=None, input_type=_MODELSREADINSTANCEREQUEST, output_type=_MODELSREADINSTANCERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='RemoveMetadata', full_name='nvidia.clara.platform.Models.RemoveMetadata', index=12, containing_service=None, input_type=_MODELSREMOVEMETADATAREQUEST, output_type=_MODELSREMOVEMETADATARESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='UpdateCatalog', full_name='nvidia.clara.platform.Models.UpdateCatalog', index=13, containing_service=None, input_type=_MODELSUPDATECATALOGREQUEST, output_type=_MODELSUPDATECATALOGRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='UpdateInstance', full_name='nvidia.clara.platform.Models.UpdateInstance', index=14, containing_service=None, input_type=_MODELSUPDATEINSTANCEREQUEST, output_type=_MODELSUPDATEINSTANCERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='UploadModel', full_name='nvidia.clara.platform.Models.UploadModel', index=15, containing_service=None, input_type=_MODELSUPLOADMODELREQUEST, output_type=_MODELSUPLOADMODELRESPONSE, serialized_options=None, ), ]) _sym_db.RegisterServiceDescriptor(_MODELS) DESCRIPTOR.services_by_name['Models'] = _MODELS # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/models_pb2.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/clara.proto # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc from nvidia_clara.grpc import pipelines_pb2 as nvidia_dot_clara_dot_platform_dot_pipelines__pb2 class PipelinesStub(object): # missing associated documentation comment in .proto file pass def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.AddMetadata = channel.unary_unary( '/nvidia.clara.platform.Pipelines/AddMetadata', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesAddMetadataRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesAddMetadataResponse.FromString, ) self.Create = channel.stream_unary( '/nvidia.clara.platform.Pipelines/Create', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesCreateRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesCreateResponse.FromString, ) self.Details = channel.unary_stream( '/nvidia.clara.platform.Pipelines/Details', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesDetailsRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesDetailsResponse.FromString, ) self.List = channel.unary_stream( '/nvidia.clara.platform.Pipelines/List', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesListRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesListResponse.FromString, ) self.Remove = channel.unary_unary( '/nvidia.clara.platform.Pipelines/Remove', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveResponse.FromString, ) self.RemoveMetadata = channel.unary_unary( '/nvidia.clara.platform.Pipelines/RemoveMetadata', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveMetadataRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveMetadataResponse.FromString, ) self.Update = channel.stream_unary( '/nvidia.clara.platform.Pipelines/Update', request_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesUpdateRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesUpdateResponse.FromString, ) class PipelinesServicer(object): # missing associated documentation comment in .proto file pass def AddMetadata(self, request, context): """Requests the addition of metadata to a pipeline. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Create(self, request_iterator, context): """Requests the creation of a new pipeline. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Details(self, request, context): """Requests details of a pipeline. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def List(self, request, context): """Requests a listing of all pipelines known by the service. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Remove(self, request, context): """Requests the removal of a pipeline definition from the service. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def RemoveMetadata(self, request, context): """Requests the removal of specified metadata of a pipeline. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Update(self, request_iterator, context): """Requests an update to a known pipeline definition. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_PipelinesServicer_to_server(servicer, server): rpc_method_handlers = { 'AddMetadata': grpc.unary_unary_rpc_method_handler( servicer.AddMetadata, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesAddMetadataRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesAddMetadataResponse.SerializeToString, ), 'Create': grpc.stream_unary_rpc_method_handler( servicer.Create, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesCreateRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesCreateResponse.SerializeToString, ), 'Details': grpc.unary_stream_rpc_method_handler( servicer.Details, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesDetailsRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesDetailsResponse.SerializeToString, ), 'List': grpc.unary_stream_rpc_method_handler( servicer.List, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesListRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesListResponse.SerializeToString, ), 'Remove': grpc.unary_unary_rpc_method_handler( servicer.Remove, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveResponse.SerializeToString, ), 'RemoveMetadata': grpc.unary_unary_rpc_method_handler( servicer.RemoveMetadata, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveMetadataRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesRemoveMetadataResponse.SerializeToString, ), 'Update': grpc.stream_unary_rpc_method_handler( servicer.Update, request_deserializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesUpdateRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_pipelines__pb2.PipelinesUpdateResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'nvidia.clara.platform.Pipelines', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,))
clara-platform-python-client-main
nvidia_clara/grpc/pipelines_pb2_grpc.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/clara.proto # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc from nvidia_clara.grpc import payloads_pb2 as nvidia_dot_clara_dot_platform_dot_payloads__pb2 class PayloadsStub(object): # missing associated documentation comment in .proto file pass def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.AddMetadata = channel.unary_unary( '/nvidia.clara.platform.Payloads/AddMetadata', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsAddMetadataRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsAddMetadataResponse.FromString, ) self.Create = channel.unary_unary( '/nvidia.clara.platform.Payloads/Create', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsCreateRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsCreateResponse.FromString, ) self.Delete = channel.unary_unary( '/nvidia.clara.platform.Payloads/Delete', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDeleteRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDeleteResponse.FromString, ) self.Details = channel.unary_stream( '/nvidia.clara.platform.Payloads/Details', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDetailsRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDetailsResponse.FromString, ) self.Download = channel.unary_stream( '/nvidia.clara.platform.Payloads/Download', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDownloadRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDownloadResponse.FromString, ) self.Remove = channel.unary_unary( '/nvidia.clara.platform.Payloads/Remove', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveResponse.FromString, ) self.RemoveMetadata = channel.unary_unary( '/nvidia.clara.platform.Payloads/RemoveMetadata', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveMetadataRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveMetadataResponse.FromString, ) self.Upload = channel.stream_unary( '/nvidia.clara.platform.Payloads/Upload', request_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsUploadRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsUploadResponse.FromString, ) class PayloadsServicer(object): # missing associated documentation comment in .proto file pass def AddMetadata(self, request, context): """Requests the addition of metadata to a payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Create(self, request, context): """Requests the creation of a new payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Delete(self, request, context): """Requests the deletion of a known payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Details(self, request, context): """Requests the details (file listing) of a known payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Download(self, request, context): """Requests the download of a blob (file) from a known payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Remove(self, request, context): """Requests the removal, or deletion, of a blob from a known payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def RemoveMetadata(self, request, context): """Requests the removal of metadata from a payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Upload(self, request_iterator, context): """Requests the upload of a blob (file) to a known payload. When payload type is PAYLOAD_TYPE_PIPELINE, uploads are written to the ~/input/ folder of the payload. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_PayloadsServicer_to_server(servicer, server): rpc_method_handlers = { 'AddMetadata': grpc.unary_unary_rpc_method_handler( servicer.AddMetadata, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsAddMetadataRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsAddMetadataResponse.SerializeToString, ), 'Create': grpc.unary_unary_rpc_method_handler( servicer.Create, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsCreateRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsCreateResponse.SerializeToString, ), 'Delete': grpc.unary_unary_rpc_method_handler( servicer.Delete, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDeleteRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDeleteResponse.SerializeToString, ), 'Details': grpc.unary_stream_rpc_method_handler( servicer.Details, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDetailsRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDetailsResponse.SerializeToString, ), 'Download': grpc.unary_stream_rpc_method_handler( servicer.Download, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDownloadRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsDownloadResponse.SerializeToString, ), 'Remove': grpc.unary_unary_rpc_method_handler( servicer.Remove, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveResponse.SerializeToString, ), 'RemoveMetadata': grpc.unary_unary_rpc_method_handler( servicer.RemoveMetadata, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveMetadataRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsRemoveMetadataResponse.SerializeToString, ), 'Upload': grpc.stream_unary_rpc_method_handler( servicer.Upload, request_deserializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsUploadRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_payloads__pb2.PayloadsUploadResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'nvidia.clara.platform.Payloads', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,))
clara-platform-python-client-main
nvidia_clara/grpc/payloads_pb2_grpc.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/clara.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from nvidia_clara.grpc import common_pb2 as nvidia_dot_clara_dot_platform_dot_common__pb2 from nvidia_clara.grpc.common_pb2 import * DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/clara.proto', package='nvidia.clara.platform', syntax='proto3', serialized_options=_b('\n\031com.nvidia.clara.platformZ\004apis\252\002\032Nvidia.Clara.Platform.Grpc'), serialized_pb=_b('\n!nvidia/clara/platform/clara.proto\x12\x15nvidia.clara.platform\x1a\"nvidia/clara/platform/common.proto\"H\n\x10\x43laraStopRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\"J\n\x11\x43laraStopResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\"^\n\x17\x43laraUtilizationRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\r\n\x05watch\x18\x02 \x01(\x08\"\xae\x04\n\x18\x43laraUtilizationResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12S\n\x0bgpu_metrics\x18\x02 \x03(\x0b\x32>.nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization\x1a\x85\x03\n\x0eGpuUtilization\x12\x0f\n\x07node_id\x18\x01 \x01(\t\x12\x0f\n\x07pcie_id\x18\x02 \x01(\r\x12\x1b\n\x13\x63ompute_utilization\x18\x06 \x01(\x02\x12\x13\n\x0bmemory_free\x18\x07 \x01(\x04\x12\x13\n\x0bmemory_used\x18\x08 \x01(\x04\x12\x1a\n\x12memory_utilization\x18\t \x01(\x02\x12\x33\n\ttimestamp\x18\x0b \x01(\x0b\x32 .nvidia.clara.platform.Timestamp\x12\x66\n\x0fprocess_details\x18\x0c \x03(\x0b\x32M.nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.ProcessDetails\x1aQ\n\x0eProcessDetails\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x31\n\x06job_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\"K\n\x13\x43laraVersionRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\"~\n\x14\x43laraVersionResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12/\n\x07version\x18\x02 \x01(\x0b\x32\x1e.nvidia.clara.platform.Version2\xb8\x02\n\x05\x43lara\x12Y\n\x04Stop\x12\'.nvidia.clara.platform.ClaraStopRequest\x1a(.nvidia.clara.platform.ClaraStopResponse\x12p\n\x0bUtilization\x12..nvidia.clara.platform.ClaraUtilizationRequest\x1a/.nvidia.clara.platform.ClaraUtilizationResponse0\x01\x12\x62\n\x07Version\x12*.nvidia.clara.platform.ClaraVersionRequest\x1a+.nvidia.clara.platform.ClaraVersionResponseB>\n\x19\x63om.nvidia.clara.platformZ\x04\x61pis\xaa\x02\x1aNvidia.Clara.Platform.GrpcP\x00\x62\x06proto3') , dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,], public_dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,]) _CLARASTOPREQUEST = _descriptor.Descriptor( name='ClaraStopRequest', full_name='nvidia.clara.platform.ClaraStopRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ClaraStopRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=96, serialized_end=168, ) _CLARASTOPRESPONSE = _descriptor.Descriptor( name='ClaraStopResponse', full_name='nvidia.clara.platform.ClaraStopResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ClaraStopResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=170, serialized_end=244, ) _CLARAUTILIZATIONREQUEST = _descriptor.Descriptor( name='ClaraUtilizationRequest', full_name='nvidia.clara.platform.ClaraUtilizationRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ClaraUtilizationRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='watch', full_name='nvidia.clara.platform.ClaraUtilizationRequest.watch', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=246, serialized_end=340, ) _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION_PROCESSDETAILS = _descriptor.Descriptor( name='ProcessDetails', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.ProcessDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.ProcessDetails.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.ProcessDetails.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=820, serialized_end=901, ) _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION = _descriptor.Descriptor( name='GpuUtilization', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='node_id', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.node_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pcie_id', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.pcie_id', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='compute_utilization', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.compute_utilization', index=2, number=6, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='memory_free', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.memory_free', index=3, number=7, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='memory_used', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.memory_used', index=4, number=8, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='memory_utilization', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.memory_utilization', index=5, number=9, type=2, cpp_type=6, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.timestamp', index=6, number=11, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='process_details', full_name='nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.process_details', index=7, number=12, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_CLARAUTILIZATIONRESPONSE_GPUUTILIZATION_PROCESSDETAILS, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=512, serialized_end=901, ) _CLARAUTILIZATIONRESPONSE = _descriptor.Descriptor( name='ClaraUtilizationResponse', full_name='nvidia.clara.platform.ClaraUtilizationResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ClaraUtilizationResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='gpu_metrics', full_name='nvidia.clara.platform.ClaraUtilizationResponse.gpu_metrics', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_CLARAUTILIZATIONRESPONSE_GPUUTILIZATION, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=343, serialized_end=901, ) _CLARAVERSIONREQUEST = _descriptor.Descriptor( name='ClaraVersionRequest', full_name='nvidia.clara.platform.ClaraVersionRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ClaraVersionRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=903, serialized_end=978, ) _CLARAVERSIONRESPONSE = _descriptor.Descriptor( name='ClaraVersionResponse', full_name='nvidia.clara.platform.ClaraVersionResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.ClaraVersionResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='version', full_name='nvidia.clara.platform.ClaraVersionResponse.version', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=980, serialized_end=1106, ) _CLARASTOPREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _CLARASTOPRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _CLARAUTILIZATIONREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION_PROCESSDETAILS.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION_PROCESSDETAILS.containing_type = _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION.fields_by_name['timestamp'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION.fields_by_name['process_details'].message_type = _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION_PROCESSDETAILS _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION.containing_type = _CLARAUTILIZATIONRESPONSE _CLARAUTILIZATIONRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _CLARAUTILIZATIONRESPONSE.fields_by_name['gpu_metrics'].message_type = _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION _CLARAVERSIONREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _CLARAVERSIONRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _CLARAVERSIONRESPONSE.fields_by_name['version'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._VERSION DESCRIPTOR.message_types_by_name['ClaraStopRequest'] = _CLARASTOPREQUEST DESCRIPTOR.message_types_by_name['ClaraStopResponse'] = _CLARASTOPRESPONSE DESCRIPTOR.message_types_by_name['ClaraUtilizationRequest'] = _CLARAUTILIZATIONREQUEST DESCRIPTOR.message_types_by_name['ClaraUtilizationResponse'] = _CLARAUTILIZATIONRESPONSE DESCRIPTOR.message_types_by_name['ClaraVersionRequest'] = _CLARAVERSIONREQUEST DESCRIPTOR.message_types_by_name['ClaraVersionResponse'] = _CLARAVERSIONRESPONSE _sym_db.RegisterFileDescriptor(DESCRIPTOR) ClaraStopRequest = _reflection.GeneratedProtocolMessageType('ClaraStopRequest', (_message.Message,), dict( DESCRIPTOR = _CLARASTOPREQUEST, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraStopRequest) )) _sym_db.RegisterMessage(ClaraStopRequest) ClaraStopResponse = _reflection.GeneratedProtocolMessageType('ClaraStopResponse', (_message.Message,), dict( DESCRIPTOR = _CLARASTOPRESPONSE, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraStopResponse) )) _sym_db.RegisterMessage(ClaraStopResponse) ClaraUtilizationRequest = _reflection.GeneratedProtocolMessageType('ClaraUtilizationRequest', (_message.Message,), dict( DESCRIPTOR = _CLARAUTILIZATIONREQUEST, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraUtilizationRequest) )) _sym_db.RegisterMessage(ClaraUtilizationRequest) ClaraUtilizationResponse = _reflection.GeneratedProtocolMessageType('ClaraUtilizationResponse', (_message.Message,), dict( GpuUtilization = _reflection.GeneratedProtocolMessageType('GpuUtilization', (_message.Message,), dict( ProcessDetails = _reflection.GeneratedProtocolMessageType('ProcessDetails', (_message.Message,), dict( DESCRIPTOR = _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION_PROCESSDETAILS, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization.ProcessDetails) )) , DESCRIPTOR = _CLARAUTILIZATIONRESPONSE_GPUUTILIZATION, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraUtilizationResponse.GpuUtilization) )) , DESCRIPTOR = _CLARAUTILIZATIONRESPONSE, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraUtilizationResponse) )) _sym_db.RegisterMessage(ClaraUtilizationResponse) _sym_db.RegisterMessage(ClaraUtilizationResponse.GpuUtilization) _sym_db.RegisterMessage(ClaraUtilizationResponse.GpuUtilization.ProcessDetails) ClaraVersionRequest = _reflection.GeneratedProtocolMessageType('ClaraVersionRequest', (_message.Message,), dict( DESCRIPTOR = _CLARAVERSIONREQUEST, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraVersionRequest) )) _sym_db.RegisterMessage(ClaraVersionRequest) ClaraVersionResponse = _reflection.GeneratedProtocolMessageType('ClaraVersionResponse', (_message.Message,), dict( DESCRIPTOR = _CLARAVERSIONRESPONSE, __module__ = 'nvidia.clara.platform.clara_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ClaraVersionResponse) )) _sym_db.RegisterMessage(ClaraVersionResponse) DESCRIPTOR._options = None _CLARA = _descriptor.ServiceDescriptor( name='Clara', full_name='nvidia.clara.platform.Clara', file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=1109, serialized_end=1421, methods=[ _descriptor.MethodDescriptor( name='Stop', full_name='nvidia.clara.platform.Clara.Stop', index=0, containing_service=None, input_type=_CLARASTOPREQUEST, output_type=_CLARASTOPRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Utilization', full_name='nvidia.clara.platform.Clara.Utilization', index=1, containing_service=None, input_type=_CLARAUTILIZATIONREQUEST, output_type=_CLARAUTILIZATIONRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Version', full_name='nvidia.clara.platform.Clara.Version', index=2, containing_service=None, input_type=_CLARAVERSIONREQUEST, output_type=_CLARAVERSIONRESPONSE, serialized_options=None, ), ]) _sym_db.RegisterServiceDescriptor(_CLARA) DESCRIPTOR.services_by_name['Clara'] = _CLARA # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/clara_pb2.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/jobs.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from nvidia_clara.grpc import common_pb2 as nvidia_dot_clara_dot_platform_dot_common__pb2 from nvidia_clara.grpc import clara_pb2 as nvidia_dot_clara_dot_platform_dot_clara__pb2 try: nvidia_dot_clara_dot_platform_dot_common__pb2 = nvidia_dot_clara_dot_platform_dot_clara__pb2.nvidia_dot_clara_dot_platform_dot_common__pb2 except AttributeError: nvidia_dot_clara_dot_platform_dot_common__pb2 = nvidia_dot_clara_dot_platform_dot_clara__pb2.nvidia.clara.platform.common_pb2 from nvidia_clara.grpc.common_pb2 import * from nvidia_clara.grpc.clara_pb2 import * DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/jobs.proto', package='nvidia.clara.platform', syntax='proto3', serialized_options=_b('\n\031com.nvidia.clara.platformZ\004apis\252\002\032Nvidia.Clara.Platform.Grpc'), serialized_pb=_b('\n nvidia/clara/platform/jobs.proto\x12\x15nvidia.clara.platform\x1a\"nvidia/clara/platform/common.proto\x1a!nvidia/clara/platform/clara.proto\"\x81\x02\n\x16JobsAddMetadataRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\x12\x31\n\x06job_id\x18\x02 \x01(\x0b\x32!.nvidia.clara.platform.Identifier\x12M\n\x08metadata\x18\x03 \x03(\x0b\x32;.nvidia.clara.platform.JobsAddMetadataRequest.MetadataEntry\x1a/\n\rMetadataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\x84\x02\n\x17JobsAddMetadataResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x31\n\x06job_id\x18\x02 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, dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,nvidia_dot_clara_dot_platform_dot_clara__pb2.DESCRIPTOR,], public_dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,nvidia_dot_clara_dot_platform_dot_clara__pb2.DESCRIPTOR,]) _JOBOPERATORSTATUS = _descriptor.EnumDescriptor( name='JobOperatorStatus', full_name='nvidia.clara.platform.JobOperatorStatus', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='JOB_OPERATOR_STATUS_UNKNOWN', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_OPERATOR_STATUS_PENDING', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_OPERATOR_STATUS_RUNNING', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_OPERATOR_STATUS_COMPLETED', index=3, number=3, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_OPERATOR_STATUS_FAULTED', index=4, number=4, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=5629, serialized_end=5815, ) _sym_db.RegisterEnumDescriptor(_JOBOPERATORSTATUS) JobOperatorStatus = enum_type_wrapper.EnumTypeWrapper(_JOBOPERATORSTATUS) _JOBPRIORITY = _descriptor.EnumDescriptor( name='JobPriority', full_name='nvidia.clara.platform.JobPriority', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='JOB_PRIORITY_UNKNOWN', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_PRIORITY_LOWER', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_PRIORITY_NORMAL', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_PRIORITY_HIGHER', index=3, number=3, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_PRIORITY_IMMEDIATE', index=4, number=4, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=5818, serialized_end=5959, ) _sym_db.RegisterEnumDescriptor(_JOBPRIORITY) JobPriority = enum_type_wrapper.EnumTypeWrapper(_JOBPRIORITY) _JOBSTATE = _descriptor.EnumDescriptor( name='JobState', full_name='nvidia.clara.platform.JobState', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='JOB_STATE_UNKNOWN', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATE_PENDING', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATE_RUNNING', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATE_STOPPED', index=3, number=3, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=5961, serialized_end=6063, ) _sym_db.RegisterEnumDescriptor(_JOBSTATE) JobState = enum_type_wrapper.EnumTypeWrapper(_JOBSTATE) _JOBSTATUS = _descriptor.EnumDescriptor( name='JobStatus', full_name='nvidia.clara.platform.JobStatus', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='JOB_STATUS_UNKNOWN', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATUS_HEALTHY', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATUS_FAULTED', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATUS_CANCELED', index=3, number=3, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATUS_EVICTED', index=4, number=4, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JOB_STATUS_TERMINATED', index=5, number=5, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=6066, serialized_end=6225, ) _sym_db.RegisterEnumDescriptor(_JOBSTATUS) JobStatus = enum_type_wrapper.EnumTypeWrapper(_JOBSTATUS) JOB_OPERATOR_STATUS_UNKNOWN = 0 JOB_OPERATOR_STATUS_PENDING = 1 JOB_OPERATOR_STATUS_RUNNING = 2 JOB_OPERATOR_STATUS_COMPLETED = 3 JOB_OPERATOR_STATUS_FAULTED = 4 JOB_PRIORITY_UNKNOWN = 0 JOB_PRIORITY_LOWER = 1 JOB_PRIORITY_NORMAL = 2 JOB_PRIORITY_HIGHER = 3 JOB_PRIORITY_IMMEDIATE = 4 JOB_STATE_UNKNOWN = 0 JOB_STATE_PENDING = 1 JOB_STATE_RUNNING = 2 JOB_STATE_STOPPED = 3 JOB_STATUS_UNKNOWN = 0 JOB_STATUS_HEALTHY = 1 JOB_STATUS_FAULTED = 2 JOB_STATUS_CANCELED = 3 JOB_STATUS_EVICTED = 4 JOB_STATUS_TERMINATED = 5 _JOBSADDMETADATAREQUEST_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.JobsAddMetadataRequest.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.JobsAddMetadataRequest.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsAddMetadataRequest.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=341, serialized_end=388, ) _JOBSADDMETADATAREQUEST = _descriptor.Descriptor( name='JobsAddMetadataRequest', full_name='nvidia.clara.platform.JobsAddMetadataRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsAddMetadataRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsAddMetadataRequest.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.JobsAddMetadataRequest.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSADDMETADATAREQUEST_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=131, serialized_end=388, ) _JOBSADDMETADATARESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.JobsAddMetadataResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.JobsAddMetadataResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsAddMetadataResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=341, serialized_end=388, ) _JOBSADDMETADATARESPONSE = _descriptor.Descriptor( name='JobsAddMetadataResponse', full_name='nvidia.clara.platform.JobsAddMetadataResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsAddMetadataResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsAddMetadataResponse.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.JobsAddMetadataResponse.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSADDMETADATARESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=391, serialized_end=651, ) _JOBSCANCELREQUEST = _descriptor.Descriptor( name='JobsCancelRequest', full_name='nvidia.clara.platform.JobsCancelRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsCancelRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsCancelRequest.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='reason', full_name='nvidia.clara.platform.JobsCancelRequest.reason', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=654, serialized_end=794, ) _JOBSCANCELRESPONSE = _descriptor.Descriptor( name='JobsCancelResponse', full_name='nvidia.clara.platform.JobsCancelResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsCancelResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsCancelResponse.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_state', full_name='nvidia.clara.platform.JobsCancelResponse.job_state', index=2, number=3, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_status', full_name='nvidia.clara.platform.JobsCancelResponse.job_status', index=3, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=797, serialized_end=1029, ) _JOBSCREATEREQUEST_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.JobsCreateRequest.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.JobsCreateRequest.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsCreateRequest.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=341, serialized_end=388, ) _JOBSCREATEREQUEST = _descriptor.Descriptor( name='JobsCreateRequest', full_name='nvidia.clara.platform.JobsCreateRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsCreateRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.JobsCreateRequest.pipeline_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.JobsCreateRequest.name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='priority', full_name='nvidia.clara.platform.JobsCreateRequest.priority', index=3, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='input_payloads', full_name='nvidia.clara.platform.JobsCreateRequest.input_payloads', index=4, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.JobsCreateRequest.metadata', index=5, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSCREATEREQUEST_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1032, serialized_end=1411, ) _JOBSCREATERESPONSE = _descriptor.Descriptor( name='JobsCreateResponse', full_name='nvidia.clara.platform.JobsCreateResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsCreateResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsCreateResponse.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='payload_id', full_name='nvidia.clara.platform.JobsCreateResponse.payload_id', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1414, serialized_end=1595, ) _JOBSLISTREQUEST_JOBFILTER = _descriptor.Descriptor( name='JobFilter', full_name='nvidia.clara.platform.JobsListRequest.JobFilter', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='completed_before', full_name='nvidia.clara.platform.JobsListRequest.JobFilter.completed_before', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='created_after', full_name='nvidia.clara.platform.JobsListRequest.JobFilter.created_after', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='has_state', full_name='nvidia.clara.platform.JobsListRequest.JobFilter.has_state', index=2, number=3, type=14, cpp_type=8, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='has_status', full_name='nvidia.clara.platform.JobsListRequest.JobFilter.has_status', index=3, number=4, type=14, cpp_type=8, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.JobsListRequest.JobFilter.pipeline_id', index=4, number=5, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1738, serialized_end=2028, ) _JOBSLISTREQUEST = _descriptor.Descriptor( name='JobsListRequest', full_name='nvidia.clara.platform.JobsListRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsListRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='filter', full_name='nvidia.clara.platform.JobsListRequest.filter', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSLISTREQUEST_JOBFILTER, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1598, serialized_end=2028, ) _JOBSLISTRESPONSE_JOBDETAILS_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=341, serialized_end=388, ) _JOBSLISTRESPONSE_JOBDETAILS = _descriptor.Descriptor( name='JobDetails', full_name='nvidia.clara.platform.JobsListResponse.JobDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.job_id', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='payload_id', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.payload_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.pipeline_id', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_name', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.job_name', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='state', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.state', index=4, number=5, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='status', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.status', index=5, number=6, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='priority', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.priority', index=6, number=7, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='created', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.created', index=7, number=13, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='started', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.started', index=8, number=14, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='stopped', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.stopped', index=9, number=15, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.metadata', index=10, number=16, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp_created', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.timestamp_created', index=11, number=10, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=_b('\030\001'), file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp_started', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.timestamp_started', index=12, number=11, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=_b('\030\001'), file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp_stopped', full_name='nvidia.clara.platform.JobsListResponse.JobDetails.timestamp_stopped', index=13, number=12, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=_b('\030\001'), file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSLISTRESPONSE_JOBDETAILS_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2180, serialized_end=2903, ) _JOBSLISTRESPONSE = _descriptor.Descriptor( name='JobsListResponse', full_name='nvidia.clara.platform.JobsListResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsListResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_details', full_name='nvidia.clara.platform.JobsListResponse.job_details', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSLISTRESPONSE_JOBDETAILS, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2031, serialized_end=2903, ) _JOBSREADLOGSREQUEST = _descriptor.Descriptor( name='JobsReadLogsRequest', full_name='nvidia.clara.platform.JobsReadLogsRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsReadLogsRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsReadLogsRequest.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='operator_name', full_name='nvidia.clara.platform.JobsReadLogsRequest.operator_name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2906, serialized_end=3055, ) _JOBSREADLOGSRESPONSE = _descriptor.Descriptor( name='JobsReadLogsResponse', full_name='nvidia.clara.platform.JobsReadLogsResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsReadLogsResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsReadLogsResponse.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='operator_name', full_name='nvidia.clara.platform.JobsReadLogsResponse.operator_name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='logs', full_name='nvidia.clara.platform.JobsReadLogsResponse.logs', index=3, number=4, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3058, serialized_end=3223, ) _JOBSREMOVEMETADATAREQUEST = _descriptor.Descriptor( name='JobsRemoveMetadataRequest', full_name='nvidia.clara.platform.JobsRemoveMetadataRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsRemoveMetadataRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsRemoveMetadataRequest.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='keys', full_name='nvidia.clara.platform.JobsRemoveMetadataRequest.keys', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3226, serialized_end=3372, ) _JOBSREMOVEMETADATARESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=341, serialized_end=388, ) _JOBSREMOVEMETADATARESPONSE = _descriptor.Descriptor( name='JobsRemoveMetadataResponse', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.JobsRemoveMetadataResponse.metadata', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSREMOVEMETADATARESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3375, serialized_end=3641, ) _JOBSSTARTREQUEST_NAMEDVALUE = _descriptor.Descriptor( name='NamedValue', full_name='nvidia.clara.platform.JobsStartRequest.NamedValue', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.JobsStartRequest.NamedValue.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsStartRequest.NamedValue.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3840, serialized_end=3881, ) _JOBSSTARTREQUEST = _descriptor.Descriptor( name='JobsStartRequest', full_name='nvidia.clara.platform.JobsStartRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsStartRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsStartRequest.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='Variables', full_name='nvidia.clara.platform.JobsStartRequest.Variables', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSSTARTREQUEST_NAMEDVALUE, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3644, serialized_end=3881, ) _JOBSSTARTRESPONSE = _descriptor.Descriptor( name='JobsStartResponse', full_name='nvidia.clara.platform.JobsStartResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsStartResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='priority', full_name='nvidia.clara.platform.JobsStartResponse.priority', index=1, number=4, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='state', full_name='nvidia.clara.platform.JobsStartResponse.state', index=2, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='status', full_name='nvidia.clara.platform.JobsStartResponse.status', index=3, number=3, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=3884, serialized_end=4110, ) _JOBSSTATUSREQUEST = _descriptor.Descriptor( name='JobsStatusRequest', full_name='nvidia.clara.platform.JobsStatusRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsStatusRequest.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsStatusRequest.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4112, serialized_end=4236, ) _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS = _descriptor.Descriptor( name='JobOperatorDetails', full_name='nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='status', full_name='nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails.status', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='created', full_name='nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails.created', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='started', full_name='nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails.started', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='stopped', full_name='nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails.stopped', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=5139, serialized_end=5384, ) _JOBSSTATUSRESPONSE_JOBDAGNODE = _descriptor.Descriptor( name='JobDagNode', full_name='nvidia.clara.platform.JobsStatusResponse.JobDagNode', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.JobsStatusResponse.JobDagNode.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='input_dependencies', full_name='nvidia.clara.platform.JobsStatusResponse.JobDagNode.input_dependencies', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='order_dependencies', full_name='nvidia.clara.platform.JobsStatusResponse.JobDagNode.order_dependencies', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=5387, serialized_end=5577, ) _JOBSSTATUSRESPONSE_METADATAENTRY = _descriptor.Descriptor( name='MetadataEntry', full_name='nvidia.clara.platform.JobsStatusResponse.MetadataEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='nvidia.clara.platform.JobsStatusResponse.MetadataEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.JobsStatusResponse.MetadataEntry.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=341, serialized_end=388, ) _JOBSSTATUSRESPONSE = _descriptor.Descriptor( name='JobsStatusResponse', full_name='nvidia.clara.platform.JobsStatusResponse', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='nvidia.clara.platform.JobsStatusResponse.header', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='job_id', full_name='nvidia.clara.platform.JobsStatusResponse.job_id', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='pipeline_id', full_name='nvidia.clara.platform.JobsStatusResponse.pipeline_id', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='payload_id', full_name='nvidia.clara.platform.JobsStatusResponse.payload_id', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='state', full_name='nvidia.clara.platform.JobsStatusResponse.state', index=4, number=5, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='status', full_name='nvidia.clara.platform.JobsStatusResponse.status', index=5, number=6, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='nvidia.clara.platform.JobsStatusResponse.name', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='priority', full_name='nvidia.clara.platform.JobsStatusResponse.priority', index=7, number=9, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='created', full_name='nvidia.clara.platform.JobsStatusResponse.created', index=8, number=13, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='started', full_name='nvidia.clara.platform.JobsStatusResponse.started', index=9, number=14, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='stopped', full_name='nvidia.clara.platform.JobsStatusResponse.stopped', index=10, number=15, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='operator_details', full_name='nvidia.clara.platform.JobsStatusResponse.operator_details', index=11, number=16, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='metadata', full_name='nvidia.clara.platform.JobsStatusResponse.metadata', index=12, number=17, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='dag', full_name='nvidia.clara.platform.JobsStatusResponse.dag', index=13, number=18, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='messages', full_name='nvidia.clara.platform.JobsStatusResponse.messages', index=14, number=8, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp_created', full_name='nvidia.clara.platform.JobsStatusResponse.timestamp_created', index=15, number=10, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=_b('\030\001'), file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp_started', full_name='nvidia.clara.platform.JobsStatusResponse.timestamp_started', index=16, number=11, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=_b('\030\001'), file=DESCRIPTOR), _descriptor.FieldDescriptor( name='timestamp_stopped', full_name='nvidia.clara.platform.JobsStatusResponse.timestamp_stopped', index=17, number=12, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=_b('\030\001'), file=DESCRIPTOR), ], extensions=[ ], nested_types=[_JOBSSTATUSRESPONSE_JOBOPERATORDETAILS, _JOBSSTATUSRESPONSE_JOBDAGNODE, _JOBSSTATUSRESPONSE_METADATAENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=4239, serialized_end=5626, ) _JOBSADDMETADATAREQUEST_METADATAENTRY.containing_type = _JOBSADDMETADATAREQUEST _JOBSADDMETADATAREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSADDMETADATAREQUEST.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSADDMETADATAREQUEST.fields_by_name['metadata'].message_type = _JOBSADDMETADATAREQUEST_METADATAENTRY _JOBSADDMETADATARESPONSE_METADATAENTRY.containing_type = _JOBSADDMETADATARESPONSE _JOBSADDMETADATARESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSADDMETADATARESPONSE.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSADDMETADATARESPONSE.fields_by_name['metadata'].message_type = _JOBSADDMETADATARESPONSE_METADATAENTRY _JOBSCANCELREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSCANCELREQUEST.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSCANCELRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSCANCELRESPONSE.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSCANCELRESPONSE.fields_by_name['job_state'].enum_type = _JOBSTATE _JOBSCANCELRESPONSE.fields_by_name['job_status'].enum_type = _JOBSTATUS _JOBSCREATEREQUEST_METADATAENTRY.containing_type = _JOBSCREATEREQUEST _JOBSCREATEREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSCREATEREQUEST.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSCREATEREQUEST.fields_by_name['priority'].enum_type = _JOBPRIORITY _JOBSCREATEREQUEST.fields_by_name['input_payloads'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSCREATEREQUEST.fields_by_name['metadata'].message_type = _JOBSCREATEREQUEST_METADATAENTRY _JOBSCREATERESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSCREATERESPONSE.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSCREATERESPONSE.fields_by_name['payload_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSLISTREQUEST_JOBFILTER.fields_by_name['completed_before'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSLISTREQUEST_JOBFILTER.fields_by_name['created_after'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSLISTREQUEST_JOBFILTER.fields_by_name['has_state'].enum_type = _JOBSTATE _JOBSLISTREQUEST_JOBFILTER.fields_by_name['has_status'].enum_type = _JOBSTATUS _JOBSLISTREQUEST_JOBFILTER.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSLISTREQUEST_JOBFILTER.containing_type = _JOBSLISTREQUEST _JOBSLISTREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSLISTREQUEST.fields_by_name['filter'].message_type = _JOBSLISTREQUEST_JOBFILTER _JOBSLISTRESPONSE_JOBDETAILS_METADATAENTRY.containing_type = _JOBSLISTRESPONSE_JOBDETAILS _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['payload_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['state'].enum_type = _JOBSTATE _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['status'].enum_type = _JOBSTATUS _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['priority'].enum_type = _JOBPRIORITY _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['created'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['started'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['stopped'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['metadata'].message_type = _JOBSLISTRESPONSE_JOBDETAILS_METADATAENTRY _JOBSLISTRESPONSE_JOBDETAILS.containing_type = _JOBSLISTRESPONSE _JOBSLISTRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSLISTRESPONSE.fields_by_name['job_details'].message_type = _JOBSLISTRESPONSE_JOBDETAILS _JOBSREADLOGSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSREADLOGSREQUEST.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSREADLOGSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSREADLOGSRESPONSE.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSREMOVEMETADATAREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSREMOVEMETADATAREQUEST.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSREMOVEMETADATARESPONSE_METADATAENTRY.containing_type = _JOBSREMOVEMETADATARESPONSE _JOBSREMOVEMETADATARESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSREMOVEMETADATARESPONSE.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSREMOVEMETADATARESPONSE.fields_by_name['metadata'].message_type = _JOBSREMOVEMETADATARESPONSE_METADATAENTRY _JOBSSTARTREQUEST_NAMEDVALUE.containing_type = _JOBSSTARTREQUEST _JOBSSTARTREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSSTARTREQUEST.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSSTARTREQUEST.fields_by_name['Variables'].message_type = _JOBSSTARTREQUEST_NAMEDVALUE _JOBSSTARTRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSSTARTRESPONSE.fields_by_name['priority'].enum_type = _JOBPRIORITY _JOBSSTARTRESPONSE.fields_by_name['state'].enum_type = _JOBSTATE _JOBSSTARTRESPONSE.fields_by_name['status'].enum_type = _JOBSTATUS _JOBSSTATUSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER _JOBSSTATUSREQUEST.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS.fields_by_name['status'].enum_type = _JOBOPERATORSTATUS _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS.fields_by_name['created'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS.fields_by_name['started'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS.fields_by_name['stopped'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS.containing_type = _JOBSSTATUSRESPONSE _JOBSSTATUSRESPONSE_JOBDAGNODE.fields_by_name['input_dependencies'].message_type = _JOBSSTATUSRESPONSE_JOBDAGNODE _JOBSSTATUSRESPONSE_JOBDAGNODE.fields_by_name['order_dependencies'].message_type = _JOBSSTATUSRESPONSE_JOBDAGNODE _JOBSSTATUSRESPONSE_JOBDAGNODE.containing_type = _JOBSSTATUSRESPONSE _JOBSSTATUSRESPONSE_METADATAENTRY.containing_type = _JOBSSTATUSRESPONSE _JOBSSTATUSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER _JOBSSTATUSRESPONSE.fields_by_name['job_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSSTATUSRESPONSE.fields_by_name['pipeline_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSSTATUSRESPONSE.fields_by_name['payload_id'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._IDENTIFIER _JOBSSTATUSRESPONSE.fields_by_name['state'].enum_type = _JOBSTATE _JOBSSTATUSRESPONSE.fields_by_name['status'].enum_type = _JOBSTATUS _JOBSSTATUSRESPONSE.fields_by_name['priority'].enum_type = _JOBPRIORITY _JOBSSTATUSRESPONSE.fields_by_name['created'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSSTATUSRESPONSE.fields_by_name['started'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSSTATUSRESPONSE.fields_by_name['stopped'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP _JOBSSTATUSRESPONSE.fields_by_name['operator_details'].message_type = _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS _JOBSSTATUSRESPONSE.fields_by_name['metadata'].message_type = _JOBSSTATUSRESPONSE_METADATAENTRY _JOBSSTATUSRESPONSE.fields_by_name['dag'].message_type = _JOBSSTATUSRESPONSE_JOBDAGNODE DESCRIPTOR.message_types_by_name['JobsAddMetadataRequest'] = _JOBSADDMETADATAREQUEST DESCRIPTOR.message_types_by_name['JobsAddMetadataResponse'] = _JOBSADDMETADATARESPONSE DESCRIPTOR.message_types_by_name['JobsCancelRequest'] = _JOBSCANCELREQUEST DESCRIPTOR.message_types_by_name['JobsCancelResponse'] = _JOBSCANCELRESPONSE DESCRIPTOR.message_types_by_name['JobsCreateRequest'] = _JOBSCREATEREQUEST DESCRIPTOR.message_types_by_name['JobsCreateResponse'] = _JOBSCREATERESPONSE DESCRIPTOR.message_types_by_name['JobsListRequest'] = _JOBSLISTREQUEST DESCRIPTOR.message_types_by_name['JobsListResponse'] = _JOBSLISTRESPONSE DESCRIPTOR.message_types_by_name['JobsReadLogsRequest'] = _JOBSREADLOGSREQUEST DESCRIPTOR.message_types_by_name['JobsReadLogsResponse'] = _JOBSREADLOGSRESPONSE DESCRIPTOR.message_types_by_name['JobsRemoveMetadataRequest'] = _JOBSREMOVEMETADATAREQUEST DESCRIPTOR.message_types_by_name['JobsRemoveMetadataResponse'] = _JOBSREMOVEMETADATARESPONSE DESCRIPTOR.message_types_by_name['JobsStartRequest'] = _JOBSSTARTREQUEST DESCRIPTOR.message_types_by_name['JobsStartResponse'] = _JOBSSTARTRESPONSE DESCRIPTOR.message_types_by_name['JobsStatusRequest'] = _JOBSSTATUSREQUEST DESCRIPTOR.message_types_by_name['JobsStatusResponse'] = _JOBSSTATUSRESPONSE DESCRIPTOR.enum_types_by_name['JobOperatorStatus'] = _JOBOPERATORSTATUS DESCRIPTOR.enum_types_by_name['JobPriority'] = _JOBPRIORITY DESCRIPTOR.enum_types_by_name['JobState'] = _JOBSTATE DESCRIPTOR.enum_types_by_name['JobStatus'] = _JOBSTATUS _sym_db.RegisterFileDescriptor(DESCRIPTOR) JobsAddMetadataRequest = _reflection.GeneratedProtocolMessageType('JobsAddMetadataRequest', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _JOBSADDMETADATAREQUEST_METADATAENTRY, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsAddMetadataRequest.MetadataEntry) )) , DESCRIPTOR = _JOBSADDMETADATAREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsAddMetadataRequest) )) _sym_db.RegisterMessage(JobsAddMetadataRequest) _sym_db.RegisterMessage(JobsAddMetadataRequest.MetadataEntry) JobsAddMetadataResponse = _reflection.GeneratedProtocolMessageType('JobsAddMetadataResponse', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _JOBSADDMETADATARESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsAddMetadataResponse.MetadataEntry) )) , DESCRIPTOR = _JOBSADDMETADATARESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsAddMetadataResponse) )) _sym_db.RegisterMessage(JobsAddMetadataResponse) _sym_db.RegisterMessage(JobsAddMetadataResponse.MetadataEntry) JobsCancelRequest = _reflection.GeneratedProtocolMessageType('JobsCancelRequest', (_message.Message,), dict( DESCRIPTOR = _JOBSCANCELREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsCancelRequest) )) _sym_db.RegisterMessage(JobsCancelRequest) JobsCancelResponse = _reflection.GeneratedProtocolMessageType('JobsCancelResponse', (_message.Message,), dict( DESCRIPTOR = _JOBSCANCELRESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsCancelResponse) )) _sym_db.RegisterMessage(JobsCancelResponse) JobsCreateRequest = _reflection.GeneratedProtocolMessageType('JobsCreateRequest', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _JOBSCREATEREQUEST_METADATAENTRY, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsCreateRequest.MetadataEntry) )) , DESCRIPTOR = _JOBSCREATEREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsCreateRequest) )) _sym_db.RegisterMessage(JobsCreateRequest) _sym_db.RegisterMessage(JobsCreateRequest.MetadataEntry) JobsCreateResponse = _reflection.GeneratedProtocolMessageType('JobsCreateResponse', (_message.Message,), dict( DESCRIPTOR = _JOBSCREATERESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsCreateResponse) )) _sym_db.RegisterMessage(JobsCreateResponse) JobsListRequest = _reflection.GeneratedProtocolMessageType('JobsListRequest', (_message.Message,), dict( JobFilter = _reflection.GeneratedProtocolMessageType('JobFilter', (_message.Message,), dict( DESCRIPTOR = _JOBSLISTREQUEST_JOBFILTER, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsListRequest.JobFilter) )) , DESCRIPTOR = _JOBSLISTREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsListRequest) )) _sym_db.RegisterMessage(JobsListRequest) _sym_db.RegisterMessage(JobsListRequest.JobFilter) JobsListResponse = _reflection.GeneratedProtocolMessageType('JobsListResponse', (_message.Message,), dict( JobDetails = _reflection.GeneratedProtocolMessageType('JobDetails', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _JOBSLISTRESPONSE_JOBDETAILS_METADATAENTRY, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsListResponse.JobDetails.MetadataEntry) )) , DESCRIPTOR = _JOBSLISTRESPONSE_JOBDETAILS, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsListResponse.JobDetails) )) , DESCRIPTOR = _JOBSLISTRESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsListResponse) )) _sym_db.RegisterMessage(JobsListResponse) _sym_db.RegisterMessage(JobsListResponse.JobDetails) _sym_db.RegisterMessage(JobsListResponse.JobDetails.MetadataEntry) JobsReadLogsRequest = _reflection.GeneratedProtocolMessageType('JobsReadLogsRequest', (_message.Message,), dict( DESCRIPTOR = _JOBSREADLOGSREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsReadLogsRequest) )) _sym_db.RegisterMessage(JobsReadLogsRequest) JobsReadLogsResponse = _reflection.GeneratedProtocolMessageType('JobsReadLogsResponse', (_message.Message,), dict( DESCRIPTOR = _JOBSREADLOGSRESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsReadLogsResponse) )) _sym_db.RegisterMessage(JobsReadLogsResponse) JobsRemoveMetadataRequest = _reflection.GeneratedProtocolMessageType('JobsRemoveMetadataRequest', (_message.Message,), dict( DESCRIPTOR = _JOBSREMOVEMETADATAREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsRemoveMetadataRequest) )) _sym_db.RegisterMessage(JobsRemoveMetadataRequest) JobsRemoveMetadataResponse = _reflection.GeneratedProtocolMessageType('JobsRemoveMetadataResponse', (_message.Message,), dict( MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _JOBSREMOVEMETADATARESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsRemoveMetadataResponse.MetadataEntry) )) , DESCRIPTOR = _JOBSREMOVEMETADATARESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsRemoveMetadataResponse) )) _sym_db.RegisterMessage(JobsRemoveMetadataResponse) _sym_db.RegisterMessage(JobsRemoveMetadataResponse.MetadataEntry) JobsStartRequest = _reflection.GeneratedProtocolMessageType('JobsStartRequest', (_message.Message,), dict( NamedValue = _reflection.GeneratedProtocolMessageType('NamedValue', (_message.Message,), dict( DESCRIPTOR = _JOBSSTARTREQUEST_NAMEDVALUE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStartRequest.NamedValue) )) , DESCRIPTOR = _JOBSSTARTREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStartRequest) )) _sym_db.RegisterMessage(JobsStartRequest) _sym_db.RegisterMessage(JobsStartRequest.NamedValue) JobsStartResponse = _reflection.GeneratedProtocolMessageType('JobsStartResponse', (_message.Message,), dict( DESCRIPTOR = _JOBSSTARTRESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStartResponse) )) _sym_db.RegisterMessage(JobsStartResponse) JobsStatusRequest = _reflection.GeneratedProtocolMessageType('JobsStatusRequest', (_message.Message,), dict( DESCRIPTOR = _JOBSSTATUSREQUEST, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStatusRequest) )) _sym_db.RegisterMessage(JobsStatusRequest) JobsStatusResponse = _reflection.GeneratedProtocolMessageType('JobsStatusResponse', (_message.Message,), dict( JobOperatorDetails = _reflection.GeneratedProtocolMessageType('JobOperatorDetails', (_message.Message,), dict( DESCRIPTOR = _JOBSSTATUSRESPONSE_JOBOPERATORDETAILS, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStatusResponse.JobOperatorDetails) )) , JobDagNode = _reflection.GeneratedProtocolMessageType('JobDagNode', (_message.Message,), dict( DESCRIPTOR = _JOBSSTATUSRESPONSE_JOBDAGNODE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStatusResponse.JobDagNode) )) , MetadataEntry = _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), dict( DESCRIPTOR = _JOBSSTATUSRESPONSE_METADATAENTRY, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStatusResponse.MetadataEntry) )) , DESCRIPTOR = _JOBSSTATUSRESPONSE, __module__ = 'nvidia.clara.platform.jobs_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.JobsStatusResponse) )) _sym_db.RegisterMessage(JobsStatusResponse) _sym_db.RegisterMessage(JobsStatusResponse.JobOperatorDetails) _sym_db.RegisterMessage(JobsStatusResponse.JobDagNode) _sym_db.RegisterMessage(JobsStatusResponse.MetadataEntry) DESCRIPTOR._options = None _JOBSADDMETADATAREQUEST_METADATAENTRY._options = None _JOBSADDMETADATARESPONSE_METADATAENTRY._options = None _JOBSCREATEREQUEST_METADATAENTRY._options = None _JOBSLISTRESPONSE_JOBDETAILS_METADATAENTRY._options = None _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['timestamp_created']._options = None _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['timestamp_started']._options = None _JOBSLISTRESPONSE_JOBDETAILS.fields_by_name['timestamp_stopped']._options = None _JOBSREMOVEMETADATARESPONSE_METADATAENTRY._options = None _JOBSSTATUSRESPONSE_METADATAENTRY._options = None _JOBSSTATUSRESPONSE.fields_by_name['timestamp_created']._options = None _JOBSSTATUSRESPONSE.fields_by_name['timestamp_started']._options = None _JOBSSTATUSRESPONSE.fields_by_name['timestamp_stopped']._options = None _JOBS = _descriptor.ServiceDescriptor( name='Jobs', full_name='nvidia.clara.platform.Jobs', file=DESCRIPTOR, index=0, serialized_options=None, serialized_start=6228, serialized_end=7034, methods=[ _descriptor.MethodDescriptor( name='AddMetadata', full_name='nvidia.clara.platform.Jobs.AddMetadata', index=0, containing_service=None, input_type=_JOBSADDMETADATAREQUEST, output_type=_JOBSADDMETADATARESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Cancel', full_name='nvidia.clara.platform.Jobs.Cancel', index=1, containing_service=None, input_type=_JOBSCANCELREQUEST, output_type=_JOBSCANCELRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Create', full_name='nvidia.clara.platform.Jobs.Create', index=2, containing_service=None, input_type=_JOBSCREATEREQUEST, output_type=_JOBSCREATERESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='List', full_name='nvidia.clara.platform.Jobs.List', index=3, containing_service=None, input_type=_JOBSLISTREQUEST, output_type=_JOBSLISTRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='ReadLogs', full_name='nvidia.clara.platform.Jobs.ReadLogs', index=4, containing_service=None, input_type=_JOBSREADLOGSREQUEST, output_type=_JOBSREADLOGSRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='RemoveMetadata', full_name='nvidia.clara.platform.Jobs.RemoveMetadata', index=5, containing_service=None, input_type=_JOBSREMOVEMETADATAREQUEST, output_type=_JOBSREMOVEMETADATARESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Start', full_name='nvidia.clara.platform.Jobs.Start', index=6, containing_service=None, input_type=_JOBSSTARTREQUEST, output_type=_JOBSSTARTRESPONSE, serialized_options=None, ), _descriptor.MethodDescriptor( name='Status', full_name='nvidia.clara.platform.Jobs.Status', index=7, containing_service=None, input_type=_JOBSSTATUSREQUEST, output_type=_JOBSSTATUSRESPONSE, serialized_options=None, ), ]) _sym_db.RegisterServiceDescriptor(_JOBS) DESCRIPTOR.services_by_name['Jobs'] = _JOBS # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/jobs_pb2.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/clara.proto # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc from nvidia_clara.grpc import jobs_pb2 as nvidia_dot_clara_dot_platform_dot_jobs__pb2 class JobsStub(object): # missing associated documentation comment in .proto file pass def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.AddMetadata = channel.unary_unary( '/nvidia.clara.platform.Jobs/AddMetadata', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsAddMetadataRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsAddMetadataResponse.FromString, ) self.Cancel = channel.unary_unary( '/nvidia.clara.platform.Jobs/Cancel', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCancelRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCancelResponse.FromString, ) self.Create = channel.unary_unary( '/nvidia.clara.platform.Jobs/Create', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCreateRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCreateResponse.FromString, ) self.List = channel.unary_stream( '/nvidia.clara.platform.Jobs/List', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsListRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsListResponse.FromString, ) self.ReadLogs = channel.unary_stream( '/nvidia.clara.platform.Jobs/ReadLogs', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsReadLogsRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsReadLogsResponse.FromString, ) self.RemoveMetadata = channel.unary_unary( '/nvidia.clara.platform.Jobs/RemoveMetadata', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsRemoveMetadataRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsRemoveMetadataResponse.FromString, ) self.Start = channel.unary_unary( '/nvidia.clara.platform.Jobs/Start', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStartRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStartResponse.FromString, ) self.Status = channel.unary_unary( '/nvidia.clara.platform.Jobs/Status', request_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStatusRequest.SerializeToString, response_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStatusResponse.FromString, ) class JobsServicer(object): # missing associated documentation comment in .proto file pass def AddMetadata(self, request, context): """Requests the addition of metadata to a job. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Cancel(self, request, context): """Request cancellation of a running job. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Create(self, request, context): """Requests creation of a new job based on a known pipeline. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def List(self, request, context): """Requests a filtered list of all known jobs, or a list of all running jobs if no filter is provided. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ReadLogs(self, request, context): """Requests the download of logs for an operator of a job. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def RemoveMetadata(self, request, context): """Requests the removal of metadata from a job. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Start(self, request, context): """Request starting of a job created by the Create RPC. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Status(self, request, context): """Requests the status of a known job. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_JobsServicer_to_server(servicer, server): rpc_method_handlers = { 'AddMetadata': grpc.unary_unary_rpc_method_handler( servicer.AddMetadata, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsAddMetadataRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsAddMetadataResponse.SerializeToString, ), 'Cancel': grpc.unary_unary_rpc_method_handler( servicer.Cancel, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCancelRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCancelResponse.SerializeToString, ), 'Create': grpc.unary_unary_rpc_method_handler( servicer.Create, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCreateRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsCreateResponse.SerializeToString, ), 'List': grpc.unary_stream_rpc_method_handler( servicer.List, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsListRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsListResponse.SerializeToString, ), 'ReadLogs': grpc.unary_stream_rpc_method_handler( servicer.ReadLogs, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsReadLogsRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsReadLogsResponse.SerializeToString, ), 'RemoveMetadata': grpc.unary_unary_rpc_method_handler( servicer.RemoveMetadata, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsRemoveMetadataRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsRemoveMetadataResponse.SerializeToString, ), 'Start': grpc.unary_unary_rpc_method_handler( servicer.Start, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStartRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStartResponse.SerializeToString, ), 'Status': grpc.unary_unary_rpc_method_handler( servicer.Status, request_deserializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStatusRequest.FromString, response_serializer=nvidia_dot_clara_dot_platform_dot_jobs__pb2.JobsStatusResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'nvidia.clara.platform.Jobs', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,))
clara-platform-python-client-main
nvidia_clara/grpc/jobs_pb2_grpc.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: nvidia/clara/platform/common.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='nvidia/clara/platform/common.proto', package='nvidia.clara.platform', syntax='proto3', serialized_options=_b('\n\031com.nvidia.clara.platformZ\004apis\252\002\032Nvidia.Clara.Platform.Grpc'), serialized_pb=_b('\n\"nvidia/clara/platform/common.proto\x12\x15nvidia.clara.platform\"\x1b\n\nIdentifier\x12\r\n\x05value\x18\x01 \x01(\t\"E\n\x07Version\x12\r\n\x05major\x18\x01 \x01(\x05\x12\r\n\x05minor\x18\x02 \x01(\x05\x12\r\n\x05patch\x18\x03 \x01(\x05\x12\r\n\x05label\x18\x04 \x01(\t\"X\n\rRequestHeader\x12\x33\n\x0b\x61pi_version\x18\x01 \x01(\x0b\x32\x1e.nvidia.clara.platform.Version\x12\x12\n\nuser_agent\x18\x02 \x01(\t\"0\n\x0eResponseHeader\x12\x0c\n\x04\x63ode\x18\x01 \x01(\x11\x12\x10\n\x08messages\x18\x02 \x03(\t\"\x1a\n\tTimestamp\x12\r\n\x05value\x18\x01 \x01(\x12\x42>\n\x19\x63om.nvidia.clara.platformZ\x04\x61pis\xaa\x02\x1aNvidia.Clara.Platform.Grpcb\x06proto3') ) _IDENTIFIER = _descriptor.Descriptor( name='Identifier', full_name='nvidia.clara.platform.Identifier', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.Identifier.value', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=61, serialized_end=88, ) _VERSION = _descriptor.Descriptor( name='Version', full_name='nvidia.clara.platform.Version', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='major', full_name='nvidia.clara.platform.Version.major', index=0, number=1, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='minor', full_name='nvidia.clara.platform.Version.minor', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='patch', full_name='nvidia.clara.platform.Version.patch', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='label', full_name='nvidia.clara.platform.Version.label', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=90, serialized_end=159, ) _REQUESTHEADER = _descriptor.Descriptor( name='RequestHeader', full_name='nvidia.clara.platform.RequestHeader', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='api_version', full_name='nvidia.clara.platform.RequestHeader.api_version', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='user_agent', full_name='nvidia.clara.platform.RequestHeader.user_agent', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=161, serialized_end=249, ) _RESPONSEHEADER = _descriptor.Descriptor( name='ResponseHeader', full_name='nvidia.clara.platform.ResponseHeader', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='code', full_name='nvidia.clara.platform.ResponseHeader.code', index=0, number=1, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='messages', full_name='nvidia.clara.platform.ResponseHeader.messages', index=1, number=2, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=251, serialized_end=299, ) _TIMESTAMP = _descriptor.Descriptor( name='Timestamp', full_name='nvidia.clara.platform.Timestamp', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='value', full_name='nvidia.clara.platform.Timestamp.value', index=0, number=1, type=18, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=301, serialized_end=327, ) _REQUESTHEADER.fields_by_name['api_version'].message_type = _VERSION DESCRIPTOR.message_types_by_name['Identifier'] = _IDENTIFIER DESCRIPTOR.message_types_by_name['Version'] = _VERSION DESCRIPTOR.message_types_by_name['RequestHeader'] = _REQUESTHEADER DESCRIPTOR.message_types_by_name['ResponseHeader'] = _RESPONSEHEADER DESCRIPTOR.message_types_by_name['Timestamp'] = _TIMESTAMP _sym_db.RegisterFileDescriptor(DESCRIPTOR) Identifier = _reflection.GeneratedProtocolMessageType('Identifier', (_message.Message,), dict( DESCRIPTOR = _IDENTIFIER, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.Identifier) )) _sym_db.RegisterMessage(Identifier) Version = _reflection.GeneratedProtocolMessageType('Version', (_message.Message,), dict( DESCRIPTOR = _VERSION, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.Version) )) _sym_db.RegisterMessage(Version) RequestHeader = _reflection.GeneratedProtocolMessageType('RequestHeader', (_message.Message,), dict( DESCRIPTOR = _REQUESTHEADER, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.RequestHeader) )) _sym_db.RegisterMessage(RequestHeader) ResponseHeader = _reflection.GeneratedProtocolMessageType('ResponseHeader', (_message.Message,), dict( DESCRIPTOR = _RESPONSEHEADER, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.ResponseHeader) )) _sym_db.RegisterMessage(ResponseHeader) Timestamp = _reflection.GeneratedProtocolMessageType('Timestamp', (_message.Message,), dict( DESCRIPTOR = _TIMESTAMP, __module__ = 'nvidia.clara.platform.common_pb2' # @@protoc_insertion_point(class_scope:nvidia.clara.platform.Timestamp) )) _sym_db.RegisterMessage(Timestamp) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)
clara-platform-python-client-main
nvidia_clara/grpc/common_pb2_grpc.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import signal import time import grpc import grpc_testing from grpc.framework.foundation import logging_pool import nvidia_clara.grpc.common_pb2 as common_pb2 import nvidia_clara.grpc.jobs_pb2 as jobs_pb2 import nvidia_clara.grpc.jobs_pb2_grpc as jobs_pb2_grpc import nvidia_clara.grpc.payloads_pb2 as payloads_pb2 import nvidia_clara.grpc.payloads_pb2_grpc as payloads_pb2_grpc import nvidia_clara.grpc.pipelines_pb2 as pipelines_pb2 import nvidia_clara.grpc.pipelines_pb2_grpc as pipelines_pb2_grpc SERVICES = { 'Pipelines': pipelines_pb2.DESCRIPTOR.services_by_name, 'Jobs': jobs_pb2.DESCRIPTOR.services_by_name, 'Payloads': payloads_pb2.DESCRIPTOR.services_by_name } def get_stubs(service, channel): if service == 'Jobs': return jobs_pb2_grpc.JobsStub(channel) elif service == 'Payloads': return payloads_pb2_grpc.PayloadsStub(channel) elif service == 'Pipelines': return pipelines_pb2_grpc.PipelinesStub(channel) class Timeout(Exception): pass # Reference: https://github.com/grpc/grpc/blob/master/src/python/grpcio_tests/tests/testing/_client_test.py def verify_request(channel, stub_method, call_sig, expected_requests, responses, timeout=1): def timeout_handler(signum, frame): raise Timeout('Timeout while taking requests') try: # setting up timeout handler because grpc_testing module doesn't support timeout for take_xxx_xxx methods signal.signal(signal.SIGALRM, timeout_handler) signal.alarm(timeout) if call_sig == 'stream_unary': invocation_metadata, rpc = channel.take_stream_unary(stub_method) rpc.send_initial_metadata(()) for expected_request in expected_requests: request = rpc.take_request() assert expected_request == request rpc.requests_closed() rpc.terminate(next(iter(responses)), (), grpc.StatusCode.OK, '') elif call_sig == 'unary_stream': invocation_metadata, request, rpc = channel.take_unary_stream(stub_method) assert next(iter(expected_requests)) == request rpc.send_initial_metadata(()) for response in responses: rpc.send_response(response) rpc.terminate((), grpc.StatusCode.OK, '') elif call_sig == 'unary_unary': invocation_metadata, request, rpc = channel.take_unary_unary(stub_method) assert next(iter(expected_requests)) == request rpc.send_initial_metadata(()) rpc.terminate(next(iter(responses)), (), grpc.StatusCode.OK, '') except Timeout: raise finally: signal.alarm(0) def run_client_test(service_name, method_name, test_method, stub_method_handlers, *args, **kwargs): fake_time = grpc_testing.strict_fake_time( time.time()) channel = grpc_testing.channel(SERVICES[service_name].values(), fake_time) stub = get_stubs(service_name, channel) service = SERVICES[service_name][service_name] client_execution_thread_pool = logging_pool.pool(1) try: test_client_only = kwargs.pop('_test_client_only', None) application_future = client_execution_thread_pool.submit( test_method, stub, method_name, *args, **kwargs) # if the client method call is expected to raise exception before grpc call if test_client_only: pass # do not simulate grpc response else: for stub_method_name, call_sig, handlers in stub_method_handlers: expected_requests, responses = handlers stub_method = service.methods_by_name[stub_method_name] verify_request(channel, stub_method, call_sig, expected_requests, responses) application_return_value = application_future.result() application_exception = application_future.exception() if application_exception: raise application_exception return application_return_value except Timeout: raise finally: client_execution_thread_pool.shutdown(False) del channel
clara-platform-python-client-main
tests/test_client_tools.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import nvidia_clara.grpc.common_pb2 as common_pb2 import nvidia_clara.grpc.payloads_pb2 as payloads_pb2 from nvidia_clara.base_client import BaseClient from nvidia_clara.payloads_client import PayloadsClient import nvidia_clara.payload_types as payload_types from tests.test_jobs_client import run_client_test def run_payload_client(stub, method_name, *args, **kwargs): with PayloadsClient(target='10.0.0.1:50051', stub=stub) as client: response = getattr(client, method_name)(*args, **kwargs) return response class MockClaraPayloadServiceClient: stub_method_handlers = [] def __init__(self, channel, stub=None, request_header=None, logger=None): pass def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): return False def create_payload(self, *args, **kwargs): return run_client_test( 'Payloads', 'create_payload', run_payload_client, stub_method_handlers=MockClaraPayloadServiceClient.stub_method_handlers, *args, **kwargs) def download_from(self, *args, **kwargs): return run_client_test( 'Payloads', 'download_from', run_payload_client, stub_method_handlers=MockClaraPayloadServiceClient.stub_method_handlers, *args, **kwargs) def upload(self, *args, **kwargs): return run_client_test( 'Payloads', 'upload', run_payload_client, stub_method_handlers=MockClaraPayloadServiceClient.stub_method_handlers, *args, **kwargs) def close(self): pass def test_create_payload(): requests = [ payloads_pb2.PayloadsCreateRequest( header=BaseClient.get_request_header() ) ] responses = [ payloads_pb2.PayloadsCreateResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), payload_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ), type=payloads_pb2.PAYLOAD_TYPE_REUSABLE ) ] stub_method_handlers = [( 'Create', 'unary_unary', ( requests, responses ) )] # set handlers MockClaraPayloadServiceClient.stub_method_handlers = stub_method_handlers with MockClaraPayloadServiceClient('localhost:50051') as client: payload_details = client.create_payload() print(payload_details.payload_id) print(payload_details.payload_type) assert payload_details.payload_id.value == '92656d79fa414db6b294069c0e9e6df5' assert payload_details.payload_type == 2 MHD_TEXT = '''ObjectType = Image NDims = 3 BinaryData = True BinaryDataByteOrderMSB = False CompressedData = False TransformMatrix = -1 0 0 0 1 0 0 0 1 Offset = 0 0 0 CenterOfRotation = 0 0 0 AnatomicalOrientation = RAI ElementSpacing = 0.98 0.98 1.5 DimSize = 460 286 1182 ElementType = MET_SHORT ElementDataFile = highResCT.raw ''' def test_download_file(): fake_payload_id = '7ac5c691e13d4f45894a3a70d9925936' fake_request_file_name = '/input/highResCT.mhd' requests = [ payloads_pb2.PayloadsDownloadRequest( header=BaseClient.get_request_header(), payload_id=common_pb2.Identifier(value=fake_payload_id), name=fake_request_file_name) ] responses = [ payloads_pb2.PayloadsDownloadResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), details=payloads_pb2.PayloadFileDetails(mode=0, name=fake_request_file_name, size=len(MHD_TEXT)), data=MHD_TEXT.encode('utf-8') ) ] stub_method_handlers = [( 'Download', 'unary_stream', ( requests, responses ) )] MockClaraPayloadServiceClient.stub_method_handlers = stub_method_handlers with MockClaraPayloadServiceClient('localhost:50051') as client: if os.path.exists('./highResCT.mhd'): os.remove('./highResCT.mhd') with open('./highResCT.mhd', 'wb+') as wb: file_details = client.download_from(payload_id=payload_types.PayloadId(fake_payload_id), blob_name=fake_request_file_name, dest_obj=wb) assert file_details.mode == 0 assert file_details.name == fake_request_file_name assert file_details.size == len(MHD_TEXT) data = '' with open('./highResCT.mhd', 'r') as file: data = file.read() os.remove('./highResCT.mhd') print("Data Returned: ") print(data) assert data == MHD_TEXT def test_upload(tmp_path): fake_payload_id = '7ac5c691e13d4f45894a3a70d9925936' fake_file_name = './image.mhd' fake_response_file_name = './input/image.mhd' requests = [ payloads_pb2.PayloadsUploadRequest( header=BaseClient.get_request_header(), payload_id=common_pb2.Identifier(value=fake_payload_id), details=payloads_pb2.PayloadFileDetails(mode=0, name=fake_response_file_name, size=len(MHD_TEXT)), data=MHD_TEXT.encode('utf-8') ) ] responses = [ payloads_pb2.PayloadsUploadResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), details=payloads_pb2.PayloadFileDetails(mode=0, name=fake_response_file_name, size=len(MHD_TEXT)) ) ] stub_method_handlers = [( 'Upload', 'stream_unary', ( requests, responses ) )] MockClaraPayloadServiceClient.stub_method_handlers = stub_method_handlers with MockClaraPayloadServiceClient('localhost:50051') as client: if os.path.exists(fake_file_name): os.remove(fake_file_name) with open(fake_file_name, 'w') as wb: wb.write(MHD_TEXT) file_details = None with open(fake_file_name, 'rb+') as fp: file_details = client.upload(payload_id=payload_types.PayloadId(fake_payload_id), blob_name=fake_response_file_name, file_object=fp) os.remove(fake_file_name) print(file_details.mode, file_details.name, file_details.size) assert file_details.mode == 0 assert file_details.name == fake_response_file_name assert file_details.size == len(MHD_TEXT)
clara-platform-python-client-main
tests/test_payloads_client.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import nvidia_clara.grpc.common_pb2 as common_pb2 import nvidia_clara.grpc.pipelines_pb2 as pipelines_pb2 from nvidia_clara.base_client import BaseClient from nvidia_clara.pipelines_client import PipelinesClient import nvidia_clara.pipeline_types as pipeline_types from tests.test_client_tools import run_client_test def run_pipeline_client(stub, method_name, *args, **kwargs): with PipelinesClient(target='10.0.0.1:50051', stub=stub) as client: response = getattr(client, method_name)(*args, **kwargs) return response class MockClaraPipelineServiceClient: stub_method_handlers = [] def __init__(self, channel, stub=None, request_header=None, logger=None): pass def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): return False def create_pipeline(self, *args, **kwargs): return run_client_test( 'Pipelines', 'create_pipeline', run_pipeline_client, stub_method_handlers=MockClaraPipelineServiceClient.stub_method_handlers, *args, **kwargs) def list_pipelines(self, *args, **kwargs): return run_client_test( 'Pipelines', 'list_pipelines', run_pipeline_client, stub_method_handlers=MockClaraPipelineServiceClient.stub_method_handlers, *args, **kwargs) def close(self): pass PIPELINE_TEXT = '''api-version: 0.2.0 name: sample-pipeline operators: - name: producer import: path: producer.yaml - name: consumer import: path: consumer.yaml args: input-from: producer ''' def test_create_pipeline(): pipeline_yaml = 'pipeline.yaml' requests = [ pipelines_pb2.PipelinesCreateRequest( header=BaseClient.get_request_header(), definition=pipelines_pb2.PipelineDefinitionFile( path='pipeline.yaml', content=PIPELINE_TEXT) ) ] responses = [ pipelines_pb2.PipelinesCreateResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), pipeline_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ) ) ] stub_method_handlers = [( 'Create', 'stream_unary', ( requests, responses ) )] # set handlers MockClaraPipelineServiceClient.stub_method_handlers = stub_method_handlers def_list = [ pipeline_types.PipelineDefinition(name=pipeline_yaml, content=PIPELINE_TEXT) ] with MockClaraPipelineServiceClient('localhost:50051') as client: pipeline_id = client.create_pipeline(definition=def_list) print(pipeline_id) assert pipeline_id.value == '92656d79fa414db6b294069c0e9e6df5' def test_create_pipeline_with_id(): pipeline_yaml = 'pipeline.yaml' requests = [ pipelines_pb2.PipelinesCreateRequest( header=BaseClient.get_request_header(), pipeline_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ), definition=pipelines_pb2.PipelineDefinitionFile( path='pipeline.yaml', content=PIPELINE_TEXT) ) ] responses = [ pipelines_pb2.PipelinesCreateResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), pipeline_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ) ) ] stub_method_handlers = [( 'Create', 'stream_unary', ( requests, responses ) )] # set handlers MockClaraPipelineServiceClient.stub_method_handlers = stub_method_handlers def_list = [ pipeline_types.PipelineDefinition(name=pipeline_yaml, content=PIPELINE_TEXT) ] pipeline_id = pipeline_types.PipelineId('92656d79fa414db6b294069c0e9e6df5') with MockClaraPipelineServiceClient('localhost:50051') as client: pipeline_id = client.create_pipeline(definition=def_list, pipeline_id=pipeline_id) print(pipeline_id) assert pipeline_id.value == '92656d79fa414db6b294069c0e9e6df5' def test_list_pipeline(): requests = [ pipelines_pb2.PipelinesListRequest( header=BaseClient.get_request_header() ) ] responses = [ pipelines_pb2.PipelinesListResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), details=pipelines_pb2.PipelinesListResponse.PipelineDetails( name='Pipeline_1', pipeline_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ) ) ), pipelines_pb2.PipelinesListResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), details=pipelines_pb2.PipelinesListResponse.PipelineDetails( name='Pipeline_2', pipeline_id=common_pb2.Identifier( value='21656d79fa414db6b294069c0e9e6r23' ) ) ) ] stub_method_handlers = [( 'List', 'unary_stream', ( requests, responses ) )] # set handlers MockClaraPipelineServiceClient.stub_method_handlers = stub_method_handlers with MockClaraPipelineServiceClient('localhost:50051') as client: pipeline_list = client.list_pipelines() print(pipeline_list) assert len(pipeline_list) == 2 assert pipeline_list[0].pipeline_id.value == '92656d79fa414db6b294069c0e9e6df5' assert pipeline_list[1].pipeline_id.value == '21656d79fa414db6b294069c0e9e6r23'
clara-platform-python-client-main
tests/test_pipelines_client.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import datetime import nvidia_clara.grpc.common_pb2 as common_pb2 import nvidia_clara.grpc.jobs_pb2 as jobs_pb2 from nvidia_clara.base_client import BaseClient from nvidia_clara.jobs_client import JobsClient import nvidia_clara.pipeline_types as pipeline_types import nvidia_clara.job_types as job_types from tests.test_client_tools import run_client_test def run_job_client(stub, method_name, *args, **kwargs): with JobsClient(target='10.0.0.1:50051', stub=stub) as client: response = getattr(client, method_name)(*args, **kwargs) return response class MockClaraJobsServiceClient: stub_method_handlers = [] def __init__(self, channel, stub=None, request_header=None, logger=None): pass def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): return False def create_job(self, *args, **kwargs): return run_client_test( 'Jobs', 'create_job', run_job_client, stub_method_handlers=MockClaraJobsServiceClient.stub_method_handlers, *args, **kwargs) def cancel_job(self, *args, **kwargs): return run_client_test( 'Jobs', 'cancel_job', run_job_client, stub_method_handlers=MockClaraJobsServiceClient.stub_method_handlers, *args, **kwargs) def get_status(self, *args, **kwargs): return run_client_test( 'Jobs', 'get_status', run_job_client, stub_method_handlers=MockClaraJobsServiceClient.stub_method_handlers, *args, **kwargs) def list_jobs(self, *args, **kwargs): return run_client_test( 'Jobs', 'list_jobs', run_job_client, stub_method_handlers=MockClaraJobsServiceClient.stub_method_handlers, *args, **kwargs) def start_job(self, *args, **kwargs): return run_client_test( 'Jobs', 'start_job', run_job_client, stub_method_handlers=MockClaraJobsServiceClient.stub_method_handlers, *args, **kwargs) def job_logs(self, *args, **kwargs): return run_client_test( 'Jobs', 'job_logs', run_job_client, stub_method_handlers=MockClaraJobsServiceClient.stub_method_handlers, *args, **kwargs) def close(self): pass def test_create_job(): requests = [ jobs_pb2.JobsCreateRequest( header=BaseClient.get_request_header(), name='test job', pipeline_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ), priority=jobs_pb2.JOB_PRIORITY_NORMAL ) ] responses = [ jobs_pb2.JobsCreateResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ), payload_id=common_pb2.Identifier( value='7ac5c691e13d4f45894a3a70d9925936' ) ) ] stub_method_handlers = [( 'Create', 'unary_unary', ( requests, responses ) )] MockClaraJobsServiceClient.stub_method_handlers = stub_method_handlers with MockClaraJobsServiceClient('localhost:50051') as client: job_info = client.create_job( job_name='test job', pipeline_id=pipeline_types.PipelineId('92656d79fa414db6b294069c0e9e6df5') ) print(job_info.job_id.value, job_info.payload_id.value) assert job_info.job_id.value == '432b274a8f754968888807fe1eba237b' assert job_info.payload_id.value == '7ac5c691e13d4f45894a3a70d9925936' def test_cancel_job(): requests = [ jobs_pb2.JobsCancelRequest( header=BaseClient.get_request_header(), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ) ) ] responses = [ jobs_pb2.JobsCancelResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ), job_state=jobs_pb2.JOB_STATE_STOPPED, job_status=jobs_pb2.JOB_STATUS_CANCELED ) ] stub_method_handlers = [( 'Cancel', 'unary_unary', ( requests, responses ) )] MockClaraJobsServiceClient.stub_method_handlers = stub_method_handlers with MockClaraJobsServiceClient('10.0.0.1:50051') as client: job_token = client.cancel_job( job_id=job_types.JobId(value='432b274a8f754968888807fe1eba237b') ) print(job_token.job_id.value, job_token.job_state, job_token.job_status) assert job_token.job_id.value == '432b274a8f754968888807fe1eba237b' assert job_token.job_state == 3 assert job_token.job_status == 3 def test_get_status(): requests = [ jobs_pb2.JobsStatusRequest( header=BaseClient.get_request_header(), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ) ) ] fake_seconds_from_epoch = 63763345820 responses = [ jobs_pb2.JobsStatusResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), name="job_1", job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ), pipeline_id=common_pb2.Identifier( value='92656d79fa414db6b294069c0e9e6df5' ), payload_id=common_pb2.Identifier( value='7ac5c691e13d4f45894a3a70d9925936' ), state=jobs_pb2.JOB_STATE_RUNNING, status=jobs_pb2.JOB_STATUS_HEALTHY, created=common_pb2.Timestamp(value=fake_seconds_from_epoch) ) ] stub_method_handlers = [( 'Status', 'unary_unary', ( requests, responses ) )] MockClaraJobsServiceClient.stub_method_handlers = stub_method_handlers with MockClaraJobsServiceClient('10.0.0.1:50051') as client: job_details = client.get_status( job_id=job_types.JobId(value='432b274a8f754968888807fe1eba237b') ) print(job_details.job_id.value, job_details.job_state, job_details.job_status) print(job_details.date_created) print(datetime.datetime.fromtimestamp(float(fake_seconds_from_epoch) - 62135596800)) assert job_details.name == "job_1" assert job_details.job_id.value == '432b274a8f754968888807fe1eba237b' assert job_details.pipeline_id.value == '92656d79fa414db6b294069c0e9e6df5' assert job_details.payload_id.value == '7ac5c691e13d4f45894a3a70d9925936' assert job_details.job_state == 2 assert job_details.job_status == 1 assert job_details.date_created == datetime.datetime.fromtimestamp( float(fake_seconds_from_epoch) - 62135596800).astimezone(datetime.timezone.utc) def test_list_jobs(): requests = [ jobs_pb2.JobsListRequest( header=BaseClient.get_request_header() ) ] responses = [ jobs_pb2.JobsListResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), job_details=jobs_pb2.JobsListResponse.JobDetails( job_name="job_1", job_id=common_pb2.Identifier( value="432b274a8f754968888807fe1eba237b" ), payload_id=common_pb2.Identifier( value='532b274a8f754968888807fe1eba237b' ), pipeline_id=common_pb2.Identifier( value='932b274a8f754968888807fe1eba237b' ), created=common_pb2.Timestamp( value=63750823591 ) ) ), jobs_pb2.JobsListResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), job_details=jobs_pb2.JobsListResponse.JobDetails( job_name="job_2", job_id=common_pb2.Identifier( value='212b274a8f754968888807fe1eba237b' ), payload_id=common_pb2.Identifier( value='212b274a8f754968888807fe1eba237b' ), pipeline_id=common_pb2.Identifier( value='322b274a8f754968888807fe1eba237b' ), created=common_pb2.Timestamp( value=63750823591 ) ) ) ] stub_method_handlers = [( 'List', 'unary_stream', ( requests, responses ) )] MockClaraJobsServiceClient.stub_method_handlers = stub_method_handlers with MockClaraJobsServiceClient('10.0.0.1:50051') as client: list_jobs = client.list_jobs() print("Length of list response: " + str(len(list_jobs))) assert len(list_jobs) == 2 assert list_jobs[0].name == "job_1" assert list_jobs[0].job_id.value == "432b274a8f754968888807fe1eba237b" assert list_jobs[0].payload_id.value == "532b274a8f754968888807fe1eba237b" assert list_jobs[0].pipeline_id.value == "932b274a8f754968888807fe1eba237b" assert list_jobs[0].date_created == datetime.datetime(2021, 3, 8, 18, 6, 31, tzinfo=datetime.timezone.utc) assert list_jobs[1].name == "job_2" assert list_jobs[1].job_id.value == '212b274a8f754968888807fe1eba237b' assert list_jobs[1].payload_id.value == '212b274a8f754968888807fe1eba237b' assert list_jobs[1].pipeline_id.value == '322b274a8f754968888807fe1eba237b' assert list_jobs[1].date_created == datetime.datetime(2021, 3, 8, 18, 6, 31, tzinfo=datetime.timezone.utc) def test_start_job(): requests = [ jobs_pb2.JobsStartRequest( header=BaseClient.get_request_header(), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ) ) ] responses = [ jobs_pb2.JobsStartResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), state=jobs_pb2.JOB_STATE_RUNNING, status=jobs_pb2.JOB_STATUS_HEALTHY, priority=jobs_pb2.JOB_PRIORITY_NORMAL ) ] stub_method_handlers = [( 'Start', 'unary_unary', ( requests, responses ) )] MockClaraJobsServiceClient.stub_method_handlers = stub_method_handlers with MockClaraJobsServiceClient('10.0.0.1:50051') as client: job_token = client.start_job( job_id=job_types.JobId(value='432b274a8f754968888807fe1eba237b') ) print(job_token.job_id.value, job_token.job_state, job_token.job_status) assert job_token.job_id.value == '432b274a8f754968888807fe1eba237b' assert job_token.job_state == 2 assert job_token.job_status == 1 def test_read_logs(): requests = [ jobs_pb2.JobsReadLogsRequest( header=BaseClient.get_request_header(), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ), operator_name="dicom-reader" ) ] responses = [ jobs_pb2.JobsReadLogsResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ), operator_name="Dicom Reader", logs=["Log_String_0", "Log_String_1"] ), jobs_pb2.JobsReadLogsResponse( header=common_pb2.ResponseHeader( code=0, messages=[]), job_id=common_pb2.Identifier( value='432b274a8f754968888807fe1eba237b' ), operator_name="Dicom Reader", logs=["Log_String_2", "Log_String_3"] ) ] stub_method_handlers = [( 'ReadLogs', 'unary_stream', ( requests, responses ) )] MockClaraJobsServiceClient.stub_method_handlers = stub_method_handlers with MockClaraJobsServiceClient('10.0.0.1:50051') as client: job_logs = client.job_logs( job_id=job_types.JobId(value='432b274a8f754968888807fe1eba237b'), operator_name="dicom-reader" ) print(len(job_logs)) assert len(job_logs) == 4 assert job_logs[0] == "Log_String_0" assert job_logs[1] == "Log_String_1" assert job_logs[2] == "Log_String_2" assert job_logs[3] == "Log_String_3"
clara-platform-python-client-main
tests/test_jobs_client.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nvidia_clara.payloads_client import PayloadsClient import nvidia_clara.payload_types as payload_types # Client Creation with IP and Port of running instance of Clara clara_ip_address = "10.0.0.1" clara_port = "30031" payload_client = PayloadsClient(target=clara_ip_address, port=clara_port) # Create static re-usable Payload payload_details = payload_client.create_payload() # Delete Payload payload_client.delete_payload(payload_id=payload_details.payload_id) # Download from existing Payload ex. Payload with identifier '61a477bf-6bcc-4fdd-abad-ccb8886eb52f' with blob/file name ./input/I114.dcm example_payload_identifier = '61a477bf-6bcc-4fdd-abad-ccb8886eb52f' # Create BinaryIO stream object with write permissions and download from payload identifier: example_payload_identifier with open('output.dcm', 'wb') as wb: payload_client.download_from(payload_id=payload_types.PayloadId(example_payload_identifier), blob_name='./input/I114.dcm', dest_obj=wb) # Uploading BinaryIO stream to a new blob # Create BinaryIO stream with read permissions (for sake of example: reading previous output stream) with open('output.dcm', 'rb') as rb: payload_client.upload(payload_id=payload_types.PayloadId(example_payload_identifier), blob_name='./test/new_blob.dcm', file_object=rb) # Get Details of a Payload confirming_details = payload_client.get_details( payload_id=payload_types.PayloadId(example_payload_identifier))
clara-platform-python-client-main
examples/payloads_client_example.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nvidia_clara.jobs_client import JobsClient from nvidia_clara.pipelines_client import PipelinesClient from nvidia_clara.payloads_client import PayloadsClient import nvidia_clara.pipeline_types as pipeline_types import os from pathlib import Path # Clients creation clara_ip_address = "10.0.0.1" clara_port = "30031" jobs_client = JobsClient(target=clara_ip_address, port=clara_port) payloads_client = PayloadsClient(target=clara_ip_address, port=clara_port) pipeline_client = PipelinesClient(target=clara_ip_address, port=clara_port) # Create list of pipeline_types.PipelineDefinition with local path to pipeline .yaml file_path = "../spleen_pipeline.yaml" definitions = [pipeline_types.PipelineDefinition(name=file_path, content=Path(file_path).read_text())] # Create Pipeline with definition list created pipeline_id = pipeline_client.create_pipeline(definition=definitions) # Create Job with newly created Pipeline job_info = jobs_client.create_job(job_name="spleenjob", pipeline_id=pipeline_types.PipelineId(pipeline_id.value)) job_id = job_info.job_id payload_id = job_info.payload_id # Local path to directory of files to upload to the job's payload on the Server input_path = "../app_spleen-input_v1/dcm" # Go through files in directory and upload to the job using the payload identifier for file in os.listdir(input_path): file_path = input_path + "/" + str(file) with open(file_path, 'rb') as fp: payloads_client.upload(payload_id=payload_id, blob_name=file, file_object=fp) # Get a List of the jobs job_list = jobs_client.list_jobs() # Start Job job_token = jobs_client.start_job(job_id=job_id) # Loop until job completes job_status = jobs_client.get_status(job_id=job_id) while job_status.job_state != 3: job_status = jobs_client.get_status(job_id=job_id) # Get Payload Details - Used to get list of payload files payload_details = payloads_client.get_details(payload_id=payload_id) # Download files from payload if pertaining to output payload directory (ex. "/operators) for file in payload_details.file_details: # Get file path on Server (ex. /operators/dicom-reader/example_file.raw") file_name = file.name # Split file path name (ex. ['','operators','dicom-reader','example_file.raw'] name = file_name.split('/') # Check if file pertains to output directory (ex. "/operators) if name[1] == 'operators': # Download file to a local results directory to a file with same name on server (ex. example_file.raw) with open("./results/"+name[-1], 'wb+') as wb: payloads_client.download_from(payload_id=payload_id, blob_name="."+file_name, dest_obj=wb) # Gets list of operator logs from job jobs_logs = jobs_client.job_logs(job_id=job_id, operator_name="dicom-reader")
clara-platform-python-client-main
examples/combined_example.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nvidia_clara.jobs_client import JobsClient import nvidia_clara.job_types as job_types import nvidia_clara.pipeline_types as pipeline_types # Client Creation with IP and Port of running instance of Clara clara_ip_address = "10.0.0.1" clara_port = "30031" jobs_client = JobsClient(target=clara_ip_address, port=clara_port) # Creates Filter of Healthy Jobs - Additionally could filter by Pipeline Id, State, Completion Time, and Creation Time job_filter = job_types.JobFilter(has_job_status=[job_types.JobStatus.Healthy]) # List Current Jobs with Optional Filter job_list = jobs_client.list_jobs(job_filter=job_filter) print(job_list) # Identifier of created pipeline (ex. colon tumor segmentation) colon_tumor_pipeline_id = "f9a843935e654a30beb9d1b8352bfaac" # Create Job job_info = jobs_client.create_job(job_name="colontumor",pipeline_id=pipeline_types.PipelineId(colon_tumor_pipeline_id)) print(job_info.job_id.value) # Start Job job_token = jobs_client.start_job(job_id=job_info.job_id) print(job_token.job_state) print(job_token.job_status) # Get Status of Job from Identifier job_details = jobs_client.get_status(job_id=job_token.job_id) print(job_details.job_state) print(job_details.job_status) # Gets List of Operators print(job_details.operator_details.keys()) # Try Canceling Job (if still running) try: job_details = jobs_client.cancel_job(job_id=job_token.job_id) except: print("Scheduler Rejected Request")
clara-platform-python-client-main
examples/jobs_client_example.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nvidia_clara.clara_client import ClaraClient import nvidia_clara.clara_types as clara_types clara_ip_address = "10.0.0.1" clara_port = "30031" clara_client = ClaraClient(target=clara_ip_address, port=clara_port) # Get Clara Version version = clara_client.version() # Getting Gpu Utilization # Option 1: Getting list which will give snapshot of current GPU Utilization utilization_list = clara_client.list_utilization() # Option 2: Obtaining generator which will provide steam of GPU Utilization utilization_stream = clara_client.stream_utilization() # Stop Pipeline Service and Triton clara_client.stop()
clara-platform-python-client-main
examples/clara_client_example.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path from nvidia_clara.pipelines_client import PipelinesClient import nvidia_clara.pipeline_types as pipeline_types # Client Creation with IP and Port of running instance of Clara clara_ip_address = "10.0.0.1" clara_port = "30031" pipeline_client = PipelinesClient(target=clara_ip_address, port=clara_port) # Create list of pipeline_types.PipelineDefinition with local path to pipeline .yaml file_path = "./liver-tumor-pipeline.yaml" definitions = [pipeline_types.PipelineDefinition(name=file_path, content=Path(file_path).read_text())] # Create Pipeline with definition list created pipeline_id = pipeline_client.create_pipeline(definition=definitions) print(pipeline_id) # Get List of Created Pipelines PipelinesClient.list_pipelines() pipelines = [(pipe_info.pipeline_id.value, pipe_info.name) for pipe_info in pipeline_client.list_pipelines()] print(pipelines) # Get Details of Pipeline with PipelinesClient.pipeline_details() pipeline_details = pipeline_client.pipeline_details(pipeline_id=pipeline_id) # Remove Pipeline pipeline_client.remove_pipeline(pipeline_id=pipeline_id)
clara-platform-python-client-main
examples/pipelines_client_example.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from rapidAligner.util import * from rapidAligner.ED import *
rapidAligner-main
rapidAligner/__init__.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from numba import cuda class cudaTimer: def __init__(self, label='', gpu=0): self.label = label self.gpu = gpu self.start = cuda.event() self.end = cuda.event() cuda.select_device(self.gpu) self.start.record(), def __enter__(self): pass def __exit__(self, *args): cuda.select_device(self.gpu) suffix = 'ms ('+self.label+')' if self.label else 'ms' self.end.record() self.end.synchronize() time = cuda.event_elapsed_time(self.start, self.end) print('elapsed time:', int(time), suffix)
rapidAligner-main
rapidAligner/util/Timer.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from rapidAligner.util.Timer import cudaTimer as Timer from rapidAligner.util.Loader import *
rapidAligner-main
rapidAligner/util/__init__.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. __all__ = ['FakeSeriesGenerator', 'ECGLoader'] import os import urllib import zipfile import cupy as cp from scipy.io import loadmat class ECGLoader: def __init__(self, root='./data/ECG', url=None): self.root = root assert url != None, \ "provide the URL to 22h of ECG data stated on the bottom of https://www.cs.ucr.edu/~eamonn/UCRsuite.html" filename = os.path.join(root, 'ECG_one_day.zip') if not os.path.isdir(root): os.makedirs(root) if not os.path.isfile(filename): urllib.request.urlretrieve(url, filename) with zipfile.ZipFile(filename, 'r') as zip_ref: zip_ref.extractall(root) @property def subject(self, alpha=400.0, beta=50.0): return alpha*loadmat(os.path.join(self.root, 'ECG_one_day','ECG.mat'))['ECG'].flatten()+beta @property def query(self): return loadmat(os.path.join(self.root, 'ECG_one_day','ECG_query.mat'))['ecg_query'].flatten() @property def data(self): return self.query, self.subject class FakeSeriesGenerator: def __init__(self, query_length=3600, subject_length=2**20, seed=None, beta=1.0): self.query_length = query_length self.subject_length = subject_length self.beta = beta assert isinstance(query_length, int) and query_length > 0 assert isinstance(subject_length, int) and subject_length > 0 assert query_length <= subject_length assert isinstance(beta, float) and beta >= 0 if isinstance(seed, int): cp.random.seed(seed) noise = cp.random.uniform(-1, +1, self.subject_length+self.query_length) kernel = cp.exp(-self.beta*cp.linspace(0, 1, self.subject_length+self.query_length)) kernel /= cp.sqrt(cp.sum(kernel**2)) self.signal = cp.fft.irfft(cp.fft.rfft(noise)*cp.fft.rfft(kernel), n=self.subject_length+self.query_length) @property def subject(self): return self.signal[self.query_length:].get() @property def query(self, alpha=2.0, beta=1.20): signal = self.signal[:self.query_length] mean = cp.mean(signal) return (alpha*(signal-mean)+beta*mean).get() @property def data(self): return self.query, self.subject
rapidAligner-main
rapidAligner/util/Loader.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import cupy as cp ############################################################################### # helpers to avoid redundant code ############################################################################### def cumsum(x, Kahan=0): """ Wrapper for exclusive prefix sum computation with an optional refinement step using a approach similar to Kahan summation. This function is not exposed to the user. Arguments: ------- x: cupy.core.core.ndarray the input array of length n to be scanned with operation + Kahan: int non-negative number of Kahan summation adjustment rounds Returns ------- cupy.core.core.ndarray the computed exclusive prefix scan of length n+1 """ assert(isinstance(Kahan, int) and Kahan >= 0) # allocate an empty array with leading 0 y = cp.empty(len(x)+1, dtype=x.dtype) y[0] = 0 # compute the inclusive prefix sum starting at entry 1 cp.cumsum(x, out=y[1:]) # basically exploit that (d/dt int f(t) dt) - f(t) = r = 0 forall f(t) # in case delta is non-vanishing due to numeric inaccuracies, we add # the prefix scan of r to the final result (inaccuracies might add up) if Kahan: r = x-cp.diff(y) if(cp.max(cp.abs(r))): y += cumsum(r, Kahan-1) return y def mnorm(x): """ Mean-adjustment of a given time series. Afterwards the time series has vanishing mean, i.e. sum_i x[i] = 0 Arguments: ------- x: cupy.core.core.ndarray the input array of length n to be normalized Returns ------- cupy.core.core.ndarray the mean-adjusted array of length n """ return x-cp.mean(x) def znorm(x, epsilon): """ Mean- and amplitude-adjustment of a given time series. Afterwards the time series has vanishing mean, i.e. sum_i x[i] = 0 and unit standard devitation i.e. sum_i x[i]*x[i] = n where n is the length of the sequence x Arguments: ------- x: cupy.core.core.ndarray the input array of length n to be normalized Returns ------- cupy.core.core.ndarray the mean-adjusted array of length n """ return (x-cp.mean(x))/max(cp.std(x, ddof=0), epsilon)
rapidAligner-main
rapidAligner/ED/stream_dists_helpers.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from rapidAligner.ED.stream_dists_fft import fft_sdist, fft_mdist, fft_zdist from rapidAligner.ED.stream_dists_kernels import sdist_kernel, mdist_kernel, zdist_kernel from rapidAligner.ED.stream_dists_helpers import mnorm, znorm import cupy as cp from numba import cuda __all__ = ["sdist", "mdist", "zdist"] def sdist(Q, S, mode="fft"): """ Rolling Euclidean Distance Arguments: ------- Q: cupy.core.core.ndarray or numba.cuda.DeviceNDArray or cudf.Series or numpy.ndarray the input query of length m to be aligned S: cupy.core.core.ndarray or numba.cuda.DeviceNDArray or cudf.Series or numpy.ndarray the input stream of length n>=m to be scanned mode: str either "naive" or "fft" Returns ------- cupy.core.core.ndarray the computed distance array of length n-m+1 """ if not isinstance(Q, cp.core.core.ndarray): Q = cp.asarray(Q) if not isinstance(S, cp.core.core.ndarray): S = cp.asarray(S) assert(Q.dtype == S.dtype) assert((len(Q.shape) == len(S.shape) == 1 and Q.shape[0] <= S.shape[0])) if mode == "fft": Z = fft_sdist(Q, S) else: stream = cuda.stream() Z = cp.empty(len(S)-len(Q)+1, dtype=Q.dtype) sdist_kernel[80*32, 64, stream](Q, S, Z) stream.synchronize() return Z def mdist(Q, S, mode="fft"): """ Rolling mean-adjusted Euclidean Distance Arguments: ------- Q: cupy.core.core.ndarray or numba.cuda.DeviceNDArray or cudf.Series or numpy.ndarray the input query of length m to be aligned S: cupy.core.core.ndarray or numba.cuda.DeviceNDArray or cudf.Series or numpy.ndarray the input stream of length n>=m to be scanned mode: str either "naive" or "fft" Returns ------- cupy.core.core.ndarray the computed distance array of length n-m+1 """ if not isinstance(Q, cp.core.core.ndarray): Q = cp.asarray(Q) if not isinstance(S, cp.core.core.ndarray): S = cp.asarray(S) assert(Q.dtype == S.dtype) assert((len(Q.shape) == len(S.shape) == 1 and Q.shape[0] <= S.shape[0])) if mode == "fft": Z = fft_mdist(Q, S) else: stream = cuda.stream() Z = cp.empty(len(S)-len(Q)+1, dtype=Q.dtype) mdist_kernel[80*32, 64, stream](mnorm(Q), S, Z) stream.synchronize() return Z def zdist(Q, S, mode="fft", epsilon=1e-6): """ Rolling mean- and amplitude-adjusted Euclidean Distance Arguments: ------- Q: cupy.core.core.ndarray or numba.cuda.DeviceNDArray or cudf.Series or numpy.ndarray the input query of length m to be aligned S: cupy.core.core.ndarray or numba.cuda.DeviceNDArray or cudf.Series or numpy.ndarray the input stream of length n>=m to be scanned epsilon: float non-negative number for regularizing zero stdev mode: str either "naive" or "fft" Returns ------- cupy.core.core.ndarray the computed distance array of length n-m+1 """ if not isinstance(Q, cp.core.core.ndarray): Q = cp.asarray(Q) if not isinstance(S, cp.core.core.ndarray): S = cp.asarray(S) assert(epsilon > 0) assert(Q.dtype == S.dtype) assert((len(Q.shape) == len(S.shape) == 1 and Q.shape[0] <= S.shape[0])) assert(cp.std(Q, ddof=0) > 0) if mode == "fft": Z = fft_zdist(Q, S, epsilon) else: stream = cuda.stream() Z = cp.empty(len(S)-len(Q)+1, dtype=Q.dtype) zdist_kernel[80*32, 64, stream](znorm(Q, epsilon), S, Z, epsilon) stream.synchronize() return Z
rapidAligner-main
rapidAligner/ED/__init__.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from numba import cuda, float64 from math import sqrt ############################################################################### # plain rolling Euclidean distance ############################################################################### @cuda.jit def sdist_kernel(Q, S, out): """Euclidean Distance naive kernel: nothing cached""" warpDim = cuda.blockDim.x // 32 warpIdx = cuda.threadIdx.x // 32 laneIdx = cuda.threadIdx.x % 32 lower = cuda.blockIdx.x*warpDim+warpIdx stride = cuda.gridDim.x*warpDim for position in range(lower, S.shape[0]-Q.shape[0]+1, stride): accum = float64(0) for index in range(laneIdx, Q.shape[0], 32): value = Q[index]-S[position+index] accum += value*value for delta in [16, 8, 4, 2, 1]: value = cuda.shfl_down_sync(0xFFFFFFFF, accum, delta) accum += value if laneIdx == 0: out[position] = accum ############################################################################### # mean-adjusted rolling Euclidean distance ############################################################################### @cuda.jit(max_registers=63) def mdist_kernel(Q, S, out): """mean-adjusted Euclidean Distance naive kernel: nothing cached""" warpDim = cuda.blockDim.x // 32 warpIdx = cuda.threadIdx.x // 32 laneIdx = cuda.threadIdx.x % 32 lower = cuda.blockIdx.x*warpDim+warpIdx stride = cuda.gridDim.x*warpDim for position in range(lower, S.shape[0]-Q.shape[0]+1, stride): accum = float64(0) for index in range(laneIdx, Q.shape[0], 32): accum += S[position+index] for delta in [16, 8, 4, 2, 1]: accum += cuda.shfl_xor_sync(0xFFFFFFFF, accum, delta) mean = accum/Q.shape[0] accum = float64(0) for index in range(laneIdx, Q.shape[0], 32): value = Q[index]-S[position+index]+mean accum += value*value for delta in [16, 8, 4, 2, 1]: value = cuda.shfl_down_sync(0xFFFFFFFF, accum, delta) accum += value if laneIdx == 0: out[position] = accum ############################################################################### # mean- and amplitude-adjusted rolling Euclidean distance ############################################################################### @cuda.jit(max_registers=63) def zdist_kernel(Q, S, out, epsilon): """z-normalized Euclidean Distance naive kernel: nothing cached""" warpDim = cuda.blockDim.x // 32 warpIdx = cuda.threadIdx.x // 32 laneIdx = cuda.threadIdx.x % 32 lower = cuda.blockIdx.x*warpDim+warpIdx stride = cuda.gridDim.x*warpDim for position in range(lower, S.shape[0]-Q.shape[0]+1, stride): accum1 = float64(0) accum2 = float64(0) for index in range(laneIdx, Q.shape[0], 32): value = S[position+index] accum1 += value accum2 += value*value for delta in [16, 8, 4, 2, 1]: accum1 += cuda.shfl_xor_sync(0xFFFFFFFF, accum1, delta) accum2 += cuda.shfl_xor_sync(0xFFFFFFFF, accum2, delta) mean = accum1/Q.shape[0] sigma = accum2/Q.shape[0]-mean*mean sigma = sqrt(sigma) if sigma > 0.0 else epsilon accum = float64(0) for index in range(laneIdx, Q.shape[0], 32): value = Q[index]-(S[position+index]-mean)/sigma accum += value*value for delta in [16, 8, 4, 2, 1]: accum += cuda.shfl_down_sync(0xFFFFFFFF, accum, delta) if laneIdx == 0: out[position] = accum
rapidAligner-main
rapidAligner/ED/stream_dists_kernels.py
# Copyright (c) 2020, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from rapidAligner.ED.stream_dists_helpers import cumsum from rapidAligner.ED.stream_dists_helpers import mnorm, znorm import cupy as cp import math def fft_sdist(Q, S, alignment=10000, Kahan=0): """ Rolling Euclidean Distance using FFT to run in loglinear time Equation exploiting cross-correlation (Fourier) theorem: d[k] = sum_i (Q[i] - S[i+k])**2 = sum_i (Q[i]**2 - 2*Q[i]*S[i+k] + S[i+k]**2) = sum_i Q[i]**2 - 2*correlation[k] + sum_i S[i+k]**2 = sum_i Q[i]**2 - 2*correlation[k] + Y[k] Arguments: ------- Q: cupy.core.core.ndarray the input query of length m to be aligned S: cupy.core.core.ndarray the input stream of length n>=m to be scanned Kahan: int non-negative number of Kahan summation adjustment rounds Returns ------- cupy.core.core.ndarray the computed distance array of length n-m+1 """ assert(Q.dtype == S.dtype) m = len(Q) n = (len(S)+alignment-1)//alignment*alignment iS = cp.zeros(n, dtype=S.dtype) iS[:len(S)] = S Y = cumsum(iS**2, Kahan) Y = Y[+m:]-Y[:-m] E = cp.zeros(n, dtype=Q.dtype) E[:m] = Q R = cp.fft.irfft(cp.fft.rfft(E).conj()*cp.fft.rfft(iS), n=n) return (cp.sum(cp.square(Q))-2*R[:-m+1]+Y)[:len(S)-m+1] def fft_mdist(Q, S, alignment=10000, Kahan=0): """ Rolling mean-adjusted Euclidean Distance using FFT to run in loglinear time Equation exploiting cross-correlation (Fourier) theorem: d[k] = sum_i (f(Q[i]) - f(S[i+k]))**2 = sum_i (f(Q[i])**2 - 2*f(Q[i])*f(S[i+k]) + f(S[i+k])**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*(S[i+k]-mu[k]) + (S[i+k]-mu[k])**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + 2*f(Q[i])*mu[k] + (S[i+k]-mu[k])**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + (S[i+k]-mu[k])**2) since sum_i f(Q[i]) = 0 by definition = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + S[i+k]**2 - 2*S[i+k]*mu[k] + mu[k]**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + S[i+k]**2 - 2*|Q|*mu[k]*mu[k] + mu[k]**2) = sum_i f(Q[i])**2 - 2*correlation(k) + Y[k] - 2*X[k]**2/|Q| + X[k]**2/|Q| = sum_i f(Q[i])**2 - 2*correlation(k) + Y[k] - X[k]**2/|Q| = sum_i f(Q[i])**2 - 2*correlation(k) + |Q|*variance[k] Arguments: ------- Q: cupy.core.core.ndarray the input query of length m to be aligned S: cupy.core.core.ndarray the input stream of length n>=m to be scanned Kahan: int non-negative number of Kahan summation adjustment rounds Returns ------- cupy.core.core.ndarray the computed distance array of length n-m+1 """ m, Q = len(Q), mnorm(Q) n = (len(S)+alignment-1)//alignment*alignment iS = cp.zeros(n).astype(S.dtype) iS[:len(S)] = S X, Y = cumsum(iS, Kahan), cumsum(iS**2, Kahan) X = X[+m:]-X[:-m] Y = Y[+m:]-Y[:-m] Z = Y-X*X/m E = cp.zeros(n, dtype=Q.dtype) E[:m] = Q R = cp.fft.irfft(cp.fft.rfft(E).conj()*cp.fft.rfft(iS), n=n) return (cp.sum(cp.square(Q))-2*R[:-m+1]+Z)[:len(S)-m+1] def fft_zdist(Q, S, epsilon, alignment=10000, Kahan=0): """ Rolling mean- and amplitude-adjusted Euclidean Distance using FFT to run in loglinear time Equation exploiting cross-correlation (Fourier) theorem: d[k] = sum_i (f(Q[i]) - f(S[i+k]))**2 = sum_i (f(Q[i])**2 - 2*f(Q[i])*f(S[i+k]) + f(S[i+k])**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*(S[i+k]-mu[k]) + (S[i+k]-mu[k])**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + 2*f(Q[i])*mu[k] + (S[i+k]-mu[k])**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + (S[i+k]-mu[k])**2) since sum_i f(Q[i]) = 0 by definition = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + S[i+k]**2 - 2*S[i+k]*mu[k] + mu[k]**2) = sum_i (f(Q[i])**2 - 2*f(Q[i])*S[i+k] + S[i+k]**2 - 2*|Q|*mu[k]*mu[k] + mu[k]**2) = sum_i f(Q[i])**2 - 2*correlation(k) + Y[k] - 2*X[k]**2/|Q| + X[k]**2/|Q| = sum_i f(Q[i])**2 - 2*correlation(k) + Y[k] - X[k]**2/|Q| = sum_i f(Q[i])**2 - 2*correlation(k) + |Q|*variance[k] Arguments: ------- Q: cupy.core.core.ndarray the input query of length m to be aligned S: cupy.core.core.ndarray the input stream of length n>=m to be scanned epsilon: float non-negative number for regularizing zero stdev Kahan: int non-negative number of Kahan summation adjustment rounds Returns ------- cupy.core.core.ndarray the computed distance array of length n-m+1 """ assert(epsilon > 0) m, Q = len(Q), znorm(Q, epsilon) n = (len(S)+alignment-1)//alignment*alignment iS = cp.zeros(n, dtype=S.dtype) iS[:len(S)] = S delta = n-len(S) X, Y = cumsum(iS, Kahan), cumsum(iS**2, Kahan) X = X[+m:]-X[:-m] Y = Y[+m:]-Y[:-m] Z = cp.sqrt(cp.maximum(Y/m-cp.square(X/m), 0)) E = cp.zeros(n, dtype=Q.dtype) E[:m] = Q R = cp.fft.irfft(cp.fft.rfft(E).conj()*cp.fft.rfft(iS), n=n) F = cp.where(Z > 0 , 2*(m-R[:-m+1]/Z), m*cp.ones_like(Z)) return F[:len(S)-m+1]
rapidAligner-main
rapidAligner/ED/stream_dists_fft.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
__init__.py
# ! /usr/bin/python # -*- coding: utf-8 -*- # Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Setup for pip package.""" import codecs import importlib.util import os import subprocess from distutils import cmd as distutils_cmd from distutils import log as distutils_log from itertools import chain import setuptools spec = importlib.util.spec_from_file_location('package_info', 'nemo_text_processing/package_info.py') package_info = importlib.util.module_from_spec(spec) spec.loader.exec_module(package_info) __contact_emails__ = package_info.__contact_emails__ __contact_names__ = package_info.__contact_names__ __description__ = package_info.__description__ __download_url__ = package_info.__download_url__ __homepage__ = package_info.__homepage__ __keywords__ = package_info.__keywords__ __license__ = package_info.__license__ __package_name__ = package_info.__package_name__ __repository_url__ = package_info.__repository_url__ __version__ = package_info.__version__ if os.path.exists('README.md'): with open("README.md", "r", encoding='utf-8') as fh: long_description = fh.read() long_description_content_type = "text/markdown" elif os.path.exists('README.rst'): # codec is used for consistent encoding long_description = codecs.open( os.path.join(os.path.abspath(os.path.dirname(__file__)), 'README.rst'), 'r', encoding='utf-8', ).read() long_description_content_type = "text/x-rst" else: long_description = 'See ' + __homepage__ ############################################################################### # Dependency Loading # # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # def req_file(filename, folder="requirements"): with open(os.path.join(folder, filename), encoding='utf-8') as f: content = f.readlines() # you may also want to remove whitespace characters # Example: `\n` at the end of each line return [x.strip() for x in content] install_requires = req_file("requirements.txt") extras_require = { # User packages 'test': req_file("requirements_test.txt") } extras_require['all'] = list(chain(extras_require.values())) # Add lightning requirements as needed # extras_require['nemo_text_processing'] = list(chain([extras_require['nemo_text_processing']])) # extras_require['test'] = list( # chain( # [ # extras_require['nemo_text_processing'], # ] # ) # ) tests_requirements = extras_require["test"] ############################################################################### # Code style checkers # # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # class StyleCommand(distutils_cmd.Command): __LINE_WIDTH = 119 __ISORT_BASE = ( 'isort ' # These two lines makes isort compatible with black. '--multi-line=3 --trailing-comma --force-grid-wrap=0 ' f'--use-parentheses --line-width={__LINE_WIDTH} -rc -ws' ) __BLACK_BASE = f'black --skip-string-normalization --line-length={__LINE_WIDTH}' description = 'Checks overall project code style.' user_options = [ ('scope=', None, 'Folder of file to operate within.'), ('fix', None, 'True if tries to fix issues in-place.'), ] def __call_checker(self, base_command, scope, check): command = list(base_command) command.append(scope) if check: command.extend(['--check', '--diff']) self.announce( msg='Running command: %s' % str(' '.join(command)), level=distutils_log.INFO, ) return_code = subprocess.call(command) return return_code def _isort(self, scope, check): return self.__call_checker(base_command=self.__ISORT_BASE.split(), scope=scope, check=check,) def _black(self, scope, check): return self.__call_checker(base_command=self.__BLACK_BASE.split(), scope=scope, check=check,) def _pass(self): self.announce(msg='\033[32mPASS\x1b[0m', level=distutils_log.INFO) def _fail(self): self.announce(msg='\033[31mFAIL\x1b[0m', level=distutils_log.INFO) # noinspection PyAttributeOutsideInit def initialize_options(self): self.scope = '.' self.fix = '' def run(self): scope, check = self.scope, not self.fix isort_return = self._isort(scope=scope, check=check) black_return = self._black(scope=scope, check=check) if isort_return == 0 and black_return == 0: self._pass() else: self._fail() exit(isort_return if isort_return != 0 else black_return) def finalize_options(self): pass ############################################################################### setuptools.setup( name=__package_name__, # Versions should comply with PEP440. For a discussion on single-sourcing # the version across setup.py and the project code, see # https://packaging.python.org/en/latest/single_source_version.html version=__version__, description=__description__, long_description=long_description, long_description_content_type=long_description_content_type, # The project's main homepage. url=__repository_url__, download_url=__download_url__, # Author details author=__contact_names__, author_email=__contact_emails__, # maintainer Details maintainer=__contact_names__, maintainer_email=__contact_emails__, # The licence under which the project is released license=__license__, classifiers=[ # How mature is this project? Common values are # 1 - Planning # 2 - Pre-Alpha # 3 - Alpha # 4 - Beta # 5 - Production/Stable # 6 - Mature # 7 - Inactive 'Development Status :: 5 - Production/Stable', # Indicate who your project is intended for 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Intended Audience :: Information Technology', # Indicate what your project relates to 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Image Recognition', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Software Development :: Libraries', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Utilities', # Pick your license as you wish (should match "license" above) 'License :: OSI Approved :: Apache Software License', # Supported python versions 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', # Additional Setting 'Environment :: Console', 'Natural Language :: English', 'Operating System :: OS Independent', ], packages=setuptools.find_packages(), install_requires=install_requires, # setup_requires=['pytest-runner'], tests_require=tests_requirements, # List additional groups of dependencies here (e.g. development # dependencies). You can install these using the following syntax, # $ pip install -e ".[all]" # $ pip install nemo_toolkit[all] extras_require=extras_require, # Add in any packaged data. include_package_data=True, exclude=['tools', 'tests', 'data'], package_data={'': ['*.tsv', '*.far', '*.fst']}, zip_safe=False, # PyPI package information. keywords=__keywords__, # Custom commands. cmdclass={'style': StyleCommand}, )
NeMo-text-processing-main
setup.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import time from argparse import ArgumentParser import pynini from nemo_text_processing.text_normalization.en.graph_utils import generator_main # This script exports compiled grammars inside nemo_text_processing into OpenFst finite state archive files # tokenize_and_classify.far and verbalize.far for production purposes def itn_grammars(**kwargs): d = {} d['classify'] = { 'TOKENIZE_AND_CLASSIFY': ITNClassifyFst( cache_dir=kwargs["cache_dir"], overwrite_cache=kwargs["overwrite_cache"], whitelist=kwargs["whitelist"], input_case=kwargs["input_case"], ).fst } d['verbalize'] = {'ALL': ITNVerbalizeFst().fst, 'REDUP': pynini.accep("REDUP")} return d def tn_grammars(**kwargs): d = {} d['classify'] = { 'TOKENIZE_AND_CLASSIFY': TNClassifyFst( input_case=kwargs["input_case"], deterministic=True, cache_dir=kwargs["cache_dir"], overwrite_cache=kwargs["overwrite_cache"], whitelist=kwargs["whitelist"], ).fst } d['verbalize'] = {'ALL': TNVerbalizeFst(deterministic=True).fst, 'REDUP': pynini.accep("REDUP")} return d def export_grammars(output_dir, grammars): """ Exports tokenizer_and_classify and verbalize Fsts as OpenFst finite state archive (FAR) files. Args: output_dir: directory to export FAR files to. Subdirectories will be created for tagger and verbalizer respectively. grammars: grammars to be exported """ for category, graphs in grammars.items(): out_dir = os.path.join(output_dir, category) if not os.path.exists(out_dir): os.makedirs(out_dir) time.sleep(1) if category == "classify": category = "tokenize_and_classify" generator_main(f"{out_dir}/{category}.far", graphs) def parse_args(): parser = ArgumentParser() parser.add_argument("--output_dir", help="output directory for grammars", required=True, type=str) parser.add_argument( "--language", help="language", choices=["en", "de", "es", "pt", "ru", 'fr', 'hu', 'sv', 'vi', 'zh', 'ar', 'it', 'es_en'], type=str, default='en', ) parser.add_argument( "--grammars", help="grammars to be exported", choices=["tn_grammars", "itn_grammars"], type=str, required=True ) parser.add_argument( "--input_case", help="input capitalization", choices=["lower_cased", "cased"], default="cased", type=str ) parser.add_argument( "--whitelist", help="Path to a file with with whitelist replacements," "e.g., for English whitelist files are stored under inverse_normalization/en/data/whitelist. If None," "the default file will be used.", default=None, type=lambda x: None if x == "None" else x, ) parser.add_argument("--overwrite_cache", help="set to True to re-create .far grammar files", action="store_true") parser.add_argument( "--cache_dir", help="path to a dir with .far grammar file. Set to None to avoid using cache", default=None, type=str, ) return parser.parse_args() if __name__ == '__main__': args = parse_args() if args.language in ['pt', 'ru', 'vi', 'es_en'] and args.grammars == 'tn_grammars': raise ValueError('Only ITN grammars could be deployed in Sparrowhawk for the selected languages.') if args.language == 'en': from nemo_text_processing.inverse_text_normalization.en.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.en.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.en.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.en.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'de': from nemo_text_processing.inverse_text_normalization.de.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.de.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.de.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.de.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'ru': from nemo_text_processing.inverse_text_normalization.ru.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.ru.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) elif args.language == 'es': from nemo_text_processing.inverse_text_normalization.es.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.es.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.es.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.es.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'pt': from nemo_text_processing.inverse_text_normalization.pt.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.pt.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) elif args.language == 'fr': from nemo_text_processing.inverse_text_normalization.fr.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.fr.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.fr.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.fr.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'hu': from nemo_text_processing.text_normalization.hu.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.hu.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'sv': from nemo_text_processing.inverse_text_normalization.sv.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.sv.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.sv.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.sv.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'vi': from nemo_text_processing.inverse_text_normalization.vi.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.vi.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) elif args.language == 'zh': from nemo_text_processing.inverse_text_normalization.zh.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.zh.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.zh.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.zh.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'ar': from nemo_text_processing.inverse_text_normalization.ar.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.ar.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) from nemo_text_processing.text_normalization.ar.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) elif args.language == 'it': from nemo_text_processing.text_normalization.it.taggers.tokenize_and_classify import ( ClassifyFst as TNClassifyFst, ) from nemo_text_processing.text_normalization.it.verbalizers.verbalize import VerbalizeFst as TNVerbalizeFst elif args.language == 'es_en': from nemo_text_processing.inverse_text_normalization.es_en.taggers.tokenize_and_classify import ( ClassifyFst as ITNClassifyFst, ) from nemo_text_processing.inverse_text_normalization.es_en.verbalizers.verbalize import ( VerbalizeFst as ITNVerbalizeFst, ) output_dir = os.path.join(args.output_dir, args.language) export_grammars( output_dir=output_dir, grammars=locals()[args.grammars]( input_case=args.input_case, cache_dir=args.cache_dir, overwrite_cache=args.overwrite_cache, whitelist=args.whitelist, ), )
NeMo-text-processing-main
tools/text_processing_deployment/pynini_export.py
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. MAJOR = 0 MINOR = 2 PATCH = 0 PRE_RELEASE = 'rc0' # Use the following formatting: (major, minor, patch, pre-release) VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE) __shortversion__ = '.'.join(map(str, VERSION[:3])) __version__ = '.'.join(map(str, VERSION[:3])) + ''.join(VERSION[3:]) __package_name__ = 'nemo_text_processing' __contact_names__ = 'NVIDIA' __contact_emails__ = '[email protected]' __homepage__ = 'https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/' __repository_url__ = 'https://github.com/nvidia/nemo-text-processing' __download_url__ = 'https://github.com/NVIDIA/NeMo-text-processing/releases' __description__ = 'NeMo text processing for ASR and TTS' __license__ = 'Apache2' __keywords__ = ' NeMo, nvidia, tts, asr, text processing, text normalization, inverse text normalization, language'
NeMo-text-processing-main
nemo_text_processing/package_info.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from argparse import ArgumentParser from time import perf_counter from typing import List from nemo_text_processing.text_normalization.data_loader_utils import load_file, write_file from nemo_text_processing.text_normalization.en.graph_utils import INPUT_CASED, INPUT_LOWER_CASED from nemo_text_processing.text_normalization.normalize import Normalizer from nemo_text_processing.text_normalization.token_parser import TokenParser class InverseNormalizer(Normalizer): """ Inverse normalizer that converts text from spoken to written form. Useful for ASR postprocessing. Input is expected to have no punctuation outside of approstrophe (') and dash (-) and be lower cased. Args: input_case: Input text capitalization, set to 'cased' if text contains capital letters. This flag affects normalization rules applied to the text. Note, `lower_cased` won't lower case input. lang: language specifying the ITN whitelist: path to a file with whitelist replacements. (each line of the file: written_form\tspoken_form\n), e.g. nemo_text_processing/inverse_text_normalization/en/data/whitelist.tsv cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. overwrite_cache: set to True to overwrite .far files max_number_of_permutations_per_split: a maximum number of permutations which can be generated from input sequence of tokens. """ def __init__( self, input_case: str = INPUT_LOWER_CASED, lang: str = "en", whitelist: str = None, cache_dir: str = None, overwrite_cache: bool = False, max_number_of_permutations_per_split: int = 729, ): assert input_case in ["lower_cased", "cased"] if lang == 'en': # English from nemo_text_processing.inverse_text_normalization.en.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.en.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'es': # Spanish (Espanol) from nemo_text_processing.inverse_text_normalization.es.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.es.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'pt': # Portuguese (Português) from nemo_text_processing.inverse_text_normalization.pt.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.pt.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'ru': # Russian (Russkiy Yazyk) from nemo_text_processing.inverse_text_normalization.ru.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.ru.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'de': # German (Deutsch) from nemo_text_processing.inverse_text_normalization.de.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.de.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'fr': # French (Français) from nemo_text_processing.inverse_text_normalization.fr.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.fr.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'sv': # Swedish (Svenska) from nemo_text_processing.inverse_text_normalization.sv.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.sv.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'vi': # Vietnamese (Tiếng Việt) from nemo_text_processing.inverse_text_normalization.vi.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'ar': # Arabic from nemo_text_processing.inverse_text_normalization.ar.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.ar.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'es_en': # Arabic from nemo_text_processing.inverse_text_normalization.es_en.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.es_en.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) elif lang == 'zh': # Mandarin from nemo_text_processing.inverse_text_normalization.zh.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.zh.verbalizers.verbalize_final import ( VerbalizeFinalFst, ) self.tagger = ClassifyFst( cache_dir=cache_dir, whitelist=whitelist, overwrite_cache=overwrite_cache, input_case=input_case ) self.verbalizer = VerbalizeFinalFst() self.parser = TokenParser() self.lang = lang self.max_number_of_permutations_per_split = max_number_of_permutations_per_split def inverse_normalize_list(self, texts: List[str], verbose=False) -> List[str]: """ NeMo inverse text normalizer Args: texts: list of input strings verbose: whether to print intermediate meta information Returns converted list of input strings """ return self.normalize_list(texts=texts, verbose=verbose) def inverse_normalize(self, text: str, verbose: bool) -> str: """ Main function. Inverse normalizes tokens from spoken to written form e.g. twelve kilograms -> 12 kg Args: text: string that may include semiotic classes verbose: whether to print intermediate meta information Returns: written form """ return self.normalize(text=text, verbose=verbose) def parse_args(): parser = ArgumentParser() input = parser.add_mutually_exclusive_group() input.add_argument("--text", dest="input_string", help="input string", type=str) input.add_argument("--input_file", dest="input_file", help="input file path", type=str) parser.add_argument('--output_file', dest="output_file", help="output file path", type=str) parser.add_argument( "--language", help="language", choices=['en', 'de', 'es', 'pt', 'ru', 'fr', 'sv', 'vi', 'ar', 'es_en', 'zh'], default="en", type=str, ) parser.add_argument( "--input_case", help="Input text capitalization, set to 'cased' if text contains capital letters." "This flag affects normalization rules applied to the text. Note, `lower_cased` won't lower case input.", choices=[INPUT_CASED, INPUT_LOWER_CASED], default=INPUT_LOWER_CASED, type=str, ) parser.add_argument( "--whitelist", help="Path to a file with with whitelist replacements," "e.g., inverse_normalization/en/data/whitelist.tsv", default=None, type=str, ) parser.add_argument("--verbose", help="print info for debugging", action='store_true') parser.add_argument("--overwrite_cache", help="set to True to re-create .far grammar files", action="store_true") parser.add_argument( "--cache_dir", help="path to a dir with .far grammar file. Set to None to avoid using cache", default=None, type=str, ) return parser.parse_args() if __name__ == "__main__": args = parse_args() whitelist = os.path.abspath(args.whitelist) if args.whitelist else None start_time = perf_counter() inverse_normalizer = InverseNormalizer( input_case=args.input_case, lang=args.language, cache_dir=args.cache_dir, overwrite_cache=args.overwrite_cache, whitelist=whitelist, ) print(f'Time to generate graph: {round(perf_counter() - start_time, 2)} sec') if args.input_string: print(inverse_normalizer.inverse_normalize(args.input_string, verbose=args.verbose)) elif args.input_file: print("Loading data: " + args.input_file) data = load_file(args.input_file) print("- Data: " + str(len(data)) + " sentences") prediction = inverse_normalizer.inverse_normalize_list(data, verbose=args.verbose) if args.output_file: write_file(args.output_file, prediction) print(f"- Denormalized. Writing out to {args.output_file}") else: print(prediction)
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/inverse_normalize.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nemo_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser from nemo_text_processing.inverse_text_normalization.inverse_normalize import InverseNormalizer from nemo_text_processing.text_normalization.data_loader_utils import ( evaluate, known_types, load_files, training_data_to_sentences, training_data_to_tokens, ) ''' Runs Evaluation on data in the format of : <semiotic class>\t<unnormalized text>\t<`self` if trivial class or normalized text> like the Google text normalization data https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish ''' def parse_args(): parser = ArgumentParser() parser.add_argument("--input", help="input file path", type=str) parser.add_argument( "--lang", help="language", choices=['en', 'de', 'es', 'pt', 'ru', 'fr', 'vi'], default="en", type=str ) parser.add_argument( "--cat", dest="category", help="focus on class only (" + ", ".join(known_types) + ")", type=str, default=None, choices=known_types, ) parser.add_argument("--filter", action='store_true', help="clean data for inverse normalization purposes") return parser.parse_args() if __name__ == "__main__": # Example usage: # python run_evaluate.py --input=<INPUT> --cat=<CATEGORY> --filter args = parse_args() if args.lang == 'en': from nemo_text_processing.inverse_text_normalization.en.clean_eval_data import filter_loaded_data file_path = args.input inverse_normalizer = InverseNormalizer(lang=args.lang) print("Loading training data: " + file_path) training_data = load_files([file_path]) if args.filter: training_data = filter_loaded_data(training_data) if args.category is None: print("Sentence level evaluation...") sentences_un_normalized, sentences_normalized, _ = training_data_to_sentences(training_data) print("- Data: " + str(len(sentences_normalized)) + " sentences") sentences_prediction = inverse_normalizer.inverse_normalize_list(sentences_normalized) print("- Denormalized. Evaluating...") sentences_accuracy = evaluate( preds=sentences_prediction, labels=sentences_un_normalized, input=sentences_normalized ) print("- Accuracy: " + str(sentences_accuracy)) print("Token level evaluation...") tokens_per_type = training_data_to_tokens(training_data, category=args.category) token_accuracy = {} for token_type in tokens_per_type: print("- Token type: " + token_type) tokens_un_normalized, tokens_normalized = tokens_per_type[token_type] print(" - Data: " + str(len(tokens_normalized)) + " tokens") tokens_prediction = inverse_normalizer.inverse_normalize_list(tokens_normalized) print(" - Denormalized. Evaluating...") token_accuracy[token_type] = evaluate(tokens_prediction, tokens_un_normalized, input=tokens_normalized) print(" - Accuracy: " + str(token_accuracy[token_type])) token_count_per_type = {token_type: len(tokens_per_type[token_type][0]) for token_type in tokens_per_type} token_weighted_accuracy = [ token_count_per_type[token_type] * accuracy for token_type, accuracy in token_accuracy.items() ] print("- Accuracy: " + str(sum(token_weighted_accuracy) / sum(token_count_per_type.values()))) print(" - Total: " + str(sum(token_count_per_type.values())), '\n') for token_type in token_accuracy: if token_type not in known_types: raise ValueError("Unexpected token type: " + token_type) if args.category is None: c1 = ['Class', 'sent level'] + known_types c2 = ['Num Tokens', len(sentences_normalized)] + [ token_count_per_type[known_type] if known_type in tokens_per_type else '0' for known_type in known_types ] c3 = ["Denormalization", sentences_accuracy] + [ token_accuracy[known_type] if known_type in token_accuracy else '0' for known_type in known_types ] for i in range(len(c1)): print(f'{str(c1[i]):10s} | {str(c2[i]):10s} | {str(c3[i]):5s}') else: print(f'numbers\t{token_count_per_type[args.category]}') print(f'Denormalization\t{token_accuracy[args.category]}')
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/run_evaluate.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import string from pathlib import Path from typing import Dict import pynini from pynini import Far from pynini.export import export from pynini.lib import byte, pynutil, utf8 NEMO_CHAR = utf8.VALID_UTF8_CHAR NEMO_DIGIT = byte.DIGIT NEMO_LOWER = pynini.union(*string.ascii_lowercase).optimize() NEMO_UPPER = pynini.union(*string.ascii_uppercase).optimize() NEMO_ALPHA = pynini.union(NEMO_LOWER, NEMO_UPPER).optimize() NEMO_ALNUM = pynini.union(NEMO_DIGIT, NEMO_ALPHA).optimize() NEMO_HEX = pynini.union(*string.hexdigits).optimize() NEMO_NON_BREAKING_SPACE = "\u00A0" NEMO_SPACE = " " NEMO_WHITE_SPACE = pynini.union(" ", "\t", "\n", "\r", "\u00A0").optimize() NEMO_NOT_SPACE = pynini.difference(NEMO_CHAR, NEMO_WHITE_SPACE).optimize() NEMO_NOT_QUOTE = pynini.difference(NEMO_CHAR, r'"').optimize() NEMO_PUNCT = pynini.union(*map(pynini.escape, string.punctuation)).optimize() NEMO_GRAPH = pynini.union(NEMO_ALNUM, NEMO_PUNCT).optimize() NEMO_SIGMA = pynini.closure(NEMO_CHAR) delete_space = pynutil.delete(pynini.closure(NEMO_WHITE_SPACE)) insert_space = pynutil.insert(" ") delete_extra_space = pynini.cross(pynini.closure(NEMO_WHITE_SPACE, 1), " ") # French frequently compounds numbers with hyphen. delete_hyphen = pynutil.delete(pynini.closure("-", 0, 1)) insert_hyphen = pynutil.insert("-") TO_LOWER = pynini.union(*[pynini.cross(x, y) for x, y in zip(string.ascii_uppercase, string.ascii_lowercase)]) TO_UPPER = pynini.invert(TO_LOWER) def generator_main(file_name: str, graphs: Dict[str, pynini.FstLike]): """ Exports graph as OpenFst finite state archive (FAR) file with given file name and rule name. Args: file_name: exported file name graphs: Mapping of a rule name and Pynini WFST graph to be exported """ exporter = export.Exporter(file_name) for rule, graph in graphs.items(): exporter[rule] = graph.optimize() exporter.close() logging.info(f"Created {file_name}") def convert_space(fst) -> "pynini.FstLike": """ Converts space to nonbreaking space. Used only in tagger grammars for transducing token values within quotes, e.g. name: "hello kitty" This is making transducer significantly slower, so only use when there could be potential spaces within quotes, otherwise leave it. Args: fst: input fst Returns output fst where breaking spaces are converted to non breaking spaces """ return fst @ pynini.cdrewrite(pynini.cross(NEMO_SPACE, NEMO_NON_BREAKING_SPACE), "", "", NEMO_SIGMA) class GraphFst: """ Base class for all grammar fsts. Args: name: name of grammar class kind: either 'classify' or 'verbalize' deterministic: if True will provide a single transduction option, for False multiple transduction are generated (used for audio-based normalization) """ def __init__(self, name: str, kind: str, deterministic: bool = True): self.name = name self.kind = kind self._fst = None self.deterministic = deterministic self.far_path = Path(os.path.dirname(__file__) + "/grammars/" + kind + "/" + name + ".far") if self.far_exist(): self._fst = Far(self.far_path, mode="r", arc_type="standard", far_type="default").get_fst() def far_exist(self) -> bool: """ Returns true if FAR can be loaded """ return self.far_path.exists() @property def fst(self) -> "pynini.FstLike": return self._fst @fst.setter def fst(self, fst): self._fst = fst def add_tokens(self, fst) -> "pynini.FstLike": """ Wraps class name around to given fst Args: fst: input fst Returns: Fst: fst """ return pynutil.insert(f"{self.name} {{ ") + fst + pynutil.insert(" }") def delete_tokens(self, fst) -> "pynini.FstLike": """ Deletes class name wrap around output of given fst Args: fst: input fst Returns: Fst: fst """ res = ( pynutil.delete(f"{self.name}") + delete_space + pynutil.delete("{") + delete_space + fst + delete_space + pynutil.delete("}") ) return res @ pynini.cdrewrite(pynini.cross("\u00A0", " "), "", "", NEMO_SIGMA)
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/graph_utils.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nemo_text_processing.inverse_text_normalization.vi.taggers.tokenize_and_classify import ClassifyFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.verbalize import VerbalizeFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.verbalize_final import VerbalizeFinalFst
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os def get_abs_path(rel_path): """ Get absolute path Args: rel_path: relative path to this file Returns absolute path """ return os.path.dirname(os.path.abspath(__file__)) + "/" + rel_path
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/utils.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( GraphFst, convert_space, delete_extra_space, delete_space, insert_space, ) from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class TimeFst(GraphFst): """ Finite state transducer for classifying time e.g. hai rưỡi -> time { hours: "2" minutes: "30" } e.g. chín giờ kém hai mươi -> time { hours: "8" minutes: "40" } e.g. ba phút hai giây -> time { minutes: "3" seconds: "2" } e.g. mười giờ chín phút bốn mươi lăm giây -> time { hours: "10" minutes: "9" seconds: "45" } """ def __init__(self): super().__init__(name="time", kind="classify") # hours, minutes, seconds, suffix, zone, style, speak_period graph_hours_to = pynini.string_file(get_abs_path("data/time/hours_to.tsv")) graph_minutes_to = pynini.string_file(get_abs_path("data/time/minutes_to.tsv")) graph_hours = pynini.string_file(get_abs_path("data/time/hours.tsv")) graph_minutes = pynini.string_file(get_abs_path("data/time/minutes.tsv")) time_zone_graph = pynini.invert(pynini.string_file(get_abs_path("data/time/time_zone.tsv"))) graph_half = pynini.cross("rưỡi", "30") oclock = pynini.cross("giờ", "") minute = pynini.cross("phút", "") optional_minute = pynini.closure(delete_space + minute, 0, 1) second = pynini.cross("giây", "") final_graph_hour = pynutil.insert('hours: "') + graph_hours + pynutil.insert('"') + delete_space + oclock graph_minute = graph_minutes + optional_minute graph_second = graph_minutes + delete_space + second final_time_zone_optional = pynini.closure( delete_space + insert_space + pynutil.insert('zone: "') + convert_space(time_zone_graph) + pynutil.insert('"'), 0, 1, ) graph_hm = ( final_graph_hour + delete_extra_space + pynutil.insert('minutes: "') + (graph_minute | graph_half) + pynutil.insert('"') ) graph_hms = ( final_graph_hour + delete_extra_space + pynutil.insert('minutes: "') + graph_minutes + delete_space + minute + pynutil.insert('"') + delete_extra_space + pynutil.insert('seconds: "') + graph_second + pynutil.insert('"') ) graph_ms = ( pynutil.insert('minutes: "') + graph_minutes + delete_space + minute + pynutil.insert('"') + delete_extra_space + pynutil.insert('seconds: "') + (graph_second | graph_half) + pynutil.insert('"') ) graph_hours_to_component = graph_hours @ graph_hours_to graph_minutes_to_component = graph_minutes @ graph_minutes_to graph_time_to = ( pynutil.insert('hours: "') + graph_hours_to_component + pynutil.insert('"') + delete_space + oclock + delete_space + pynutil.delete("kém") + delete_extra_space + pynutil.insert('minutes: "') + graph_minutes_to_component + pynutil.insert('"') + optional_minute ) final_graph = (final_graph_hour | graph_hm | graph_hms) + final_time_zone_optional final_graph |= graph_ms final_graph |= graph_time_to final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/time.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( GraphFst, convert_space, delete_extra_space, delete_space, ) from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class MeasureFst(GraphFst): """ Finite state transducer for classifying measure e.g. trừ mười hai ki lô gam -> measure { negative: "true" cardinal { integer: "12" } units: "kg" } Args: cardinal: CardinalFst decimal: DecimalFst """ def __init__(self, cardinal: GraphFst, decimal: GraphFst): super().__init__(name="measure", kind="classify") cardinal_graph = cardinal.graph_no_exception graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")) graph_four = pynini.cross("tư", "4") graph_one = pynini.cross("mốt", "1") graph_half = pynini.cross("rưỡi", "5") graph_unit = pynini.string_file(get_abs_path("data/measurements.tsv")) graph_unit_singular = pynini.invert(graph_unit) # singular -> abbr optional_graph_negative = pynini.closure( pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"true"') + delete_extra_space, 0, 1, ) unit_singular = convert_space(graph_unit_singular) unit_misc = pynutil.insert("/") + pynutil.delete("trên") + delete_space + convert_space(graph_unit_singular) unit_singular = ( pynutil.insert('units: "') + (unit_singular | unit_misc | pynutil.add_weight(unit_singular + delete_space + unit_misc, 0.01)) + pynutil.insert('"') ) subgraph_decimal = ( pynutil.insert("decimal { ") + optional_graph_negative + decimal.final_graph_wo_negative + pynutil.insert(" }") + delete_extra_space + unit_singular ) subgraph_cardinal = ( pynutil.insert("cardinal { ") + optional_graph_negative + pynutil.insert('integer: "') + cardinal_graph + pynutil.insert('"') + pynutil.insert(" }") + delete_extra_space + unit_singular ) fraction_graph = ( delete_extra_space + pynutil.insert('fractional_part: "') + (graph_digit | graph_half | graph_one | graph_four) + pynutil.insert('"') ) subgraph_cardinal |= ( pynutil.insert("cardinal { ") + optional_graph_negative + pynutil.insert('integer: "') + cardinal_graph + pynutil.insert('" }') + delete_extra_space + unit_singular + fraction_graph ) final_graph = subgraph_decimal | subgraph_cardinal final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/measure.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst, delete_extra_space, delete_space from pynini.lib import pynutil class FractionFst(GraphFst): """ Finite state transducer for classifying fraction e.g. 2 phần 3 -> tokens { fraction { numerator: "2" denominator: "3" } } e.g. 2 trên 3 -> tokens { fraction { numerator: "2" denominator: "3" } } e.g. 2 chia 3 -> tokens { fraction { numerator: "2" denominator: "3" } } Args: cardinal: OrdinalFst """ def __init__(self, cardinal: GraphFst): super().__init__(name="fraction", kind="classify") # integer_part # numerator # denominator graph_cardinal = cardinal.graph_no_exception graph_four = pynini.cross("tư", "4") numerator = pynutil.insert('numerator: "') + graph_cardinal + pynutil.insert('"') fraction_component = pynutil.delete(pynini.union("phần", "trên", "chia")) denominator = pynutil.insert('denominator: "') + (graph_cardinal | graph_four) + pynutil.insert('"') graph_fraction_component = numerator + delete_space + fraction_component + delete_extra_space + denominator self.graph_fraction_component = graph_fraction_component graph = graph_fraction_component graph = graph.optimize() self.final_graph_wo_negative = graph optional_graph_negative = pynini.closure( pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"true"') + delete_extra_space, 0, 1, ) graph = optional_graph_negative + graph final_graph = self.add_tokens(graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/fraction.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst, delete_space from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class TelephoneFst(GraphFst): """ Finite state transducer for classifying telephone numbers, e.g. một hai ba một hai ba năm sáu bảy tám -> { number_part: "1231235678" } """ def __init__(self): super().__init__(name="telephone", kind="classify") graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv")) graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")) digit = graph_digit | graph_zero last_digit = digit | pynini.cross("mốt", "1") | pynini.cross("tư", "4") | pynini.cross("lăm", "5") graph_number_part = pynini.closure(digit + delete_space, 2) + last_digit number_part = pynutil.insert('number_part: "') + graph_number_part + pynutil.insert('"') graph = number_part final_graph = self.add_tokens(graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/telephone.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst, delete_space from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class OrdinalFst(GraphFst): """ Finite state transducer for classifying ordinal e.g. thứ nhất -> ordinal { integer: "1" } """ def __init__(self): super().__init__(name="ordinal", kind="classify") graph_digit = pynini.string_file(get_abs_path("data/ordinals/digit.tsv")) graph_ordinal = pynini.cross("thứ", "") graph = graph_digit self.graph = graph final_graph = pynutil.insert('integer: "') + graph_ordinal + delete_space + self.graph + pynutil.insert('"') final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/ordinal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst, convert_space from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class WhiteListFst(GraphFst): """ Finite state transducer for classifying whitelisted tokens e.g. misses -> tokens { name: "mrs." } This class has highest priority among all classifier grammars. Whitelisted tokens are defined and loaded from "data/whitelist.tsv" (unless input_file specified). Args: input_file: path to a file with whitelist replacements (each line of the file: written_form\tspoken_form\n), e.g. nemo_text_processing/inverse_text_normalization/en/data/whitelist.tsv """ def __init__(self, input_file: str = None): super().__init__(name="whitelist", kind="classify") if input_file: whitelist = pynini.string_file(input_file).invert() else: whitelist = pynini.string_file(get_abs_path("data/whitelist.tsv")).invert() graph = pynutil.insert('name: "') + convert_space(whitelist) + pynutil.insert('"') self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/whitelist.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( GraphFst, delete_extra_space, delete_space, generator_main, ) from nemo_text_processing.inverse_text_normalization.vi.taggers.cardinal import CardinalFst from nemo_text_processing.inverse_text_normalization.vi.taggers.date import DateFst from nemo_text_processing.inverse_text_normalization.vi.taggers.decimal import DecimalFst from nemo_text_processing.inverse_text_normalization.vi.taggers.electronic import ElectronicFst from nemo_text_processing.inverse_text_normalization.vi.taggers.fraction import FractionFst from nemo_text_processing.inverse_text_normalization.vi.taggers.measure import MeasureFst from nemo_text_processing.inverse_text_normalization.vi.taggers.money import MoneyFst from nemo_text_processing.inverse_text_normalization.vi.taggers.ordinal import OrdinalFst from nemo_text_processing.inverse_text_normalization.vi.taggers.punctuation import PunctuationFst from nemo_text_processing.inverse_text_normalization.vi.taggers.telephone import TelephoneFst from nemo_text_processing.inverse_text_normalization.vi.taggers.time import TimeFst from nemo_text_processing.inverse_text_normalization.vi.taggers.whitelist import WhiteListFst from nemo_text_processing.inverse_text_normalization.vi.taggers.word import WordFst from nemo_text_processing.text_normalization.en.graph_utils import INPUT_LOWER_CASED from pynini.lib import pynutil class ClassifyFst(GraphFst): """ Final class that composes all other classification grammars. This class can process an entire sentence, that is lower cased. For deployment, this grammar will be compiled and exported to OpenFst Finite State Archive (FAR) File. More details to deployment at NeMo/tools/text_processing_deployment. Args: cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. overwrite_cache: set to True to overwrite .far files whitelist: path to a file with whitelist replacements input_case: accepting either "lower_cased" or "cased" input. """ def __init__( self, cache_dir: str = None, overwrite_cache: bool = False, whitelist: str = None, input_case: str = INPUT_LOWER_CASED, ): super().__init__(name="tokenize_and_classify", kind="classify") far_file = None if cache_dir is not None and cache_dir != "None": os.makedirs(cache_dir, exist_ok=True) far_file = os.path.join(cache_dir, f"vi_itn_{input_case}.far") if not overwrite_cache and far_file and os.path.exists(far_file): self.fst = pynini.Far(far_file, mode="r")["tokenize_and_classify"] logging.info(f"ClassifyFst.fst was restored from {far_file}.") else: logging.info(f"Creating ClassifyFst grammars.") cardinal = CardinalFst() cardinal_graph = cardinal.fst fraction = FractionFst(cardinal) fraction_graph = fraction.fst ordinal = OrdinalFst() ordinal_graph = ordinal.fst decimal = DecimalFst(cardinal) decimal_graph = decimal.fst measure_graph = MeasureFst(cardinal=cardinal, decimal=decimal).fst date_graph = DateFst(cardinal=cardinal).fst word_graph = WordFst().fst time_graph = TimeFst().fst money_graph = MoneyFst(cardinal=cardinal, decimal=decimal).fst whitelist_graph = WhiteListFst(input_file=whitelist).fst punct_graph = PunctuationFst().fst electronic_graph = ElectronicFst().fst telephone_graph = TelephoneFst().fst classify = ( pynutil.add_weight(whitelist_graph, 1.01) | pynutil.add_weight(time_graph, 1.05) | pynutil.add_weight(date_graph, 1.09) | pynutil.add_weight(decimal_graph, 1.08) | pynutil.add_weight(measure_graph, 1.1) | pynutil.add_weight(cardinal_graph, 1.1) | pynutil.add_weight(ordinal_graph, 1.1) | pynutil.add_weight(fraction_graph, 1.09) | pynutil.add_weight(money_graph, 1.07) | pynutil.add_weight(telephone_graph, 1.1) | pynutil.add_weight(electronic_graph, 1.1) | pynutil.add_weight(word_graph, 100) ) punct = pynutil.insert("tokens { ") + pynutil.add_weight(punct_graph, weight=1.1) + pynutil.insert(" }") token = pynutil.insert("tokens { ") + classify + pynutil.insert(" }") token_plus_punct = ( pynini.closure(punct + pynutil.insert(" ")) + token + pynini.closure(pynutil.insert(" ") + punct) ) graph = token_plus_punct + pynini.closure(delete_extra_space + token_plus_punct) graph = delete_space + graph + delete_space self.fst = graph.optimize() if far_file: generator_main(far_file, {"tokenize_and_classify": self.fst}) logging.info(f"ClassifyFst grammars are saved to {far_file}.")
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/tokenize_and_classify.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst from pynini.lib import pynutil class PunctuationFst(GraphFst): """ Finite state transducer for classifying punctuation e.g. a, -> tokens { name: "a" } tokens { name: "," } """ def __init__(self): super().__init__(name="punctuation", kind="classify") s = "!#$%&'()*+,-./:;<=>?@^_`{|}~" punct = pynini.union(*s) graph = pynutil.insert('name: "') + punct + pynutil.insert('"') self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/punctuation.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_DIGIT, GraphFst, delete_extra_space, delete_space, ) from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")) def get_quantity(decimal: "pynini.FstLike", cardinal_up_to_hundred: "pynini.FstLike") -> "pynini.FstLike": """ Returns FST that transforms either a cardinal or decimal followed by a quantity into a numeral, e.g. một triệu -> integer_part: "1" quantity: "triệu" e.g. một tỷ rưỡi -> integer_part: "1" fractional_part: "5" quantity: "tỷ" Args: decimal: decimal FST cardinal_up_to_hundred: cardinal FST """ numbers = cardinal_up_to_hundred @ ( pynutil.delete(pynini.closure("0")) + pynini.difference(NEMO_DIGIT, "0") + pynini.closure(NEMO_DIGIT) ) suffix = pynini.union("triệu", "tỉ", "tỷ", "vạn") graph_four = pynini.cross("tư", "4") graph_one = pynini.cross("mốt", "1") graph_half = pynini.cross("rưỡi", "5") last_digit_exception = pynini.project(pynini.cross("năm", "5"), "input") last_digit = pynini.union( (pynini.project(graph_digit, "input") - last_digit_exception.arcsort()) @ graph_digit, graph_one, graph_four, graph_half, ) optional_fraction_graph = pynini.closure( delete_extra_space + pynutil.insert('fractional_part: "') + (last_digit | graph_half | graph_one | graph_four) + pynutil.insert('"'), 0, 1, ) res = ( pynutil.insert('integer_part: "') + numbers + pynutil.insert('"') + delete_extra_space + pynutil.insert('quantity: "') + suffix + pynutil.insert('"') + optional_fraction_graph ) res |= ( decimal + delete_extra_space + pynutil.insert('quantity: "') + (suffix | "ngàn" | "nghìn") + pynutil.insert('"') ) return res class DecimalFst(GraphFst): """ Finite state transducer for classifying decimal e.g. âm hai hai phẩy không năm tư năm tỉ -> decimal { negative: "true" integer_part: "22" fractional_part: "054" quantity: "tỉ" } e.g. không chấm ba lăm -> decimal { integer_part: "0" fractional_part: "35" } e.g. một triệu rưỡi -> decimal { integer_part: "1" quantity: "triệu" fractional_part: "5" } Args: cardinal: CardinalFst """ def __init__(self, cardinal: GraphFst): super().__init__(name="decimal", kind="classify") cardinal_graph = cardinal.graph_no_exception graph_decimal = graph_digit | pynini.string_file(get_abs_path("data/numbers/zero.tsv")) graph_one = pynini.cross("mốt", "1") graph_four = pynini.cross("tư", "4") graph_five = pynini.cross("lăm", "5") graph_decimal = pynini.union( graph_decimal, graph_four, pynini.closure(graph_decimal + delete_space, 1) + (graph_decimal | graph_four | graph_five | graph_one), ) self.graph = graph_decimal point = pynutil.delete("chấm") | pynutil.delete("phẩy") optional_graph_negative = pynini.closure( pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"true"') + delete_extra_space, 0, 1, ) graph_fractional = pynutil.insert('fractional_part: "') + graph_decimal + pynutil.insert('"') graph_integer = pynutil.insert('integer_part: "') + cardinal_graph + pynutil.insert('"') final_graph_wo_sign = ( pynini.closure(graph_integer + delete_extra_space, 0, 1) + point + delete_extra_space + graph_fractional ) final_graph = optional_graph_negative + final_graph_wo_sign self.final_graph_wo_negative = final_graph_wo_sign | get_quantity( final_graph_wo_sign, cardinal.graph_hundred_component_at_least_one_none_zero_digit, ) final_graph |= optional_graph_negative + get_quantity( final_graph_wo_sign, cardinal.graph_hundred_component_at_least_one_none_zero_digit, ) final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/decimal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_DIGIT, GraphFst, convert_space, delete_extra_space, ) from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class MoneyFst(GraphFst): """ Finite state transducer for classifying money e.g. mười hai đô la mỹ -> money { integer_part: "12" currency: "$" } e.g. mười phẩy chín đồng -> money { integer_part: "10.9" currency: "đ" } Args: cardinal: CardinalFst decimal: DecimalFst """ def __init__(self, cardinal: GraphFst, decimal: GraphFst): super().__init__(name="money", kind="classify") # quantity, integer_part, fractional_part, currency cardinal_graph = cardinal.graph_no_exception graph_decimal_final = decimal.final_graph_wo_negative graph_half = pynini.cross("rưỡi", "5") unit = pynini.string_file(get_abs_path("data/currency.tsv")) unit_singular = pynini.invert(unit) graph_unit_singular = pynutil.insert('currency: "') + convert_space(unit_singular) + pynutil.insert('"') add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT) # twelve dollars fifty, only after integer optional_cents_suffix = pynini.closure( delete_extra_space + pynutil.insert('fractional_part: "') + (pynutil.add_weight(cardinal_graph @ add_leading_zero_to_double_digit, -0.7) | graph_half) + pynutil.insert('"'), 0, 1, ) graph_integer = ( pynutil.insert('integer_part: "') + cardinal_graph + pynutil.insert('"') + delete_extra_space + graph_unit_singular + optional_cents_suffix ) graph_decimal = graph_decimal_final + delete_extra_space + graph_unit_singular + optional_cents_suffix final_graph = graph_integer | graph_decimal final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/money.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_DIGIT, NEMO_SPACE, GraphFst, delete_space, ) from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class CardinalFst(GraphFst): """ Finite state transducer for classifying cardinals e.g. trừ hai mươi ba -> cardinal { integer: "23" negative: "-" } } e.g. hai nghìn lẻ chín -> cardinal { integer: "2009"} } Numbers below ten are not converted. """ def __init__(self): super().__init__(name="cardinal", kind="classify") graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv")) graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")) graph_ties = pynini.string_file(get_abs_path("data/numbers/ties.tsv")) graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv")) graph_one = pynini.cross("mốt", "1") graph_four = pynini.cross("tư", "4") graph_five = pynini.cross("lăm", "5") graph_half = pynini.cross("rưỡi", "5") graph_hundred = pynini.cross("trăm", "") graph_ten = pynini.cross("mươi", "") zero = pynini.cross(pynini.union("linh", "lẻ"), "0") optional_ten = pynini.closure(delete_space + graph_ten, 0, 1) last_digit_exception = pynini.project(pynini.cross("năm", "5"), "input") last_digit = pynini.union( (pynini.project(graph_digit, "input") - last_digit_exception.arcsort()) @ graph_digit, graph_one, graph_four, graph_five, ) graph_hundred_ties_component = (graph_digit | graph_zero) + delete_space + graph_hundred graph_hundred_ties_component += delete_space graph_hundred_ties_component += pynini.union( graph_teen, (graph_half | graph_four | graph_one) + pynutil.insert("0"), graph_ties + optional_ten + ((delete_space + last_digit) | pynutil.insert("0")), zero + delete_space + (graph_digit | graph_four), pynutil.insert("00"), ) graph_hundred_ties_component |= ( pynutil.insert("0") + delete_space + pynini.union( graph_teen, graph_ties + optional_ten + delete_space + last_digit, graph_ties + delete_space + graph_ten + pynutil.insert("0"), zero + delete_space + (graph_digit | graph_four), ) ) graph_hundred_component = graph_hundred_ties_component | (pynutil.insert("00") + delete_space + graph_digit) graph_hundred_component_at_least_one_none_zero_digit = graph_hundred_component @ ( pynini.closure(NEMO_DIGIT) + (NEMO_DIGIT - "0") + pynini.closure(NEMO_DIGIT) ) self.graph_hundred_component_at_least_one_none_zero_digit = ( graph_hundred_component_at_least_one_none_zero_digit ) graph_hundred_ties_zero = graph_hundred_ties_component | pynutil.insert("000") graph_thousands = pynini.union( graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete(pynini.union("nghìn", "ngàn")), pynutil.insert("000", weight=0.1), ) graph_ten_thousand = pynini.union( graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete("vạn"), pynutil.insert("0000", weight=0.1), ) graph_ten_thousand_suffix = pynini.union( graph_digit + delete_space + pynutil.delete(pynini.union("nghìn", "ngàn")), pynutil.insert("0", weight=0.1), ) graph_million = pynini.union( graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete("triệu"), pynutil.insert("000", weight=0.1), ) graph_billion = pynini.union( graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete(pynini.union("tỉ", "tỷ")), pynutil.insert("000", weight=0.1), ) graph = pynini.union( graph_billion + delete_space + graph_million + delete_space + graph_thousands + delete_space + graph_hundred_ties_zero, graph_ten_thousand + delete_space + graph_ten_thousand_suffix + delete_space + graph_hundred_ties_zero, graph_hundred_component_at_least_one_none_zero_digit + delete_space + pynutil.delete(pynini.union("nghìn", "ngàn")) + delete_space + (((last_digit | graph_half) + pynutil.insert("00")) | graph_hundred_ties_zero), graph_digit, graph_zero, ) graph = graph @ pynini.union( pynutil.delete(pynini.closure("0")) + pynini.difference(NEMO_DIGIT, "0") + pynini.closure(NEMO_DIGIT), "0", ) # don't convert cardinals from zero to nine inclusive graph_exception = pynini.project(pynini.union(graph_digit, graph_zero), "input") self.graph_no_exception = graph self.graph = (pynini.project(graph, "input") - graph_exception.arcsort()) @ graph optional_minus_graph = pynini.closure( pynutil.insert("negative: ") + pynini.cross(pynini.union("âm", "trừ"), '"-"') + NEMO_SPACE, 0, 1, ) final_graph = optional_minus_graph + pynutil.insert('integer: "') + self.graph + pynutil.insert('"') final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/cardinal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_ALPHA, GraphFst, insert_space from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil class ElectronicFst(GraphFst): """ Finite state transducer for classifying electronic: as URLs, email addresses, etc. e.g. c d f một a còng a b c dot e d u -> tokens { electronic { username: "cdf1" domain: "abc.edu" } } """ def __init__(self): super().__init__(name="electronic", kind="classify") delete_extra_space = pynutil.delete(" ") alpha_num = ( NEMO_ALPHA | pynini.string_file(get_abs_path("data/numbers/digit.tsv")) | pynini.string_file(get_abs_path("data/numbers/zero.tsv")) ) symbols = pynini.string_file(get_abs_path("data/electronic/symbols.tsv")).invert() accepted_username = alpha_num | symbols process_dot = pynini.cross("chấm", ".") username = ( pynutil.insert('username: "') + alpha_num + pynini.closure(delete_extra_space + accepted_username) + pynutil.insert('"') ) single_alphanum = pynini.closure(alpha_num + delete_extra_space) + alpha_num server = single_alphanum | pynini.string_file(get_abs_path("data/electronic/server_name.tsv")) domain = single_alphanum | pynini.string_file(get_abs_path("data/electronic/domain.tsv")) multi_domain = ( pynini.closure(process_dot + delete_extra_space + domain + delete_extra_space) + process_dot + delete_extra_space + domain ) domain_graph = pynutil.insert('domain: "') + server + delete_extra_space + multi_domain + pynutil.insert('"') graph = ( username + delete_extra_space + pynutil.delete(pynini.union("a còng", "a móc", "a vòng")) + insert_space + delete_extra_space + domain_graph ) ############# url ### protocol_end = pynini.cross(pynini.union("w w w", "www"), "www") protocol_start = (pynini.cross("h t t p", "http") | pynini.cross("h t t p s", "https")) + pynini.cross( " hai chấm sẹc sẹc ", "://" ) # .com, ending = ( delete_extra_space + symbols + delete_extra_space + (domain | pynini.closure(accepted_username + delete_extra_space) + accepted_username) ) protocol = ( pynini.closure(protocol_start, 0, 1) + protocol_end + delete_extra_space + process_dot + pynini.closure(delete_extra_space + accepted_username, 1) + pynini.closure(ending, 1, 2) ) protocol = pynutil.insert('protocol: "') + protocol + pynutil.insert('"') graph |= protocol ######## final_graph = self.add_tokens(graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/electronic.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst, delete_extra_space, delete_space from nemo_text_processing.inverse_text_normalization.vi.utils import get_abs_path from pynini.lib import pynutil graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv")).optimize() graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")).optimize() graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv")).optimize() ties_graph = pynini.string_file(get_abs_path("data/numbers/ties.tsv")).optimize() def _get_month_graph(): """ Transducer for month, e.g. march -> march """ month_graph = pynini.string_file(get_abs_path("data/months.tsv")).optimize() return month_graph def _get_ties_graph(): """ Transducer for 20-99 e.g hai ba -> 23 """ graph_one = pynini.cross("mốt", "1") graph_four = pynini.cross("tư", "4") graph_five = pynini.cross("lăm", "5") graph_ten = pynini.cross("mươi", "") optional_ten = pynini.closure(delete_space + graph_ten, 0, 1) graph = pynini.union( ties_graph + optional_ten + delete_space + (graph_digit | graph_one | graph_four | graph_five), ties_graph + delete_space + graph_ten + pynutil.insert("0"), ) return graph def _get_year_graph(): """ Transducer for year, e.g. hai không hai mươi -> 2020 """ def _get_digits_graph(): zero = pynini.cross((pynini.union("linh", "lẻ")), "0") four = pynini.cross("tư", "4") graph = pynini.union(zero + delete_space + (graph_digit | four), graph_zero + delete_space + graph_digit,) graph.optimize() return graph def _get_hundreds_graph(graph_ties, graph_digits): graph = ( graph_digit + delete_space + pynutil.delete("trăm") + delete_space + (graph_teen | graph_ties | graph_digits) ) return graph def _get_thousands_graph(graph_ties, graph_digits): graph_hundred_component = ( (graph_digit | graph_zero) + delete_space + pynutil.delete("trăm") ) | pynutil.insert("0") graph = ( graph_digit + delete_space + pynutil.delete(pynini.union("nghìn", "ngàn")) + delete_space + graph_hundred_component + delete_space + (graph_teen | graph_ties | graph_digits) ) return graph graph_ties = _get_ties_graph() graph_digits = _get_digits_graph() graph_hundreds = _get_hundreds_graph(graph_ties, graph_digits) graph_thousands = _get_thousands_graph(graph_ties, graph_digits) year_graph = ( # 20 19, 40 12, 2012, 2 0 0 5, 2 0 17, 938 - assuming no limit on the year graph_digit + delete_space + (graph_digit | graph_zero) + delete_space + (graph_teen | graph_ties | graph_digits) | graph_thousands | graph_hundreds | (graph_digit + pynutil.insert("0") + delete_space + (graph_ties | graph_digits | graph_teen)) ) year_graph.optimize() return year_graph class DateFst(GraphFst): """ Finite state transducer for classifying date, e.g. mười lăm tháng một năm hai nghìn mười hai -> date { day: "15" month: "1" year: "2012" preserve_order: true } e.g. ngày ba mốt tháng mười hai năm một chín chín chín -> date { day: "31" month: "12" year: "2012" preserve_order: true } e.g. năm hai không hai mốt -> date { year: "2021" preserve_order: true } Args: cardinal: CardinalFst """ def __init__(self, cardinal: GraphFst): super().__init__(name="date", kind="classify") cardinal_graph = cardinal.graph_no_exception year_graph = _get_year_graph() YEAR_WEIGHT = 0.001 year_graph = pynutil.add_weight(year_graph, YEAR_WEIGHT) month_graph = _get_month_graph() month_graph = pynutil.insert('month: "') + month_graph + pynutil.insert('"') month_exception = pynini.project(pynini.cross("năm", "5"), "input") month_graph_exception = (pynini.project(month_graph, "input") - month_exception.arcsort()) @ month_graph day_graph = pynutil.insert('day: "') + cardinal_graph + pynutil.insert('"') # day_suffix = pynini.union("ngày", "mùng") # optional_day = pynini.closure(day_suffix + delete_space, 0, 1) graph_month = pynutil.delete("tháng") + delete_space + month_graph_exception graph_year = ( delete_extra_space + pynutil.delete("năm") + delete_extra_space + pynutil.insert('year: "') + pynutil.add_weight(year_graph, -YEAR_WEIGHT) + pynutil.insert('"') ) optional_graph_year = pynini.closure(graph_year, 0, 1) graph_my = pynutil.delete("tháng") + delete_space + month_graph + graph_year graph_dmy = ( day_graph + delete_space + pynutil.delete("tháng") + delete_extra_space + month_graph + optional_graph_year ) graph_year = ( pynutil.delete("năm") + delete_extra_space + pynutil.insert('year: "') + year_graph + pynutil.insert('"') ) final_graph = (graph_dmy | graph_my | graph_month | graph_year) + pynutil.insert(" preserve_order: true") final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/date.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_SPACE, GraphFst from pynini.lib import pynutil class WordFst(GraphFst): """ Finite state transducer for classifying plain tokens, that do not belong to any special class. This can be considered as the default class. e.g. sleep -> tokens { name: "sleep" } """ def __init__(self): super().__init__(name="word", kind="classify") word = pynutil.insert('name: "') + pynini.closure(NEMO_NOT_SPACE, 1) + pynutil.insert('"') self.fst = word.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/taggers/word.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_CHAR, NEMO_DIGIT, GraphFst, delete_space, insert_space, ) from pynini.lib import pynutil class TimeFst(GraphFst): """ Finite state transducer for verbalizing time, e.g. time { hours: "3" } -> 3h time { hours: "12" minutes: "30" } -> 12:30 time { hours: "1" minutes: "12" second: "22"} -> 1:12:22 time { minutes: "36" second: "45"} -> 36p45s time { hours: "2" zone: "gmt" } -> 2h gmt """ def __init__(self): super().__init__(name="time", kind="verbalize") add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT) hour = ( pynutil.delete("hours:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete('"') ) minute = ( pynutil.delete("minutes:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete('"') ) second = ( pynutil.delete("seconds:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete('"') ) zone = ( delete_space + insert_space + pynutil.delete("zone:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_CHAR - " ", 1) + pynutil.delete('"') ) optional_zone = pynini.closure(zone, 0, 1) optional_second = pynini.closure( delete_space + pynutil.insert(":") + (second @ add_leading_zero_to_double_digit), 0, 1, ) graph_h = hour + pynutil.insert("h") graph_hms = ( hour + delete_space + pynutil.insert(":") + (minute @ add_leading_zero_to_double_digit) + optional_second ) graph_ms = ( minute + delete_space + pynutil.insert("p") + (second @ add_leading_zero_to_double_digit) + pynutil.insert("s") ) graph = (graph_h | graph_ms | graph_hms) + optional_zone delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/time.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_CHAR, NEMO_NOT_QUOTE, GraphFst, delete_space, ) from pynini.lib import pynutil class MeasureFst(GraphFst): """ Finite state transducer for verbalizing measure, e.g. measure { negative: "true" cardinal { integer: "12" } units: "kg" } -> -12 kg Args: decimal: DecimalFst cardinal: CardinalFst """ def __init__(self, decimal: GraphFst, cardinal: GraphFst): super().__init__(name="measure", kind="verbalize") optional_sign = pynini.closure(pynini.cross('negative: "true"', "-"), 0, 1) unit = ( pynutil.delete("units:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_CHAR - " ", 1) + pynutil.delete('"') + delete_space ) graph_decimal = ( pynutil.delete("decimal {") + delete_space + optional_sign + delete_space + decimal.numbers + delete_space + pynutil.delete("}") ) graph_cardinal = ( pynutil.delete("cardinal {") + delete_space + optional_sign + delete_space + cardinal.numbers + delete_space + pynutil.delete("}") ) fractional = ( pynutil.insert(".") + pynutil.delete("fractional_part:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) optional_fractional = pynini.closure(fractional + delete_space, 0, 1) graph = ( (graph_cardinal | graph_decimal) + delete_space + optional_fractional + pynutil.insert(" ") + unit + delete_space ) delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/measure.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst, delete_space from pynini.lib import pynutil class FractionFst(GraphFst): """ Finite state transducer for verbalizing fraction, e.g. fraction { numerator: "2" denominator: "3" } } -> 2/3 e.g. fraction { numerator: "20" denominator: "3" negative: "true"} } -> 2/3 """ def __init__(self): super().__init__(name="fraction", kind="verbalize") optional_sign = pynini.closure(pynini.cross('negative: "true"', "-") + delete_space, 0, 1) numerator = pynutil.delete('numerator: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') denominator = ( pynutil.insert("/") + pynutil.delete('denominator: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) graph = (numerator + delete_space + denominator).optimize() self.numbers = graph delete_tokens = self.delete_tokens(optional_sign + graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/fraction.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst from pynini.lib import pynutil class TelephoneFst(GraphFst): """ Finite state transducer for verbalizing telephone, e.g. telephone { number_part: "1231235678" } -> 1231235678 """ def __init__(self): super().__init__(name="telephone", kind="verbalize") number_part = pynutil.delete('number_part: "') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') delete_tokens = self.delete_tokens(number_part) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/telephone.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst, delete_space from pynini.lib import pynutil class OrdinalFst(GraphFst): """ Finite state transducer for verbalizing ordinal, e.g. ordinal { integer: "2" } -> thứ 2 """ def __init__(self): super().__init__(name="ordinal", kind="verbalize") graph = ( pynutil.delete("integer:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) graph = pynutil.insert("thứ ") + graph delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/ordinal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.cardinal import CardinalFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.date import DateFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.decimal import DecimalFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.electronic import ElectronicFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.fraction import FractionFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.measure import MeasureFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.money import MoneyFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.ordinal import OrdinalFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.telephone import TelephoneFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.time import TimeFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.whitelist import WhiteListFst class VerbalizeFst(GraphFst): """ Composes other verbalizer grammars. For deployment, this grammar will be compiled and exported to OpenFst Finite State Archive (FAR) File. More details to deployment at NeMo/tools/text_processing_deployment. """ def __init__(self): super().__init__(name="verbalize", kind="verbalize") cardinal = CardinalFst() cardinal_graph = cardinal.fst ordinal_graph = OrdinalFst().fst decimal = DecimalFst() decimal_graph = decimal.fst fraction = FractionFst() fraction_graph = fraction.fst measure_graph = MeasureFst(decimal=decimal, cardinal=cardinal).fst money_graph = MoneyFst(decimal=decimal).fst time_graph = TimeFst().fst date_graph = DateFst().fst whitelist_graph = WhiteListFst().fst telephone_graph = TelephoneFst().fst electronic_graph = ElectronicFst().fst graph = ( time_graph | date_graph | money_graph | measure_graph | ordinal_graph | fraction_graph | decimal_graph | cardinal_graph | whitelist_graph | telephone_graph | electronic_graph ) self.fst = graph
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/verbalize.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_CHAR, NEMO_SIGMA, GraphFst, delete_space, ) from pynini.lib import pynutil class WhiteListFst(GraphFst): """ Finite state transducer for verbalizing whitelist e.g. tokens { name: "mrs." } -> mrs. """ def __init__(self): super().__init__(name="whitelist", kind="verbalize") graph = ( pynutil.delete("name:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_CHAR - " ", 1) + pynutil.delete('"') ) graph = graph @ pynini.cdrewrite(pynini.cross(u"\u00A0", " "), "", "", NEMO_SIGMA) self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/whitelist.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import GraphFst, delete_extra_space, delete_space from nemo_text_processing.inverse_text_normalization.vi.verbalizers.verbalize import VerbalizeFst from nemo_text_processing.inverse_text_normalization.vi.verbalizers.word import WordFst from pynini.lib import pynutil class VerbalizeFinalFst(GraphFst): """ Finite state transducer that verbalizes an entire sentence, e.g. tokens { name: "its" } tokens { time { hours: "12" minutes: "30" } } tokens { name: "now" } -> its 12:30 now """ def __init__(self): super().__init__(name="verbalize_final", kind="verbalize") verbalize = VerbalizeFst().fst word = WordFst().fst types = verbalize | word graph = ( pynutil.delete("tokens") + delete_space + pynutil.delete("{") + delete_space + types + delete_space + pynutil.delete("}") ) graph = delete_space + pynini.closure(graph + delete_extra_space) + graph + delete_space self.fst = graph
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/verbalize_final.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst, delete_space from pynini.lib import pynutil class DecimalFst(GraphFst): """ Finite state transducer for verbalizing decimal, e.g. decimal { negative: "true" integer_part: "12" fractional_part: "5006" quantity: "tỷ" } -> -12.5006 tỷ """ def __init__(self): super().__init__(name="decimal", kind="verbalize") optionl_sign = pynini.closure(pynini.cross('negative: "true"', "-") + delete_space, 0, 1) integer = ( pynutil.delete("integer_part:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) optional_integer = pynini.closure(integer + delete_space, 0, 1) fractional = ( pynutil.insert(".") + pynutil.delete("fractional_part:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) optional_fractional = pynini.closure(fractional + delete_space, 0, 1) quantity = ( pynutil.delete("quantity:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) optional_quantity = pynini.closure(pynutil.insert(" ") + quantity + delete_space, 0, 1) graph = optional_integer + optional_fractional + optional_quantity self.numbers = graph graph = optionl_sign + graph delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/decimal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_CHAR, GraphFst, delete_space from pynini.lib import pynutil class MoneyFst(GraphFst): """ Finite state transducer for verbalizing money, e.g. money { integer_part: "12" fractional_part: "05" currency: "$" } -> 12.05$ Args: decimal: DecimalFst """ def __init__(self, decimal: GraphFst): super().__init__(name="money", kind="verbalize") unit = ( pynutil.delete("currency:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_CHAR - " ", 1) + pynutil.delete('"') ) graph = decimal.numbers + delete_space + unit delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/money.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst, delete_space from pynini.lib import pynutil class CardinalFst(GraphFst): """ Finite state transducer for verbalizing cardinal e.g. cardinal { integer: "23" negative: "-" } -> -23 """ def __init__(self): super().__init__(name="cardinal", kind="verbalize") optional_sign = pynini.closure( pynutil.delete("negative:") + delete_space + pynutil.delete('"') + NEMO_NOT_QUOTE + pynutil.delete('"') + delete_space, 0, 1, ) graph = ( pynutil.delete("integer:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) self.numbers = graph graph = optional_sign + graph delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/cardinal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst, delete_space from pynini.lib import pynutil class ElectronicFst(GraphFst): """ Finite state transducer for verbalizing electronic e.g. tokens { electronic { username: "cdf1" domain: "abc.edu" } } -> [email protected] """ def __init__(self): super().__init__(name="electronic", kind="verbalize") user_name = ( pynutil.delete("username:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) domain = ( pynutil.delete("domain:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) protocol = ( pynutil.delete("protocol:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) graph = user_name + delete_space + pynutil.insert("@") + domain graph |= protocol delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/electronic.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import NEMO_NOT_QUOTE, GraphFst, delete_space from pynini.lib import pynutil class DateFst(GraphFst): """ Finite state transducer for verbalizing date, e.g. date { month: "1" year: "2012"} -> tháng 1 năm 2012 date { day: "5" month: "10" year: "2021" preserve_order: true } -> 5 tháng 10 năm 2021 """ def __init__(self): super().__init__(name="date", kind="verbalize") day = ( pynutil.delete("day:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) month = ( pynutil.delete("month:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete('"') ) year = ( pynutil.delete("year:") + delete_space + pynutil.delete('"') + pynini.closure(NEMO_NOT_QUOTE, 1) + delete_space + pynutil.delete('"') ) # (day) month year # day month graph_dm = day + delete_space + pynutil.insert(" tháng ") + month graph_dmy = graph_dm + delete_space + pynutil.insert(" năm ") + year graph_m = pynutil.insert("tháng ") + month graph_my = pynutil.insert("tháng ") + month + delete_space + pynutil.insert(" năm ") + year graph_y = pynutil.insert("năm ") + year optional_preserve_order = pynini.closure( pynutil.delete("preserve_order:") + delete_space + pynutil.delete("true") + delete_space | pynutil.delete("field_order:") + delete_space + pynutil.delete('"') + NEMO_NOT_QUOTE + pynutil.delete('"') + delete_space ) final_graph = (graph_y | graph_m | graph_dm | graph_dmy | graph_my) + delete_space + optional_preserve_order delete_tokens = self.delete_tokens(final_graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/date.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.vi.graph_utils import ( NEMO_CHAR, NEMO_SIGMA, GraphFst, delete_space, ) from pynini.lib import pynutil class WordFst(GraphFst): """ Finite state transducer for verbalizing plain tokens e.g. tokens { name: "sleep" } -> sleep """ def __init__(self): super().__init__(name="word", kind="verbalize") chars = pynini.closure(NEMO_CHAR - " ", 1) char = pynutil.delete("name:") + delete_space + pynutil.delete('"') + chars + pynutil.delete('"') graph = char @ pynini.cdrewrite(pynini.cross(u"\u00A0", " "), "", "", NEMO_SIGMA) self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/verbalizers/word.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/data/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/data/numbers/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/data/ordinals/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/data/math/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/data/electronic/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/vi/data/time/__init__.py
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os def get_abs_path(rel_path): """ Get absolute path Args: rel_path: relative path to this file Returns absolute path """ return os.path.dirname(os.path.abspath(__file__)) + '/' + rel_path
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/utils.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # Copyright (c) 2023, Jim O'Regan. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.sv.utils import get_abs_path from nemo_text_processing.text_normalization.en.graph_utils import NEMO_SPACE, GraphFst from nemo_text_processing.text_normalization.sv.utils import get_abs_path as get_tn_abs_path from nemo_text_processing.text_normalization.sv.utils import load_labels from pynini.lib import pynutil QUARTERS = {15: "kvart över", 30: "halv", 45: "kvart i"} def get_all_to_or_from_numbers(): output = {} for num, word in QUARTERS.items(): current_past = [] current_to = [] for i in range(1, 60): if i == num: continue elif i < num: current_to.append((str(i), str(num - i))) else: current_past.append((str(i), str(i - num))) output[word] = {} output[word]["över"] = current_past output[word]["i"] = current_to return output def get_all_to_or_from_fst(cardinal: GraphFst): numbers = get_all_to_or_from_numbers() output = {} for key in numbers: output[key] = {} for when in ["över", "i"]: map = pynini.string_map(numbers[key][when]) output[key][when] = pynini.project(map, "input") @ map @ cardinal.graph return output class TimeFst(GraphFst): """ Finite state transducer for classifying time e.g. klockan åtta e s t -> time { hours: "kl. 8" zone: "e s t" } e.g. klockan tretton -> time { hours: "kl. 13" } e.g. klockan tretton tio -> time { hours: "kl. 13" minutes: "10" } e.g. kvart i tolv -> time { minutes: "45" hours: "11" } e.g. kvart över tolv -> time { minutes: "15" hours: "12" } Args: tn_cardinal_tagger: TN cardinal verbalizer """ def __init__(self, tn_cardinal_tagger: GraphFst): super().__init__(name="time", kind="classify") suffixes = pynini.invert(pynini.string_map(load_labels(get_abs_path("data/time/suffix.tsv")))) self.suffixes = suffixes klockan = pynini.union(pynini.cross("klockan", "kl."), pynini.cross("klockan är", "kl.")) klockan_graph_piece = pynutil.insert("hours: \"") + klockan minutes_to = pynini.string_map([(str(i), str(60 - i)) for i in range(1, 60)]) minutes = pynini.string_map([str(i) for i in range(1, 60)]) minutes_inverse = pynini.invert(pynini.project(minutes_to, "input") @ tn_cardinal_tagger.graph_en) minutes = pynini.invert(pynini.project(minutes, "input") @ tn_cardinal_tagger.graph_en) self.minute_words_to_words = minutes_inverse @ minutes_to @ tn_cardinal_tagger.graph_en self.minute_words_to_words_graph = ( pynutil.insert("minutes: \"") + self.minute_words_to_words + pynutil.insert("\"") ) time_zone_graph = pynini.invert(pynini.string_file(get_tn_abs_path("data/time/time_zone.tsv"))) final_suffix = pynutil.insert("suffix: \"") + suffixes + pynutil.insert("\"") final_suffix_optional = pynini.closure(NEMO_SPACE + final_suffix, 0, 1) final_time_zone = pynutil.insert("zone: \"") + time_zone_graph + pynutil.insert("\"") final_time_zone_optional = pynini.closure(NEMO_SPACE + final_time_zone, 0, 1) both_optional_suffixes = final_suffix_optional + final_time_zone_optional one_optional_suffix = NEMO_SPACE + final_suffix + final_time_zone_optional one_optional_suffix |= final_suffix_optional + NEMO_SPACE + final_time_zone labels_hour = [str(x) for x in range(0, 24)] hours = pynini.invert(pynini.union(*labels_hour) @ tn_cardinal_tagger.graph) self.hours = hours hours_graph = pynutil.insert("hours: \"") + hours + pynutil.insert("\"") klockan_hour = klockan_graph_piece + NEMO_SPACE + hours + pynutil.insert("\"") hours_graph |= klockan_hour hour_sfx = hours_graph + one_optional_suffix def hours_to_pairs(): for x in range(1, 13): if x == 12: y = 1 else: y = x + 1 yield x, y hours_to = pynini.string_map([(str(x[0]), str(x[1])) for x in hours_to_pairs()]) hours_to = pynini.invert(hours_to @ tn_cardinal_tagger.graph) self.hours_to = hours_to hours_to_graph = pynutil.insert("hours: \"") + hours_to + pynutil.insert("\"") bare_quarters_to = pynini.string_map([(x[1], str(x[0])) for x in QUARTERS.items() if not "över" in x[1]]) bare_quarters_from = pynini.cross("kvart över", "15") self.quarters_to = bare_quarters_to self.quarters_from = bare_quarters_from prefix_minutes_to = bare_quarters_to prefix_minutes_from = bare_quarters_from from_to_output = get_all_to_or_from_fst(tn_cardinal_tagger) for _, word in QUARTERS.items(): for when in ["över", "i"]: num_part = pynini.invert(from_to_output[word][when]) num_part_end = num_part + pynutil.delete(f" {when} {word}") if word == "kvart över": prefix_minutes_from |= num_part_end else: prefix_minutes_to |= num_part_end prefix_minutes_to |= minutes_inverse + pynutil.delete(" i") prefix_minutes_from |= minutes + pynutil.delete(" över") prefix_minutes_to_graph = pynutil.insert("minutes: \"") + prefix_minutes_to + pynutil.insert("\"") graph_to_prefixed = prefix_minutes_to_graph + NEMO_SPACE + hours_to_graph prefix_minutes_from_graph = pynutil.insert("minutes: \"") + prefix_minutes_from + pynutil.insert("\"") graph_from_prefixed = prefix_minutes_from_graph + NEMO_SPACE + hours_graph minutes_graph = pynutil.insert("minutes: \"") + minutes + pynutil.insert("\"") seconds_graph = pynutil.insert("seconds: \"") + minutes + pynutil.insert("\"") hm_sfx = hours_graph + NEMO_SPACE + minutes_graph + one_optional_suffix hms_sfx = hours_graph + NEMO_SPACE + minutes_graph + NEMO_SPACE + seconds_graph + one_optional_suffix graph = graph_to_prefixed | graph_from_prefixed | klockan_hour + both_optional_suffixes | hour_sfx graph |= hm_sfx graph |= hms_sfx self.fst = self.add_tokens(graph).optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/time.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # Copyright (c) 2023, Jim O'Regan for Språkbanken Tal # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_SPACE, GraphFst, convert_space from pynini.lib import pynutil class FractionFst(GraphFst): """ Finite state transducer for classifying fraction e.g. halv -> tokens { name: "1/2" } e.g. ett och en halv -> tokens { name: "1 1/2" } e.g. tre och fyra femtedelar -> tokens { name: "3 4/5" } Args: itn_cardinal_tagger: ITN cardinal tagger tn_fraction_verbalizer: TN fraction verbalizer """ def __init__(self, itn_cardinal_tagger: GraphFst, tn_fraction_tagger: GraphFst): super().__init__(name="fraction", kind="classify") cardinal = itn_cardinal_tagger.graph_no_exception fractions = tn_fraction_tagger.fractions_any.invert().optimize() minus = pynini.cross("minus ", "-") optional_minus = pynini.closure(minus, 0, 1) no_numerator = pynini.cross("och ", "1/") integer = optional_minus + cardinal self.graph = pynini.union( integer + NEMO_SPACE + no_numerator + fractions, integer + NEMO_SPACE + cardinal + pynini.cross(" ", "/") + fractions, integer + pynini.cross(" och ", " ") + cardinal + pynini.cross(" ", "/") + fractions, integer + pynini.cross(" och ", " ") + pynini.cross("en halv", "1/2"), cardinal + pynini.cross(" ", "/") + fractions, ) graph = pynutil.insert("name: \"") + convert_space(self.graph) + pynutil.insert("\"") self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/fraction.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_SPACE, GraphFst, convert_space from pynini.lib import pynutil class TelephoneFst(GraphFst): """ Finite state transducer for classifying telephone numbers, e.g. noll åtta sjuhundraåttionio femtiotvå tjugofem -> tokens { name: "08-789 52 25" } Args: tn_cardinal_tagger: TN Cardinal Tagger """ def __init__(self, tn_cardinal_tagger: GraphFst, tn_telephone_tagger: GraphFst): super().__init__(name="telephone", kind="classify") # country_plus_area_code = pynini.invert(tn_telephone_tagger.country_plus_area_code).optimize() area_codes = pynini.invert(tn_telephone_tagger.area_codes).optimize() # lead = (country_plus_area_code | area_codes) + pynini.cross(" ", "-") lead = area_codes + pynini.cross(" ", "-") two_digits = pynini.invert(tn_cardinal_tagger.two_digits_read).optimize() three_digits = pynini.invert(tn_cardinal_tagger.three_digits_read).optimize() base_number_part = pynini.union( three_digits + NEMO_SPACE + three_digits + NEMO_SPACE + two_digits, three_digits + NEMO_SPACE + two_digits + NEMO_SPACE + two_digits, two_digits + NEMO_SPACE + two_digits + NEMO_SPACE + two_digits, three_digits + NEMO_SPACE + two_digits, ) graph = convert_space(lead + base_number_part) final_graph = pynutil.insert("name: \"") + graph + pynutil.insert("\"") self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/telephone.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import GraphFst from pynini.lib import pynutil class OrdinalFst(GraphFst): """ Finite state transducer for classifying ordinal e.g. hundraandra -> tokens { name: "102." } Args: tn_ordinal_verbalizer: TN Ordinal Verbalizer """ def __init__(self, tn_ordinal: GraphFst): super().__init__(name="ordinal", kind="classify") graph = pynini.arcmap(tn_ordinal.bare_ordinals, map_type="rmweight").invert().optimize() self.bare_ordinals = graph self.ordinals = graph + pynutil.insert(".") forsta_andra = pynini.project(pynini.union("1", "2") @ tn_ordinal.bare_ordinals, "output") graph = ((pynini.project(graph, "input") - forsta_andra.arcsort()) @ graph) + pynutil.insert(".") graph = pynutil.insert("name: \"") + graph + pynutil.insert("\"") self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/ordinal.py
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # Copyright 2015 and onwards Google, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from nemo_text_processing.inverse_text_normalization.sv.utils import get_abs_path from nemo_text_processing.text_normalization.en.graph_utils import ( INPUT_LOWER_CASED, GraphFst, convert_space, string_map_cased, ) from pynini.lib import pynutil class WhiteListFst(GraphFst): """ Finite state transducer for classifying whitelisted tokens e.g. sankt -> tokens { name: "s:t" } This class has highest priority among all classifier grammars. Whitelisted tokens are defined and loaded from "data/whitelist.tsv" (unless input_file specified). Args: input_file: path to a file with whitelist replacements (each line of the file: written_form\tspoken_form\n), e.g. nemo_text_processing/inverse_text_normalization/sv/data/whitelist.tsv input_case: accepting either "lower_cased" or "cased" input. """ def __init__(self, input_case: str = INPUT_LOWER_CASED, input_file: str = None): super().__init__(name="whitelist", kind="classify") if input_file is None: input_file = get_abs_path("data/whitelist.tsv") if not os.path.exists(input_file): raise ValueError(f"Whitelist file {input_file} not found") whitelist = string_map_cased(input_file, input_case) graph = pynutil.insert("name: \"") + convert_space(whitelist) + pynutil.insert("\"") self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/whitelist.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import pynini from nemo_text_processing.inverse_text_normalization.en.taggers.punctuation import PunctuationFst from nemo_text_processing.inverse_text_normalization.en.taggers.word import WordFst from nemo_text_processing.inverse_text_normalization.sv.taggers.cardinal import CardinalFst from nemo_text_processing.inverse_text_normalization.sv.taggers.date import DateFst from nemo_text_processing.inverse_text_normalization.sv.taggers.decimal import DecimalFst from nemo_text_processing.inverse_text_normalization.sv.taggers.electronic import ElectronicFst from nemo_text_processing.inverse_text_normalization.sv.taggers.fraction import FractionFst from nemo_text_processing.inverse_text_normalization.sv.taggers.ordinal import OrdinalFst from nemo_text_processing.inverse_text_normalization.sv.taggers.telephone import TelephoneFst from nemo_text_processing.inverse_text_normalization.sv.taggers.time import TimeFst from nemo_text_processing.inverse_text_normalization.sv.taggers.whitelist import WhiteListFst from nemo_text_processing.text_normalization.en.graph_utils import ( INPUT_LOWER_CASED, GraphFst, delete_extra_space, delete_space, generator_main, ) from nemo_text_processing.text_normalization.sv.taggers.cardinal import CardinalFst as TNCardinalTagger from nemo_text_processing.text_normalization.sv.taggers.date import DateFst as TNDateTagger from nemo_text_processing.text_normalization.sv.taggers.decimal import DecimalFst as TNDecimalTagger from nemo_text_processing.text_normalization.sv.taggers.electronic import ElectronicFst as TNElectronicTagger from nemo_text_processing.text_normalization.sv.taggers.fraction import FractionFst as TNFractionTagger from nemo_text_processing.text_normalization.sv.taggers.ordinal import OrdinalFst as TNOrdinalTagger from nemo_text_processing.text_normalization.sv.taggers.telephone import TelephoneFst as TNTelephoneTagger from nemo_text_processing.text_normalization.sv.verbalizers.electronic import ElectronicFst as TNElectronicVerbalizer from pynini.lib import pynutil class ClassifyFst(GraphFst): """ Final class that composes all other classification grammars. This class can process an entire sentence, that is lower cased. For deployment, this grammar will be compiled and exported to OpenFst Finite State Archive (FAR) File. More details to deployment at NeMo/tools/text_processing_deployment. Args: cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache. overwrite_cache: set to True to overwrite .far files whitelist: path to a file with whitelist replacements input_case: accepting either "lower_cased" or "cased" input. """ def __init__( self, cache_dir: str = None, overwrite_cache: bool = False, whitelist: str = None, input_case: str = INPUT_LOWER_CASED, ): super().__init__(name="tokenize_and_classify", kind="classify") far_file = None if cache_dir is not None and cache_dir != 'None': os.makedirs(cache_dir, exist_ok=True) far_file = os.path.join(cache_dir, f"sv_itn_{input_case}.far") if not overwrite_cache and far_file and os.path.exists(far_file): self.fst = pynini.Far(far_file, mode="r")["tokenize_and_classify"] logging.info(f"ClassifyFst.fst was restored from {far_file}.") else: logging.info(f"Creating ClassifyFst grammars.") tn_cardinal_tagger = TNCardinalTagger(deterministic=False) tn_ordinal_tagger = TNOrdinalTagger(cardinal=tn_cardinal_tagger, deterministic=False) tn_date_tagger = TNDateTagger(cardinal=tn_cardinal_tagger, ordinal=tn_ordinal_tagger, deterministic=False) tn_decimal_tagger = TNDecimalTagger(cardinal=tn_cardinal_tagger, deterministic=False) tn_fraction_tagger = TNFractionTagger( cardinal=tn_cardinal_tagger, ordinal=tn_ordinal_tagger, deterministic=True ) tn_electronic_tagger = TNElectronicTagger(deterministic=False) tn_electronic_verbalizer = TNElectronicVerbalizer(deterministic=False) tn_telephone_tagger = TNTelephoneTagger(deterministic=False) cardinal = CardinalFst(tn_cardinal_tagger=tn_cardinal_tagger) cardinal_graph = cardinal.fst ordinal = OrdinalFst(tn_ordinal=tn_ordinal_tagger) ordinal_graph = ordinal.fst decimal = DecimalFst(itn_cardinal_tagger=cardinal, tn_decimal_tagger=tn_decimal_tagger) decimal_graph = decimal.fst fraction = FractionFst(itn_cardinal_tagger=cardinal, tn_fraction_tagger=tn_fraction_tagger) fraction_graph = fraction.fst date_graph = DateFst(tn_date_tagger=tn_date_tagger).fst word_graph = WordFst().fst time_graph = TimeFst(tn_cardinal_tagger=tn_cardinal_tagger).fst whitelist_graph = WhiteListFst(input_file=whitelist, input_case=input_case).fst punct_graph = PunctuationFst().fst electronic_graph = ElectronicFst( tn_electronic_tagger=tn_electronic_tagger, tn_electronic_verbalizer=tn_electronic_verbalizer ).fst telephone_graph = TelephoneFst( tn_cardinal_tagger=tn_cardinal_tagger, tn_telephone_tagger=tn_telephone_tagger ).fst classify = ( pynutil.add_weight(cardinal_graph, 1.1) | pynutil.add_weight(whitelist_graph, 1.0) | pynutil.add_weight(time_graph, 1.1) | pynutil.add_weight(date_graph, 1.1) | pynutil.add_weight(decimal_graph, 1.1) | pynutil.add_weight(ordinal_graph, 1.1) | pynutil.add_weight(fraction_graph, 1.1) | pynutil.add_weight(telephone_graph, 1.1) | pynutil.add_weight(electronic_graph, 1.1) | pynutil.add_weight(word_graph, 100) ) punct = pynutil.insert("tokens { ") + pynutil.add_weight(punct_graph, weight=1.1) + pynutil.insert(" }") token = pynutil.insert("tokens { ") + classify + pynutil.insert(" }") token_plus_punct = ( pynini.closure(punct + pynutil.insert(" ")) + token + pynini.closure(pynutil.insert(" ") + punct) ) graph = token_plus_punct + pynini.closure(delete_extra_space + token_plus_punct) graph = delete_space + graph + delete_space self.fst = graph.optimize() if far_file: generator_main(far_file, {"tokenize_and_classify": self.fst}) logging.info(f"ClassifyFst grammars are saved to {far_file}.")
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/tokenize_and_classify.py
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_SIGMA, GraphFst from nemo_text_processing.text_normalization.sv.taggers.decimal import get_quantity from pynini.lib import pynutil class DecimalFst(GraphFst): """ Finite state transducer for classifying decimal e.g. minus elva komma två nulla nulla sex biljoner -> decimal { negative: "true" integer_part: "11" fractional_part: "2006" quantity: "biljoner" } e.g. en biljon -> decimal { integer_part: "1" quantity: "biljon" } Args: itn_cardinal_tagger: ITN Cardinal tagger tn_decimal_tagger: TN decimal tagger """ def __init__(self, itn_cardinal_tagger: GraphFst, tn_decimal_tagger: GraphFst): super().__init__(name="decimal", kind="classify") self.graph = tn_decimal_tagger.graph_itn self.graph = self.graph @ pynini.cdrewrite(pynini.cross(" ", ""), "", "", NEMO_SIGMA) delete_point = pynutil.delete(" komma") graph_fractional = pynutil.insert("fractional_part: \"") + self.graph + pynutil.insert("\"") hundreds = itn_cardinal_tagger.graph_hundred_component_at_least_one_non_zero_digit hundreds = (pynini.project(hundreds, "input") - "en" - "ett") @ hundreds hundreds_no_one = hundreds hundreds |= pynini.cross("en", "1") hundreds |= pynini.cross("ett", "1") graph_integer = pynutil.insert("integer_part: \"") + hundreds + pynutil.insert("\"") self.graph_integer = graph_integer final_graph_wo_sign = graph_integer + delete_point + pynini.accep(" ") + graph_fractional self.final_graph_wo_sign = final_graph_wo_sign self.final_graph_wo_negative = ( final_graph_wo_sign | get_quantity(final_graph_wo_sign, None, hundreds_no_one, None, False, True,) ).optimize() optional_minus_graph = pynini.closure(pynini.cross("minus ", "negative: \"true\" "), 0, 1) final_graph = optional_minus_graph + self.final_graph_wo_negative final_graph += pynutil.insert(" preserve_order: true") final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/decimal.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_SIGMA, GraphFst from pynini.lib import pynutil class CardinalFst(GraphFst): """ Finite state transducer for classifying cardinals. Numbers below ten are not converted. Allows both compound numeral strings or separated by whitespace. e.g. minus tjugoen -> cardinal { negative: "-" integer: "21" } } e.g. minus tjugoett -> cardinal { negative: "-" integer: "21" } } Args: tn_cardinal_tagger: TN cardinal tagger """ def __init__(self, tn_cardinal_tagger: GraphFst): super().__init__(name="cardinal", kind="classify") graph = pynini.invert(pynini.arcmap(tn_cardinal_tagger.graph, map_type="rmweight")).optimize() graph = graph @ pynini.cdrewrite(pynini.cross(" ", ""), "", "", NEMO_SIGMA) self.graph = graph no_ones = pynini.project(graph, "input") - "en" - "ett" graph = no_ones @ graph self.graph_no_ones = graph self.graph_hundred_component_at_least_one_non_zero_digit = pynini.invert( pynini.arcmap(tn_cardinal_tagger.graph_hundreds_component_at_least_one_non_zero_digit, map_type="rmweight") ).optimize() self.graph_hundred_component_at_least_one_non_zero_digit_no_one = ( pynini.project(self.graph_hundred_component_at_least_one_non_zero_digit, "input") - "en" - "ett" ) @ self.graph_hundred_component_at_least_one_non_zero_digit self.graph_ties = (tn_cardinal_tagger.two_digit_non_zero).invert().optimize() # this is to make sure if there is an ambiguity with decimal, decimal is chosen, e.g. 1000000 vs. 1 million graph = pynutil.add_weight(graph, weight=0.001) self.graph_no_exception = graph self.digit = pynini.arcmap(tn_cardinal_tagger.digit, map_type="rmweight").invert().optimize() self.optional_minus_graph = pynini.closure(pynini.cross("minus ", "negative: \"-\" "), 0, 1) final_graph = self.optional_minus_graph + pynutil.insert("integer: \"") + graph + pynutil.insert("\"") final_graph = self.add_tokens(final_graph) self.fst = final_graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/cardinal.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import GraphFst from pynini.lib import pynutil class ElectronicFst(GraphFst): """ Finite state transducer for classifying electronic: email addresses, etc. e.g. c d f ett at a b c punkt e d u -> tokens { name: "cdf1.abc.edu" } Args: tn_electronic_tagger: TN eletronic tagger tn_electronic_verbalizer: TN eletronic verbalizer """ def __init__(self, tn_electronic_tagger: GraphFst, tn_electronic_verbalizer: GraphFst): super().__init__(name="electronic", kind="classify") tagger = pynini.invert(tn_electronic_verbalizer.graph).optimize() verbalizer = pynini.invert(tn_electronic_tagger.graph).optimize() final_graph = tagger @ verbalizer graph = pynutil.insert("name: \"") + final_graph + pynutil.insert("\"") self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/electronic.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # Copyright (c) 2023, Jim O'Regan for Språkbanken Tal # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_DIGIT, NEMO_SPACE, GraphFst from pynini.lib import pynutil class DateFst(GraphFst): """ Finite state transducer for classifying date, in the form of (day) month (year) or year e.g. andra januari tjugohundraett -> tokens { name: "2001-01-02" } e.g. tjugotredje januari -> tokens { name: "23. jan." } e.g. tjugotjugo -> tokens { name: "2020" } Args: tn_date_tagger: TN date tagger """ def __init__( self, tn_date_tagger: GraphFst, ): super().__init__(name="date", kind="classify") def force_double_digits(fst: GraphFst): double = (NEMO_DIGIT + NEMO_DIGIT) @ fst single = (pynutil.insert("0") + NEMO_DIGIT) @ (NEMO_DIGIT @ fst) return single | double year = tn_date_tagger.year.invert().optimize() decade = tn_date_tagger.decade.invert().optimize() era_words = tn_date_tagger.era_words.invert().optimize() day = tn_date_tagger.digit_day.invert().optimize() day_double = tn_date_tagger.digit_day_zero.invert().optimize() month_double = force_double_digits(tn_date_tagger.number_to_month).invert().optimize() month_abbr = tn_date_tagger.month_abbr.invert().optimize() self.month_to_number = tn_date_tagger.number_to_month.invert().optimize() graph_year = pynutil.insert("year: \"") + year + pynutil.insert("\"") graph_month = pynutil.insert("month: \"") + month_double + pynutil.insert("\"") graph_month_abbr = pynutil.insert("month: \"") + month_abbr + pynutil.insert("\"") graph_day = pynutil.insert("day: \"") + day_double + pynutil.insert("\"") graph_day_ord = pynutil.insert("day: \"") + day + pynutil.insert("\"") graph_era = pynutil.insert("era: \"") + era_words + pynutil.insert("\"") optional_era = pynini.closure(NEMO_SPACE + graph_era, 0, 1) graph_decade = pynutil.insert("year: \"") + decade + pynutil.insert("\"") preserve = pynutil.insert(" preserve_order: true") optional_preserve = pynini.closure(preserve, 0, 1) year_era = graph_year + NEMO_SPACE + graph_era + preserve graph_dm = graph_day_ord + NEMO_SPACE + graph_month_abbr + preserve dmy = graph_day + NEMO_SPACE + graph_month + NEMO_SPACE + graph_year graph_dmy = dmy + optional_era ydm = graph_year + NEMO_SPACE + graph_month + NEMO_SPACE + graph_day graph_ydm = ydm + optional_era + preserve + optional_preserve final_graph = year_era | graph_dmy | graph_dm | graph_ydm | graph_decade graph = self.add_tokens(final_graph) self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/taggers/date.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import ( NEMO_DIGIT, NEMO_NOT_QUOTE, NEMO_SPACE, GraphFst, delete_space, ) from pynini.lib import pynutil class TimeFst(GraphFst): """ Finite state transducer for verbalizing time, e.g. time { hours: "8" minutes: "30" zone: "e s t" } -> 08:30 est time { hours: "8" } -> kl. 8 time { hours: "8" minutes: "30" seconds: "10" } -> 08:30:10 """ def __init__(self, deterministic: bool = True): super().__init__(name="time", kind="verbalize", deterministic=deterministic) add_leading_zero_to_double_digit = (NEMO_DIGIT + NEMO_DIGIT) | (pynutil.insert("0") + NEMO_DIGIT) hour = pynutil.delete("hours: \"") + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete("\"") kl_hour = ( pynutil.delete("hours: \"") + pynini.accep("kl. ") + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete("\"") ) minute = pynutil.delete("minutes: \"") + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete("\"") zeroed_hour = hour @ add_leading_zero_to_double_digit lead_hour = zeroed_hour | kl_hour lead_minute = minute @ add_leading_zero_to_double_digit second = pynutil.delete("seconds: \"") + pynini.closure(NEMO_DIGIT, 1) + pynutil.delete("\"") lead_second = second @ add_leading_zero_to_double_digit ANY_NOT_QUOTE = pynini.closure(NEMO_NOT_QUOTE, 1) final_suffix = pynutil.delete("suffix: \"") + ANY_NOT_QUOTE + pynutil.delete("\"") optional_suffix = pynini.closure(NEMO_SPACE + final_suffix, 0, 1) zone = pynutil.delete("zone: \"") + ANY_NOT_QUOTE + pynutil.delete("\"") optional_zone = pynini.closure(NEMO_SPACE + zone, 0, 1) one_optional_suffix = NEMO_SPACE + final_suffix + optional_zone one_optional_suffix |= optional_suffix + NEMO_SPACE + zone graph = ( delete_space + pynutil.insert(":") + lead_minute + pynini.closure(delete_space + pynutil.insert(":") + lead_second, 0, 1) + optional_suffix + optional_zone ) graph_h = hour + one_optional_suffix graph_klh = kl_hour + optional_suffix + optional_zone graph_hm = lead_hour + graph final_graph = graph_hm | graph_h | graph_klh self.fst = self.delete_tokens(final_graph).optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/time.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nemo_text_processing.inverse_text_normalization.sv.verbalizers.cardinal import CardinalFst from nemo_text_processing.inverse_text_normalization.sv.verbalizers.date import DateFst from nemo_text_processing.inverse_text_normalization.sv.verbalizers.decimal import DecimalFst from nemo_text_processing.inverse_text_normalization.sv.verbalizers.time import TimeFst from nemo_text_processing.text_normalization.en.graph_utils import GraphFst from nemo_text_processing.text_normalization.sv.verbalizers.cardinal import CardinalFst as TNCardinalVerbalizer class VerbalizeFst(GraphFst): """ Composes other verbalizer grammars. For deployment, this grammar will be compiled and exported to OpenFst Finite State Archive (FAR) File. More details to deployment at NeMo/tools/text_processing_deployment. """ def __init__(self, deterministic: bool = True): super().__init__(name="verbalize", kind="verbalize", deterministic=deterministic) tn_cardinal_verbalizer = TNCardinalVerbalizer(deterministic=False) cardinal = CardinalFst(tn_cardinal_verbalizer=tn_cardinal_verbalizer) cardinal_graph = cardinal.fst date_graph = DateFst().fst decimal = DecimalFst() decimal_graph = decimal.fst time_graph = TimeFst().fst graph = time_graph | decimal_graph | cardinal_graph | date_graph self.fst = graph
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/verbalize.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.inverse_text_normalization.en.verbalizers.word import WordFst from nemo_text_processing.inverse_text_normalization.sv.verbalizers.verbalize import VerbalizeFst from nemo_text_processing.text_normalization.en.graph_utils import GraphFst, delete_extra_space, delete_space from pynini.lib import pynutil class VerbalizeFinalFst(GraphFst): """ Finite state transducer that verbalizes an entire sentence, e.g. tokens { name: "klockan" } tokens { name: "är" } tokens { time { hours: "12" minutes: "30" } } -> klockan är 12:30 """ def __init__(self, deterministic: bool = True): super().__init__(name="verbalize_final", kind="verbalize", deterministic=deterministic) verbalize = VerbalizeFst().fst word = WordFst().fst types = verbalize | word graph = ( pynutil.delete("tokens") + delete_space + pynutil.delete("{") + delete_space + types + delete_space + pynutil.delete("}") ) graph = delete_space + pynini.closure(graph + delete_extra_space) + graph + delete_space self.fst = graph
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/verbalize_final.py
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import ( NEMO_NOT_QUOTE, GraphFst, delete_preserve_order, delete_space, ) from nemo_text_processing.text_normalization.sv.graph_utils import ensure_space from pynini.lib import pynutil class DecimalFst(GraphFst): """ Finite state transducer for verbalizing decimal, e.g. decimal { negative: "true" integer_part: "12" fractional_part: "5006" quantity: "biljoner" } -> -12,5006 biljoner """ def __init__(self, deterministic: bool = True): super().__init__(name="decimal", kind="verbalize", deterministic=deterministic) optional_sign = pynini.closure(pynini.cross("negative: \"true\"", "-") + delete_space, 0, 1) integer = pynutil.delete("integer_part: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") fractional = pynutil.delete("fractional_part: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") quantity = pynutil.delete("quantity: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") number = pynini.union( optional_sign + integer, optional_sign + integer + pynini.cross(" ", ",") + fractional, pynutil.insert(",") + fractional, ) number_quantity = number + ensure_space + quantity optional_delete_preserve_order = pynini.closure(delete_preserve_order, 0, 1) graph = (number | number_quantity | quantity).optimize() self.graph = graph graph = self.graph + optional_delete_preserve_order delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/decimal.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_NOT_QUOTE, GraphFst from pynini.lib import pynutil class CardinalFst(GraphFst): """ Finite state transducer for verbalizing cardinal e.g. cardinal { integer: "23" negative: "-" } -> -23 Args: tn_cardinal_verbalizer: TN cardinal verbalizer """ def __init__(self, tn_cardinal_verbalizer: GraphFst, deterministic: bool = True): super().__init__(name="cardinal", kind="verbalize", deterministic=deterministic) self.numbers = tn_cardinal_verbalizer.numbers optional_sign = pynini.closure(pynutil.delete("negative: \"") + NEMO_NOT_QUOTE + pynutil.delete("\" "), 0, 1) graph = optional_sign + self.numbers delete_tokens = self.delete_tokens(graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/cardinal.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import ( NEMO_NOT_QUOTE, NEMO_SPACE, GraphFst, delete_preserve_order, delete_space, ) from pynini.lib import pynutil class DateFst(GraphFst): """ Finite state transducer for verbalizing date, e.g. date { day: "1." month: "jan." preserve_order: true } -> 1. jan. """ def __init__(self): super().__init__(name="date", kind="verbalize") year = pynutil.delete("year: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") month = pynutil.delete("month: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") day = pynutil.delete("day: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") era = pynutil.delete("era: \"") + pynini.closure(NEMO_NOT_QUOTE, 1) + pynutil.delete("\"") optional_era = pynini.closure(NEMO_SPACE + era, 0, 1) space_to_hyphen = pynini.cross(" ", "-") optional_preserve_order = pynini.closure( pynutil.delete(" preserve_order:") + delete_space + pynutil.delete("true") + delete_space | pynutil.delete(" field_order:") + delete_space + pynutil.delete("\"") + NEMO_NOT_QUOTE + pynutil.delete("\"") ) # day month year_era = year + optional_era + optional_preserve_order graph_dm = day + NEMO_SPACE + month + delete_preserve_order graph_ydm = year + space_to_hyphen + month + space_to_hyphen + day + optional_era + optional_preserve_order final_graph = graph_dm | graph_ydm | year_era delete_tokens = self.delete_tokens(final_graph) self.fst = delete_tokens.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/verbalizers/date.py
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/data/__init__.py
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/sv/data/time/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/ru/__init__.py
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pynini from nemo_text_processing.text_normalization.en.graph_utils import NEMO_SPACE, GraphFst from nemo_text_processing.text_normalization.ru.verbalizers.time import TimeFst as TNTimeVerbalizer from pynini.lib import pynutil class TimeFst(GraphFst): """ Finite state transducer for classifying time, e.g. "два часа пятнадцать минут" -> time { hours: "02:15" } Args: tn_time: Text Normalization Time graph deterministic: if True will provide a single transduction option, for False multiple transduction are generated (used for audio-based normalization) """ def __init__(self, tn_time: GraphFst, deterministic: bool = True): super().__init__(name="time", kind="classify", deterministic=deterministic) tn_time_tagger = tn_time.graph_preserve_order tn_time_verbalizer = TNTimeVerbalizer().graph tn_time_graph_preserve_order = pynini.compose(tn_time_tagger, tn_time_verbalizer).optimize() graph_preserve_order = pynini.invert(tn_time_graph_preserve_order).optimize() graph_preserve_order = pynutil.insert("hours: \"") + graph_preserve_order + pynutil.insert("\"") # "пятнадцать минут шестого" -> 17:15 # Requires permutations for the correct verbalization m_next_h = ( pynutil.insert("minutes: \"") + pynini.invert(tn_time.minutes).optimize() + pynutil.insert("\"") + pynini.accep(NEMO_SPACE) + pynutil.insert("hours: \"") + pynini.invert(tn_time.increment_hour_ordinal).optimize() + pynutil.insert("\"") ).optimize() # "без пятнадцати минут шесть" -> 17:45 # Requires permutation for the correct verbalization m_to_h = ( pynini.cross("без ", "minutes: \"") + pynini.invert(tn_time.mins_to_h) + pynutil.insert("\"") + pynini.accep(NEMO_SPACE) + pynutil.insert("hours: \"") + pynini.invert(tn_time.increment_hour_cardinal).optimize() + pynutil.insert("\"") ) graph_reserve_order = m_next_h | m_to_h graph = graph_preserve_order | graph_reserve_order graph = self.add_tokens(graph) self.fst = graph.optimize()
NeMo-text-processing-main
nemo_text_processing/inverse_text_normalization/ru/taggers/time.py