alessandro trinca tornidor
commited on
Commit
·
606a7d7
1
Parent(s):
893aaca
refactor: sam-quantized submodule now referrenced without symlinks, update dockerfile
Browse files- Dockerfile +1 -1
- README.md +2 -3
- app.py +1 -1
- dockerfiles/dockerfile-samgis-base-with-lambda-support +1 -1
- docs/conf.py +1 -1
- machine_learning_models +0 -1
Dockerfile
CHANGED
@@ -1,4 +1,4 @@
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FROM registry.gitlab.com/aletrn/gis-prediction:1.11.
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# Include global arg in this stage of the build
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ARG WORKDIR_ROOT="/var/task"
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FROM registry.gitlab.com/aletrn/gis-prediction:1.11.8
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# Include global arg in this stage of the build
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ARG WORKDIR_ROOT="/var/task"
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README.md
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@@ -15,9 +15,8 @@ I tested these instructions on macOS, but should work on linux as well.
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## Segment Anything models
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It's possible to prepare the model files using <https://github.com/vietanhdev/samexporter/> or using the ones
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from <https://huggingface.co/aletrn/sam-quantized
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In this case after the clone of this repository it's best to initialize the `sam-quantized` submodule:
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```bash
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git submodule update --init --recursive
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## Segment Anything models
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It's possible to prepare the model files using <https://github.com/vietanhdev/samexporter/> or using the ones
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from <https://huggingface.co/aletrn/sam-quantized>. By default the project contains that submodule.
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If you want to use this (recommended) after the clone of this repository it's best to initialize the `sam-quantized` submodule:
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```bash
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git submodule update --init --recursive
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app.py
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@@ -23,7 +23,7 @@ from starlette.responses import JSONResponse
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load_dotenv()
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project_root_folder = Path(globals().get("__file__", "./_")).absolute().parent
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workdir = os.getenv("WORKDIR", project_root_folder)
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model_folder = Path(project_root_folder / "machine_learning_models")
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log_level = os.getenv("LOG_LEVEL", "INFO")
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setup_logging(log_level=log_level)
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load_dotenv()
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project_root_folder = Path(globals().get("__file__", "./_")).absolute().parent
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workdir = os.getenv("WORKDIR", project_root_folder)
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model_folder = Path(project_root_folder / "sam-quantized" / "machine_learning_models")
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log_level = os.getenv("LOG_LEVEL", "INFO")
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setup_logging(log_level=log_level)
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dockerfiles/dockerfile-samgis-base-with-lambda-support
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@@ -126,6 +126,6 @@ FROM runtime
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ARG FASTAPI_STATIC
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RUN mkdir ${FASTAPI_STATIC}
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COPY ./machine_learning_models ${LAMBDA_TASK_ROOT}/machine_learning_models
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COPY --from=node_prod_deps /appnode/node_modules* ${FASTAPI_STATIC}/node_modules
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COPY --from=node_build /appnode/dist* ${FASTAPI_STATIC}/dist
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ARG FASTAPI_STATIC
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RUN mkdir ${FASTAPI_STATIC}
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COPY ./sam-quantized/machine_learning_models ${LAMBDA_TASK_ROOT}/machine_learning_models
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COPY --from=node_prod_deps /appnode/node_modules* ${FASTAPI_STATIC}/node_modules
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COPY --from=node_build /appnode/dist* ${FASTAPI_STATIC}/dist
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docs/conf.py
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@@ -43,7 +43,7 @@ typehints_defaults = "comma"
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templates_path = ['_templates']
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exclude_patterns = [
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'_build', 'Thumbs.db', '.DS_Store', 'build/*', 'machine_learning_models', 'machine_learning_models/*'
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]
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source_suffix = {
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templates_path = ['_templates']
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exclude_patterns = [
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'_build', 'Thumbs.db', '.DS_Store', 'build/*', 'machine_learning_models', 'machine_learning_models/*', "sam-quantized/machine_learning_models", "sam-quantized/machine_learning_models/*"
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]
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source_suffix = {
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machine_learning_models
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@@ -1 +0,0 @@
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sam-quantized/machine_learning_models
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