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[frontend] Run Comparison Refresh button broken
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[] | 2022-06-07T20:10:24 | 2022-06-14T23:53:02 | 2022-06-14T23:53:02 |
CONTRIBUTOR
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.2
### Steps to reproduce
1. Go to the "Runs" tab on KFP
2. Clone an existing run (preferably one that completes successfully and quickly)
3. On a new tab, go to the "Runs" tab on KFP
4. On this new tab, select the original run and the cloned run, and click "Compare runs"
5. Refresh the original tab to determine when the cloned run completes
6. Once this cloned run completes, go to the Run Comparison page and click the button that says "Refresh" on the page
### Expected result
You should see that the page does not update and says that the run is still pending, even though it has completed.
### Materials and Reference
The below video shows how the "Refresh" button does not appropriately update the page contents. However, it is important to note that by viewing the "Network" tab, the runs are being re-fetched accurately, but just the page does not display this new information.
https://user-images.githubusercontent.com/7987279/172473134-27648b73-bd43-43f6-b429-c01973579e58.mp4
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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| 1,263,467,889 |
I_kwDOB-71UM5LTv1x
| 7,851 |
[feature] How to pass secret pipeline parameters?
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[
"Maybe try using a container op and wrap the secret parameter inside?\r\nhttps://kubernetes.io/docs/concepts/configuration/secret/#using-secrets-as-files-from-a-pod\r\n\r\nHere is an example of passing gcp secret:\r\nhttps://github.com/kubeflow/examples/blob/871895c54402f68685c8e227c954d86a81c0575f/pipelines/mnist-pipelines/mnist_pipeline.py#L97\r\nMaybe AWS has some similar function.",
"Thanks for the suggestion, @Linchin! \r\n\r\nOur \"api_key\" is slightly more complicated: Every user has a different one, so our backend system can check which data each user is actually allowed to access in their pipeline runs, and track who created output data. But we want these API keys to be unknown to other users, so they cannot impersonate each other.\r\n\r\nWe are using RSA encryption now to pass secret parameters: Our client application uses a public key to encrypt the \"api_key\" parameter before submitting through the Kubeflow API. Then the container code gets the private key as an AWS SSM Parameter (Similar to the GCP secret you mentioned above) and decrypts the \"api_key\". This makes it sufficiently hard for users who see other people's pipelines to access their secret API keys.\r\n\r\nI have one doubt about this solution though, which applies to any secrets that Kubeflow jobs can access: AFAIK, every user who can see pipeline runs in Kubeflow can also upload and run their own pipeline. So they could upload a malicious pipeline that simply prints the value of an internal secret (like our private key stored as an AWS SSM Parameter).\r\n\r\nDo you know any solution to this, other than really implementing secret parameters in Kubeflow?"
] | 2022-06-07T15:18:09 | 2022-08-16T08:27:40 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
- Given a pipeline with a parameter "api_key",
- when a user triggers a run (via the UI or the CLI)
- then his API key is publicly visible in the UI currently,
- but we would like to hide it.
### What is the use case or pain point?
We would like to hide such secret parameters as much as possible. Since the API key belongs to a separate system, it would be nice if not even the kubeflow admins could access it.
### Is there a workaround currently?
I'm aware of [kubeflow secrets](https://kubernetes.io/docs/concepts/configuration/secret/), but this does not seem to be targeted at parameters if I understand it correctly. However, it's easily possible that I've missed something. I currently see two workarounds:
- pass secrets, which are only valid for a certain time
- pass encrypted secrets and store the key for decryption as kubeflow secret (or in AWS secret manager or similar)
Finally, [this post ](https://github.com/StatCan/daaas/issues/91#issuecomment-651282886) states that pipeline arguments are not visible in the UI, but I observe differently?!
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| 1,262,357,865 |
I_kwDOB-71UM5LPg1p
| 7,848 |
[feature] Support labels in the HyperparameterTuningJob for GCP
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[
"Could you please specify which hyperparameter you are referring to? A link would help.",
"I am referring to setting the GCP tags on [this](https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/experimental/hyperparameter_tuning_job/component.yaml) component to track the cost of the infrastructure."
] | 2022-06-06T20:13:32 | 2022-06-10T06:27:28 | null |
NONE
| null |
### Feature Area
/area components
### What feature would you like to see?
Support labels in the HyperparameterTuningJob
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
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[bug] google_cloud_pipeline_components wait_gcp_resources breaks cache
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[
"Labels:\r\n\r\n/area sdk\r\n/area components",
"Here's an exemple run output:\r\n\r\nCanceled:\r\n\r\n\r\nCached:\r\n\r\n",
"/assign @IronPan \r\n/cc @chongyouquan "
] | 2022-06-06T15:28:24 | 2022-06-16T22:54:42 | null |
NONE
| null |
### What steps did you take
Create a pipeline with the followings tasks:
- A dataflow job
- WaitGcpResourcesOp on that dataflow job
### What happened:
If the run fails or is canceled while WaitGcpResourcesOp is running, the dataflow job gets cached as successful (only the WaitGcpResourcesOp step is marked as canceled/failed). This means that re-running the pipeline will never work, since the WaitGcpResourcesOp will receive a cached gcp_resources of a task that is already canceled.
### What did you expect to happen:
Since the job failed or was canceled, I expect kubeflow to not cache that step. So that if a pipeline is canceled on a WaitGcpResourcesOp step, the task that created the resource that is being waited on is not cached (cache is invalidated?)
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
GCP Vertex AI pipelines
* KFP SDK version:
Python kfp 1.8.11
### Anything else you would like to add:
I have also developed my own components that wait on other types of resources (e.g. Custom Jobs) and would be interested in a way to fix this. Is there a way to manually invalidate a cache for a component run?
### Labels
/area sdk
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5LL2Yn
| 7,845 |
Can you pass TypedDicts to components or pipelines?
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[
"Hello @cgebbe , can you describe how the `TypedDict` support will help you check typo?\r\n\r\n/assign @connor-mccarthy "
] | 2022-06-06T06:12:04 | 2022-06-23T22:56:30 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
/area components
### What feature would you like to see?
Given the following code example: https://github.com/kubeflow/pipelines/issues/3412#issuecomment-1147075404 , it would be nice to pass `aws_config` not as a simple dictionary, but instead as a `TypedDict`. More exactly, we can split this up into:
- passing TypeDicts in between components
- passing TypeDicts as a parameter to pipeline to validate the parameter very early
### What is the use case or pain point?
Small typos can delay development.
### Is there a workaround currently?
Yes, simply use simpler dicts.
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| 1,260,644,140 |
I_kwDOB-71UM5LI-cs
| 7,842 |
Show parameters comparison for V2 Run Comparison page
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[
"The parameter comparison was implemented in https://github.com/kubeflow/pipelines/pull/8105, and a separate issue for scalability was raised at https://github.com/kubeflow/pipelines/issues/8131. For reference, the Parameters section uses an analogous organization algorithm to the KFPv1 Run Comparison page, and the Parameters are already fetched alongside the run list."
] | 2022-06-04T05:06:23 | 2022-08-10T19:11:36 | 2022-08-10T19:11:36 |
COLLABORATOR
| null |
# Description
1. Retrieve parameters from backend
2. Determine the strategy about how to show parameter comparison
3. Scalability: How to handle comparison when number of parameters is very high?
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| 1,260,644,126 |
I_kwDOB-71UM5LI-ce
| 7,841 |
Compare various metrics for V2 Run Comparison page
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[
"This page has now been implemented, and these points are elaborated on in detail in the internal design doc as well as PR descriptions. We do not provide much functionality for users to adjust UI, though this will be an option if the parallel-coordinate plot is implemented for Scalar Metrics or Parameters."
] | 2022-06-04T05:06:19 | 2022-08-10T19:08:38 | 2022-08-10T19:08:38 |
COLLABORATOR
| null |
# Description
1. List the types of metrics we want to compare
2. Implement an efficient strategy to fetch these metrics
3. Provide functionality for users to adjust UI (For example: dropdown/checkbox for better comparison)
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I_kwDOB-71UM5LI-cM
| 7,840 |
Design Run Comparison page for V2
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[
"This page has now been implemented; these questions are answered in detail in the internal design doc, and next steps are provided via GitHub Issues ([[1]](https://github.com/kubeflow/pipelines/issues/8129), [[2]](https://github.com/kubeflow/pipelines/issues/8130), [[3]](https://github.com/kubeflow/pipelines/issues/8131), [[4]](https://github.com/kubeflow/pipelines/issues/8132), [[5]](https://github.com/kubeflow/pipelines/issues/8133)) and on an internal \"next steps\" doc."
] | 2022-06-04T05:06:15 | 2022-08-10T19:02:56 | 2022-08-10T19:02:55 |
COLLABORATOR
| null |
# Description
Design the Run Comparison page for KFPv2. The main discussion points are:
1. What are critical user journeys?
2. How to handle compatibility with KFPv1?
3. What metrics does Run Comparison page compare? (for example: parameters, metrics, etc.)
4. How to compare metrics? (Side-by-side, combined chart, pick-and-choose comparison, etc.)
5. How to design the layout so that Run Comparison page is both easy-to-use and optimized for performance?
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I_kwDOB-71UM5LH07K
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Please support the traffic_percentage argument in the deploy_endpoint Google Cloud Pipeline Component
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[
"Note that the traffic_percentage argument is not supported in the V1 [ModelDeployOp](https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/v1/endpoint/deploy_model/component.yaml) either.",
"The ModelDeployOp interface aligns with the [API definition](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints/deployModel) instead of the Vertex SDK which is another wrapper around it. It doesn't align with the [Vertex SDK](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/models.py#L629) interface unfortunately. This is by design."
] | 2022-06-03T20:25:23 | 2023-01-27T23:45:25 | 2023-01-27T23:45:25 |
NONE
| null |
The [aiplatform.Model.deploy](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/models.py#L629
) method allows users to specify either a traffic percentage or a traffic split parameter. Either, but not both, can be used to specify the amount of traffic to route to a newly deployed model.
The Google Cloud Pipeline Components [ModelDeployOp](https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/aiplatform/endpoint/deploy_model/component.yaml#L14), however, does not support the traffic percentage parameter.
Please support the traffic percentage parameter in a future version of ModelDeployOp. The traffic percentage parameter makes it much easier to create canary deployments of new models. This is because the traffic split parameter requires advance knowledge of the ids of all other models deployed to the Endpoint when splits other than {'0': 100} are specified.
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I_kwDOB-71UM5LDwIK
| 7,832 |
[feature] add `component_id` and `component_version` to Component YAML spec
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[
"We are considering supporting this feature in the future, but don't have immediate plan for now. For V1 yaml, you can use metadata field to store version info. You can also use hash as reference for versions."
] | 2022-06-03T02:45:31 | 2022-06-09T22:41:22 | null |
MEMBER
| null |
### Feature Area
/area components
### What feature would you like to see?
Right now, Kubeflow Pipelines has no native concept of "versioning" the same Component YAML over time.
If changes are made to a Component's YAML, existing pipelines may break or do unexpected things.
Given this, the only sensible strategy is __treating each Component YAML as atomic__, and creating a new YAML file for every change (never updating the old ones).
This is fine, but we can make this workflow easier, by providing:
1. A first-class way to indicate that two Component YAMLs actually describe the same component:
- For example, a `component_id` field that specifies a unique identifier for their component
2. A first-class way to indicate what version this YAML represents:
- For example, a `component_version` field that specifies the version of the component
### Example:
These examples show two "versions" of the same component.
```yaml
name: My Component
description: ""
component_id: example.com/my-component
component_version: 1.0.0
inputs:
- ...
outputs:
- ...
implementation:
...
```
```yaml
name: My Component
description: ""
component_id: example.com/my-component
component_version: 2.0.0
inputs:
- ...
outputs:
- ...
implementation:
...
```
### Other Comments:
I am aware that some components [use an annotation called `cannonical_location`](https://github.com/kubeflow/pipelines/blob/340318625c527836af1c9abc0fd0d76c0a466333/components/contrib/git/clone/component.yaml#L6) to uniquely idenfiy components.
However, I think adding id/version as a first-class part of the spec will provide many benefits, including the possibility of building proper "catalogs" of KFP components (which the Python SDK can be natively aware of).
Related to https://github.com/kubeflow/pipelines/issues/1025 (from 2019)
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
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I_kwDOB-71UM5LCiwu
| 7,827 |
[frontend] Run Comparison filter mechanism broken
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[] | 2022-06-02T23:25:40 | 2022-06-06T16:46:30 | 2022-06-06T16:46:30 |
CONTRIBUTOR
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.2
### Steps to reproduce
1. Go to the "Runs" tab on KFP
2. Select at least two runs, and click "Compare runs"
3. Type something into the "Filter runs" field which should filter out the runs by "Run name"
### Expected result
You should see the runs filter by their "Run name" (as they do on the "Runs" tab), but nothing changes.
### Materials and Reference
The below video shows how the filter mechanism works on the "Runs" tab, but does not work on the Run Comparison page.
https://user-images.githubusercontent.com/7987279/171753804-2493547e-7f9a-41ca-a04d-3bcfa6e3d74f.mp4
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5LAig-
| 7,823 |
[Question] Plans for 2.0 release?
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[
"bumping this question. Also when will there be another alpha release? I wanted to get [this patch](https://github.com/kubeflow/pipelines/pull/8343).",
"@chensun @Linchin hey, just bumping again to ask if any releases are planned?",
"Hi @davidxia, thank you for asking. The v2 beta version was planned to be released at end of year 2022. We are working on the last bit of it, so it will be released as soon as we finish. Please let me know if you have any more questions. :)\r\n",
"Hi folks, somewhat related, is there a proposed timeline for when v2 will go from beta to \"full\" release? Additionally, what are the plans for upgrading from v1 to v2? I apologize if it's documented somewhere already -- I was unable to find any information when I looked around.",
"Hi @anishasthana, we are planning to release the GA version for KFP V2 by the end of 2023H1. We will keep supporting v1 for at least a year, but no new feature will be developed there. After that we will gradually deprecate v1."
] | 2022-06-02T16:34:24 | 2023-01-12T21:35:09 | null |
CONTRIBUTOR
| null |
Hi folks, qq, is there any estimate on dates for kfp==2.0.0rc0 release and 2.0.0 full support from backend (trying to undestand better the current story around this).
I have seen that 2.0.0alpha is going out in a new release of Kubeflow, but would be interesting to learn when is 2.0 release anticipated (or if it's months away) to plan around adoption and migration.
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I_kwDOB-71UM5K3lqP
| 7,818 |
[feature] Add authorization to both ReadArtifacts and ReportMetrics endpoints
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[
"/assign @chensun ",
"Related: https://github.com/kubeflow/pipelines/issues/4649"
] | 2022-06-01T14:01:48 | 2022-08-04T10:24:07 | 2022-08-04T10:24:07 |
MEMBER
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
</area backend >
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Ensure that the user has permission to call [ReadArtifacts](https://github.com/kubeflow/pipelines/blob/665d826b38b10f559c4ac105f37fb24c17ef5561/backend/src/apiserver/server/run_server.go#L303) and [ReportMetrics](https://github.com/kubeflow/pipelines/blob/665d826b38b10f559c4ac105f37fb24c17ef5561/backend/src/apiserver/server/run_server.go#L278)
`ReadArtifacts` and `ReportMetrics` could lavarage an existsing function - [`canAccessRun` ](https://github.com/kubeflow/pipelines/blob/665d826b38b10f559c4ac105f37fb24c17ef5561/backend/src/apiserver/server/run_server.go#L359) to enable authorization.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Currently, both [ReadArtifacts](https://github.com/kubeflow/pipelines/blob/665d826b38b10f559c4ac105f37fb24c17ef5561/backend/src/apiserver/server/run_server.go#L303) and [ReportMetrics](https://github.com/kubeflow/pipelines/blob/665d826b38b10f559c4ac105f37fb24c17ef5561/backend/src/apiserver/server/run_server.go#L278) does not check if the user has permissions to call these endpoints.
### Is there a workaround currently?
No.
A security issue is exposed
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 1,255,565,352 |
I_kwDOB-71UM5K1mgo
| 7,817 |
[Question]: Replace Kubeflow Minio
|
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[
"You can replace your minio image in https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/third-party/minio/base/minio-deployment.yaml#L33"
] | 2022-06-01T10:04:49 | 2022-06-16T23:16:56 | null |
NONE
| null |
I want to replace Kubeflow Minio component with the enterprise Minio that my company maintains.
What changes should I make in order to achieve this.
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| 1,253,747,492 |
I_kwDOB-71UM5Kuqsk
| 7,812 |
[bug] Artifacts UI gets populated only when using pipelines SDK v2, It doesn't show any data if using kfp SDK v1
|
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[] | null |
[
"Facing same issue, can we get any resolution to this?",
"Hello @pranavaninadam , there's no artifact in your example 1. Here is the basic definition of artifacts and parameters: https://www.kubeflow.org/docs/components/pipelines/sdk-v2/v2-component-io/#artifacts-and-parameters. Please take a look at it. ",
"Hi @jlyaoyuli thanks for the response, as you mentioned there are no artifacts in example-1, really curious to know if it is possible to get the input or output parameters to show in the Artifacts UI, the reason being I remember the Artifacts UI getting populated when example-1 was executed and not working all of a sudden. \r\nwould like to know your thoughts on this.\r\nthanks."
] | 2022-05-31T11:46:45 | 2022-06-21T02:25:21 | 2022-06-16T22:42:24 |
NONE
| null |
### Artifacts UI gets populated only when using pipelines SDK v2, It doesn't show any data if using kfp SDK v1
### What happened:
**Example-1**
```
NAMESPACE = "kubeflow-user-example-com"
EXPERIMENT_NAME = "sample-artifacts"
import kfp
import kfp.components as components
import kfp.dsl as dsl
from kfp.components import create_component_from_func
def add(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
return a + b
add_op = create_component_from_func(
add, output_component_file='add_component.yaml')
@dsl.pipeline(
name='Addition pipeline',
description='An example pipeline that performs addition calculations.'
)
def add_pipeline(
a='1',
b='7',
):
first_add_task = add_op(a, 14)
second_add_task = add_op(first_add_task.output, b)
arguments = {'a': '9', 'b': '28'}
client = kfp.Client()
experiment = client.create_experiment(
name=EXPERIMENT_NAME,
description="A sample pipeline to validate Artufacts",
namespace=NAMESPACE
)
client.create_run_from_pipeline_func(
add_pipeline,
arguments=arguments,
run_name="sample-artifacts",
experiment_name=EXPERIMENT_NAME
)
```
when I run this sample pipeline the Artifacts UI doesn't show any Artifacts.
**Example-2**
```
from typing import Dict, List
import kfp
from kfp.v2 import dsl
from kfp.v2.dsl import Input, InputPath, Output, OutputPath, Dataset, Model, component
EXPERIMENT_NAME = "Artifacts Test"
NAMESPACE = "kubeflow-user-example-com"
@component
def stepOne(msg: str, output_dataset: Output[Dataset]):
with open(output_dataset.path, 'w') as f:
f.write(msg)
@component
def stepTwo(dataset_path: InputPath('Dataset')):
with open(dataset_path, 'r') as input_file:
dataset_contents = input_file.read()
line = (f'dataset_contents: {dataset_contents}')
print(line)
@dsl.pipeline(pipeline_root='', name='my-test-pipeline-beta')
def pipeline(msg: str = 'Sample message to test Artifacts UI'):
step_one = stepOne(msg=msg)
step_two = stepTwo(dataset_path=step_one.outputs['output_dataset'])
client = kfp.Client()
experiment = client.create_experiment(
name=EXPERIMENT_NAME,
description="A sample pipeline to test artifacts",
namespace=NAMESPACE
)
client.create_run_from_pipeline_func(
pipeline,
arguments={},
run_name="sample-artifacts-test",
experiment_name=EXPERIMENT_NAME,
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE
)
```
when I run this sample pipeline the Artifacts UI shows Artifacts.
### What did you expect to happen:
The Expectation is the Artifacts UI will show the Input/Output data.
Artifacts should show up in example 1 as well.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)? : We deployed using kubeflow AWS manifests on an EKS cluster.
* Kubeflow Version 1.4.0
* Kubeflow Pipelines Version 1.7.0
* KFP SDK version: kfp==1.8.12
When I manually insert data into metadb database, the Artifacts UI gets populated, so I can confirm it is not an issue from frontend.
### Labels
<!-- /area backend -->
<!-- /area sdk -->
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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| 1,252,046,275 |
I_kwDOB-71UM5KoLXD
| 7,810 |
[bug] Failed to clone a Run started by recurring Run
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[
"Would you mind sharing which version that you are using?",
"version 1.8.1 ",
"In the process of upgrading Kubeflow 1.2 to 1.5 on GCP\r\nEnvironment:\r\nHow do you deploy Kubeflow Pipelines (KFP)? Our platform team deploys with a customized GCP procedure.\r\nVersion:\r\nkfp==1.8.12\r\nkfp-pipeline-spec==0.1.14\r\nkfp-server-api==1.8.1",
"any updates on this @Linchin? We have the same issue running on pipelines version: 1.8.3, kubeflow: 1.6.0. \r\nAlso clicking on the recurring run pipeline, it shows error for graph",
"<img width=\"1520\" alt=\"image\" src=\"https://user-images.githubusercontent.com/117266846/202159481-794458b4-f7dc-4410-a1c9-390155801044.png\">\r\n\r\nSeems like the issue come from how the recurring run write run manifest into the database.\r\nI tried adding missing objects, `apiVersion` and `kind`, into the manifest within the Kubeflow backend db `mlpipeline.run_details` at the `WorkflowSpecManifest` and `WorkflowRuntimeManifest` column, then the preview and cloning works perfectly after.\r\n\r\n<img width=\"1515\" alt=\"image\" src=\"https://user-images.githubusercontent.com/117266846/202159525-45415cbf-d470-4249-bf27-04393fa623a2.png\">\r\n\r\n\r\nmissing objects within the manifest:\r\n`\"apiVersion\": \"argoproj.io/v1alpha1\", \"kind\": \"Workflow\",`",
"Hello @tleewongjaro-agoda - The missing objects(apiVersion, kind) were present in the initial pipeline yaml but when creating an experiment run from the given pipeline, those objects are going away when navigating through pipeline version link. Ultimately these clone run and unable to view the graph issues are still present. Any guidance please ? we are using kubelfow verion: 1.6. Do you think we need to install patch ? kindly suggest",
"Looks like we are missing `apiVersion` and `kind` from the `GetRun` API. @Linchin Can you take a look at why is this happening for recurring run?",
"Looks like the issue was fixed in KFP 2.0.0-beta.0. You can upgrade to the latest version to try.",
"Thanks much @zijianjoy i will give a try and let you know asap.",
"@zijianjoy Tried upgrading to 2.0.0-beta.0 and this issue is resolved. Thanks.",
"@zijianjoy @tleewongjaro-agoda @jkchang - As suggested, I have upgraded the KFP and corresponding KFP-Server-API but now i see a different error which is more problematic to all pipelines :( Can you please suggest ? Our team generates YAML files to be able to upload to kubeflow as a pipeline. Error and sample code snippet below...\r\n\r\nError: 'kfp.dsl' has no attribute 'ContainerOp'\r\n------\r\nSample code:\r\n-------------\r\nfrom kfp import dsl \r\nop = dsl.ContainerOp(\r\n name=name,\r\n image='{0}.azurecr.io/{1}:{2}'.format(self._REGISTRY_NAME, self._IMAGE_NAME, version_tag),\r\n command=[ \"python\", self._WRAPPER_SCRPT],\r\n arguments=[\r\n '--env', self._ENV,\r\n '--sql file', sqlfile,\r\n '--whse', wh,\r\n '--loglevel',\"INFO\"\r\n ]\r\n )",
"You might want to still use KFP SDK 1.8 for now. Because ContainerOp is replaced in KFP SDK v2. Another approach is to migrate your pipeline code to KFP v2. Reference: https://kubeflow-pipelines.readthedocs.io/en/2.0.0b9/source/dsl.html?h=containerop#module-kfp.dsl"
] | 2022-05-30T00:56:25 | 2023-03-02T20:01:28 | null |
NONE
| null |
### What steps did you take
1. Create a "hello-world" pipeline using: https://raw.githubusercontent.com/kubeflow/pipelines/master/backend/test/resources/hello-world.yaml
2. Create a recurring run using pipeline "hello-world"
3. Wait after a Run is started and completed.
4. Clone the Run, and click Start button, it fails with error:
### What happened:
Run creation failed
{"error":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid","code":3,"message":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid","details":[{"@type":"type.googleapis.com/api.Error","error_message":"unknown template format","error_details":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid"}]}
### What did you expect to happen:
It should start a new run from the clone.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 7,809 |
[frontend] Error when downloading Artifact that is Folder via UI
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[
"Would you please share which code you are using? Thank you.",
"For sure. We started with the declaration of pipelines via DSL syntax. Here I tried to minimize the number of steps, just creating a TensorFlow model and saving it as an Artifact:\r\n```\r\nimport kfp\r\nimport kfp.dsl as dsl\r\nfrom kfp.v2.dsl import (\r\n component,\r\n Input,\r\n Output,\r\n Dataset,\r\n Metrics,\r\n Model,\r\n)\r\n\r\n@component(base_image='tensorflow/tensorflow', packages_to_install=['pandas'])\r\ndef create_model(saved_model: Output[Model]):\r\n \r\n import pandas as pd\r\n import tensorflow as tf\r\n \r\n csv_file = tf.keras.utils.get_file('heart.csv', 'https://storage.googleapis.com/download.tensorflow.org/data/heart.csv')\r\n df = pd.read_csv(csv_file)\r\n \r\n train_features_data = df[['age', 'thalach', 'trestbps', 'chol', 'oldpeak']]\r\n \r\n normalizer = tf.keras.layers.Normalization(axis=-1)\r\n normalizer.adapt(train_features_data)\r\n \r\n model = tf.keras.Sequential([\r\n normalizer,\r\n tf.keras.layers.Dense(10, activation='relu'),\r\n tf.keras.layers.Dense(10, activation='relu'),\r\n tf.keras.layers.Dense(1)\r\n ])\r\n\r\n model.compile(optimizer='adam',\r\n loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\r\n metrics=['accuracy'])\r\n \r\n print(model.summary())\r\n \r\n model.save(saved_model.path)\r\n\r\[email protected](name=\"artifacts-example\", description=\"artifacts-example\")\r\ndef example_pipeline():\r\n create_model_task = create_model() \r\n \r\nclient = kfp.Client()\r\nprint(client.list_experiments())\r\n\r\nclient.create_experiment(name=\"client_test_1\", description=\"client_test_desc\", namespace=\"my-team\")\r\nclient.create_run_from_pipeline_func(\r\n example_pipeline,\r\n None, run_name=\"test_run\",\r\n experiment_name=\"client_test_1\",\r\n namespace=\"my-team\",\r\n enable_caching=False,\r\n mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE) \r\n```\r\nAs a result, the pipeline runs fine and green, but the artifact (that is a folder) is not available on UI:\r\n\r\n\r\nIn case of clicking on minio link just a blank page is opened in the browser\r\n\r\nIn case of clicking on View All button:\r\n500 error http response is returned with a message:\r\n`Failed to get object in bucket mlpipeline at path v2/artifacts/pipeline/artifacts-example/ea79a102-8eff-4289-ad3a-99af24960c3c/create-model/saved_model: S3Error: The specified key does not exist.`\r\n(I'm using default vanilla build with minio under the hood)\r\n\r\nWhen I use V1 DSL syntax all artifacts (single file or folders are packed as tar archives automatically), so I guess for me (or not only for me) V2 pipelines just store artifacts (files and folders) as it is. Probably it is fine and even cool, plus from what I see 'folder' artifacts are passed fine between pipeline staps.\r\n\r\nBut still it fails to download it on UI by some reason ",
"Another thing that maybe makes sense to mention, and maybe could help is that on the Artifacts UI page I see that all my Artifacts generated by all V2 syntax pipelines are under 'uknown' pipeline by some reason:\r\n\r\n\r\nThis is super confusing. The same time artifacts generated by V1 pipelines are there and look fine:\r\n\r\n "
] | 2022-05-28T08:58:47 | 2022-06-10T08:28:37 | null |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
AWS EKS k8s cluster + vanilla installation with multi-user auth
* KFP version:
1.7
### Steps to reproduce
In Kubeflow pipelines V2 when an artifact is a folder it is not possible to download the Artifact via UI. Data is passed between components in a pipeline fine. When we are using V1 syntax folder (and non folders) are packed in tar archive and everything works in the same environment.
### Expected result
Artifact is packed automatically in archive or just downloadable as a folder when it is a folder originally
### Materials and Reference
There is closed issues on github, but I think it was a fix only for pipelines V1:
https://github.com/kubeflow/pipelines/issues/3667
https://github.com/kubeflow/pipelines/issues/4799
Here is ho it looks on UI when we are trying to download an artifact that was a folder:
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5KgJDT
| 7,794 |
create_component_from_func vs func_to_container_op
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[
"What is the difference between create_component_from_func and func_to_container_op?\r\nThey are equivalent, but func_to_container_op is deprecated in v2 of the SDK. For more information on what has been deprecated see here:\r\nhttps://github.com/kubeflow/pipelines/issues/6133\r\n\r\nCredit: Alexandre Brown in Community Slack\r\n\r\nref: https://journal.arrikto.com/kubeflow-tips-tricks-january-2022-238fde12f7d0",
"We are moving away from `func_to_container_op` in KFPv1, we suggest to use `create_component_from_func` in KFPv1.\r\n\r\nFor KFPv2, we are moving away from both approaches above. We recommend using `@component` decorator in KFPv2. \r\n\r\nFeel free to reopen this issue if you need more help.",
"@zijianjoy will create_component_from_func add `extra_code`? It's sometimes useful when defaults in a function are not a python primitive.",
"cc @connor-mccarthy Do we support `extra_code` in `create_component_from_func`?",
"`create_component_from_func` does not support `extra_code`, though for some use-cases you can work around this by using `subprocess.call` or `os.system` to execute a subprocess within the Python code itself",
"This won't work as we need to add extra code _before_ the function is instantiated in order to avoid issues on default values from the parsed function.",
"@casassg, I see. Yes, unfortunately that's one of the use-cases where this workaround will not work. The other approach is creating a containerized component that implements this.\r\n\r\nI suspect this is a fairly common use-case. If you feel strongly about having `extra_code` functionality, please feel free to open a feature request issue requesting that this be included in v2, then the community can upvote it."
] | 2022-05-26T19:05:42 | 2022-07-20T17:47:16 | 2022-06-23T22:49:35 |
NONE
| null |
I don't seem to understand the behavior behind these two functions.
I have been using `create_component_from_func` for all of my pipelines, however all tutorials and guides I'm seeing online use `func_to_container_op` and the docstrings don't explain them enough (They both say `Converts a Python function to a component and returns a task`).
Can anyone elaborate on this as the documentation doesn't include much info about this?
|
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I_kwDOB-71UM5KfqLj
| 7,792 |
How to pass in already existing VertexDataset or VertexModel to a KF/Vertex Pipeline?
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[
"For those that encounter this issue, the solution is here:\r\n\r\nhttps://cloud.google.com/vertex-ai/docs/pipelines/use-components#use_python_function-based_components"
] | 2022-05-26T16:53:37 | 2022-05-26T17:59:50 | 2022-05-26T17:59:49 |
NONE
| null |
What seems to be the only supported means of passing a VertexDataset to an AutoML training job in a pipeline is to create the dataset as part of the same pipeline.
I have not found a way to pass the URI or resource name of an existing dataset to an AutoML Training task in a Vertex Pipeline, tasks that take a model or dataset as input expect an artifact of type `google.VertexDataset` or `google.VertexModel`. However, creating these at runtime is proving impossible.
I've found a way to construct a VertexDataset or VertexModel artifact, but deserialization in the training task fails with this error:
`ValueError: Resource {{$.inputs.artifacts['dataset'].metadata['resourceName']}} is not a valid resource id.`
Looking in the GCS URI for that output artifact, the `executor_output.json`, in fact, has an empty object for the `metadata` key. So the error message is accurate. How do I properly serialize this type of artifact? Additionally, is there a better way to pass an already created model or dataset to a given task that expects the `google.VertexDataset` or `google.VertexModel` data type?
```python
def get_dataset(name: str, dataset_resource_name: str, uri: str) -> \
NamedTuple('DatasetOutput', [('dataset', Artifact)]):
from google_cloud_pipeline_components.types.artifact_types import VertexDataset
from collections import namedtuple
ds = VertexDataset(name=name, dataset_resource_name=dataset_resource_name, uri=uri)
output = namedtuple('DatasetOutput', ['dataset'])
return output(ds)
create_component_from_func(get_dataset, output_component_file='get_dataset_component.yaml',
packages_to_install=['google-cloud-pipeline-components'])
```
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[question]Can not visualize the results of my pipelines component until component is completed
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[] | 2022-05-24T13:36:12 | 2022-06-09T22:52:53 | null |
NONE
| null |
I want to visualize tensorboard while training, but according to the [doc](https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/), I can not show tensorboard until the training component is completed.
I have 2 questions:
1. Is there a way I can use tensorboard while training component is working(not finshed)?
2. For now all kinds of visualizations have to wait for component finished, and then to show in the visualization tab, right?
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I_kwDOB-71UM5KRITO
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kfp_server_api.exceptions.ApiException: (500) - failed to authenticate with kubeflow pipeline
|
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[
"By further research, found [How to do programmatic authentication with Dex? #140](https://github.com/kubeflow/kfctl/issues/140#issuecomment-719894529). By modifying the code as per the solution provided there, it worked.\r\n\r\nPlease clarify if this we need to do. If so, please confirm where it is documented?\r\n\r\n```\r\nimport requests\r\nimport kfp\r\nimport kfp.dsl as dsl\r\nfrom kfp.components import create_component_from_func\r\n\r\n\r\n# Does not work\r\n#import kfp\r\n#client = kfp.Client(host='http://localhost:3000', namespace='kubeflow')\r\n#print(client.list_experiments(namespace='kubeflow'))\r\n\r\n\r\n# --------------------------------------------------------------------------------\r\n# https://github.com/kubeflow/kfctl/issues/140#issuecomment-719894529\r\n# How to do programmatic authentication with Dex? #140\r\n# --------------------------------------------------------------------------------\r\nHOST = \"http://localhost:8080/\"\r\nUSERNAME = \"[email protected]\"\r\nPASSWORD = \"12341234\"\r\nNAMESPACE = \"kubeflow-user-example-com\"\r\n\r\nsession = requests.Session()\r\nresponse = session.get(HOST)\r\n\r\nheaders = {\r\n \"Content-Type\": \"application/x-www-form-urlencoded\",\r\n}\r\n\r\ndata = {\"login\": USERNAME, \"password\": PASSWORD}\r\nsession.post(response.url, headers=headers, data=data)\r\nsession_cookie = session.cookies.get_dict()[\"authservice_session\"]\r\n\r\nclient = kfp.Client(\r\n host=f\"{HOST}/pipeline\",\r\n cookies=f\"authservice_session={session_cookie}\",\r\n namespace=NAMESPACE,\r\n)\r\n\r\nprint(client.list_pipelines())\r\n\r\n\r\ndef add(a: float, b: float) -> float:\r\n '''Calculates sum of two arguments'''\r\n return a + b\r\n\r\n\r\nadd_op = create_component_from_func(\r\n add, output_component_file='add_component.yaml')\r\n\r\n\r\[email protected](\r\n name='Addition pipeline',\r\n description='An example pipeline that performs addition calculations.'\r\n)\r\ndef add_pipeline(\r\n a='1',\r\n b='7',\r\n):\r\n # Passes a pipeline parameter and a constant value to the `add_op` factory\r\n # function.\r\n first_add_task = add_op(a, 4)\r\n # Passes an output reference from `first_add_task` and a pipeline parameter\r\n # to the `add_op` factory function. For operations with a single return\r\n # value, the output reference can be accessed as `task.output` or\r\n # `task.outputs['output_name']`.\r\n second_add_task = add_op(first_add_task.output, b)\r\n\r\n# Specify argument values for your pipeline run.\r\narguments = {'a': '7', 'b': '8'}\r\n\r\n# Create a pipeline run, using the client you initialized in a prior step.\r\n#client.create_run_from_pipeline_func(add_pipeline, arguments=arguments)\r\n\r\nkfp.compiler.Compiler().compile(\r\n pipeline_func=add_pipeline,\r\n package_path='pipeline.yaml')\r\n```",
"`client = kfp.Client(host='http://localhost:3000', namespace='kubeflow')` is wrong. `client = kfp.Client()` is enough for an in-cluster jupyterlab (jupyter notebook is wrong, a notebook is a file). And especially localhost is nonsense. You want the apiserver from the kubeflow namespace. You should really spend some time alone with the Kubeflow architecture and code/documentation.\r\nAlso kubectl port-forward svc/ml-pipeline-ui 3000:80 --namespace kubeflow\\ shows that you do not understand the Kubeflow architecture. You are trying to abuse the frontend as apiserver.",
"@oonisim Please confirm that you are able to use Kubeflow dashboard without port forwarding. Then you can create KFP client using the URL endpoint to pipeline."
] | 2022-05-24T05:27:54 | 2022-06-16T23:02:28 | null |
NONE
| null |
# Objective
Please provide the steps how to implement the authentication for Kubeflow pipeline addressed in [Add authentication with ServiceAccountToken #5138](https://github.com/kubeflow/pipelines/issues/5138)
# Background
The [Kubeflow sdk - error in client.list_experiments()][1] refers to [Kubeflow sdk - error in client.list_experiments() #6120][2] github issue through which the author looks fixed the issue.
>I received feedback from the developers (see the [closed issue](https://github.com/kubeflow/pipelines/issues/6120)). This is one of the current caveats of multi-user mode (see [documentation](https://www.kubeflow.org/docs/components/pipelines/multi-user/#in-cluster-api-request-authentication)). This usage is now being supported through [#5138](https://github.com/kubeflow/pipelines/issues/5138).
However, I could not figure out what is exactly the cause and how to fix it. It looks [Connecting to Kubeflow Pipelines using the SDK client][3] gives the configurations but not sure how exactly I need to do.
# Reproduction steps
1. Deployed Minikube on a remote instance and setup kubectl connection.
2. Deployed Kubeflow 1.5.0 by following [Install with a single command][4].
3. Verify the connection and confirm pods are running from the local laptop.
```
$ kubectl get pods -n kubeflow
NAME READY STATUS RESTARTS AGE
admission-webhook-deployment-7df7558c67-drdzw 1/1 Running 5 2d18h
cache-deployer-deployment-6f4bcc969-8kpm6 2/2 Running 15 2d18h
cache-server-575d97c95-k7rv4 2/2 Running 10 2d18h
centraldashboard-5dd4f57bbd-gcxn5 2/2 Running 10 2d18h
jupyter-web-app-deployment-5886974887-8c2cf 1/1 Running 5 2d18h
katib-controller-58ddb4b856-mzq2l 1/1 Running 46 2d18h
katib-db-manager-6df878f5b8-c9dhr 1/1 Running 8 2d18h
katib-mysql-6dcb447c6f-lz5b8 1/1 Running 5 2d18h
katib-ui-f787b9d88-8h25n 1/1 Running 6 2d18h
kfserving-controller-manager-0 2/2 Running 50 2d18h
kfserving-models-web-app-7884f597cf-m9n59 2/2 Running 10 2d18h
kserve-models-web-app-5c64c8d8bb-bpdsb 2/2 Running 10 2d18h
kubeflow-pipelines-profile-controller-84bcbdb899-669hr 1/1 Running 5 2d18h
metacontroller-0 1/1 Running 6 2d18h
metadata-envoy-deployment-7b847ff6c5-d2fjv 1/1 Running 5 2d18h
metadata-grpc-deployment-6f6f7776c5-2vqp6 2/2 Running 21 2d18h
metadata-writer-78fc7d5bb8-q8hfq 2/2 Running 11 2d18h
minio-5b65df66c9-fttpm 2/2 Running 10 2d18h
ml-pipeline-75b5c59d7f-k7mm7 2/2 Running 59 2d18h
ml-pipeline-persistenceagent-87b6888c4-swv8k 2/2 Running 10 2d18h
ml-pipeline-scheduledworkflow-665847bb9-4b5vr 2/2 Running 10 2d18h
ml-pipeline-ui-68cc764f66-892rz 2/2 Running 14 2d18h
ml-pipeline-viewer-crd-68777557fb-6lq88 2/2 Running 16 2d18h
ml-pipeline-visualizationserver-58ccb76855-qz2rc 2/2 Running 12 2d18h
mysql-f7b9b7dd4-2dpqv 2/2 Running 10 2d18h
notebook-controller-deployment-6c5f5d6cfc-mxmzw 2/2 Running 17 2d18h
profiles-deployment-5cdc5dc577-szhjk 3/3 Running 61 2d18h
tensorboard-controller-controller-manager-5cbddb7fb5-xgq2v 3/3 Running 21 2d18h
tensorboards-web-app-deployment-7c5db448d7-t8xqp 1/1 Running 5 2d18h
training-operator-7b8cc9865d-qr8hm 1/1 Running 7 2d18h
volumes-web-app-deployment-87484c848-qvsnc 1/1 Running 5 2d18h
workflow-controller-6bf87db995-snfdn 2/2 Running 20 2d18h
```
4. Installed kubeflow SDK in the local laptop.
```
$ pip list | grep kfp
kfp 1.8.12
kfp-pipeline-spec 0.1.15
kfp-server-api 1.8.1
```
5. Connected to the kubeflow pipeline as per [Connecting to Kubeflow Pipelines using the SDK client][5]
> kubectl port-forward svc/ml-pipeline-ui 3000:80 --namespace kubeflow\
6. Verified the pipeline ui appears as in the document.
> You can verify that port forwarding is working properly by visiting http://localhost:3000 in your browser. If port forwarding is working properly, the Kubeflow Pipelines UI appears.
[![enter image description here][6]][6]
7. Applied the YAML by kubectl.
```
apiVersion: kubeflow.org/v1alpha1
kind: PodDefault
metadata:
name: access-ml-pipeline
namespace: "kubeflow"
spec:
desc: Allow access to Kubeflow Pipelines
selector:
matchLabels:
access-ml-pipeline: "true"
volumes:
- name: volume-kf-pipeline-token
projected:
sources:
- serviceAccountToken:
path: token
expirationSeconds: 7200
audience: pipelines.kubeflow.org
volumeMounts:
- mountPath: /var/run/secrets/kubeflow/pipelines
name: volume-kf-pipeline-token
readOnly: true
env:
- name: KF_PIPELINES_SA_TOKEN_PATH
value: /var/run/secrets/kubeflow/pipelines/token
```
and
```
apiVersion: kubeflow.org/v1alpha1
kind: PodDefault
metadata:
name: access-ml-pipeline
namespace: "kubeflow-user-example-com"
spec:
desc: Allow access to Kubeflow Pipelines
selector:
matchLabels:
access-ml-pipeline: "true"
volumes:
- name: volume-kf-pipeline-token
projected:
sources:
- serviceAccountToken:
path: token
expirationSeconds: 7200
audience: pipelines.kubeflow.org
volumeMounts:
- mountPath: /var/run/secrets/kubeflow/pipelines
name: volume-kf-pipeline-token
readOnly: true
env:
- name: KF_PIPELINES_SA_TOKEN_PATH
value: /var/run/secrets/kubeflow/pipelines/token
```
8. Run the code from the local laptop.
```
import kfp
client = kfp.Client(host='http://localhost:3000', namespace='kubeflow')
print(client.list_experiments(namespace='kubeflow'))
```
Got the error.
```
Traceback (most recent call last):
File "connect_kubeflow_pipeline.py", line 8, in <module>
print(client.list_experiments(namespace='kubeflow'))
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp/_client.py", line 540, in list_experiments
filter=filter)
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/api/experiment_service_api.py", line 567, in list_experiment
return self.list_experiment_with_http_info(**kwargs) # noqa: E501
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/api/experiment_service_api.py", line 682, in list_experiment_with_http_info
collection_formats=collection_formats)
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 369, in call_api
_preload_content, _request_timeout, _host)
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 185, in __call_api
_request_timeout=_request_timeout)
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 393, in request
headers=headers)
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/rest.py", line 234, in GET
query_params=query_params)
File "/Users/1245095/venv/ml/lib/python3.7/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (500)
Reason: Internal Server Error
HTTP response headers: HTTPHeaderDict({'X-Powered-By': 'Express', 'content-type': 'application/json', 'date': 'Tue, 24 May 2022 04:58:42 GMT', 'x-envoy-upstream-service-time': '2', 'server': 'envoy', 'connection': 'close', 'transfer-encoding': 'chunked'})
HTTP response body: {"error":"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\nFailed to authorize with API resource references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1089\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1091\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed to authorize with API resource references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1089\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1091\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357","code":13,"message":"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\nFailed to authorize with API resource references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1089\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1091\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed to authorize with API resource references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1089\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:1091\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email 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protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed to authorize with API resource 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```
[1]: https://stackoverflow.com/a/69886126/4281353
[2]: https://github.com/kubeflow/pipelines/issues/6120
[3]: https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#connect-to-kubeflow-pipelines-from-the-same-cluster
[4]: https://github.com/kubeflow/manifests#install-with-a-single-command
[5]: https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/
[6]: https://i.stack.imgur.com/GX0GQ.png
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I_kwDOB-71UM5KAwUe
| 7,757 |
[backend] cache-server's behavior on pod annotations cause pipeline to fail
|
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[
"Hi @we10710aa, you can try disable caching following this instruction to block yourself. https://www.kubeflow.org/docs/components/pipelines/overview/caching/#disablingenabling-caching\r\n\r\nAnd in the WIP KFP v2, we have a different implementation, maybe it will be compatible with your use case."
] | 2022-05-19T12:39:25 | 2022-05-26T22:59:46 | null |
NONE
| null |
When cache-server find correspond result in cache, it remove the init container of pipeline pod and keep all annotations. This will cause pod with following annotations fail to be created.
``container.apparmor.security.beta.kubernetes.io/init=runtime/default``
The kube-apiserver will reject the create api call because it can't find corresond container in pod manifest.
The error message will be like this
```
[metadata.annotations[container.apparmor.security.beta.kubernetes.io/init]: Invalid value: "init": container not found
```
Behavior like this will cause any cluster that enable ``PodSecurityPolicy`` ( or any cluster that use this annotation for security reason ) to fail to run the same pipeline more than once, because this annotation is added automatically.
### Environment
* How did you deploy Kubeflow Pipelines (KFP)? Using Kubeflow manifest
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: v1.5
* Kubernetes v1.23
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
1. Install KF Pipeline on cluster that enable PodSecurityPolicy
2. Run any example pipeline twice, and the error will appear like below
<img width="1688" alt="image" src="https://user-images.githubusercontent.com/16017026/169294925-f34d4931-6a9e-4ef0-817e-0df3bd0cc393.png">
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
The pipeline can run any number of times
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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Create a new page for V2 Run Comparison
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[] | 2022-05-18T23:19:32 | 2022-05-31T18:09:11 | 2022-05-31T18:09:11 |
COLLABORATOR
| null |
# Description
Create a new V2 Run Comparison page, when two or more V2 pipelines are selected, the `Compare Run` button is enabled. If the selected runs are all V2 pipeline runs, redirect users to V2 Run Comparison page. We only need to show a message for now to tell whether it is V2 Run Comparison page, no need for other content.
# Reference
V1 Run Comparison page: https://github.com/kubeflow/pipelines/blob/master/frontend/src/pages/Compare.tsx
Detect whether a pipeline run is V1 or V2: V1 has workflow_manifest, while V2 has pipeline_manifest: https://deploy-preview-3245--competent-brattain-de2d6d.netlify.app/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec-v1latest/#/definitions/apiPipelineRuntime
# Note
- Please use functional component instead of class component for new pages: https://reactjs.org/docs/components-and-props.html#function-and-class-components.
- Guard this feature behind V2_ALPHA feature flag. Reference: https://github.com/kubeflow/pipelines/tree/master/frontend#large-features-development
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| 7,740 |
[feature] Add startup probe to pipeline API deployment
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[
"Resolved by https://github.com/kubeflow/pipelines/pull/7741. "
] | 2022-05-17T15:30:29 | 2022-05-23T18:25:41 | 2022-05-23T18:25:40 |
CONTRIBUTOR
| null |
### Feature Area
/area backend
### What feature would you like to see?
When we pivoted from an in-cluster mysql deployment to an externally hosted RDS instance for the metadata store, we ran into a scenario where the ml-pipeline-apiserver pod couldn't start because it didn't have a long enough grace period to complete its database migration.
We fixed this with a startup probe. I'll submit the associated PR momentarily. Filing this issue for proper book-keeping.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
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[bug] The pipeline UI shows that the pipeline has been running, but I have turned it off
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"\r\n",
"cc/ @zijianjoy ",
"The already started step will continue running, only the subsequent step will be terminated. Does it answer your question?",
"It is still going on without stopping, as if it were to be executed forever.",
"@ErraticO What is your kubernetes version and what is your KFP backend version? You might need to upgrade to a newer version of KFP (like KFP Backend 1.8) to give it another try. "
] | 2022-05-16T08:33:59 | 2022-06-23T06:45:07 | null |
NONE
| null |
### What steps did you take
When the scheduled task is executed, I delete it and turn off the automatically generated run.
### What happened:
The pipeline UI shows that the pipeline has been running, but I have turned it off
### What did you expect to happen:
The UI interface should show the status of closing rather than running all the time
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
1. Set pipeline scheduled task in Experiments
2. After the scheduled task takes effect, the pipeline execution is terminated
3. Delete scheduled task, A new pipeline run will be regenerated
4. Immediately terminate the pipeline running, and a bug occurs at this time
* KFP version:
kfp==1.6.6
python:3.8.6
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,236,576,257 |
I_kwDOB-71UM5JtKgB
| 7,730 |
[grpc-gateway] upgrade grpc-gateway to v2
|
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[
"cc/ @zijianjoy ",
"Thank you @hsinhoyeh ! Indeed we are still using grpc-gateway v1 and I hope we can upgrade to v2. However, there are breaking changes that I don't have enough bandwidth to look into yet. I am hoping that we can introduce a new API package which can keep this upgrade contained. If you have tried to upgrade, do you know the breaking changes which are caused by upgrading from v1 to v2?\r\n\r\ncc @Linchin @annajung @difince "
] | 2022-05-16T03:46:40 | 2022-05-29T03:12:28 | null |
CONTRIBUTOR
| null |
### Feature Area
[grpc gateway](https://github.com/grpc-ecosystem/grpc-gateway) provides an HTTP way to communicate the underlying GRPC services (defined in the same proto file), and since version v1.14, the community suggests that the v1 is under maintenance mode and suggest to upgrade to v2. https://github.com/grpc-ecosystem/grpc-gateway/releases/tag/v1.14.7
However, the current Kubeflow pipeline is still using v1 (as you can see here https://github.com/kubeflow/pipelines/blob/master/backend/api/hack/generator.sh#L33)
It is well understood that upgrading from v1 to v2 of grpc-gateway might not be straightforward. just wondering that is this need imporoving? or you would need some hands?
/area backend
### What feature would you like to see?
use grpc-gateway v2
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
some binaries are gone since v2, it's hard to track them.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
currently, the upstream of kf pipeine use v1 which is under maintaince mode of grpc-gateway.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
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I_kwDOB-71UM5JqncZ
| 7,728 |
[feature] create run from pipeline func locally
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NONE
| null |
In feature request #6956 to run all type of kfp pipelines and kfp components locally, it was point out that kfp supports components with different types of formats, languages, base image, etc.
In this current feature request, i would like to ask support to run kfp pipelines locally in the particular case when all components run a single custom made image with a custom made library.*
Here you have a proposal https://github.com/iuiu34/kfp-py-func-local**
*this would be very similar to switch in `prefect` from `flow.run_config = KubernetesRun()` to `flow.run_config = LocalRun()` [link](https://docs.prefect.io/orchestration/agents/kubernetes.html#requirements)
**while making the proposal, i founded package [kfp-local](https://pypi.org/project/kfp-local/1.1.0/). But i can't make it work, neither i can find the github repo to know if it's for the same purpose
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Can pipeline components provide http services within the cluster?
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[
"Now I can create the pipeline with the following code:\r\n```\r\nimport kfp\r\nimport kfp.dsl as dsl\r\nimport kfp.components as comp\r\n\r\[email protected](\r\n name='test demo pipeline',\r\n description='test demo pipeline'\r\n)\r\ndef prepare_data_pipeline():\r\n \r\n start_service_op = kfp.dsl.ContainerOp(\r\n 'start_service', \r\n 'kserve:v1',\r\n 'python', \r\n ['/root/test.py', '1']).add_pod_label('app','hello-pod')\r\n \r\npipeline_func = prepare_data_pipeline\r\nkfp.Client().create_run_from_pipeline_func(pipeline_func, arguments={})\r\n \r\n``` \r\nAlso create a service to access the component:\r\n```\r\napiVersion: v1\r\nkind: Service\r\nmetadata:\r\n name: hello-pod\r\n namespace: test\r\nspec:\r\n ports:\r\n - port: 8889\r\n targetPort: 8889\r\n protocol: TCP\r\n nodePort: 30010\r\n type: NodePort\r\n selector:\r\n app: hello-pod\r\n```\r\nBut you will get a warning:\r\n\r\n> /opt/conda/lib/python3.8/site-packages/kfp/dsl/_container_op.py:1257: FutureWarning: Please create reusable components instead of constructing ContainerOp instances directly. Reusable components are shareable, portable and have compatibility and support guarantees. Please see the documentation: https://www.kubeflow.org/docs/pipelines/sdk/component-development/#writing-your-component-definition-file The components can be created manually (or, in case of python, using kfp.components.create_component_from_func or func_to_container_op) and then loaded using kfp.components.load_component_from_file, load_component_from_uri or load_component_from_text: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_file\r\n> warnings.warn(\r\n\r\nI wonder if there is a way other than ContainerOp? It seems that other ways to create components cannot be used in conjunction with services",
"Thanks, @grapefruitL. As indicated in the error message, `ContainerOp` is deprecated. Have you tried creating the same component using YAML and reading in using `kfp.components.load_component_from_file`?\r\n\r\nFYI, the KFP v2 SDK will eventually have a `ContainerOp` alternative with similar functionality, though this is not yet available.",
"Solved, after using `load_component_from_file` to generate `op `variables, you need to call the `add_pod_label `method"
] | 2022-05-14T06:13:39 | 2022-05-20T00:43:00 | 2022-05-20T00:43:00 |
NONE
| null |
Can pipeline components provide http services within the cluster? For example, I have a data processing and analysis pipeline, which is designed to run for 1 hour. Other pods in the cluster access this pipeline through http requests to process the data and obtain results. After 1 hour, the pipeline runs and analyzes the request data it receives to obtain the analysis results.
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I_kwDOB-71UM5Jof_4
| 7,719 |
[bug] Cannot find how to type pipeline parameters for JsonArray or JsonObject
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[
"I recognize this isn't a bug, however I do not know what else to describe it as, nor where to find this information. I've tried the KFP documentation and the myriad of oddly organized and incomplete GCP documentation on Vertex AI Pipelines.",
"For anyone who comes across this, [`jsonobject`](https://pypi.org/project/jsonobject/) is the package that contains the classes required. This should be in the documentation somewhere.",
"@dboshardy\r\n\r\nPython's `list` type is mapped to `JsonArray`\r\nPython's `dict` type is mapped to `JsonObject`.\r\n\r\n>kfp.dsl.types.InconsistentTypeException: Incompatible argument passed to the input \"gcs_source_uris\" of component \"model_batch_predict\": Argument type \"typing.List[str]\" is incompatible with the input type \"JsonArray\"\r\n\r\n`typing.List[str]` should work the same as `list`, but there might be newly introduced bugs in v2 that break that.\r\n\r\n>For anyone who comes across this, [jsonobject](https://pypi.org/project/jsonobject/) is the package that contains the classes required. This should be in the documentation somewhere.\r\n\r\nThe `jsonobject` package has no relation to the `JsonObject` type name in KFP components.",
"I don't know what to say other than using `lists` as the type did not work but using `jsonobject.JsonArray` did. Maybe this has changed in later versions. \n\n",
"The `param: list` annotations has worked since the first releases of KPF. Maybe KFPv2 has disrupted something recently for some time. \r\nIn any case, specifying arbitrary type is very easy - just annotate with the type name\r\n`param: \"JsonArray\"`\r\nOr\r\n`some_path: InputPath(\"JsonArray\") `\r\nThis works with all types. ",
"Thanks, changing my PipelineParam type hint from `List[str]` to `list` worked.",
"I have a question: how can we use the for loop for `gcs_source_uris ` at `batch_predict_pipeline`, not inside the component which was passed. Thanks"
] | 2022-05-13T14:57:49 | 2022-12-10T10:34:54 | 2022-05-16T18:45:05 |
NONE
| null |
### What steps did you take
I'm trying to utilize the ModelBatchPredictOp from the GCP Components. There are two fields, one with a type of `JsonObject` and the other with the type `JsonArray`. The pipeline accepts the fields for this operator as input parameters to the pipeline and passes them to the operator in the constructor. Since they're in the pipeline params, they must be typed. However, I am unable to find any type configuration that coerces to `JsonArray` or `JsonObject`, and I cannot find _any_ documentation on how to do this.
How do I pass these values?
For example, `gcs_source_uris` is supposed to be a `JsonArray`. When passing this through as a pipeline param typed to `List[str]` it fails to compile with this error:
```
kfp.dsl.types.InconsistentTypeException: Incompatible argument passed to the input "gcs_source_uris" of component "model_batch_predict": Argument type "typing.List[str]" is incompatible with the input type "JsonArray"
```
```python
@kfp.v2.dsl.pipeline(
name="batch-predict-v0",
pipeline_root=pipeline_gcs_root_path)
def batch_predict_pipeline(
...
gcs_source_uris: List[str],
...
):
predict_op = ModelBatchPredictOp(
...
gcs_source_uris=gcs_source_uris,
....
)
```
I understand the error, and I've exhausted any other potential types I could think of. I've also tried to find any references to `JsonObject` or `JsonArray` in the SDK and none of them reference any Python classes I can import to type these parameters as. I'm gathering this is some kind of protobuf type, but when trying to use that, it still fails.
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5JoXFR
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[sdk] v2: mount_pvc fails with parameters if other parameters are given to component
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"following_url": "https://api.github.com/users/laserK3000/following{/other_user}",
"gists_url": "https://api.github.com/users/laserK3000/gists{/gist_id}",
"starred_url": "https://api.github.com/users/laserK3000/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/laserK3000/subscriptions",
"organizations_url": "https://api.github.com/users/laserK3000/orgs",
"repos_url": "https://api.github.com/users/laserK3000/repos",
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|
[
{
"id": 1073153908,
"node_id": "MDU6TGFiZWwxMDczMTUzOTA4",
"url": "https://api.github.com/repos/kubeflow/pipelines/labels/kind/bug",
"name": "kind/bug",
"color": "fc2515",
"default": false,
"description": ""
},
{
"id": 1136110037,
"node_id": "MDU6TGFiZWwxMTM2MTEwMDM3",
"url": "https://api.github.com/repos/kubeflow/pipelines/labels/area/sdk",
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}
] |
open
| false | null |
[] | null |
[
"Thanks, @laserK3000. V2 compatible mode is deprecated and not currently supported. And, unfortunately, this is not yet a feature in V2. I would proceed with the previous approach that is successful in V1."
] | 2022-05-13T14:25:43 | 2022-05-19T22:56:58 | null |
NONE
| null |
### Environment
* KFP version:
standalone
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version:
1.8.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
kfp 1.8.12
kfp-pipeline-spec 0.1.14
kfp-server-api 1.8.1
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
I want to mount a pvc using `mount_pvc(...)` with a pvc name coming from a pipeline parameter. This works only if there are no other parameters given to the pipeline step.
### Expected result
Should work independently of other parameters.
### Materials and Reference
sample code
```
import kfp
from kfp.onprem import mount_pvc
from kfp.v2.dsl import component
@component
def test_component(greeting: str = "World"):
print(f"Hello {greeting}")
@kfp.dsl.pipeline(name='pvc mounting pipeline')
def test_pipeline_fail(
pvc_name: str = 'pcv-name',
greeting: str = "World"
):
test_task = test_component(greeting=greeting).apply(mount_pvc(pvc_name, 'pvc', '/mnt/pipeline'))
@kfp.dsl.pipeline(name='pvc mounting pipeline')
def test_pipeline_pass(
pvc_name: str = 'pcv-name',
greeting: str = "World"
):
test_task = test_component().apply(mount_pvc(pvc_name, 'pvc', '/mnt/pipeline'))
if __name__ == '__main__':
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(test_pipeline_fail, 'test_pipeline_fail.yaml')
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(test_pipeline_pass, 'test_pipeline_pass.yaml')
```
Comparing the generated pipeline definitions, one can see that the failing pipeline lacks the input parameter `pvc_name`. This worked in v1 without problems.
Uploading the pipeline results in error:
```
kfp_server_api.exceptions.ApiException: (500)
Reason: Internal Server Error
HTTP response headers: HTTPHeaderDict({'X-Powered-By': 'Express', 'content-type': 'application/json', 'date': 'Fri, 13 May 2022 13:48:42 GMT', 'connection': 'close', 'transfer-encoding': 'chunked'})
HTTP response body: {"error":"Internal error: templates.pvc-mounting-pipeline templates.pvc-mounting-pipeline.tasks.test-component templates.test-component: failed to resolve {{inputs.parameters.pvc_name}}\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:55\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.ValidateWorkflow\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/workflow/validate/validate.go:228\ngithub.com/kubeflow/pipelines/backend/src/apiserver/template.ValidateWorkflow\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/template/argo_template.go:193\ngithub.com/kubeflow/pipelines/backend/src/apiserver/template.NewArgoTemplate\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/template/argo_template.go:123\ngithub.com/kubeflow/pipelines/backend/src/apiserver/template.New\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/template/template.go:142\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:351\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:135\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed to create a new run.\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:137\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357","code":13,"message":"Internal error: templates.pvc-mounting-pipeline templates.pvc-mounting-pipeline.tasks.test-component templates.test-component: failed to resolve {{inputs.parameters.pvc_name}}\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:55\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.ValidateWorkflow\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email 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run.\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:137\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal error: templates.pvc-mounting-pipeline templates.pvc-mounting-pipeline.tasks.test-component templates.test-component: failed to resolve {{inputs.parameters.pvc_name}}\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:55\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.ValidateWorkflow\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email 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protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357","error_details":"Internal error: templates.pvc-mounting-pipeline templates.pvc-mounting-pipeline.tasks.test-component templates.test-component: failed to resolve {{inputs.parameters.pvc_name}}\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/errors/errors.go:55\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.ValidateWorkflow\n\t/go/pkg/mod/github.com/argoproj/argo-workflows/[email protected]/workflow/validate/validate.go:228\ngithub.com/kubeflow/pipelines/backend/src/apiserver/template.ValidateWorkflow\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/template/argo_template.go:193\ngithub.com/kubeflow/pipelines/backend/src/apiserver/template.NewArgoTemplate\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/template/argo_template.go:123\ngithub.com/kubeflow/pipelines/backend/src/apiserver/template.New\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/template/template.go:142\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:351\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:135\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed to create a new run.\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:279\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:137\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1286\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:1609\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/[email protected]/server.go:934\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357"}]}
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,235,143,415 |
I_kwDOB-71UM5Jnsr3
| 7,717 |
[backend] cannot enter chroot for container named "main" error with Kubeflow Pipelines version 1.8.1
|
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"id": 39456672,
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"events_url": "https://api.github.com/users/Priyanka4pc/events{/privacy}",
"received_events_url": "https://api.github.com/users/Priyanka4pc/received_events",
"type": "User",
"site_admin": false
}
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[] | null |
[
"Hi @Priyanka4pc, we don't officially support minikube deployment, maybe you can try one of the these local deployment choices: https://www.kubeflow.org/docs/components/pipelines/installation/localcluster-deployment/",
"The same also happens when we use kind nonetheless. Same steps to reproduce except we use `kind create cluster`.\r\n\r\n```shell\r\n# I am on Apple-M1 Chip\r\nkind create cluster\r\n\r\nPIPELINE_VERSION=1.8.5 \r\n\r\nkubectl apply -k \"github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION\"\r\n\r\nkubectl wait --for condition=established --timeout=60s crd/[applications.app.k8s.io](http://applications.app.k8s.io/)\r\n\r\nkubectl apply -k \"github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION\"\r\n```",
"@AdityaSoni19031997 any luck with this error? I'm getting same error on kind.",
"It worked for me when I switched to `Emissary Executor` as mentioned here: https://www.kubeflow.org/docs/components/pipelines/v1/installation/choose-executor/#emissary-executor"
] | 2022-05-13T11:53:40 | 2022-10-12T15:38:17 | 2022-05-26T22:45:16 |
NONE
| null |
### Describe the issue
* We are getting a random error PID error when using version 1.8.1 of the Standalone deployment of Kubeflow pipelines with both emissary and pns executors.
* All the runs where this errors comes, the clone of the run or retry works completely fine. So a guess is that this error only comes when a new image is being pulled.
* This error comes in the wait container of the pod and is not always reproducible but we got it by running the demo pipeline of Kubeflow and it comes randomly in one of the steps.
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Step 1:
minikube start --auto-update-drivers=true --cpus='4' --delete-on-failure=true --disk-size='20g' --driver='hyperkit' --kubernetes-version='stable' --memory='10g'
Step 2:
export PIPELINE_VERSION=1.8.1
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-emissary?ref=$PIPELINE_VERSION"
### Steps to reproduce
* kubectl port-forward $(kubectl get pods -n kubeflow | grep ml-pipeline-ui | cut -d' ' -f1) 31380:3000 -n kubeflow &
* Run [Demo] XGBoost - Iterative model training
Error Message:
> This step is in Error state with this message: Error (exit code 1): cannot enter chroot for container named "main": no PID known - maybe short running container
Logs of the containers of pod with this error
[container_wait.log](https://github.com/kubeflow/pipelines/files/8686671/container_wait.log)
[container_main.log](https://github.com/kubeflow/pipelines/files/8686672/container_main.log)
[container_init.log](https://github.com/kubeflow/pipelines/files/8686673/container_init.log)
### Expected result
Pipeline runs successfully
---
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,234,930,293 |
I_kwDOB-71UM5Jm4p1
| 7,716 |
[bug] ERROR1040: too many connections
|
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[] | null |
[
"Thanks for raising this and sorry to hear you're having issues. Unfortunately, KFP 0.7 is not currently supported.\r\n\r\nPlease consider trying KFP 1.8.1 and let us know if the issue exists in the 1.8 distribution.",
"@connor-mccarthy thank you for the suggestions. We'll try."
] | 2022-05-13T08:51:13 | 2022-05-20T16:50:21 | 2022-05-20T16:50:21 |
NONE
| null |
### What steps did you take
I have a use case that calling kubeflow backend service to get run id in a very high rps: about 200. However, I got HTTP 400 on my proxy. After looking into the root cause, I found error messages in ml-pipeline pod.
### What happened:
InternalServerError: Failed to get run: Error 1040: Too many connections. The detailed error:
```
github.com/kubeflow/pipelines/backend/api/go_client._RunService_GetRun_Handler
bazel-out/k8-opt/bin/backend/api/linux_amd64_stripped/go_client_go_proto%/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:1289
google.golang.org/grpc.(*Server).processUnaryRPC
external/org_golang_google_grpc/server.go:966
google.golang.org/grpc.(*Server).handleStream
external/org_golang_google_grpc/server.go:1245
google.golang.org/grpc.(*Server).serveStreams.func1.1
external/org_golang_google_grpc/server.go:685
runtime.goexit
GOROOT/src/runtime/asm_amd64.s:1333
I0513 08:47:18.882841 1 interceptor.go:29] /api.RunService/GetRun handler starting
I0513 08:47:18.883430 1 interceptor.go:29] /api.RunService/GetRun handler starting
I0513 08:47:18.884657 1 error.go:218] Error 1040: Too many connections
InternalServerError: Failed to get run: Error 1040: Too many connections
github.com/kubeflow/pipelines/backend/src/common/util.NewInternalServerError
backend/src/common/util/error.go:142
github.com/kubeflow/pipelines/backend/src/apiserver/storage.(*RunStore).GetRun
backend/src/apiserver/storage/run_store.go:182
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).GetRun
backend/src/apiserver/resource/resource_manager.go:277
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).GetRun
backend/src/apiserver/server/run_server.go:43
github.com/kubeflow/pipelines/backend/api/go_client._RunService_GetRun_Handler.func1
```
### What did you expect to happen:
with 100 rps, the pipeline works fine. I hope it still works with high rps.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 0.7
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 0.7
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
I did some research, and some suggested set `db.SetMaxOpenConns(NUM)` ([reference](https://stackoverflow.com/questions/28135580/golang-mysql-error-1040-too-many-connections))
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5JmffT
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[bug] CrashLoopBackOff deploying standalone KFP to custom namespace
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[
"Created PR #7715 ",
"Hi @markgeejw what is the k8s engine you're using with your on-prem cluster, and secondly in which namespace did you deploy minio?\r\nAfter you changed the manifest, does it work for you?",
"Iβm using kubeadm with containerd and calico. I deployed to a namespace with my own name, and yes after changing the manifest it worked."
] | 2022-05-13T07:06:56 | 2023-02-13T21:07:30 | 2023-02-13T21:07:30 |
CONTRIBUTOR
| null |
### What steps did you take
Deploy kfp standalone to on-premise k8s cluster, with custom namespace described in [docs](https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#change-deployment-namespace).
### What happened:
ml-pipeline-viewer-crd pod goes into CrashLoopBackOff with logs:
```
I0513 06:52:22.650451 1 main.go:96] Starting controller for the Viewer CRD
E0513 06:52:22.651800 1 reflector.go:138] pkg/mod/k8s.io/[email protected]/tools/cache/reflector.go:167: Failed to watch *v1.Service: failed to list *v1.Service: services is forbidden: User "system:serviceaccount:<custom-ns>:ml-pipeline-viewer-crd-service-account" cannot list resource "services" in API group "" in the namespace "kubeflow"
E0513 06:52:22.652079 1 reflector.go:138] pkg/mod/k8s.io/[email protected]/tools/cache/reflector.go:167: Failed to watch *v1.Deployment: failed to list *v1.Deployment: deployments.apps is forbidden: User "system:serviceaccount:<custom-ns>:ml-pipeline-viewer-crd-service-account" cannot list resource "deployments" in API group "apps" in the namespace "kubeflow"
E0513 06:52:22.652104 1 reflector.go:138] pkg/mod/k8s.io/[email protected]/tools/cache/reflector.go:167: Failed to watch *v1beta1.Viewer: failed to list *v1beta1.Viewer: viewers.kubeflow.org is forbidden: User "system:serviceaccount:<custom-ns>:ml-pipeline-viewer-crd-service-account" cannot list resource "viewers" in API group "kubeflow.org" in the namespace "kubeflow"
```
### What did you expect to happen:
Successful deployment with custom namespace
### Environment:
On-premise k8s cluster
* How do you deploy Kubeflow Pipelines (KFP)?
Standalone deployment using kustomize
* KFP version:
1.8.1
* KFP SDK version:
NA
### Anything else you would like to add:
The problem comes from the deployment still looking at the namespace `kubeflow` even though we specified a difference namespace in the relevant `kustomization.yaml` files.
After some digging, I found that the bug is because in [`backend/Dockerfile.viewercontroller`](https://github.com/kubeflow/pipelines/blob/c8a18bde299f2fdf5f72144f15887915b8d11520/backend/Dockerfile.viewercontroller#L43), the env var is actually `NAMESPACE` not `MINIO_NAMESPACE` as in [`manifests/kfp/base/pipeline/ml-pipeline-viewer-crd-deployment.yaml`](https://github.com/kubeflow/pipelines/blob/c8a18bde299f2fdf5f72144f15887915b8d11520/manifests/kustomize/base/pipeline/ml-pipeline-viewer-crd-deployment.yaml#L25). I am writing a PR to fix this.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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I_kwDOB-71UM5Jkow8
| 7,709 |
[sdk] Function names with underscores lead to pipeline errors when used in v2 compatible mode
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[] | null |
[
"OK, I think I have mixed v1 an v2 mode here by using `kfp.components.create_component_from_func` on a v2 decorated function. Should this case be catched by an error message?",
"> OK, I think I have mixed v1 an v2 mode here by using `kfp.components.create_component_from_func` on a v2 decorated function. Should this case be catched by an error message?\r\n\r\nYes, first of all, the v1 `kfp.components.create_component_from_func` doesn't work with v2 components. Secondly, `create_component_from_func` accept the original function as the input, not the one being decorated. In fact, `create_component_from_func` itself can be used as a decorator if your intention is to use v1 (run on KFP v1):\r\n\r\n```python\r\n@create_component_from_func(\r\n output_component_file='add.component.yaml',\r\n)\r\ndef add_underscore(a: float, b: float) -> float:\r\n return a + b\r\n```\r\n"
] | 2022-05-12T18:29:07 | 2022-05-26T22:41:51 | 2022-05-26T22:41:51 |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
standalone
* KFP version:
Version: 1.8.1
* KFP SDK version:
kfp 1.8.12
kfp-pipeline-spec 0.1.14
kfp-server-api 1.8.1
### Steps to reproduce
When building a component from a Python function that contains an underscore the component executes with error as the underscore is converted into a whitespace.
minimal example:
```
import kfp
from kfp.v2.dsl import component
@component
def add_underscore(a: float, b: float) -> float:
return a + b
add_op = kfp.components.create_component_from_func(
func=add_underscore,
output_component_file='add.component.yaml',
)
```
leads to a component file with
```
name: Add underscore
description: Add underscore
inputs:
- {name: a, type: Float}
- {name: b, type: Float}
implementation:
container:
image: python:3.7
command:
- sh
- -ec
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |
def pass_locals():
return dict_func(locals()) # noqa: F821 TODO
import argparse
_parser = argparse.ArgumentParser(prog='Add underscore', description='Add underscore')
_parser.add_argument("--a", dest="a", type=float, required=True, default=argparse.SUPPRESS)
_parser.add_argument("--b", dest="b", type=float, required=True, default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = Add underscore(**_parsed_args)
args:
- --a
- {inputValue: a}
- --b
- {inputValue: b}
```
The problem arises in the line containing the function name `_outputs = Add underscore(**_parsed_args)`
### Expected result
Function names should sanitized in a way that allows the underscore character.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,234,093,116 |
I_kwDOB-71UM5JjsQ8
| 7,706 |
[feature] Compile processing of PipelineParam
|
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[
"I've also been wrestling with this for a while now.\r\n\r\nWould like to highlight another problem that comes from the same behavior -- if you try to add string PipelineParams to a list within a component, the compiler fails because PipelineParams are not JSON serializable. \r\n\r\nFor example:\r\n```\r\nfrom typing import List\r\n@component\r\ndef submit_dataproc_job(job_args: List):\r\n # submit job using dataproc client -- job args for dataproc jobs have to be strings\r\n\r\n@pipeline \r\ndef example_pipeline(\r\n dataproc_job_arg1: str,\r\n dataproc_job_arg2: str,\r\n dataproc_job_arg3: str,\r\n):\r\n submit_dp_job_task = submit_dataproc_job(\r\n job_args=[\r\n dataproc_job_arg1,\r\n dataproc_job_arg2,\r\n dataproc_job_arg3,\r\n ]\r\n )\r\n\r\nif __name__ == \"__main__\":\r\n compiler.Compiler().compile(\r\n pipeline_func=example_pipeline,\r\n package_path=f\"example_pipeline.json\",\r\n )\r\n```\r\n\r\nRunning this code results in\r\n`TypeError: Object of type PipelineParam is not JSON serializable`\r\n\r\nIf you try to cast the parameter values to strings (which you'd expect them to be handled as, given the type hint) like so:\r\n```\r\njob_args=[\r\n str(dataproc_job_arg1),\r\n str(dataproc_job_arg2),\r\n str(dataproc_job_arg3),\r\n]\r\n```\r\n\r\nyou get an error like the original one shown in this issue. From the VAI UI, `job_args` is type `string` and set to:\r\n\r\n```\r\n[\"{{pipelineparam:op=;name=dataproc_job_arg1}}\", \"{{pipelineparam:op=;name=dataproc_job_arg2}}\", \"{{pipelineparam:op=;name=dataproc_job_arg3}}\"]\r\n```\r\n\r\nWorth noting I have been able to work around this by passing in `job_args` as a pipeline level parameter as \r\n```\r\n@pipeline \r\ndef example_pipeline(job_args: List):\r\n submit_dp_job_task = submit_dataproc_job(\r\n job_args=job_args\r\n )\r\n```\r\n\r\nThe problem here is that I'd like to be able to use the original pipeline parameters across several components without having to unpack them from a list. Discrete parameters are also more convenient for comparing runs.\r\n\r\n---\r\n\r\nIf we were able to access the value of a PipelineParam, working around this behavior wouldn't be too difficult. Perhaps that's already possible? My IDE only provides completion hints for `str` because of the type hint on these params.\r\n\r\nEDIT:\r\nIt is absolutely possible to access the value of a PipelineParam by calling `.value`, but the values all come out to `null`. \r\n\r\nThe docs for PipelineParam say that `value` is \"The actual value of the PipelineParam. If provided, the PipelineParam is 'resolved' immediately. For now, we support string only.\" \r\nI'm not sure where PipelineParams are created, but I'd assume it's in the pipeline decorator logic. I'd have expected that the PipelineParam `value` would be set based on the default value in the pipeline function definition, if one was set, and then updated later when the `RuntimeConfigBuilder` is created, using its `_parameter_values` property which is defined when an actual pipeline run is started.",
"Has anyone succeeded with retrieving the `value` parameter from within the pipeline function and constructing a new string with it? I couldn't find any reference of doing so\r\nhttps://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.PipelineParam",
"@yonaroze \r\n\r\nIn short, no, it isnβt possible to manipulate pipeline parameters outside of components. \r\n\r\nWhat we do instead is create components which preprocess pipeline parameters into the form thatβs consumable by your components. "
] | 2022-05-12T14:42:41 | 2023-05-10T15:57:44 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Currently, KFP compiler compiles pipeline arguments into PipelineParam. Hence, it alters the string processing behavior of pipeline arguments at compile-time and runtime.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
I am building a Vertex Pipeline for batch prediction using Kubeflow Pipeline SDK. I want to process the pipeline argument `bq_table` as a string. The pipeline compiles successfully but it fails at runtime (see screenshot below).
```python
@dsl.pipeline(name='batch-prediction-pipeline')
def batch_prediction_pipeline(
project: str,
region: str,
gcs_source: str,
bq_location: str = 'us',
bq_table: str = 'ml.testing_data',
):
bq_dataset = str(bq_table).split('.')[0] # parse BigQuery dataset name
bq_table_uri = f'{project}.{bq_table}'
bq_dataset_uri = f'{project}.{bq_dataset}'
bq_query = f'''CREATE OR REPLACE EXTERNAL TABLE
{bq_table_uri}
OPTIONS (
format = 'CSV',
uris = ['{gcs_source}']
);'''
bq_query_op = BigqueryQueryJobOp(
project=project,
location=bq_location,
query=bq_query
)
.
.
.
batch_op = gcc_aip.ModelBatchPredictOp(
project=project,
location=region,
bigquery_source_input_uri=f'bq://{bq_table_uri}',
bigquery_destination_output_uri=f'bq://{bq_dataset_uri}',
).after(bq_query_op)
```
As seen in the screenshot, the value of `bigquery_destination_output_uri` doesn't include the `split` logic which is implemented. In this case, I am using the pre-built [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction) so I can't change the component implementation.
Other related issues: #2725, #2937
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Yes. I can define more granular pipeline arguments (e.g. `bq_dataset`) to support my use case.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 1,234,006,091 |
I_kwDOB-71UM5JjXBL
| 7,705 |
[question] All possible status codes of Kubeflow Runs
|
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[
"Hi, @yarnabrina. You can find the set of possible status values in the `run.proto` definition here: https://github.com/kubeflow/pipelines/blob/0932905ff58a5effae510a9eeb30bbe86fc3adf1/backend/api/run.proto#L254-L255.\r\n\r\nPlease re-open if you have any other questions on this topic."
] | 2022-05-12T13:41:10 | 2022-05-20T10:38:20 | 2022-05-20T10:38:20 |
NONE
| null |
We have a [`streamlit`](https://streamlit.io/) app, which triggers a pipeline run based on user inputs. We regularly check the status of the submitted run and inform the run state in the UI using the following function.
```python
import kfp
def get_run_status(run_id: str) -> str:
client = kfp.Client()
run_details = client.get_run(run_id)
run_status = getattr(getattr(run_details, "run"), "status")
return run_status
```
We have decided to add some colour formatting based on the current run status. For that, we need to know all possible status codes, but we are not able to find anything comprehensive at [`kfp.dsl` documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html).
So far in our pipelines, we came across `Succeeded` and `Failed`, and rarely `Error`. We don't know if there are more or not, so some guidelines for this will be super helpful.
/area backend
/area sdk
/kind question
|
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I_kwDOB-71UM5JfacU
| 7,701 |
[feature] Allow Graph Based XY Data as Artifact Type in UI + SDK (v2)
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[
"We are planning to implement Run Comparison feature, which can achieve the same goal without introducing new Artifact type.",
"Should be covered by https://github.com/kubeflow/pipelines/issues/8794"
] | 2022-05-11T17:28:17 | 2023-02-23T19:31:41 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
The feature would essentially allow us log XY and metrics for a graph along with titles in the metadata. This would be similar functionality for the ROC curve where we log a `List[float]` for TPR and FPR.
In the Pipelines UI I think this would be adding support for an additional Viewer like `XYMetricsViewer` or similar. In the SDK, it would be a matter of implementing `Metric` subtype like `RegressionMetric` or something, with the ability to log XY data in metadata.
### What is the use case or pain point?
These graphs a super useful in comparing metrics evolution over time. Currently we are only able to log single scalar metrics / tabular data, which is a big pain point when comparing experiment runs.
### Is there a workaround currently?
We currently log data to tensorboard, which requires a separate pod instance and we have to manually keep track of the pipeline run linked to the graph that is produced. As a result, developers do not really log many visualisations, which hinders progress.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 7,700 |
"Data dimensions X do not match the number of labels passed X" error
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[
"/assign @zijianjoy ",
"Hello @easkerov , v2 compatible mode has been discontinued. Can you try the V2 alpha? (I means using KFP backend 2.0.0-alpha.2 and KFP SDK 2.0.0-alpha.3) Feel free to comment here if using v2 alpha doesn't work."
] | 2022-05-11T13:26:41 | 2022-05-23T21:28:58 | null |
NONE
| null |
Hi,
I am using kubeflow pipelines SDK in v2 compatibility mode and trying to output the confusion matrix as documented [here](https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#confusion-matrix)
I have a multiclass classifier and produce matrix using `sklearn`. It creates a confusion matrix 3x3 which is OK for the number of classes (labels) equals 3.
E.g.:
```
[[13 0 0]
[ 0 10 6]
[ 0 0 9]]
```
Below is the code:
````
Y_predictions = classifier.predict(X_test)
cm = confusion_matrix(Y_test, Y_predictions)
metrics.log_confusion_matrix(
['Class 1', 'Class 2', 'Class 3'],
cm.tolist()
````
After pipeline execution, when switching to the **Visualisation** tab I see the following error: `Data dimensions 3 do not match the number of labels passed 3`
I suspect `log_confusion_matrix` expects some other format of confusion matrix, or am I doing something wrong here?
Thanks!
|
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I_kwDOB-71UM5Jd9n3
| 7,699 |
[bug] pod is forbidden: failed quota: kf-resource-quota: must specify cpu,memory
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[
"Hello @edwardhuanghz , do you want to parse the content of your `kf-resource-quota` resource? It looks like some quota restriction is applied to your cluster.",
"I'm a little confused. I'm running into this issue too. I have read that setting a user quota is the most sensible thing to do regarding cluster configuration. I'm confused, as I want to request resources through kubeflow pipelines for an algorithm that could use up to 32 cores for an embarrassingly parallel task. \r\n\r\nIs this possible with either the v1.8 or 2.0 specs?",
"Thanks to Benjamin Tan, I can comment that the above issue seems to be fixed when each component is run like the following:\r\n\r\n\r\n```python\r\ndef gunzip(infile:str, outfile:str):\r\n \"\"\" Infile like 'path/to/example.txt.gz', outfile like 'path/to/example.txt' \"\"\"\r\n\r\n return dsl.ContainerOp(\r\n name='gunzip',\r\n image='bitnami/minideb:latest',\r\n command=[\r\n '/bin/zcat $0 > input.txt ',\r\n '|| ',\r\n 'mv $0 input.txt'\r\n ],\r\n file_outputs={\r\n 'output': 'input.txt'\r\n }\r\n )\r\n\r\n\r\[email protected](\r\n name='kubeflow-barebone-demo',\r\n description='kubeflow demo with minimal setup'\r\n)\r\ndef pipeline(fastq1:str):\r\n # Step 1: training component\r\n\r\n gunzip1 = gunzip(infile=infile, outfile = outfile).set_cpu_request('1').set_cpu_limit('1').set_memory_request('512Mi').set_memory_limit('512Mi')\r\n\r\n```"
] | 2022-05-11T12:51:29 | 2023-01-15T15:37:34 | null |
NONE
| null |
### What steps did you take
script below~
import kfp
from kfp.components import create_component_from_func
from kfp.dsl import pipeline
@create_component_from_func
def print_and_return_number(number: int) -> int:
print(number)
return(number)
@create_component_from_func
def sum_and_print_numbers(number_1: int,number_2: int):
print(number_1+number_2)
@pipeline(name="example_pipeline")
def example_pipeline(number_1: int, number_2: int):
number_1_rst = print_and_return_number(number_1)
number_2_rst = print_and_return_number(number_2)
sum_result = sum_and_print_numbers(number_1_rst.output,number_2=number_2_rst.output)
if __name__ == "__main__":
kfp.compiler.Compiler().compile(example_pipeline,"example_pipeline.yaml")
<!-- A clear and concise description of what the bug is.-->
### What happened:
--Run of example_pipeline (7b8fc)
This step is in Error state with this message: task 'example-pipeline-vlklw.print-and-return-number' errored: pods "example-pipeline-vlklw-3223166133" is forbidden: failed quota: kf-resource-quota: must specify cpu,memory
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.6.3
kfp-pipeline-spec 0.1.12
kfp-server-api 1.6.0
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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I_kwDOB-71UM5Jcki4
| 7,697 |
[Inquiry]Is there a way to display a figure when running pipeline?
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[
"Tried this:\r\nIn component code, output one html file:\r\n\r\n metadata = {\r\n 'outputs': [{\r\n 'storage': 'inline',\r\n 'source': '<h1>Hello, World!</h1>',\r\n 'type': 'web-app',\r\n }]\r\n }\r\n print(\" html path \", args.html)\r\n with open(args.html, 'w') as f:\r\n json.dump(metadata, f)\r\n\r\nThen in component.yaml file, define output type:\r\n\r\n```outputs:\r\n- {name: MLPipeline HTML, type: HTML, description: 'Figure'}\r\n```\r\n\r\n\r\nBut no idea why in Run Output Tab UI, hello world does not show up\r\n\r\n",
"Hello @yangyang919 , The `Run Output Tab` UI is currently broken. You can click the node which generates HTML visualization, it will open side panel. Click `Visualization` tab in the side panel, you should be able to see HTML visualization. https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#single-html-file",
"Hi @zijianjoy , thanks for answering!\r\n\r\nI am able to display a simple HTML, but still could't display a figure output by component. I think it's the way how I do it, could you take a look and give some suggestions?\r\n\r\n myPlot = myDataframe.plot().get_figure()\r\n myPlot.savefig('my_plot')\r\n metadata = {\r\n 'outputs': [\r\n {\r\n 'type': 'web-app',\r\n 'storage': 'inline',\r\n 'source': \"<img src='/tmp/outputs/MLPipeline_UI_metadata/data/my_plot.png'/>\",\r\n }]\r\n }\r\n visualization_output = namedtuple('VisualizationOutput', [\r\n 'mlpipeline_ui_metadata'])\r\n with open(args.html, 'w') as out_file:\r\n json.dump(metadata, out_file)",
"@yangyang919 Your HTML is rendering files from an internal location `/tmp/outputs/MLPipeline_UI_metadata/data/my_plot.png`. However, currently the HTML rendering happens on your browser. That means you need to grant your HTML or JS script within this HTML file to be able to pull images from this location. \r\n\r\nFor simplicity, if you just publish this image to an Internet-public location, you will just need to parse the full URL of this image in HTML file.",
"thanks @zijianjoy , I figured out a way to display dataframe plot into html. So I close this ticket accordingly.",
"@yangyang919 can you share your solution ? I am struggling with visualizations, a lot of bugs",
"@yangyang919 Could you please explain on how you solved this particular issue? I'm also trying to do visualization of image results of a classification problem"
] | 2022-05-11T08:44:20 | 2023-01-04T08:24:16 | 2022-05-17T02:19:21 |
NONE
| null |
We have deployed one kubeflow pipeline which has several components, fetch data, preprocess, train model, etc. We are wondering whether in one of these components, we can draw a figure using plt.plot() on dataframe object, output this figure and then in Experiments UI, we can view this figure in Run Output section?
I am thinking about embed figure into HTML, and output html, but failed. Anyone has similar experiences or suggestions? Thanks a lot!
|
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[bug] Pipeline component CI failure - ERROR: No matching distribution found for google-cloud-storage==2.2.1
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[
"/assign @chensun for fixing the python3.6 issue.",
"@chensun Do we have any fix to unblock this PR ?",
"@chensun \r\npulled the latest changes from master. build failing with following error\r\n\r\n```\r\nERROR: Could not find a version that satisfies the requirement cachetools==5.0.0 (from versions: 0.0.0, 0.1.0, 0.2.0, 0.3.0, 0.3.1, 0.4.0, 0.5.0, 0.5.1, 0.6.0, 0.7.0, 0.7.1, 0.8.0, 0.8.1, 0.8.2, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.1.6, 2.0.0, 2.0.1, 2.1.0, 3.0.0, 3.1.0, 3.1.1, 4.0.0, 4.1.0, 4.1.1, 4.2.0, 4.2.1, 4.2.2, 4.2.3, 4.2.4)\r\nERROR: No matching distribution found for cachetools==5.0.0\r\n```",
"cc: @zijianjoy @james-jwu @theadactyl The PR - https://github.com/kubeflow/pipelines/pull/7615 is long pending due to the build failures. ",
"@shrinath-suresh, the issue with google-cloud-storage no long exists, but I found a few other issues and sent a couple of PRs that should fix the test failure, should be resolved by this week."
] | 2022-05-10T10:34:01 | 2022-06-09T00:02:47 | null |
CONTRIBUTOR
| null |
### What steps did you take
We have updated the pytorch samples present in kubeflow pipelines to work with latest version of kserve (kserve v2 protocol). The PR(https://github.com/kubeflow/pipelines/pull/7615) has been blocked due to the CI failure
```
ERROR: Could not find a version that satisfies the requirement google-cloud-storage==2.2.1 (from versions: 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.23.1, 1.0.0, 1.1.0, 1.1.1, 1.2.0, 1.3.0, 1.3.1, 1.3.2, 1.4.0, 1.5.0, 1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.11.1, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 1.13.2, 1.13.3, 1.14.0, 1.14.1, 1.15.0, 1.15.1, 1.15.2, 1.16.0, 1.16.1, 1.16.2, 1.17.0, 1.17.1, 1.18.0, 1.18.1, 1.19.0, 1.19.1, 1.20.0, 1.21.0, 1.22.0, 1.23.0, 1.24.0, 1.24.1, 1.25.0, 1.26.0, 1.27.0, 1.28.0, 1.28.1, 1.29.0, 1.30.0, 1.31.0, 1.31.1, 1.31.2, 1.32.0, 1.33.0, 1.34.0, 1.35.0, 1.35.1, 1.36.0, 1.36.1, 1.36.2, 1.37.0, 1.37.1, 1.38.0, 1.39.0, 1.40.0, 1.41.0, 1.41.1, 1.42.0, 1.42.1, 1.42.2, 1.42.3, 1.43.0, 1.44.0, 2.0.0)
ERROR: No matching distribution found for google-cloud-storage==2.2.1
```
### What happened:
Looks like the change in google-cloud-storage package version is causing an error and its blocking the current PR.
### What did you expect to happen:
All the CI runs should pass
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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I_kwDOB-71UM5JU87O
| 7,681 |
[feature] Frontend - pods summary tab
|
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[
"It is a nice feature to have, but KFPv2 has different mechanism than relying directly on ArgoWorkflow nodes. So it probably worths a formal design before introducing this feature. Will you be interested in composing it?",
"@zijianjoy Hi, Let me take a look at KFPv2 (as we are using v1.7.1) so I can check the new mechanism and I will update here.",
"I have looked at v2 versus v1.\r\nIs it a design choice not to expose the pod and logs data?\r\nFor us, it helps a lot to look at failed pods and quickly access the logs and understand the reason for failures.",
"@aviklai We haven't had a design for exposing pod and logs data yet, but we are planning on supporting it.",
"@zijianjoy thanks - so it means that it will be available in the api in the future and then it will be possible to implement this feature?",
"@aviklai I am not sure if we can going to have a KFP API for log access yet. But once we figure it out, it should be implemented and available to use. We are aware that log data access is important, but we need to consider how to expose pod info."
] | 2022-05-09T20:40:15 | 2022-05-16T20:13:41 | null |
NONE
| null |
### Feature Area
area frontend
### What feature would you like to see?
A summary page that will include the status of all the nodes in the graph in a specific run.
The idea is to implement a new tab that is much easier to navigate than a large graph.
We already implemented it for our kubeflow installation at our company but I would like to understand if it's something that can be relevant for all kubeflow users:
and clicking on a node gives the node details - same as when clicking on a node in the graph.
### What is the use case or pain point?
When a run has many nodes in the graph (meaning many pods), it's hard to keep track in the graph of which nodes finished, successfully, which didn't finish successfully and which are still pending.
### Is there a workaround currently?
Looking at all the nodes in the graph and searching for failed nodes.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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[feature] Frontend - pods summary tab
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[] | 2022-05-09T20:33:34 | 2022-05-09T20:38:17 | 2022-05-09T20:38:17 |
NONE
| null |
### Feature Area
area frontend
### What feature would you like to see?
A summary page that will include the status of all the nodes in the graph in a specific run.
The idea is to implement a new tab that is much easier to navigate than a large graph.
We already implemented it for our kubeflow installation at our company but I would like to understand if it's something that can be relevant for all kubeflow users:
and clicking on a node gives the node details - same as when clicking on a node in the graph.
### What is the use case or pain point?
When a run has many nodes in the graph (meaning many pods), it's hard to keep track in the graph of which nodes finished, successfully, which didn't finish successfully and which are still pending.
### Is there a workaround currently?
Looking at all the nodes in the graph and searching for failed nodes.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 1,228,476,217 |
I_kwDOB-71UM5JOQ85
| 7,676 |
[backend] Output on GKE giving unclear error related to "container not found"
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[
"Just to be clear, this may be a GKE thing. I have an EKS deployment and it seems to run fine. But the error is very confusing regardless.",
"For what it's worth, I solved it by changing how I deployed kubeflow from manifests. You can read more about it here - https://github.com/SAME-Project/same-project/issues/126\r\n\r\nHOWEVER, I think this is still a bug in KFP. The error message should be clearer what's going wrong - not being able to find a container can't be correct.",
"It appears this error has been touched on here - https://github.com/kubeflow/pipelines/issues/1471 - but, i'd propose the core issue is that the error message is not helpful. We should work on that.",
"This is probably due to Emissary executor not being used in the latest version of Kubernetes cluster.\r\n\r\nAs an overall point of view, I am looking for advice about what kind of error message it should show instead of the current message. Open to contributor or discussion about how to provide a proper error message.",
"Genuinely unclear what the error is. If emissary has not been setup correctly, can we detect it? Could we output that error?",
"Follow instruction in https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/ to detect workflow executor setup."
] | 2022-05-07T00:40:58 | 2022-06-29T22:18:41 | null |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Just deployed directly from kubeflow manifests
* KFP version:
1.4.1, 1.5, 2.0.0 alpha
* KFP SDK version:
1.8.12
### Steps to reproduce
Upload the following yaml as a pipeline - run it: https://gist.github.com/aronchick/25d6fca71df0ef86846c40bec5cbc2c3
### Expected result
Runs correctly.
### Actual
```
This step is in Error state with this message: failed to save outputs: Error response from daemon: No such container: 77f84d932adba6d4b35769fbba02bfab4b48cf2128985f3c105d3744978abf9f
```
(The container name changes every time, the container name does not appear in the yaml)
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,228,284,426 |
I_kwDOB-71UM5JNiIK
| 7,674 |
[feature] ml-pipeline-ui is absent in Full kubeflow deployment
|
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"Full Kubeflow deployment should have ml-pipeline-ui pod. Can you check the kubernetes resource status (`kubectl get all -n kubeflow`) to see whether your Kubeflow deployment is successful?"
] | 2022-05-06T18:47:33 | 2022-05-12T22:49:05 | null |
NONE
| null |
### Feature Area
[feature] ml-pipeline-ui is absent in Full kubeflow deployment
As i understand this https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/
i am able to run kubeflow pipeline from my PyCharm/localhost to the remote cluster via
```
client = kfp.Client()
print(client.list_experiments())
```
only for cluster in standalone mode because it has endpoint pod ml-pipeline-ui.
But why can't i use it for cluster in Full kubeflow deployment?
(As i see we have no ml-pipeline-ui pod in this mode now)
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| 1,228,074,445 |
I_kwDOB-71UM5JMu3N
| 7,673 |
[backend] KFPv2: Backend always return JSON format PipelineSpec template even if I upload yaml
|
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[] | 2022-05-06T16:36:23 | 2022-06-22T01:13:25 | 2022-06-22T01:13:25 |
COLLABORATOR
| null |
### Environment
KFP backend 2.0.0-alpha.1
### Steps to reproduce
1. Upload a pipeline using KFPv2 yaml format
2. Get the pipeline template by calling `GET Pipeline Template` API
3. The response of template is in JSON format.
Side note: If you upload a KFPv2 json format, it returns json format template, which is expected.
### Expected result
We should preserve the format when storing pipeline template. For example, when user uploads a YAML format, it should return YAML format when calling `GET Pipeline Template` API. When user uploads a JSON format, it should return JSON format.
/assign @Linchin
/cc @jlyaoyuli
---
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Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5JJu-L
| 7,671 |
Create automated tooling to run test
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"Merged https://github.com/kubeflow/kubeflow/pull/6478"
] | 2022-05-06T00:02:04 | 2022-11-08T21:59:23 | 2022-11-08T21:59:23 |
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| null | null |
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I_kwDOB-71UM5JGVjw
| 7,667 |
Does pipeline support mounting existing pv?
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[
"Thank you for the suggestion! Would you please share the code snippet that you used?",
"@Linchin Below is the definition code of my pipeline:\r\n```\r\n# Define the pipeline\r\[email protected](\r\n name='data volum demo pipeline',\r\n description='A toy pipeline that performs model training and prediction.'\r\n)\r\n\r\n# Define parameters to be fed into pipeline\r\ndef demo_pipeline( \r\n run_id:int,\r\n input_path:str='/input',\r\n output_path:str='/ouput',\r\n base_config_path='yolov3_d53_mstrain-416_273e_coco.py',\r\n args:dict={\r\n \"model.bbox_head.num_classes\":13,\r\n \"runner.type\":\"EpochBasedRunner\",\r\n \"runner.max_epochs\": 20,\r\n\r\n \"load_from\":\"checkpoints/latest.pth\" ,\r\n \"gpu_ids\":[0]} ): \r\n # Define volume to share data between components.\r\n \r\n input_vop = dsl.VolumeOp(\r\n name=\"name-0506-1\",\r\n resource_name=\"resource_name-0506-1\", \r\n size=\"5Gi\", \r\n modes=dsl.VOLUME_MODE_RWM) \r\n \r\n output_vop = dsl.VolumeOp(\r\n name=\"name-0506-2\",\r\n resource_name=\"resource_name-0506-2\", \r\n size=\"5Gi\", \r\n modes=dsl.VOLUME_MODE_RWM)\r\n \r\n #Create config-building component\r\n config_op = kfp.components.load_component_from_file('config_component.yaml')\r\n config_task = config_op(input_path,output_path,base_config_path, args).add_pvolumes({output_path: output_vop.volume})\r\n\r\n # Create trainning component.\r\n train_op = kfp.components.load_component_from_file('train_component.yaml') \r\n train_task = train_op(input_path, output_path, config_task.outputs['dst_config']) \\\r\n .add_pvolumes({input_path:input_vop.volume,output_path:output_vop.volume})\r\n # Create testing component. \r\n test_op = kfp.components.load_component_from_file('test_component.yaml')\r\n test_task = test_op(input_path, output_path, config_task.outputs['dst_config'], train_task.outputs['latest_weight'])\\\r\n .add_pvolumes({input_path:input_vop.volume,output_path:output_vop.volume}) \r\n \r\n train_task.after(config_task)\r\n test_task.after(train_task)\r\n```\r\nThe training data should be stored in input_vop.volume but now it is a new empty pv which is not what I want, my data is stored in an existing pv:\r\n```\r\n(base) [root@foxk8s ~]# k get pv|grep test/test11-volume\r\npvc-ffe761a6-525d-4296-b95f-b281f74e38cb 10Gi RWO Delete Bound test/test11-volume nfs-client-storageclass 41h\r\n```\r\nHow can I mount this pv to my pipeline?",
"I noticed that a specific pv can be mounted by specifying `volume_name`, but the training data is stored in the pv, which may be used by multiple pipelines at the same time, but different pipelines create different pvcs, they cannot mount the same pv, how about this problem Solve it? Any suggestions?",
"It seems that PR [#6582](https://github.com/kubeflow/pipelines/pull/6582) was created to serve this functionality. Try to set `generate_unique_name` to be `False` in your `VolumeOp` definition (as seen in the following link), and see if it works.\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/4621aec14611bd022253d35e3b6bb9d3b4a57a3c/sdk/python/kfp/deprecated/dsl/_volume_op.py#L72",
"@Linchin Thank you for your reply, I tried it with the following code\r\n```\r\nfrom kfp import components, dsl, compiler\r\n\r\[email protected]_component_from_func\r\ndef write_to_volume():\r\n with open(\"/mnt/file.txt\", \"w\") as file:\r\n file.write(\"Hello world\")\r\n\r\n\r\[email protected](\r\n name=\"volumeop-basic\",\r\n description=\"A Basic Example on VolumeOp Usage.\"\r\n)\r\ndef volumeop_basic(size: str=\"1Gi\"):\r\n vop = dsl.VolumeOp(\r\n name=\"name-test0509-2\",\r\n resource_name=\"volumeop-basic-bkb2f-resource-name-test0505-1\",\r\n modes=dsl.VOLUME_MODE_RWO,\r\n size=size,\r\n generate_unique_name = False\r\n )\r\n\r\n write_to_volume().add_pvolumes({\"/mnt\": vop.volume})\r\n\r\nimport kfp\r\nkfp.Client().create_run_from_pipeline_func(volumeop_basic, arguments={})\r\n\r\n```\r\n volumeop-basic-bkb2f-resource-name-test0505-1 is an existing pvc:\r\n```\r\n(base) [root@foxk8s ~]# k get pvc -A |grep volumeop-basic-bkb2f-resource-name-test0505-1\r\ntest volumeop-basic-bkb2f-resource-name-test0505-1 Bound pvc-dd7746b9-9721-40fc-abdf-5f6dfe888a86 1Gi RWO nfs-client-storageclass 4d17h\r\n\r\n```\r\nbut an error occurred:\r\n`This step is in Failed state with this message: Error (exit code 1): no more retries Error from server (AlreadyExists): error when creating \"/tmp/manifest.yaml\": persistentvolumeclaims \"volumeop-basic-bkb2f-resource-name-test0505-1\" already exists`\r\n\r\n",
"Add another argument that specifies `action='apply'`. I tried and it works.\r\n\r\nExample:\r\n```\r\[email protected](\r\n name=\"volumeop-basic\",\r\n description=\"A Basic Example on VolumeOp Usage.\"\r\n)\r\ndef volumeop_basic(size: str=\"1Gi\"):\r\n vop = dsl.VolumeOp(\r\n name=\"create-pvc\",\r\n resource_name=\"my-pvc-unique-name-false\",\r\n modes=dsl.VOLUME_MODE_RWO,\r\n size=size,\r\n generate_unique_name=False,\r\n action='apply',\r\n )\r\n write_to_volume().add_pvolumes({\"/mnt'\": vop.volume})\r\n```",
"Thanks! it really works"
] | 2022-05-05T08:59:07 | 2022-05-10T05:28:21 | 2022-05-10T05:28:21 |
NONE
| null |
If I know the name of a pv and the data is stored in it, I want to use it directly
`VolumeOp `method will create a new empty pv which is not what I want
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I_kwDOB-71UM5JFAqT
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[backend] KFP v2 currently doesn't support "Retry run" function
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[
"/assign @chensun ",
"Hi, customer here :)\r\n\r\nI'm having an issue that might be related to this ( according to https://issuetracker.google.com/issues/226569351 ). Happy to post elsewhere if not.\r\n\r\nThe `set_retry` does not appear to work when setting an integer `backoff_duration`. We get the error `AttributeError: 'int' object has no attribute 'replace'`.\r\n\r\nIt seems integer coercion to string is not implemented. Integer `backoff_duration` is allowed in the docs here https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.BaseOp.set_retry \r\n\r\nThough it admittedly does violate the type hint.\r\n\r\nFull error below.\r\n\r\nTo reproduce: add something like `.set_retry(3, backoff_duration = 10)` to a ContainerOp.\r\nThis comes through as something along the lines of:\r\n```\r\nfrom kfp.v2.compiler.compiler_utils import make_retry_policy_proto\r\nmake_retry_policy_proto(\r\n max_retry_count=3,\r\n backoff_duration=10,\r\n backoff_factor=None,\r\n backoff_max_duration=None\r\n)\r\n```\r\ngiving the full error:\r\n```\r\n[...] in compile_pipeline(output_filename, pipeline_func)\r\n 10 compiler.Compiler().compile(\r\n 11 pipeline_func = pipeline_func,\r\n---> 12 package_path = output_filename\r\n 13 )\r\n 14 \r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in compile(self, pipeline_func, package_path, pipeline_name, pipeline_parameters, type_check)\r\n 1304 pipeline_func=pipeline_func,\r\n 1305 pipeline_name=pipeline_name,\r\n-> 1306 pipeline_parameters_override=pipeline_parameters)\r\n 1307 self._write_pipeline(pipeline_job, package_path)\r\n 1308 finally:\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in _create_pipeline_v2(self, pipeline_func, pipeline_name, pipeline_parameters_override)\r\n 1249 pipeline_spec = self._create_pipeline_spec(\r\n 1250 args_list_with_defaults,\r\n-> 1251 dsl_pipeline,\r\n 1252 )\r\n 1253 \r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in _create_pipeline_spec(self, args, pipeline)\r\n 1055 op_to_parent_groups=op_name_to_parent_groups,\r\n 1056 opgroup_to_parent_groups=opgroup_name_to_parent_groups,\r\n-> 1057 op_name_to_for_loop_op=op_name_to_for_loop_op,\r\n 1058 )\r\n 1059 \r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in _group_to_dag_spec(self, group, inputs, dependencies, pipeline_spec, deployment_config, rootgroup_name, op_to_parent_groups, opgroup_to_parent_groups, op_name_to_for_loop_op)\r\n 751 backoff_duration=subgroup.backoff_duration,\r\n 752 backoff_factor=subgroup.backoff_factor,\r\n--> 753 backoff_max_duration=subgroup.backoff_max_duration,\r\n 754 )\r\n 755 subgroup.task_spec.retry_policy.CopyFrom(retry_policy_proto)\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler_utils.py in make_retry_policy_proto(max_retry_count, backoff_duration, backoff_factor, backoff_max_duration)\r\n 153 backoff_max_duration = backoff_max_duration or '3600s'\r\n 154 \r\n--> 155 backoff_duration_seconds = f'{convert_duration_to_seconds(backoff_duration)}s'\r\n 156 backoff_max_duration_seconds = f'{min(convert_duration_to_seconds(backoff_max_duration), 3600)}s'\r\n 157 \r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler_utils.py in convert_duration_to_seconds(duration)\r\n 209 int: The number of seconds in the duration.\r\n 210 \"\"\"\r\n--> 211 duration = normalize_time_string(duration)\r\n 212 seconds_per_unit = {'s': 1, 'm': 60, 'h': 3_600, 'd': 86_400, 'w': 604_800}\r\n 213 if duration[-1] not in seconds_per_unit.keys():\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler_utils.py in normalize_time_string(duration)\r\n 180 str: The normalized duration string.\r\n 181 \"\"\"\r\n--> 182 no_ws_duration = duration.replace(' ', '')\r\n 183 duration_split = [el for el in re.split(r'(\\D+)', no_ws_duration) if el]\r\n 184 \r\n\r\nAttributeError: 'int' object has no attribute 'replace'\r\n```",
"Retry run is supported now, see #8804"
] | 2022-05-04T23:06:16 | 2023-08-23T23:19:40 | 2023-08-23T23:19:40 |
COLLABORATOR
| null |
For the KFP v2, **Retry Run** function hasn't been implemented. Currently, when users click retry button, they are blocked by this error message: https://screenshot.googleplex.com/3rFyhstqcjeyXAG.
Refer to:
https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/resource/resource_manager.go#L557-L559
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[backend] CreatePipeline API not handling multi-user mode properly
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[] | 2022-05-04T16:21:04 | 2022-05-05T22:46:16 | null |
CONTRIBUTOR
| null |
Please see https://github.com/kubeflow/pipelines/blob/be682e4d728f7ac566a232213f0794553abe5d5f/backend/src/apiserver/server/pipeline_server.go#L134.
It directly calls isAuthorized(...), which doesn't add the API group to resourceAttributes. I think that is why I ended up with an error message like below:
```
Failed to create pipeline from URL 'https://storage.googleapis.com/ml-pipeline-dataset/sequential.yaml'. Raw error from the service: Failed to authorize with API resource references: PermissionDenied: User 'system:serviceaccount:jamesjwu:sleep' is not authorized with reason: (request: &ResourceAttributes{Namespace:jamesjwu,Verb:create,Group:,Version:,Resource:pipelines,Subresource:,Name:,}): Unauthorized access (code: 7)", externalStatusCode:0xd}
```
Notice ```Group``` is empty string. It should be
```
RbacPipelinesGroup = "pipelines.kubeflow.org"
```
Another strange issue to me is that PipelineServer.CreatePipeline() seems to take resource references. However, I thought Pipeline isn't namespaced. After creating a pipeline with namespace, it doesn't seem to get listed or deleted. I know it exists because I get an 'already existed' error when creating the pipeline the 2nd time.
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I_kwDOB-71UM5JAZKG
| 7,655 |
[frontend] Failed to display a pipeline execution graph: "No graph to show"
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[
"Hello @pahask8 , would you mind providing the example you used for this run, so that we can reproduce the result for this issue. In addition, we would like to ask if you were using v1 mode or v2 mode for compiling the pipeline definition.",
"Hello @jlyaoyuli, thank you for quick update. I used v1 mode for compiling the pipeline. I can try to provide an example of this , can you please suggest, will the complete yaml work for you? We use this pipeline in production and have some private data there. Thanks",
"Hello @pahask8 , yes the complete yaml works. Can provide a simplified version of pipeline (yaml file)? Thank you :) Noted: We think that the potential reason for this error is because we upgrade node from 12 to 14, which doesn't allow using Buffer API on the Browser side. https://github.com/kubeflow/pipelines/blob/a8a3b93aded13c9c8f5c9207dd1c233488aaf29f/frontend/src/lib/Utils.tsx#L391",
"Hello @jlyaoyuli, please find the attached pipeline file. Please note I masked all sensitive data there. Hope that helps. Thank you!\r\n[pipeline.yaml.zip](https://github.com/kubeflow/pipelines/files/8756506/pipeline.yaml.zip)\r\n",
"It's definitely something to do with compressedNodes. The problem starts happening as soon as the pipeline gets big enough to trigger Argo to start using the compressedNodes method of storing the run state.\r\n\r\nAny update on when this will be addressed?",
"Also upping this as something that is impacting our team. We're downgrading KFP because this issue makes the latest versions of KFP unusable for us."
] | 2022-05-04T01:26:47 | 2022-10-15T01:14:52 | 2022-10-15T01:14:52 |
CONTRIBUTOR
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Manifests [files](https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize) from
* KFP version: 2.0.0-alpha.1 (worked well in 1.7.0, first observed in 1.8.1).
### Steps to reproduce
1. Pipeline was uploaded to Kubeflow using Kubeflow Python SDK (version 1.8.1)
2. Run a pipeline which is displaying as 2168 lines yaml file in Kubeflow UI.
3. Once completed, Kubeflow UI shows a message: "No graph to show" instead of pipeline execution graph. Please see a screenshot attached.
<img width="1507" alt="no_graph" src="https://user-images.githubusercontent.com/16062064/166611772-7de4eef3-6e05-4192-afdc-de0d3682507d.png">
### Expected result
Kubeflow UI shows a pipeline execution graph.
### Materials and Reference
There was a similar error described in https://github.com/kubeflow/pipelines/issues/4289.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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[backend] Unable to upload a specific testing pipeline via URL
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[
"/assign @Linchin ",
"Potential cause: This pipeline contains OptionalInput.",
"> Potential cause: This pipeline contains OptionalInput.\r\n\r\nThen this should have been resolved via https://github.com/kubeflow/pipelines/issues/8716. @Linchin to verify",
"Just tried using beta.0, it's still not working. It turned out that when the backend pulls yaml doc from URLs, there is an implicit size limitation caused by package [bufio](https://pkg.go.dev/bufio#pkg-constants), thus the loaded file would not be complete when it's large."
] | 2022-05-03T23:03:34 | 2023-04-04T21:33:04 | 2023-04-04T21:33:03 |
COLLABORATOR
| null |
For the user journey test of upload pipeline. The testing pipeline https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler_cli_tests/test_data/xgboost_sample_pipeline.yaml was not able to be uploaded via URL.
|
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| 1,219,866,151 |
I_kwDOB-71UM5Ita4n
| 7,641 |
Can not connect to the mongodb
|
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[] | 2022-04-29T05:10:26 | 2022-05-05T22:39:45 | null |
NONE
| null |
Hi! I'm try to use kubeflow pipeline to run my project οΌand my project need to store data to mongodb . My mongodb is deploy in the K8s and it work fine, but when I use the kubeflow pipeline to create a mongodb connection, I got this error.
my mongodb uri is : "mongodb://testUser:passwd@localhost:8000/?authMechanism=SCRAM-SHA-256&authSource=admin&directConnection=true"
and my mongodb's pipeline code is :
And I port-forward my mongodb svc to the 8000:27017
I try to use this mongodb uri on the mongodb client and I connect complete. But I don't know why I use this uri on the pipeline and get this error.
Here is my complete connect mongodb picture.
|
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| 1,219,238,444 |
I_kwDOB-71UM5IrBos
| 7,638 |
[feature] Pipeline level exit handler
|
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[] | 2022-04-28T20:17:28 | 2022-05-09T22:28:39 | 2022-05-09T22:28:39 |
COLLABORATOR
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 1,218,118,499 |
I_kwDOB-71UM5ImwNj
| 7,631 |
SDK: Accept newer `typing-extensions` versions
|
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[] | 2022-04-28T02:54:26 | 2022-04-30T00:01:25 | 2022-04-30T00:01:25 |
CONTRIBUTOR
| null |
### Feature Area
The SDK currently requires [`typing-extensions>=3.7.4,<4`](https://github.com/kubeflow/pipelines/blob/2.0.0-alpha.1/sdk/python/requirements.in#L45).
### What feature would you like to see?
I would like the SDK to accept higher versions - `>=4` specifically.
### What is the use case or pain point?
I would like to use the SDK on Cloud Composer (a.k.a Google-managed Apache Airflow). However, I can't install the SDK because Cloud Composer is using `looker-sdk 22.4.0` which requires `typing-extensions>=4.1.1`
### Is there a workaround currently?
There are some workarounds but I think it's no problem that the SDK accepts newer `typing-extensions` versions.
|
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I_kwDOB-71UM5IlXkT
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[backend] OutputPath is giving "permission denied" - why?
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[
"Additional data - here's the generated yaml - line 317 is the one that worries me. It APPEARS it's putting in the string of `output_context_path` when shouldn't that be a variable? is that substituted at runtime? --\r\n\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n generateName: root-pipeline-compilation-\r\n annotations:\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.12\r\n pipelines.kubeflow.org/pipeline_compilation_time: '2022-04-29T18:04:24.336927'\r\n pipelines.kubeflow.org/pipeline_spec: '{\"inputs\": [{\"default\": \"\", \"name\": \"context\",\r\n \"optional\": true, \"type\": \"String\"}, {\"default\": \"\", \"name\": \"metadata_url\",\r\n \"optional\": true, \"type\": \"String\"}, {\"default\": \"\", \"name\": \"pipeline-root\"},\r\n {\"default\": \"pipeline/root_pipeline_compilation\", \"name\": \"pipeline-name\"}],\r\n \"name\": \"root_pipeline_compilation\"}'\r\n pipelines.kubeflow.org/v2_pipeline: \"true\"\r\n labels:\r\n pipelines.kubeflow.org/v2_pipeline: \"true\"\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.12\r\nspec:\r\n entrypoint: root-pipeline-compilation\r\n templates:\r\n - name: root-pipeline-compilation\r\n inputs:\r\n parameters:\r\n - {name: metadata_url}\r\n - {name: pipeline-name}\r\n - {name: pipeline-root}\r\n dag:\r\n tasks:\r\n - name: run-info-fn\r\n template: run-info-fn\r\n arguments:\r\n parameters:\r\n - {name: pipeline-name, value: '{{inputs.parameters.pipeline-name}}'}\r\n - {name: pipeline-root, value: '{{inputs.parameters.pipeline-root}}'}\r\n - name: same-step-000-d5554cccadc4445f91f51849eb5f2de6-fn\r\n template: same-step-000-d5554cccadc4445f91f51849eb5f2de6-fn\r\n dependencies: [run-info-fn]\r\n arguments:\r\n parameters:\r\n - {name: metadata_url, value: '{{inputs.parameters.metadata_url}}'}\r\n - {name: pipeline-name, value: '{{inputs.parameters.pipeline-name}}'}\r\n - {name: pipeline-root, value: '{{inputs.parameters.pipeline-root}}'}\r\n - {name: run-info-fn-run_info, value: '{{tasks.run-info-fn.outputs.parameters.run-info-fn-run_info}}'}\r\n - name: run-info-fn\r\n container:\r\n args:\r\n - sh\r\n - -c\r\n - |2\r\n\r\n if ! [ -x \"$(command -v pip)\" ]; then\r\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\r\n fi\r\n\r\n PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'kfp' 'dill' 'kfp==1.8.12' && \"$0\" \"$@\"\r\n - sh\r\n - -ec\r\n - |\r\n program_path=$(mktemp -d)\r\n printf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\r\n python3 -m kfp.v2.components.executor_main --component_module_path \"$program_path/ephemeral_component.py\" \"$@\"\r\n - |2+\r\n\r\n import kfp\r\n from kfp.v2 import dsl\r\n from kfp.v2.dsl import *\r\n from typing import *\r\n\r\n def run_info_fn(\r\n run_id: str,\r\n ) -> NamedTuple(\"RunInfoOutput\", [(\"run_info\", str),]):\r\n from base64 import urlsafe_b64encode\r\n from collections import namedtuple\r\n import datetime\r\n import base64\r\n import dill\r\n import kfp\r\n\r\n client = kfp.Client(host=\"http://ml-pipeline:8888\")\r\n run_info = client.get_run(run_id=run_id)\r\n\r\n run_info_dict = {\r\n \"run_id\": run_info.run.id,\r\n \"name\": run_info.run.name,\r\n \"created_at\": run_info.run.created_at.isoformat(),\r\n \"pipeline_id\": run_info.run.pipeline_spec.pipeline_id,\r\n }\r\n\r\n # Track kubernetes resources associated wth the run.\r\n for r in run_info.run.resource_references:\r\n run_info_dict[f\"{r.key.type.lower()}_id\"] = r.key.id\r\n\r\n # Base64-encoded as value is visible in kubeflow ui.\r\n output = urlsafe_b64encode(dill.dumps(run_info_dict))\r\n\r\n return namedtuple(\"RunInfoOutput\", [\"run_info\"])(\r\n str(output, encoding=\"ascii\")\r\n )\r\n\r\n - --executor_input\r\n - '{{$}}'\r\n - --function_to_execute\r\n - run_info_fn\r\n command: [/kfp-launcher/launch, --mlmd_server_address, $(METADATA_GRPC_SERVICE_HOST),\r\n --mlmd_server_port, $(METADATA_GRPC_SERVICE_PORT), --runtime_info_json, $(KFP_V2_RUNTIME_INFO),\r\n --container_image, $(KFP_V2_IMAGE), --task_name, run-info-fn, --pipeline_name,\r\n '{{inputs.parameters.pipeline-name}}', --run_id, $(KFP_RUN_ID), --run_resource,\r\n workflows.argoproj.io/$(WORKFLOW_ID), --namespace, $(KFP_NAMESPACE), --pod_name,\r\n $(KFP_POD_NAME), --pod_uid, $(KFP_POD_UID), --pipeline_root, '{{inputs.parameters.pipeline-root}}',\r\n --enable_caching, $(ENABLE_CACHING), --, 'run_id={{workflow.uid}}', --]\r\n env:\r\n - name: KFP_POD_NAME\r\n valueFrom:\r\n fieldRef: {fieldPath: metadata.name}\r\n - name: KFP_POD_UID\r\n valueFrom:\r\n fieldRef: {fieldPath: metadata.uid}\r\n - name: KFP_NAMESPACE\r\n valueFrom:\r\n fieldRef: {fieldPath: metadata.namespace}\r\n - name: WORKFLOW_ID\r\n valueFrom:\r\n fieldRef: {fieldPath: 'metadata.labels[''workflows.argoproj.io/workflow'']'}\r\n - name: KFP_RUN_ID\r\n valueFrom:\r\n fieldRef: {fieldPath: 'metadata.labels[''pipeline/runid'']'}\r\n - name: ENABLE_CACHING\r\n valueFrom:\r\n fieldRef: {fieldPath: 'metadata.labels[''pipelines.kubeflow.org/enable_caching'']'}\r\n - {name: KFP_V2_IMAGE, value: 'python:3.7'}\r\n - {name: KFP_V2_RUNTIME_INFO, value: '{\"inputParameters\": {\"run_id\": {\"type\":\r\n \"STRING\"}}, \"inputArtifacts\": {}, \"outputParameters\": {\"run_info\": {\"type\":\r\n \"STRING\", \"path\": \"/tmp/outputs/run_info/data\"}}, \"outputArtifacts\": {}}'}\r\n envFrom:\r\n - configMapRef: {name: metadata-grpc-configmap, optional: true}\r\n image: python:3.7\r\n volumeMounts:\r\n - {mountPath: /kfp-launcher, name: kfp-launcher}\r\n inputs:\r\n parameters:\r\n - {name: pipeline-name}\r\n - {name: pipeline-root}\r\n outputs:\r\n parameters:\r\n - name: run-info-fn-run_info\r\n valueFrom: {path: /tmp/outputs/run_info/data}\r\n artifacts:\r\n - {name: run-info-fn-run_info, path: /tmp/outputs/run_info/data}\r\n metadata:\r\n annotations:\r\n pipelines.kubeflow.org/v2_component: \"true\"\r\n pipelines.kubeflow.org/component_ref: '{}'\r\n pipelines.kubeflow.org/arguments.parameters: '{\"run_id\": \"{{workflow.uid}}\"}'\r\n labels:\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.12\r\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\r\n pipelines.kubeflow.org/v2_component: \"true\"\r\n pipelines.kubeflow.org/enable_caching: \"true\"\r\n initContainers:\r\n - command: [launcher, --copy, /kfp-launcher/launch]\r\n image: gcr.io/ml-pipeline/kfp-launcher:1.8.7\r\n name: kfp-launcher\r\n mirrorVolumeMounts: true\r\n volumes:\r\n - {name: kfp-launcher}\r\n - name: same-step-000-d5554cccadc4445f91f51849eb5f2de6-fn\r\n container:\r\n args:\r\n - sh\r\n - -c\r\n - |2\r\n\r\n if ! [ -x \"$(command -v pip)\" ]; then\r\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\r\n fi\r\n\r\n PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'dill' 'requests' 'kfp==1.8.12' && \"$0\" \"$@\"\r\n - sh\r\n - -ec\r\n - |\r\n program_path=$(mktemp -d)\r\n printf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\r\n python3 -m kfp.v2.components.executor_main --component_module_path \"$program_path/ephemeral_component.py\" \"$@\"\r\n - |2+\r\n\r\n import kfp\r\n from kfp.v2 import dsl\r\n from kfp.v2.dsl import *\r\n from typing import *\r\n\r\n def same_step_000_d5554cccadc4445f91f51849eb5f2de6_fn(\r\n input_context_path: InputPath(str),\r\n output_context_path: OutputPath(str),\r\n run_info: str = \"gAR9lC4=\",\r\n metadata_url: str = \"\",\r\n ):\r\n from base64 import urlsafe_b64encode, urlsafe_b64decode\r\n from pathlib import Path\r\n import datetime\r\n import requests\r\n import tempfile\r\n import dill\r\n import os\r\n\r\n input_context = None\r\n with Path(input_context_path).open(\"rb\") as reader:\r\n input_context = reader.read()\r\n\r\n # Helper function for posting metadata to mlflow.\r\n def post_metadata(json):\r\n if metadata_url == \"\":\r\n return\r\n\r\n try:\r\n req = requests.post(metadata_url, json=json)\r\n req.raise_for_status()\r\n except requests.exceptions.HTTPError as err:\r\n print(f\"Error posting metadata: {err}\")\r\n\r\n # Move to writable directory as user might want to do file IO.\r\n # TODO: won't persist across steps, might need support in SDK?\r\n os.chdir(tempfile.mkdtemp())\r\n\r\n # Load information about the current experiment run:\r\n run_info = dill.loads(urlsafe_b64decode(run_info))\r\n\r\n # Post session context to mlflow.\r\n if len(input_context) > 0:\r\n input_context_str = urlsafe_b64encode(input_context)\r\n post_metadata({\r\n \"experiment_id\": run_info[\"experiment_id\"],\r\n \"run_id\": run_info[\"run_id\"],\r\n \"step_id\": \"same_step_000\",\r\n \"metadata_type\": \"input\",\r\n \"metadata_value\": input_context_str,\r\n \"metadata_time\": datetime.datetime.now().isoformat(),\r\n })\r\n\r\n # User code for step, which we run in its own execution frame.\r\n user_code = f\"\"\"\r\n import dill\r\n\r\n # Load session context into global namespace:\r\n if { len(input_context) } > 0:\r\n dill.load_session(\"{ input_context_path }\")\r\n\r\n {dill.loads(urlsafe_b64decode(\"gASVGAAAAAAAAACMFHByaW50KCJIZWxsbyB3b3JsZCIplC4=\"))}\r\n\r\n # Remove anything from the global namespace that cannot be serialised.\r\n # TODO: this will include things like pandas dataframes, needs sdk support?\r\n _bad_keys = []\r\n _all_keys = list(globals().keys())\r\n for k in _all_keys:\r\n try:\r\n dill.dumps(globals()[k])\r\n except TypeError:\r\n _bad_keys.append(k)\r\n\r\n for k in _bad_keys:\r\n del globals()[k]\r\n\r\n # Save new session context to disk for the next component:\r\n dill.dump_session(\"{output_context_path}\")\r\n \"\"\"\r\n\r\n # Runs the user code in a new execution frame. Context from the previous\r\n # component in the run is loaded into the session dynamically, and we run\r\n # with a single globals() namespace to simulate top-level execution.\r\n exec(user_code, globals(), globals())\r\n\r\n # Post new session context to mlflow:\r\n with Path(output_context_path).open(\"rb\") as reader:\r\n context = urlsafe_b64encode(reader.read())\r\n post_metadata({\r\n \"experiment_id\": run_info[\"experiment_id\"],\r\n \"run_id\": run_info[\"run_id\"],\r\n \"step_id\": \"same_step_000\",\r\n \"metadata_type\": \"output\",\r\n \"metadata_value\": context,\r\n \"metadata_time\": datetime.datetime.now().isoformat(),\r\n })\r\n\r\n - --executor_input\r\n - '{{$}}'\r\n - --function_to_execute\r\n - same_step_000_d5554cccadc4445f91f51849eb5f2de6_fn\r\n command: [/kfp-launcher/launch, --mlmd_server_address, $(METADATA_GRPC_SERVICE_HOST),\r\n --mlmd_server_port, $(METADATA_GRPC_SERVICE_PORT), --runtime_info_json, $(KFP_V2_RUNTIME_INFO),\r\n --container_image, $(KFP_V2_IMAGE), --task_name, same-step-000-d5554cccadc4445f91f51849eb5f2de6-fn,\r\n --pipeline_name, '{{inputs.parameters.pipeline-name}}', --run_id, $(KFP_RUN_ID),\r\n --run_resource, workflows.argoproj.io/$(WORKFLOW_ID), --namespace, $(KFP_NAMESPACE),\r\n --pod_name, $(KFP_POD_NAME), --pod_uid, $(KFP_POD_UID), --pipeline_root, '{{inputs.parameters.pipeline-root}}',\r\n --enable_caching, $(ENABLE_CACHING), --, input_context_path=, 'metadata_url={{inputs.parameters.metadata_url}}',\r\n 'run_info={{inputs.parameters.run-info-fn-run_info}}', --]\r\n env:\r\n - name: KFP_POD_NAME\r\n valueFrom:\r\n fieldRef: {fieldPath: metadata.name}\r\n - name: KFP_POD_UID\r\n valueFrom:\r\n fieldRef: {fieldPath: metadata.uid}\r\n - name: KFP_NAMESPACE\r\n valueFrom:\r\n fieldRef: {fieldPath: metadata.namespace}\r\n - name: WORKFLOW_ID\r\n valueFrom:\r\n fieldRef: {fieldPath: 'metadata.labels[''workflows.argoproj.io/workflow'']'}\r\n - name: KFP_RUN_ID\r\n valueFrom:\r\n fieldRef: {fieldPath: 'metadata.labels[''pipeline/runid'']'}\r\n - name: ENABLE_CACHING\r\n valueFrom:\r\n fieldRef: {fieldPath: 'metadata.labels[''pipelines.kubeflow.org/enable_caching'']'}\r\n - {name: KFP_V2_IMAGE, value: 'public.ecr.aws/j1r0q0g6/notebooks/notebook-servers/codeserver-python:v1.5.0'}\r\n - {name: KFP_V2_RUNTIME_INFO, value: '{\"inputParameters\": {\"input_context_path\":\r\n {\"type\": \"STRING\"}, \"metadata_url\": {\"type\": \"STRING\"}, \"run_info\": {\"type\":\r\n \"STRING\"}}, \"inputArtifacts\": {}, \"outputParameters\": {\"output_context_path\":\r\n {\"type\": \"STRING\", \"path\": \"/tmp/outputs/output_context_path/data\"}}, \"outputArtifacts\":\r\n {}}'}\r\n envFrom:\r\n - configMapRef: {name: metadata-grpc-configmap, optional: true}\r\n image: public.ecr.aws/j1r0q0g6/notebooks/notebook-servers/codeserver-python:v1.5.0\r\n volumeMounts:\r\n - {mountPath: /kfp-launcher, name: kfp-launcher}\r\n inputs:\r\n parameters:\r\n - {name: metadata_url}\r\n - {name: pipeline-name}\r\n - {name: pipeline-root}\r\n - {name: run-info-fn-run_info}\r\n outputs:\r\n artifacts:\r\n - {name: same-step-000-d5554cccadc4445f91f51849eb5f2de6-fn-output_context_path,\r\n path: /tmp/outputs/output_context_path/data}\r\n metadata:\r\n annotations:\r\n pipelines.kubeflow.org/v2_component: \"true\"\r\n pipelines.kubeflow.org/component_ref: '{}'\r\n pipelines.kubeflow.org/arguments.parameters: '{\"input_context_path\": \"\", \"metadata_url\":\r\n \"{{inputs.parameters.metadata_url}}\", \"run_info\": \"{{inputs.parameters.run-info-fn-run_info}}\"}'\r\n pipelines.kubeflow.org/max_cache_staleness: P0D\r\n labels:\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.12\r\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\r\n pipelines.kubeflow.org/v2_component: \"true\"\r\n pipelines.kubeflow.org/enable_caching: \"true\"\r\n initContainers:\r\n - command: [launcher, --copy, /kfp-launcher/launch]\r\n image: gcr.io/ml-pipeline/kfp-launcher:1.8.7\r\n name: kfp-launcher\r\n mirrorVolumeMounts: true\r\n volumes:\r\n - {name: kfp-launcher}\r\n arguments:\r\n parameters:\r\n - {name: context, value: ''}\r\n - {name: metadata_url, value: ''}\r\n - {name: pipeline-root, value: ''}\r\n - {name: pipeline-name, value: pipeline/root_pipeline_compilation}\r\n serviceAccountName: pipeline-runner\r\n\r\n```",
"FYI, this is a pure regression from KFP 1.\r\n\r\nHere's two gists - one compiled in LEGACY mode and one compiled in V2 compat.\r\n\r\nusing the compiler in v1 mode - https://gist.github.com/aronchick/0dfc57d2a794c1bd4fb9bff9962cfbd6\r\nusing the compiler in v2 mode - https://gist.github.com/aronchick/473060503ae189b360fbded04d802c80\r\n\r\nFirst one executes fine. Second one gives:\r\n```\r\ntime=\"2022-04-26T21:53:30.710Z\" level=info msg=\"capturing logs\" argo=true\r\nI0426 21:53:30.745547 18 launcher.go:144] PipelineRoot defaults to \"minio://mlpipeline/v2/artifacts\".\r\nI0426 21:53:30.745908 18 cache.go:120] Connecting to cache endpoint 10.100.244.104:8887\r\nI0426 21:53:30.854201 18 launcher.go:193] enable caching\r\nF0426 21:53:30.979055 18 main.go:50] Failed to execute component: failed to create directory \"/tmp/outputs/output_context_path\" for output parameter \"output_context_path\": mkdir /tmp/outputs/output_context_path: permission denied\r\ntime=\"2022-04-26T21:53:30.980Z\" level=info msg=\"/tmp/outputs/output_context_path/data -> /var/run/argo/outputs/artifacts/tmp/outputs/output_context_path/data.tgz\" argo=true\r\ntime=\"2022-04-26T21:53:30.981Z\" level=info msg=\"Taring /tmp/outputs/output_context_path/data\"\r\nError: failed to tarball the output /tmp/outputs/output_context_path/data to /var/run/argo/outputs/artifacts/tmp/outputs/output_context_path/data.tgz: stat /tmp/outputs/output_context_path/data: permission denied\r\nfailed to tarball the output /tmp/outputs/output_context_path/data to /var/run/argo/outputs/artifacts/tmp/outputs/output_context_path/data.tgz: stat /tmp/outputs/output_context_path/data: permission denied\r\n```",
"Is there any movement here? Just FYI, this affects Vertex too. Happy to pass along to your team internally if you'd like.",
"I'm also getting this, Any solution?",
"This issue is affecting us as well. Is there any solution / workaround ? Thank You",
"Is this still a problem? We're seeing this with 2.0.0.",
"```\r\ntotal 288\r\ndrwxrwxrwt 1 root root 4096 Aug 29 20:47 .\r\ndrwxr-xr-x 1 root root 4096 Aug 29 20:47 ..\r\ndrw-r--r-- 2 user user 4096 Aug 29 20:47 kfp <------ 0644 mode\r\n-rw------- 1 root root 278952 Aug 16 20:34 tmp7xutvpq1\r\n[KFP Executor 2023-08-29 20:47:56,221 INFO]: Looking for component `square` in --component_module_path `/tmp/tmp.Y2OeYEVRGv/ephemeral_component.py`\r\n[KFP Executor 2023-08-29 20:47:56,221 INFO]: Loading KFP component \"square\" from /tmp/tmp.Y2OeYEVRGv/ephemeral_component.py (directory \"/tmp/tmp.Y2OeYEVRGv\" and module name \"ephemeral_component\")\r\n[KFP Executor 2023-08-29 20:47:56,222 INFO]: Got executor_input:\r\n{\r\n \"inputs\": {\r\n \"parameterValues\": {\r\n \"x\": 2\r\n }\r\n },\r\n \"outputs\": {\r\n \"parameters\": {\r\n \"Output\": {\r\n \"outputFile\": \"/tmp/kfp/Output\"\r\n }\r\n },\r\n \"outputFile\": \"/tmp/kfp/output_metadata.json\"\r\n }\r\n}\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\r\n \"__main__\", mod_spec)\r\n File \"/usr/local/lib/python3.7/runpy.py\", line 85, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/user/.local/lib/python3.7/site-packages/kfp/components/executor_main.py\", line 105, in <module>\r\n executor_main()\r\n File \"/home/user/.local/lib/python3.7/site-packages/kfp/components/executor_main.py\", line 101, in executor_main\r\n executor.execute()\r\n File \"/home/user/.local/lib/python3.7/site-packages/kfp/components/executor.py\", line 347, in execute\r\n self._write_executor_output(result)\r\n File \"/home/user/.local/lib/python3.7/site-packages/kfp/components/executor.py\", line 297, in _write_executor_output\r\n with open(executor_output_path, 'w') as f:\r\nPermissionError: [Errno 13] Permission denied: '/tmp/kfp/output_metadata.json'\r\n```"
] | 2022-04-27T18:50:10 | 2023-08-29T20:51:45 | null |
NONE
| null |
When I run a step that runs fine with Kubeflow v1 compatibility, it fails with the following error:
```
time="2022-04-26T21:53:30.710Z" level=info msg="capturing logs" argo=true
I0426 21:53:30.745547 18 launcher.go:144] PipelineRoot defaults to "minio://mlpipeline/v2/artifacts".
I0426 21:53:30.745908 18 cache.go:120] Connecting to cache endpoint 10.100.244.104:8887
I0426 21:53:30.854201 18 launcher.go:193] enable caching
F0426 21:53:30.979055 18 main.go:50] Failed to execute component: failed to create directory "/tmp/outputs/output_context_path" for output parameter "output_context_path": mkdir /tmp/outputs/output_context_path: permission denied
time="2022-04-26T21:53:30.980Z" level=info msg="/tmp/outputs/output_context_path/data -> /var/run/argo/outputs/artifacts/tmp/outputs/output_context_path/data.tgz" argo=true
time="2022-04-26T21:53:30.981Z" level=info msg="Taring /tmp/outputs/output_context_path/data"
Error: failed to tarball the output /tmp/outputs/output_context_path/data to /var/run/argo/outputs/artifacts/tmp/outputs/output_context_path/data.tgz: stat /tmp/outputs/output_context_path/data: permission denied
failed to tarball the output /tmp/outputs/output_context_path/data to /var/run/argo/outputs/artifacts/tmp/outputs/output_context_path/data.tgz: stat /tmp/outputs/output_context_path/data: permission denied
```
The code that produces this is here:
```
import kfp
from kfp.v2.dsl import component, Artifact, Input, InputPath, Output, OutputPath, Dataset, Model
from typing import NamedTuple
def same_step_000_afc67b36914c4108b47e8b4bb316869d_fn(
input_context_path: InputPath(str),
output_context_path: OutputPath(str),
run_info: str ="gAR9lC4=",
metadata_url: str="",
):
from base64 import urlsafe_b64encode, urlsafe_b64decode
from pathlib import Path
import datetime
import requests
import tempfile
import dill
import os
input_context = None
with Path(input_context_path).open("rb") as reader:
input_context = reader.read()
# Helper function for posting metadata to mlflow.
def post_metadata(json):
if metadata_url == "":
return
try:
req = requests.post(metadata_url, json=json)
req.raise_for_status()
except requests.exceptions.HTTPError as err:
print(f"Error posting metadata: {err}")
# Move to writable directory as user might want to do file IO.
# TODO: won't persist across steps, might need support in SDK?
os.chdir(tempfile.mkdtemp())
# Load information about the current experiment run:
run_info = dill.loads(urlsafe_b64decode(run_info))
# Post session context to mlflow.
if len(input_context) > 0:
input_context_str = urlsafe_b64encode(input_context)
post_metadata({
"experiment_id": run_info["experiment_id"],
"run_id": run_info["run_id"],
"step_id": "same_step_000",
"metadata_type": "input",
"metadata_value": input_context_str,
"metadata_time": datetime.datetime.now().isoformat(),
})
# User code for step, which we run in its own execution frame.
user_code = f"""
import dill
# Load session context into global namespace:
if { len(input_context) } > 0:
dill.load_session("{ input_context_path }")
{dill.loads(urlsafe_b64decode("gASVGAAAAAAAAACMFHByaW50KCJIZWxsbyB3b3JsZCIplC4="))}
# Remove anything from the global namespace that cannot be serialised.
# TODO: this will include things like pandas dataframes, needs sdk support?
_bad_keys = []
_all_keys = list(globals().keys())
for k in _all_keys:
try:
dill.dumps(globals()[k])
except TypeError:
_bad_keys.append(k)
for k in _bad_keys:
del globals()[k]
# Save new session context to disk for the next component:
dill.dump_session("{output_context_path}")
"""
# Runs the user code in a new execution frame. Context from the previous
# component in the run is loaded into the session dynamically, and we run
# with a single globals() namespace to simulate top-level execution.
exec(user_code, globals(), globals())
# Post new session context to mlflow:
with Path(output_context_path).open("rb") as reader:
context = urlsafe_b64encode(reader.read())
post_metadata({
"experiment_id": run_info["experiment_id"],
"run_id": run_info["run_id"],
"step_id": "same_step_000",
"metadata_type": "output",
"metadata_value": context,
"metadata_time": datetime.datetime.now().isoformat(),
})
```
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
From manifests
* KFP version:
1.8.1
* KFP SDK version:
1.8.12
### Expected result
Is there supposed to be a different way of writing this file? Do I need to mount a storage location in order to do this? (You didn't have to in KFP v2)
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,217,305,122 |
I_kwDOB-71UM5Ijpoi
| 7,627 |
[sdk] pytorch launcher create_worker_spec error
|
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open
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[] | null |
[
"@dblate Can you provide more information on this issue? Thanks. "
] | 2022-04-27T12:46:40 | 2022-05-05T22:44:48 | null |
NONE
| null |
### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,216,504,773 |
I_kwDOB-71UM5IgmPF
| 7,623 |
[backend] Unclear why compilation returns KeyError ['root']
|
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closed
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[] | null |
[
"Turns out that I had a \"@component\" decorator over the function AND was using `kfp.components.create_component_from_func`. This caused the bug.",
"> Turns out that I had a \"@component\" decorator over the function AND was using `kfp.components.create_component_from_func`. This caused the bug.\r\n\r\nI have a similar problem, in a file there are the functions (using kubeflow V2), in another file there is the pipeline.\r\n```\r\nfrom functions import addition, product\r\nfirst_comp = kfp.components.create_component_from_func(addition, base_image=base_img1)\r\n```\r\nwhat should I use instead? Thanks\r\n"
] | 2022-04-26T21:38:34 | 2022-11-09T15:50:46 | 2022-04-29T20:16:21 |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
From manifests - 1.8.1
* KFP version:
1.8.1
* KFP SDK version:
1.8.12
### Steps to reproduce
Here's the execution code:
```
from google.cloud import aiplatform
job = aiplatform.PipelineJob(
display_name="MY_DISPLAY_JOB",
template_path=compiled_pipeline_path,
project=project_id,
pipeline_root=pipeline_root,
credentials=service_account_credentials_file,
)
```
I get the following error:
```
Traceback (most recent call last):
File "/home/daaronch/code/same-project/sameproject/ops/vertex/vertex_debugger.py", line 132, in <module>
main(auto_envvar_prefix="SAME")
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/click/core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/click/core.py", line 1657, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/home/daaronch/code/same-project/sameproject/ops/vertex/vertex_debugger.py", line 102, in deploy_vertex
job = aiplatform.PipelineJob(
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/google/cloud/aiplatform/pipeline_jobs.py", line 201, in __init__
builder = pipeline_utils.PipelineRuntimeConfigBuilder.from_job_spec_json(
File "/tmp/tmp8sl1sldw/.venv/lib/python3.8/site-packages/google/cloud/aiplatform/utils/pipeline_utils.py", line 69, in from_job_spec_json
job_spec["pipelineSpec"]["root"]
KeyError: 'root'
```
And here's the yaml I generated:
```
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: root-pipeline-compilation-
annotations:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.12
pipelines.kubeflow.org/pipeline_compilation_time: '2022-04-26T20:57:42.544232'
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "", "name": "context",
"optional": true, "type": "String"}, {"default": "", "name": "metadata_url",
"optional": true, "type": "String"}, {"default": "", "name": "pipeline-root"},
{"default": "pipeline/root_pipeline_compilation", "name": "pipeline-name"}],
"name": "root_pipeline_compilation"}'
pipelines.kubeflow.org/v2_pipeline: "true"
labels:
pipelines.kubeflow.org/v2_pipeline: "true"
pipelines.kubeflow.org/kfp_sdk_version: 1.8.12
spec:
entrypoint: root-pipeline-compilation
templates:
- name: root-pipeline-compilation
inputs:
parameters:
- {name: metadata_url}
- {name: pipeline-name}
- {name: pipeline-root}
dag:
tasks:
- name: run-info-fn
template: run-info-fn
arguments:
parameters:
- {name: pipeline-name, value: '{{inputs.parameters.pipeline-name}}'}
- {name: pipeline-root, value: '{{inputs.parameters.pipeline-root}}'}
- name: same-step-000-afc67b36914c4108b47e8b4bb316869d-fn
template: same-step-000-afc67b36914c4108b47e8b4bb316869d-fn
dependencies: [run-info-fn]
arguments:
parameters:
- {name: metadata_url, value: '{{inputs.parameters.metadata_url}}'}
- {name: pipeline-name, value: '{{inputs.parameters.pipeline-name}}'}
- {name: pipeline-root, value: '{{inputs.parameters.pipeline-root}}'}
- {name: run-info-fn-run_info, value: '{{tasks.run-info-fn.outputs.parameters.run-info-fn-run_info}}'}
- name: run-info-fn
container:
args:
- sh
- -c
- |2
if ! [ -x "$(command -v pip)" ]; then
python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip
fi
PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'kfp' 'dill' 'kfp==1.8.12' && "$0" "$@"
- sh
- -ec
- |
program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
python3 -m kfp.v2.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
- |2+
import kfp
from kfp.v2 import dsl
from kfp.v2.dsl import *
from typing import *
def run_info_fn(
run_id: str,
) -> NamedTuple("RunInfoOutput", [("run_info", str),]):
from base64 import urlsafe_b64encode
from collections import namedtuple
import datetime
import base64
import dill
import kfp
client = kfp.Client(host="http://ml-pipeline:8888")
run_info = client.get_run(run_id=run_id)
run_info_dict = {
"run_id": run_info.run.id,
"name": run_info.run.name,
"created_at": run_info.run.created_at.isoformat(),
"pipeline_id": run_info.run.pipeline_spec.pipeline_id,
}
# Track kubernetes resources associated wth the run.
for r in run_info.run.resource_references:
run_info_dict[f"{r.key.type.lower()}_id"] = r.key.id
# Base64-encoded as value is visible in kubeflow ui.
output = urlsafe_b64encode(dill.dumps(run_info_dict))
return namedtuple("RunInfoOutput", ["run_info"])(
str(output, encoding="ascii")
)
- --executor_input
- '{{$}}'
- --function_to_execute
- run_info_fn
command: [/kfp-launcher/launch, --mlmd_server_address, $(METADATA_GRPC_SERVICE_HOST),
--mlmd_server_port, $(METADATA_GRPC_SERVICE_PORT), --runtime_info_json, $(KFP_V2_RUNTIME_INFO),
--container_image, $(KFP_V2_IMAGE), --task_name, run-info-fn, --pipeline_name,
'{{inputs.parameters.pipeline-name}}', --run_id, $(KFP_RUN_ID), --run_resource,
workflows.argoproj.io/$(WORKFLOW_ID), --namespace, $(KFP_NAMESPACE), --pod_name,
$(KFP_POD_NAME), --pod_uid, $(KFP_POD_UID), --pipeline_root, '{{inputs.parameters.pipeline-root}}',
--enable_caching, $(ENABLE_CACHING), --, 'run_id={{workflow.uid}}', --]
env:
- name: KFP_POD_NAME
valueFrom:
fieldRef: {fieldPath: metadata.name}
- name: KFP_POD_UID
valueFrom:
fieldRef: {fieldPath: metadata.uid}
- name: KFP_NAMESPACE
valueFrom:
fieldRef: {fieldPath: metadata.namespace}
- name: WORKFLOW_ID
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''workflows.argoproj.io/workflow'']'}
- name: KFP_RUN_ID
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''pipeline/runid'']'}
- name: ENABLE_CACHING
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''pipelines.kubeflow.org/enable_caching'']'}
- {name: KFP_V2_IMAGE, value: 'python:3.7'}
- {name: KFP_V2_RUNTIME_INFO, value: '{"inputParameters": {"run_id": {"type":
"STRING"}}, "inputArtifacts": {}, "outputParameters": {"run_info": {"type":
"STRING", "path": "/tmp/outputs/run_info/data"}}, "outputArtifacts": {}}'}
envFrom:
- configMapRef: {name: metadata-grpc-configmap, optional: true}
image: python:3.7
volumeMounts:
- {mountPath: /kfp-launcher, name: kfp-launcher}
inputs:
parameters:
- {name: pipeline-name}
- {name: pipeline-root}
outputs:
parameters:
- name: run-info-fn-run_info
valueFrom: {path: /tmp/outputs/run_info/data}
artifacts:
- {name: run-info-fn-run_info, path: /tmp/outputs/run_info/data}
metadata:
annotations:
pipelines.kubeflow.org/v2_component: "true"
pipelines.kubeflow.org/component_ref: '{}'
pipelines.kubeflow.org/arguments.parameters: '{"run_id": "{{workflow.uid}}"}'
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.12
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/v2_component: "true"
pipelines.kubeflow.org/enable_caching: "true"
initContainers:
- command: [launcher, --copy, /kfp-launcher/launch]
image: gcr.io/ml-pipeline/kfp-launcher:1.8.7
name: kfp-launcher
mirrorVolumeMounts: true
volumes:
- {name: kfp-launcher}
- name: same-step-000-afc67b36914c4108b47e8b4bb316869d-fn
container:
args:
- sh
- -c
- |2
if ! [ -x "$(command -v pip)" ]; then
python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip
fi
PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'dill' 'requests' 'kfp==1.8.12' && "$0" "$@"
- sh
- -ec
- |
program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
python3 -m kfp.v2.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
- |2+
import kfp
from kfp.v2 import dsl
from kfp.v2.dsl import *
from typing import *
def same_step_000_afc67b36914c4108b47e8b4bb316869d_fn(
input_context_path: InputPath(str),
output_context_path: OutputPath(str),
run_info: str ="gAR9lC4=",
metadata_url: str="",
):
from base64 import urlsafe_b64encode, urlsafe_b64decode
from pathlib import Path
import datetime
import requests
import tempfile
import dill
import os
input_context = None
with Path(input_context_path).open("rb") as reader:
input_context = reader.read()
# Helper function for posting metadata to mlflow.
def post_metadata(json):
if metadata_url == "":
return
try:
req = requests.post(metadata_url, json=json)
req.raise_for_status()
except requests.exceptions.HTTPError as err:
print(f"Error posting metadata: {err}")
# Move to writable directory as user might want to do file IO.
# TODO: won't persist across steps, might need support in SDK?
os.chdir(tempfile.mkdtemp())
# Load information about the current experiment run:
run_info = dill.loads(urlsafe_b64decode(run_info))
# Post session context to mlflow.
if len(input_context) > 0:
input_context_str = urlsafe_b64encode(input_context)
post_metadata({
"experiment_id": run_info["experiment_id"],
"run_id": run_info["run_id"],
"step_id": "same_step_000",
"metadata_type": "input",
"metadata_value": input_context_str,
"metadata_time": datetime.datetime.now().isoformat(),
})
# User code for step, which we run in its own execution frame.
user_code = f"""
import dill
# Load session context into global namespace:
if { len(input_context) } > 0:
dill.load_session("{ input_context_path }")
{dill.loads(urlsafe_b64decode("gASVGAAAAAAAAACMFHByaW50KCJIZWxsbyB3b3JsZCIplC4="))}
# Remove anything from the global namespace that cannot be serialised.
# TODO: this will include things like pandas dataframes, needs sdk support?
_bad_keys = []
_all_keys = list(globals().keys())
for k in _all_keys:
try:
dill.dumps(globals()[k])
except TypeError:
_bad_keys.append(k)
for k in _bad_keys:
del globals()[k]
# Save new session context to disk for the next component:
dill.dump_session("{output_context_path}")
"""
# Runs the user code in a new execution frame. Context from the previous
# component in the run is loaded into the session dynamically, and we run
# with a single globals() namespace to simulate top-level execution.
exec(user_code, globals(), globals())
# Post new session context to mlflow:
with Path(output_context_path).open("rb") as reader:
context = urlsafe_b64encode(reader.read())
post_metadata({
"experiment_id": run_info["experiment_id"],
"run_id": run_info["run_id"],
"step_id": "same_step_000",
"metadata_type": "output",
"metadata_value": context,
"metadata_time": datetime.datetime.now().isoformat(),
})
- --executor_input
- '{{$}}'
- --function_to_execute
- same_step_000_afc67b36914c4108b47e8b4bb316869d_fn
command: [/kfp-launcher/launch, --mlmd_server_address, $(METADATA_GRPC_SERVICE_HOST),
--mlmd_server_port, $(METADATA_GRPC_SERVICE_PORT), --runtime_info_json, $(KFP_V2_RUNTIME_INFO),
--container_image, $(KFP_V2_IMAGE), --task_name, same-step-000-afc67b36914c4108b47e8b4bb316869d-fn,
--pipeline_name, '{{inputs.parameters.pipeline-name}}', --run_id, $(KFP_RUN_ID),
--run_resource, workflows.argoproj.io/$(WORKFLOW_ID), --namespace, $(KFP_NAMESPACE),
--pod_name, $(KFP_POD_NAME), --pod_uid, $(KFP_POD_UID), --pipeline_root, '{{inputs.parameters.pipeline-root}}',
--enable_caching, $(ENABLE_CACHING), --, input_context_path=, 'metadata_url={{inputs.parameters.metadata_url}}',
'run_info={{inputs.parameters.run-info-fn-run_info}}', --]
env:
- name: KFP_POD_NAME
valueFrom:
fieldRef: {fieldPath: metadata.name}
- name: KFP_POD_UID
valueFrom:
fieldRef: {fieldPath: metadata.uid}
- name: KFP_NAMESPACE
valueFrom:
fieldRef: {fieldPath: metadata.namespace}
- name: WORKFLOW_ID
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''workflows.argoproj.io/workflow'']'}
- name: KFP_RUN_ID
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''pipeline/runid'']'}
- name: ENABLE_CACHING
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''pipelines.kubeflow.org/enable_caching'']'}
- {name: KFP_V2_IMAGE, value: 'public.ecr.aws/j1r0q0g6/notebooks/notebook-servers/codeserver-python:v1.5.0'}
- {name: KFP_V2_RUNTIME_INFO, value: '{"inputParameters": {"input_context_path":
{"type": "STRING"}, "metadata_url": {"type": "STRING"}, "run_info": {"type":
"STRING"}}, "inputArtifacts": {}, "outputParameters": {"output_context_path":
{"type": "STRING", "path": "/tmp/outputs/output_context_path/data"}}, "outputArtifacts":
{}}'}
envFrom:
- configMapRef: {name: metadata-grpc-configmap, optional: true}
image: public.ecr.aws/j1r0q0g6/notebooks/notebook-servers/codeserver-python:v1.5.0
volumeMounts:
- {mountPath: /kfp-launcher, name: kfp-launcher}
inputs:
parameters:
- {name: metadata_url}
- {name: pipeline-name}
- {name: pipeline-root}
- {name: run-info-fn-run_info}
outputs:
artifacts:
- {name: same-step-000-afc67b36914c4108b47e8b4bb316869d-fn-output_context_path,
path: /tmp/outputs/output_context_path/data}
metadata:
annotations:
pipelines.kubeflow.org/v2_component: "true"
pipelines.kubeflow.org/component_ref: '{}'
pipelines.kubeflow.org/arguments.parameters: '{"input_context_path": "", "metadata_url":
"{{inputs.parameters.metadata_url}}", "run_info": "{{inputs.parameters.run-info-fn-run_info}}"}'
pipelines.kubeflow.org/max_cache_staleness: P0D
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.12
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/v2_component: "true"
pipelines.kubeflow.org/enable_caching: "true"
initContainers:
- command: [launcher, --copy, /kfp-launcher/launch]
image: gcr.io/ml-pipeline/kfp-launcher:1.8.7
name: kfp-launcher
mirrorVolumeMounts: true
volumes:
- {name: kfp-launcher}
arguments:
parameters:
- {name: context, value: ''}
- {name: metadata_url, value: ''}
- {name: pipeline-root, value: ''}
- {name: pipeline-name, value: pipeline/root_pipeline_compilation}
serviceAccountName: pipeline-runner
```
### Expected result
Runs correctl
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,216,301,149 |
I_kwDOB-71UM5If0hd
| 7,617 |
[frontend] "Failed to retrieve logs" in finished pipelines.
|
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| false | null |
[] | null |
[
"We automatically recycle the pods 24 hours after creation. You can try to re-run the pipeline to get logs. Please re-open this issue if you have any more questions.",
"This was failing on microk8s install of kubeflow pipelines.\r\nOnce I switched to https://k3ai.github.io/docs/tutorials/kubeflow/ everything is great.",
"We also experience the very same issue, once our cluster Auto-Scales. \r\n\r\nAll logs are gone and NOT read from our mirror in MINIO (interface of KFP to access S3) / AWS S3 (physical location)."
] | 2022-04-26T18:03:04 | 2023-01-27T10:39:18 | 2022-05-05T22:44:07 |
NONE
| null |
Sometimes I click on old pipelines to check their logs and get the error "Failed to retrieve logs":
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| 1,215,586,951 |
I_kwDOB-71UM5IdGKH
| 7,616 |
Using API to manage pipeline in Multi-User mode
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open
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[] | null |
[
"@grapefruitL What do you mean by \"manage\"? What is your use case?",
"@jlyaoyuli thank you for your reply.\r\nDeploying, using and maintaining kubeflow requires some k8s knowledge that our data scientists don't have\r\nNow I only have the way\r\nOur data scientists are only responsible for writing the yaml of the pipeline and observing the running results, not for the startup of the pipeline"
] | 2022-04-26T08:21:52 | 2022-04-29T00:58:38 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
use [api ](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/)to manage pipeline in Multi-User mode
### What is the use case or pain point?
in our dev group, kubeflow is managed by engineers who are not familiar with python, they need to manage pipelines with pipeline api and yaml file generated from our data scientists
### Is there a workaround currently?
now our data scientists are working without any mlops tools
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5IcbNZ
| 7,614 |
Placeholder values (eg. {{workflow.status}} ) not working for v2 compiler & Vertex AI.
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[
"/assign @chensun ",
"`{{workflow.status}}` and such are Argo variables (https://argoproj.github.io/argo-workflows/variables/#global), and Vertex Pipelines is not based on Argo, so it's by design that they won't work. \r\nOn Vertex Pipelines, we provide a set of platform-agnostic placeholders. Here're some examples: https://github.com/kubeflow/pipelines/blob/6296c18c7c9ec37489f36efc2cd121a800155da6/sdk/python/kfp/compiler_cli_tests/test_data/pipeline_with_placeholders.py#L27-L46\r\n\r\n",
"Okay, I understand @chensun . Do you have any idea of how one would get the status of the run within a component, as asked in [this StackOverflow question?](https://stackoverflow.com/questions/72094768/how-to-get-the-status-of-a-pipeline-run-within-a-component-running-on-vertex-ai?noredirect=1#comment127610264_72094768) Would really appreciate your insight, since this is a key aspect of allowing us to move our infrastructure serverless on VertexAI. Thanks in advance!\r\n\r\n",
"Hello @chensun, I've also encountered this problem.\r\nWhat's the platform-agnostic alternative of `{{workflow.status}}`? It seems [kfp/dsl/\\_\\_init\\_\\_.py](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/__init__.py) doesn't have that.\r\nWIthout this, we can't migrate our in-house component which uses `{{workflow.status}}` to Vertex AI.\r\nThank you for your help in advance.\r\n\r\nI'd say you should reopen this issue as it's not solved yet.\r\n",
"@ysk24ok @msvensson222 \r\nFYI, there is a special input variable `PipelineTaskFinalStatus` for `ExitHandler`. This variable contains the workflow status, resource name, error code and so on.\r\nref: https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/pipelines/#dslexithandler\r\n\r\nYou may want the workflow status at the end of the pipeline. Instead of `{{workflow.status}}`, you can use `PipelineTaskFinalStatus.state` on the ExitHandler executed at the end of the pipeline to get the pipeline status.\r\nBut I think `PipelineTaskFinalStatus` is only available for Python-based component, not for YAML component.\r\n"
] | 2022-04-26T05:22:56 | 2022-11-08T14:12:18 | 2022-05-12T20:55:57 |
NONE
| null |
### Environment
* KFP version:
`1.8.12`
* All dependencies version:
`google-cloud-aiplatform==1.7.1`
### Steps to reproduce
I define a very simple pipeline with a single, image based, component, and compile it using the v2 compiler. After compiling it, I submit the pipeline job to Google Vertex AI. The input argument for the component is a placeholder, {{workflow.status}}. During execution on Vertex AI, the component log `{{workflow.status}}` as a string, but I was expecting `Succeeded`, or some other status value. Seems like the placeholder does not get replaced during execution.
Pipeline:
```python
from google.cloud import aiplatform
from google.cloud.aiplatform import pipeline_jobs
from kfp.v2.dsl import pipeline
from kfp.components import load_component_from_file
@pipeline(name="basic-pipeline",
description="A pipeline with image based components",
pipeline_root='gs://.../basic-pipeline')
def pipeline():
step_0_spec = load_component_from_file('./comp_0.yaml')
step_0_spec(status="{{workflow.status}}")
if __name__ == '__main__':
from kfp.v2 import compiler
compiler.Compiler().compile(
pipeline_func=pipeline,
package_path="pipeline.json"
)
from google.oauth2 import service_account
credentials = service_account.Credentials.from_service_account_file("./gcp-sa-key.json")
aiplatform.init(project='...',
location='europe-west4',
credentials=credentials)
job = pipeline_jobs.PipelineJob(
display_name="basic-pipeline",
template_path="pipeline.json",
enable_caching=False
)
job.run(service_account='...', sync=False)
```
Component (`comp_0.yaml` in the code above) specification:
```yaml
name: Step 0
description: Print inputs.
inputs:
- {name: status, type: String}
implementation:
container:
image: eu.gcr.io/.../test_component:latest
command: [
python3,
main.py,
--status, { inputValue: status }
]
```
Component code:
```python
import logging
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--status', type=str)
return parser.parse_known_args()
def main(FLAGS):
logging.basicConfig(level=logging.INFO)
logging.info(f"Status: {FLAGS.status}")
if __name__ == '__main__':
FLAGS, unparsed = get_args()
main(FLAGS)
```
When executed on Vertex AI, the component logs "Status: {{workflow.status}}"
### Expected result
I was expecting the `status` within the component to log `Succeeded`, or some other status value. This way of using placeholder works fine with the old compiler and not using Vertex AI. I am not sure if the problem is the new compiler or Vertex AI since I don't think I can use the old compiler for Vertex AI (only accepts json pipelines).
Complete compiled JSON pipeline (`pipeline.json`):
```json
{
"pipelineSpec": {
"components": {
"comp-step-0": {
"executorLabel": "exec-step-0",
"inputDefinitions": {
"parameters": {
"status": {
"type": "STRING"
}
}
}
}
},
"deploymentSpec": {
"executors": {
"exec-step-0": {
"container": {
"command": [
"python3",
"main.py",
"--status",
"{{$.inputs.parameters['status']}}"
],
"image": "eu.gcr.io/.../test_component:latest"
}
}
}
},
"pipelineInfo": {
"name": "basic-pipeline"
},
"root": {
"dag": {
"tasks": {
"step-0": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-step-0"
},
"inputs": {
"parameters": {
"status": {
"runtimeValue": {
"constantValue": {
"stringValue": "{{workflow.status}}"
}
}
}
}
},
"taskInfo": {
"name": "step-0"
}
}
}
}
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.8.12"
},
"runtimeConfig": {
"gcsOutputDirectory": "gs://.../basic-pipeline"
}
}
```
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,215,393,742 |
I_kwDOB-71UM5IcW_O
| 7,613 |
[bug] Documentation link broken
|
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[
"Same on this page:\r\nhttps://www.kubeflow.org/docs/components/pipelines/sdk-v2/build-pipeline/\r\n\r\n1. Artifact and Dataset point to broken url: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py\r\n\r\n<img width=\"696\" alt=\"Screen Shot 2022-04-26 at 1 06 05 AM\" src=\"https://user-images.githubusercontent.com/140710/165225581-bfa6d182-95f8-4360-9cd4-64502e6905e4.png\">\r\n\r\n2. Also \"Web Download component\" link doesn't work: \r\nhttps://github.com/kubeflow/pipelines/blob/master/components/web/Download/component.yaml",
"/assign @connor-mccarthy "
] | 2022-04-26T04:56:38 | 2022-05-05T22:40:28 | 2022-05-05T22:40:28 |
NONE
| null |
On this page:
https://www.kubeflow.org/docs/components/pipelines/reference/component-spec/
This link to types is broken: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/types.py
<img width="754" alt="Screen Shot 2022-04-26 at 12 55 55 AM" src="https://user-images.githubusercontent.com/140710/165224593-4f0d6547-f241-4a79-bb4a-6c8dd7e1561d.png">
|
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| 1,215,320,315 |
I_kwDOB-71UM5IcFD7
| 7,611 |
Follow up with the latest PipelineSpec proto changes
|
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| 1,215,219,641 |
I_kwDOB-71UM5Ibse5
| 7,610 |
Enable KFPv2 UI by default by creating a new feature key
|
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"/assign @jlyaoyuli "
] | 2022-04-26T01:09:42 | 2022-05-11T19:51:19 | 2022-05-11T19:51:19 |
COLLABORATOR
| null |
## Description
We are planning to enable KFPv2 UI feature when we release KFP 2.0.0-alpha.2. Therefore, we need to switch over to a new feature key which is enabled by default. And change all `FeatureKey.V2` usages to `FeatureKey.V2_ALPHA`.
## Tentative steps
1. Create a new feature key with name like `FeatureKey.V2_ALPHA`.
2. Make this feature key enabled by default.
3. Change all usage of `FeatureKey.V2` to `FeatureKey.V2_ALPHA`.
4. Validate all the V2 features are enabled by default.
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I_kwDOB-71UM5IbYkG
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[backend] JSONDecoder error?
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[
"Here's the logs:\r\n```\r\ntime=\"2022-04-26T17:38:09.547Z\" level=info msg=\"capturing logs\" argo=true\r\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: \r\nhttps://pip.pypa.io/warnings/venv\r\n[KFP Executor 2022-04-26 17:38:24,691 INFO]: Looking for component `run_info_fn` in --component_module_path `/tmp/tmp.NJW6PWXpIt/ephemeral_component.py`\r\n[KFP Executor 2022-04-26 17:38:24,691 INFO]: Loading KFP component \"run_info_fn\" from /tmp/tmp.NJW6PWXpIt/ephemeral_component.py (directory \"/tmp/tmp.NJW6PWXpIt\" and module name \"ephemeral_component\")\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\r\n \"__main__\", mod_spec)\r\n File \"/usr/local/lib/python3.7/runpy.py\", line 85, in _run_code\r\n exec(code, run_globals)\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/v2/components/executor_main.py\", line 104, in <module>\r\n executor_main()\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/v2/components/executor_main.py\", line 94, in executor_main\r\n executor_input = json.loads(args.executor_input)\r\n File \"/usr/local/lib/python3.7/json/__init__.py\", line 348, in loads\r\n return _default_decoder.decode(s)\r\n File \"/usr/local/lib/python3.7/json/decoder.py\", line 337, in decode\r\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n File \"/usr/local/lib/python3.7/json/decoder.py\", line 353, in raw_decode\r\n obj, end = self.scan_once(s, idx)\r\njson.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)\r\ntime=\"2022-04-26T17:38:24.803Z\" level=error msg=\"cannot save artifact /tmp/outputs/run_info/data\" argo=true error=\"stat /tmp/outputs/run_info/data: no such file or directory\"\r\nError: exit status 1\r\n```\r\n\r\nHere's the files to repro:\r\nroot_pipeline_04d99580c84b47c28405a2c8bcae8703.py\r\n```\r\nimport kfp.v2.components\r\nfrom kfp.v2.dsl import InputPath\r\nfrom kubernetes.client.models import V1EnvVar\r\nfrom kubernetes import client, config\r\nfrom typing import NamedTuple\r\nfrom base64 import b64encode\r\nimport kfp.v2.dsl as dsl\r\nimport kubernetes\r\nimport json\r\nimport kfp\r\n\r\nfrom run_info import run_info_fn\r\nfrom same_step_000_ce6494722c474dd3b8bef482bb976557 import same_step_000_ce6494722c474dd3b8bef482bb976557_fn\r\n\r\n\r\nrun_info_comp = kfp.v2.dsl.component(\r\n func=run_info_fn,\r\n packages_to_install=[\r\n \"kfp\",\r\n \"dill\",\r\n ],\r\n)\r\n\r\nsame_step_000_ce6494722c474dd3b8bef482bb976557_comp = kfp.v2.dsl.component(\r\n func=same_step_000_ce6494722c474dd3b8bef482bb976557_fn,\r\n base_image=\"public.ecr.aws/j1r0q0g6/notebooks/notebook-servers/codeserver-python:v1.5.0\",\r\n packages_to_install=[\r\n \"dill\",\r\n \"requests\",\r\n # TODO: make this a loop\r\n ],\r\n)\r\n\r\[email protected](name=\"root_pipeline_compilation\",)\r\ndef root(\r\n context: str='', metadata_url: str='',\r\n):\r\n # Generate secrets (if not already created)\r\n secrets_by_env = {}\r\n\r\n env_vars = {\r\n }\r\n\r\n run_info = run_info_comp(run_id=kfp.dsl.RUN_ID_PLACEHOLDER)\r\n\r\n\r\n same_step_000_ce6494722c474dd3b8bef482bb976557 = same_step_000_ce6494722c474dd3b8bef482bb976557_comp(\r\n input_context_path=\"\",\r\n run_info=run_info.outputs[\"run_info\"],\r\n metadata_url=metadata_url\r\n )\r\n\r\n same_step_000_ce6494722c474dd3b8bef482bb976557.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\r\n for k in env_vars:\r\n same_step_000_ce6494722c474dd3b8bef482bb976557.add_env_variable(V1EnvVar(name=k, value=env_vars[k]))\r\n\r\n```\r\n\r\nrun_info.py\r\n```\r\n\"\"\"\r\nThe run_info component fetches metadata about the current pipeline execution\r\nfrom kubeflow and passes it on to the user code step components.\r\n\"\"\"\r\nfrom typing import NamedTuple\r\n\r\n\r\ndef run_info_fn(\r\n run_id: str,\r\n) -> NamedTuple(\"RunInfoOutput\", [(\"run_info\", str),]):\r\n from base64 import urlsafe_b64encode\r\n from collections import namedtuple\r\n import datetime\r\n import base64\r\n import dill\r\n import kfp\r\n\r\n client = kfp.Client(host=\"http://ml-pipeline:8888\")\r\n run_info = client.get_run(run_id=run_id)\r\n\r\n run_info_dict = {\r\n \"run_id\": run_info.run.id,\r\n \"name\": run_info.run.name,\r\n \"created_at\": run_info.run.created_at.isoformat(),\r\n \"pipeline_id\": run_info.run.pipeline_spec.pipeline_id,\r\n }\r\n\r\n # Track kubernetes resources associated wth the run.\r\n for r in run_info.run.resource_references:\r\n run_info_dict[f\"{r.key.type.lower()}_id\"] = r.key.id\r\n\r\n # Base64-encoded as value is visible in kubeflow ui.\r\n output = urlsafe_b64encode(dill.dumps(run_info_dict))\r\n\r\n return namedtuple(\"RunInfoOutput\", [\"run_info\"])(\r\n str(output, encoding=\"ascii\")\r\n )\r\n```\r\n\r\nsame_step_000_ce6494722c474dd3b8bef482bb976557.py\r\n```\r\nimport kfp\r\nfrom kfp.v2.dsl import component, Artifact, Input, InputPath, Output, OutputPath, Dataset, Model\r\nfrom typing import NamedTuple\r\n\r\n\r\ndef same_step_000_ce6494722c474dd3b8bef482bb976557_fn(\r\n input_context_path: InputPath(str),\r\n output_context_path: OutputPath(str),\r\n run_info: str = \"gAR9lC4=\",\r\n metadata_url: str = \"\",\r\n):\r\n from base64 import urlsafe_b64encode, urlsafe_b64decode\r\n from pathlib import Path\r\n import datetime\r\n import requests\r\n import tempfile\r\n import dill\r\n import os\r\n\r\n input_context = None\r\n with Path(input_context_path).open(\"rb\") as reader:\r\n input_context = reader.read()\r\n\r\n # Helper function for posting metadata to mlflow.\r\n def post_metadata(json):\r\n if metadata_url == \"\":\r\n return\r\n\r\n try:\r\n req = requests.post(metadata_url, json=json)\r\n req.raise_for_status()\r\n except requests.exceptions.HTTPError as err:\r\n print(f\"Error posting metadata: {err}\")\r\n\r\n # Move to writable directory as user might want to do file IO.\r\n # TODO: won't persist across steps, might need support in SDK?\r\n os.chdir(tempfile.mkdtemp())\r\n\r\n # Load information about the current experiment run:\r\n run_info = dill.loads(urlsafe_b64decode(run_info))\r\n\r\n # Post session context to mlflow.\r\n if len(input_context) > 0:\r\n input_context_str = urlsafe_b64encode(input_context)\r\n post_metadata(\r\n {\r\n \"experiment_id\": run_info[\"experiment_id\"],\r\n \"run_id\": run_info[\"run_id\"],\r\n \"step_id\": \"same_step_000\",\r\n \"metadata_type\": \"input\",\r\n \"metadata_value\": input_context_str,\r\n \"metadata_time\": datetime.datetime.now().isoformat(),\r\n }\r\n )\r\n\r\n # User code for step, which we run in its own execution frame.\r\n user_code = f\"\"\"\r\nimport dill\r\n\r\n# Load session context into global namespace:\r\nif { len(input_context) } > 0:\r\n dill.load_session(\"{ input_context_path }\")\r\n\r\n{dill.loads(urlsafe_b64decode(\"gASVGAAAAAAAAACMFHByaW50KCJIZWxsbyB3b3JsZCIplC4=\"))}\r\n\r\n# Remove anything from the global namespace that cannot be serialised.\r\n# TODO: this will include things like pandas dataframes, needs sdk support?\r\n_bad_keys = []\r\n_all_keys = list(globals().keys())\r\nfor k in _all_keys:\r\n try:\r\n dill.dumps(globals()[k])\r\n except TypeError:\r\n _bad_keys.append(k)\r\n\r\nfor k in _bad_keys:\r\n del globals()[k]\r\n\r\n# Save new session context to disk for the next component:\r\ndill.dump_session(\"{output_context_path}\")\r\n\"\"\"\r\n\r\n # Runs the user code in a new execution frame. Context from the previous\r\n # component in the run is loaded into the session dynamically, and we run\r\n # with a single globals() namespace to simulate top-level execution.\r\n exec(user_code, globals(), globals())\r\n\r\n # Post new session context to mlflow:\r\n with Path(output_context_path).open(\"rb\") as reader:\r\n context = urlsafe_b64encode(reader.read())\r\n post_metadata(\r\n {\r\n \"experiment_id\": run_info[\"experiment_id\"],\r\n \"run_id\": run_info[\"run_id\"],\r\n \"step_id\": \"same_step_000\",\r\n \"metadata_type\": \"output\",\r\n \"metadata_value\": context,\r\n \"metadata_time\": datetime.datetime.now().isoformat(),\r\n }\r\n )\r\n```\r\n\r\nPython file to execute to run:\r\n```\r\nfrom sameproject.ops import helpers\r\nfrom pathlib import Path\r\nimport importlib\r\nimport kfp\r\n\r\n\r\ndef deploy(compiled_path: Path, root_module_name: str):\r\n with helpers.add_path(str(compiled_path)):\r\n kfp_client = kfp.Client() # only supporting 'kubeflow' namespace\r\n root_module = importlib.import_module(root_module_name)\r\n\r\n return kfp_client.create_run_from_pipeline_func(\r\n root_module.root,\r\n arguments={},\r\n )\r\n```\r\n",
"Added to stackoverflow - https://stackoverflow.com/questions/72019232/json-decoder-jsondecodeerror-when-migrating-to-kubeflow-pipelines-v2",
"Turns out it has to do with not compiling with the right execution mode on.\r\n\r\nDid a PR to improve the error message: https://github.com/kubeflow/pipelines/pull/7619 \r\n\r\nIf you're getting this, your code should look like this.\r\n\r\n```\r\n Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(pipeline_func=root_module.root, package_path=str(package_yaml_path))\r\n\r\n```"
] | 2022-04-25T23:08:38 | 2022-04-26T20:39:02 | 2022-04-26T20:39:01 |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Standard deployment to AWS
* KFP version:
1.8.1
* KFP SDK version:
1.8.12
### Steps to reproduce
Very simple pipeline:
```
import kfp.v2.components
from kfp.v2.dsl import InputPath
from kubernetes.client.models import V1EnvVar
from kubernetes import client, config
from typing import NamedTuple
from base64 import b64encode
import kfp.v2.dsl as dsl
import kubernetes
import json
import kfp
from run_info import run_info_fn
run_info_comp = kfp.components.create_component_from_func_v2(
func=run_info_fn,
packages_to_install=[
"kfp",
"dill",
],
)
@kfp.dsl.pipeline(name="root_pipeline_compilation",)
def root(
context: str='', metadata_url: str='',
):
run_info = run_info_comp(run_id=kfp.dsl.RUN_ID_PLACEHOLDER)
```
Component:
```
from typing import NamedTuple
def run_info_fn(
run_id: str,
) -> NamedTuple("RunInfoOutput", [("run_info", str),]):
from base64 import urlsafe_b64encode
from collections import namedtuple
import datetime
import base64
import dill
import kfp
client = kfp.Client(host="http://ml-pipeline:8888")
run_info = client.get_run(run_id=run_id)
run_info_dict = {
"run_id": run_info.run.id,
"name": run_info.run.name,
"created_at": run_info.run.created_at.isoformat(),
"pipeline_id": run_info.run.pipeline_spec.pipeline_id,
}
# Track kubernetes resources associated wth the run.
for r in run_info.run.resource_references:
run_info_dict[f"{r.key.type.lower()}_id"] = r.key.id
# Base64-encoded as value is visible in kubeflow ui.
output = urlsafe_b64encode(dill.dumps(run_info_dict))
return namedtuple("RunInfoOutput", ["run_info"])(
str(output, encoding="ascii")
)
```
### Expected result
Pipeline runs.
### Materials and Reference
Actual error: `json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)`
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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| 1,214,605,966 |
I_kwDOB-71UM5IZWqO
| 7,602 |
[feature] Support for "OR" condition on multiple filter predicates.
|
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[
"/area backend",
"@S-Mann Thanks for your suggestion, it's a valid feature request. Feel free to contribute if you are interested!"
] | 2022-04-25T14:34:10 | 2022-04-28T22:58:21 | null |
NONE
| null |
### Feature Area
/area backend
### What feature would you like to see?
Currently filter objects can have one or more query objects but they work with `AND` it would be great to somehow switch to `OR` as well.
For example if you want to query [runs list API](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs-get) and get the multiple runs with different names you can't do it.
```json
{
"predicates": [
{
"key": "id",
"op": "IS_SUBSTRING",
"string_value": "run1"
},
{
"key": "id",
"op": "IS_SUBSTRING",
"string_value": "run21"
}
]
}
```
And lets say you have 4 runs `["run1", "run11", "run21", "run51"]`
This filter on the API should return 3 runs `["run1", "run11", "run21"]`
As of now it returns no runs `[]`
### What is the use case or pain point?
Calling [runs list API](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs-get) multiple times (as many times as the number of predicates) is a bad design if we already have filtering why not refine it a little more and support multiple predicates with "OR" as well.
### Is there a workaround currently?
Call [runs list API](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs-get) multiple times (as many times as the number of predicates)
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
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I_kwDOB-71UM5IVWvh
| 7,601 |
[feature] Add the annotation `cluster-autoscaler.kubernetes.io/safe-to-evict: false` to the pod
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[
"/assign @chensun "
] | 2022-04-24T06:09:37 | 2022-04-28T22:48:46 | null |
CONTRIBUTOR
| null |
### Feature Area
/area sdk
### What feature would you like to see?
Hi, it would be nice if we can add the annotation `cluster-autoscaler.kubernetes.io/safe-to-evict: false` to the pipeline/workflow pod __by default__
### What is the use case or pain point?
Without this annotation, the pod will be evicted accidentally. (If the pod running for hours, it will get a bigger change to be evicted)
The user can add this by the SDK's api but I think this should be set as default.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5IU_EL
| 7,600 |
Support creating new run from pipeline, pipeline version, and existing run.
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I_kwDOB-71UM5IU_D8
| 7,599 |
Support providing protobuf.Value as parameters when creating new run.
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[
"Fixed by @jlyaoyuli "
] | 2022-04-23T21:42:27 | 2023-01-18T08:42:56 | 2023-01-18T08:42:55 |
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I_kwDOB-71UM5IU_D0
| 7,598 |
Allow users to configure pipeline root when creating new run.
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[
"I believe this is fixed. Let me know otherwise @jlyaoyuli "
] | 2022-04-23T21:42:23 | 2023-01-18T08:42:09 | 2023-01-18T08:41:23 |
COLLABORATOR
| null |
When creating new Run, user can specify a pipeline root location for artifact payload. PipelineRood definition is in https://www.kubeflow.org/docs/components/pipelines/overview/pipeline-root/.
Feature:
1. Show a `Checkbox` which asks user whether to set custom pipeline root.
2. If `Checkbox` is not checked, show nothing.
3. If `Checkbox` is checked, show an input box for accepting string value.
4. When creating new run, use the input box value to set `pipelineRoot` field inside `runtimeConfig`. If `Checkbox` is not checked, do not set `pipelineRoot` value.
|
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| 1,211,215,152 |
I_kwDOB-71UM5IMa0w
| 7,589 |
Training Pipeline fails after uploading model artifacts to Google Cloud Storage
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[
"/assign @IronPan ",
"Hi there - were you able to resolve this issue? Any tips? I'm seeing the same thing on my end. ",
"Hi there, I met the same problem too. any ideas, please?",
"I'm running into a similar problem, as well. Any solutions? ",
"same problem",
"same here, please keep in the loop",
"I have the same problem"
] | 2022-04-21T15:38:03 | 2023-03-07T09:16:35 | null |
NONE
| null |
## Environment:
KFP version 1.8.9
## Steps to Reproduce
Here's a snippet of my training code:
```
param_grid = {
"max_tokens" : [100],
"max_len" : [10],
"dropout" : [0.1],
}
gs_model = GridSearchCV(KerasClassifier(build_model), param_grid, cv=3, scoring='accuracy')
gs_model.fit(x_train, y_train, verbose = 1)
best_params = gs_model.best_params_
optimized_model = build_model(max_tokens = best_params["max_tokens"], max_len = best_params["max_len"], dropout = best_params["dropout"])
optimized_model.fit(x_train, y_train, epochs = 3, validation_split = 0.2, callbacks = tensorflow.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2, verbose = 1))
model_name = "/tmp/custom-model-test"
optimized_model.save(model_name)
print('saved model to ', model_name)
upload_from_directory(model_name, "[redacted Bucket name]", "custom-model-test")
try:
upload_blob("[redacted Bucket name]", "goback-custom-train/requirements.txt", "custom-model-test/requirements.txt")
except:
print(traceback.format_exc())
print('Upload failed')
```
Which succeeds in uploading to Google Cloud Storage. It makes use of `model.save` from Keras, and saves the created directory to my Bucket, along with a `requirements.txt` file inside it. To be clear, once the code block above is ran, a directory `custom-model-test/` is created in `gs://[redacted Bucket name]` with contents `requirements.txt` and `tmp/`. Inside `tmp/` is `keras-metadata.pb`, `saved_model.pb`, and `variables/`.
I run this container in the following codeblock in my Kubeflow Pipeline:
```
training_job_run_op = gcc_aip.CustomContainerTrainingJobRunOp(
project = project,
display_name = display_name,
container_uri=training_container_uri,
model_serving_container_image_uri=model_serving_container_image_uri,
model_serving_container_predict_route = model_serving_container_predict_route,
model_serving_container_health_route = model_serving_container_health_route,
model_serving_container_ports = [8080],
service_account = "[redacted service account]",
machine_type = "n1-highmem-2",
accelerator_type ="NVIDIA_TESLA_V100",
staging_bucket = BUCKET_NAME)
```
For some reason, after training and saving the model artifacts (the logs for the model training says it completed successfully) the pipeline fails with logs saying:
```
" File "/opt/python3.7/lib/python3.7/site-packages/google/cloud/aiplatform/training_jobs.py", line 905, in _raise_failure "
" raise RuntimeError("Training failed with:\n%s" % self._gca_resource.error) "
"RuntimeError: Training failed with: "
"code: 5
"message: "There are no files under \"gs://[redacted Bucket name]/aiplatform-custom-training-2022-04-21-14:04:46.151/model\" to copy."
"
```
What's going on here? What's the fix?
|
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I_kwDOB-71UM5IH383
| 7,587 |
[frontend] "clone recurring run" then changing the pipeline version resets run parameters to defaults
|
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[
"I don't think \"clone recurring run\" passes the parameters. If you think this is a useful feature, feel free to open a feature request and describe your use case."
] | 2022-04-20T18:00:15 | 2022-04-21T22:58:33 | null |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)? terraform
* KFP version: 1.4.0
### Steps to reproduce
Make sure you have a pipeline with more than 1 version.
Create a recurring run. Change some of the variables to non-default values. Start the run.
Then, clone the just-created recurring run. Change the pipeline version.
### Expected result
I think the values should remain what they were from the cloned recurring run and not be replaced by the defaults of the new pipeline version - creating a completely new recurring run with the new pipeline already provides that behavior.
---
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,206,046,072 |
I_kwDOB-71UM5H4s14
| 7,573 |
[backend] Can't run a data-passed pipeline many times with the same pipeline function name. Error: This step is in Error state with this message: Error (exit code 1): key unsupported: cannot get key for artifact location, because it is invalid
|
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[
"Maybe it's because the `@pipeline` decorator is missing?"
] | 2022-04-16T07:44:55 | 2022-04-21T22:55:16 | null |
NONE
| null |
### Environment
* Kubeflow Pipelines Standalone on a local cluster.
* KFP version: 1.7.0
* KFP SDK version: 1.8.2
### Steps to reproduce
Dear developers, I try to run the same pipeline many times with different arguments, which are `line` and `count` in the following official `data_pass` example.
In the beginning, the **main pipeline function** is named `pipeline_func`. And the pipeline runs successfully the 1st time, but for the 2nd time, the error happens, which is quite the same as this closed issue https://github.com/kubeflow/pipelines/issues/5948, also, has a connection with this issue https://github.com/argoproj/argo-workflows/issues/6497.
I change the **main pipeline function** name, trying to figure out the problem. Below is the process.
```bash
# (1) change the pipeline function name, success
# pipeline_func β a-new-pipeline-func-please
a-new-pipeline-func-please-s8tb5-2208969941 # launched pipeline pods
a-new-pipeline-func-please-s8tb5-2839087706
# (2) use the new name, run again, error
a-new-pipeline-func-please-snp4h-1210744786
# (3) change the pipeline function name again, success
# a-new-pipeline-func-please, success β a-new-pipeline-func-please-again
a-new-pipeline-func-please-again-wfq8v-351450082
a-new-pipeline-func-please-again-wfq8v-766454711
# (4) use the new name, run again, success
a-new-pipeline-func-please-again-pcbzd-2732903275
a-new-pipeline-func-please-again-pcbzd-98665068
# (5) use the new name, run again, error
a-new-pipeline-func-please-again-kg4w4-25440416
```
Here is the official `data_pass` example.
```py
from kfp.components import InputPath, OutputPath, func_to_container_op
import kfp
kfp_client = kfp.Client()
@func_to_container_op
def repeat_line(line: str, count: int, output_text_path: OutputPath(str)):
with open(output_text_path, 'w') as writer:
for i in range(count):
writer.write(line + '\n')
@func_to_container_op
def print_text(input_text_path: InputPath()):
with open(input_text_path, 'r') as reader:
for line in reader:
print(line, end='')
def a_new_pipeline_func_please_again(line: str = 'Hello', count: int = 10):
repeat_line_task = repeat_line(line=line, count=count)
print_text(repeat_line_task.output)
if __name__ == '__main__':
kfp_client.create_run_from_pipeline_func(
a_new_pipeline_func_please_again,
arguments={
'line': "bbb",
'count': 5,
},
)
```
### Expected result
About the artifact repository. I find some clues in the kubeflow namespace
```bash
$ kubectl get cm -n kubeflow
NAME DATA AGE
inferenceservice-config 9 87d
istio-ca-root-cert 1 160d
kfp-launcher 1 150d
kfserving-config 1 87d
kfserving-models-web-app-config-mtgm8bbd98 1 87d
metadata-grpc-configmap 2 150d
ml-pipeline-ui-configmap 1 150d
pipeline-install-config 15 150d
workflow-controller-configmap 3 150d
$ kubectl get cm workflow-controller-configmap -n kubeflow -o yaml
apiVersion: v1
data:
artifactRepository: | # the artifactRepository configuration.
archiveLogs: true
s3:
endpoint: "minio-service.kubeflow:9000"
bucket: "mlpipeline"
# keyFormat is a format pattern to define how artifacts will be organized in a bucket.
# It can reference workflow metadata variables such as workflow.namespace, workflow.name,
# pod.name. Can also use strftime formating of workflow.creationTimestamp so that workflow
# artifacts can be organized by date. If omitted, will use `{{workflow.name}}/{{pod.name}}`,
# which has potential for have collisions, because names do not guarantee they are unique
# over the lifetime of the cluster.
# Refer to https://kubernetes.io/docs/concepts/overview/working-with-objects/names/.
#
# The following format looks like:
# artifacts/my-workflow-abc123/2018/08/23/my-workflow-abc123-1234567890
# Adding date into the path greatly reduces the chance of {{pod.name}} collision.
keyFormat: "artifacts/{{workflow.name}}/{{workflow.creationTimestamp.Y}}/{{workflow.creationTimestamp.m}}/{{workflow.creationTimestamp.d}}/{{pod.name}}"
# insecure will disable TLS. Primarily used for minio installs not configured with TLS
insecure: true
accessKeySecret:
name: mlpipeline-minio-artifact
key: accesskey
secretKeySecret:
name: mlpipeline-minio-artifact
key: secretkey
containerRuntimeExecutor: docker
executor: |
imagePullPolicy: IfNotPresent
resources:
requests:
cpu: 0.01
memory: 32Mi
limits:
cpu: 0.5
memory: 512Mi
kind: ConfigMap
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"v1","data":{"artifactRepository":"archiveLogs: true\ns3:\n endpoint: \"minio-service.kubeflow:9000\"\n bucket: \"mlpipeline\"\n # keyFormat is a format pattern to define how artifacts will be organized in a bucket.\n # It can reference workflow metadata variables such as workflow.namespace, workflow.name,\n # pod.name. Can also use strftime formating of workflow.creationTimestamp so that workflow\n # artifacts can be organized by date. If omitted, will use `{{workflow.name}}/{{pod.name}}`,\n # which has potential for have collisions, because names do not guarantee they are unique\n # over the lifetime of the cluster.\n # Refer to https://kubernetes.io/docs/concepts/overview/working-with-objects/names/.\n #\n # The following format looks like:\n # artifacts/my-workflow-abc123/2018/08/23/my-workflow-abc123-1234567890\n # Adding date into the path greatly reduces the chance of {{pod.name}} collision.\n keyFormat: \"artifacts/{{workflow.name}}/{{workflow.creationTimestamp.Y}}/{{workflow.creationTimestamp.m}}/{{workflow.creationTimestamp.d}}/{{pod.name}}\"\n # insecure will disable TLS. Primarily used for minio installs not configured with TLS\n insecure: true\n accessKeySecret:\n name: mlpipeline-minio-artifact\n key: accesskey\n secretKeySecret:\n name: mlpipeline-minio-artifact\n key: secretkey\n","containerRuntimeExecutor":"docker","executor":"imagePullPolicy: IfNotPresent\nresources:\n requests:\n cpu: 0.01\n memory: 32Mi\n limits:\n cpu: 0.5\n memory: 512Mi\n"},"kind":"ConfigMap","metadata":{"annotations":{},"labels":{"application-crd-id":"kubeflow-pipelines"},"name":"workflow-controller-configmap","namespace":"kubeflow"}}
creationTimestamp: "2021-11-17T03:05:35Z"
labels:
application-crd-id: kubeflow-pipelines
name: workflow-controller-configmap
namespace: kubeflow
resourceVersion: "16984168"
selfLink: /api/v1/namespaces/kubeflow/configmaps/workflow-controller-configmap
uid: d0b5c55e-ed8a-4b37-835e-6a2b4d3c402c
```
Please help me. Many thanks!
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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| 1,205,894,338 |
I_kwDOB-71UM5H4HzC
| 7,571 |
Take a yaml format for pipelineSpec to submit them from UI to backend
|
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[
"/assign @jlyaoyuli ",
"Except for `xgboost_sample_pipeline.yaml` pipeline, all of the testing pipelines were able to be uploaded via URL and local file. DAG visualization, side panel, and `Pipeline Spec` etc. are displayed as we expect."
] | 2022-04-15T21:16:13 | 2022-05-11T19:55:34 | 2022-05-11T19:55:34 |
COLLABORATOR
| null |
# Problem
Support functionality to submit a pipeline template to backend, either from URL or a local file.
# Materials
A list of sample pipelines are stored in https://github.com/kubeflow/pipelines/tree/master/sdk/python/kfp/compiler_cli_tests/test_data.
# User journey
1. Create a new Pipeline, select to upload from local folder. Successfully create a pipeline entity which is shown on Pipeline List page.
1. Create a new Pipeline, select to import from URL. Successfully create a pipeline entity which is shown on Pipeline List page.
2. Create a new Pipeline Version, successfully create a pipeline entity.
3. Select a V2 pipeline entity, you are able to see the ReactFlow UI (New DAG visualization). It should be able to show Execution, Artifact, Sub-DAG, and side panel.
4. Select a V2 pipeline entity, you are able to navigate to `Pipeline Spec` for a view of the yaml file.
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I_kwDOB-71UM5H1iuN
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[backend] Cannot create new pipeline or upload new version to existing pipelines
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[
"The issue is solved with a workaround. \r\n\r\nFirst `kubectl exec mysql-pod -it -- mysql -h mysql`\r\n\r\nThen change the column of Description in Table pipeline_versions to accept NULL values\r\n\r\n`ALTER table pipeline_versions MODIFY Description longtext NULL;`\r\n\r\nProblem solved. Still don't know why issue happened why it's working for quite some time. I suspect there are some mismatch between frontend and backend codes, which makes the description value does not get passed correctly from UI to db",
"Maybe you could check your mysql config and see if there's any recent change? In kfp the description should be optional."
] | 2022-04-15T03:33:25 | 2022-04-21T22:51:05 | 2022-04-21T22:51:05 |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)? Full deployment
* KFP version: 1.6
* KFP SDK version: 1.6.0
### Steps to reproduce
Previously, everything looks fine. But all of a sudden, couldn't upload pipelines or create pipelines either from UI or from cli. The error messages are like follows:
Upload version to existing pipeliens:
```
I0415 03:29:46.118884 7 pipeline_upload_server.go:144] Upload pipeline version called
E0415 03:29:46.142425 7 pipeline_upload_server.go:240] Failed to upload pipelines. Error: Error creating pipeline version: Create pipeline version failed: InternalServerError: Failed to add version to pipeline version table: Error 1364: Field 'Description' doesn't have a default value: Error 1364: Field 'Description' doesn't have a default value
```
Create new pipelines:
`E0415 03:30:50.340822 7 pipeline_upload_server.go:240] Failed to upload pipelines. Error: Error creating pipeline: Create pipeline failed: InternalServerError: Failed to add pipeline version to pipeline_versions table: Error 1364: Field 'Description' doesn't have a default value: Error 1364: Field 'Description' doesn't have a default value`
No matter which yaml I use, the one generated by kfp cli, or the one in sample code, all get same errors. It looks like a global issue instead of the format in one single yaml file. No clue at why it happened...thanks for checking...
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I_kwDOB-71UM5HyVSI
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[backend] V2 pipelins can't save numpy files using Output[Dataset] notation
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[
"I am experiencing the same problem, in version kfp sdk version 1.4.0 it worked fine. I am using KFP standalone version 1.8.0.\r\n\r\nMy component code is this:\r\n\r\n```Python\r\[email protected]\r\ndef make_date_parser(datetime: str, input_format: str, output_format: str, days_to_add: int,\r\n command: str = STARTUP_COMMAND, image_name: str = IMAGE_NAME, image_tag: str = IMAGE_TAG,\r\n memory_limit: int = MEMORY_LIMIT, memory_request: int = MEMORY_REQUEST,\r\n cpu_limit: int = CPU_LIMIT, cpu_request: int = CPU_REQUEST, display_name: str = DISPLAY_NAME,\r\n ) -> ContainerOp:\r\n\r\n image = f\"{image_name}:{image_tag}\"\r\n\r\n operator = ContainerOp(\r\n name=CONTAINER_NAME,\r\n image=image,\r\n command=command,\r\n arguments=[\r\n \"--input-format\", input_format,\r\n \"--output-format\", output_format,\r\n \"--save-to\", OUTPUT_PATH,\r\n f\"--add-days={days_to_add}\",\r\n datetime\r\n ],\r\n file_outputs={\r\n 'converted_date': OUTPUT_PATH,\r\n }\r\n )\r\n\r\n operator = operator.set_display_name(display_name)\r\n operator = setup_resources(operator, memory_request, memory_limit, cpu_request, cpu_limit)\r\n\r\n return operator\r\n```\r\n\r\nMy output is similar to @ConverJens's output:\r\n\r\n```\r\ntime=\"2022-04-15T04:06:32.738Z\" level=info msg=\"capturing logs\" argo=true\r\nOriginal format: %d/%m/%Y %H:%M\r\nResulting format: %Y-%m-%d\r\nSaving transformed dates to converted_date.txt\r\nResults:\r\n{ontem} -> 2022-04-14\r\ntime=\"2022-04-15T04:06:32.739Z\" level=info msg=\"converted_date.txt -> /var/run/argo/outputs/parameters/converted_date.txt\" argo=true\r\ntime=\"2022-04-15T04:06:32.739Z\" level=error msg=\"cannot save artifact /tmp/outputs/Output/data\" argo=true error=\"stat /tmp/outputs/Output/data: no such file or directory\"\r\ntime=\"2022-04-15T04:06:32.739Z\" level=info msg=\"converted_date.txt -> /var/run/argo/outputs/artifacts/converted_date.txt.tgz\" argo=true\r\ntime=\"2022-04-15T04:06:32.739Z\" level=info msg=\"Taring /converted_date.txt\"\r\n```",
"KFP compatible mode is no longer supported, so try using KFP backend 2.0.0-alpha.1 and sdk 2.0.0-alpha.2 version and see if it works. You don't have to specify using compatible mode in v2 [ref](https://www.kubeflow.org/docs/components/pipelines/sdk-v2/v2-compatibility/#compiling-and-running-pipelines-in-v2-compatibility-mode), and also note that there is name space change. You need to change `import kfp.v2` to `import kfp`."
] | 2022-04-14T10:52:05 | 2022-04-21T22:44:38 | null |
CONTRIBUTOR
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Bundled with KF 1.5
* KFP version:
1.8.1
* KFP SDK version:
1.8.12
### Steps to reproduce
Create a simple component that saves a file in numpy format using the v2 artifact notation `Output[Dataset]`:
```
@component()
def numpy_test(output_data: Output[Dataset]):
import numpy as np
np_arr = np.array([1, 2, 3, 4])
np.save(f"{output_data.path}/data.npy", np_arr)
```
### Expected result
File should be saved succesfully
### Materials and Reference
Logs from component execution:
```
time="2022-04-14T10:50:53.395Z" level=info msg="capturing logs" argo=true
I0414 10:50:53.419935 25 launcher.go:144] PipelineRoot defaults to "minio://kubeflow-internal/artifacts/v2".
I0414 10:50:53.420141 25 cache.go:143] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
I0414 10:50:53.420147 25 cache.go:120] Connecting to cache endpoint ml-pipeline.kubeflow:8887
I0414 10:50:53.851228 25 launcher.go:193] enable caching
I0414 10:50:54.011769 25 object_store.go:305] Cannot detect minio-service in the same namespace, default to minio-service.kubeflow:9000 as MinIO endpoint.
[KFP Executor 2022-04-14 10:50:55,331 INFO]: Looking for component `numpy_test` in --component_module_path `/tmp/tmp.AgNinLaVsS/ephemeral_component.py`
[KFP Executor 2022-04-14 10:50:55,331 INFO]: Loading KFP component "numpy_test" from /tmp/tmp.AgNinLaVsS/ephemeral_component.py (directory "/tmp/tmp.AgNinLaVsS" and module name "ephemeral_component")
Traceback (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/root/pyenv/lib/python3.7/site-packages/kfp/v2/components/executor_main.py", line 104, in <module>
executor_main()
File "/root/pyenv/lib/python3.7/site-packages/kfp/v2/components/executor_main.py", line 100, in executor_main
executor.execute()
File "/root/pyenv/lib/python3.7/site-packages/kfp/v2/components/executor.py", line 309, in execute
result = self._func(**func_kwargs)
File "/tmp/tmp.AgNinLaVsS/ephemeral_component.py", line 11, in numpy_test
np.save(f"{output_data.path}/data.npy", np_arr)
File "<__array_function__ internals>", line 6, in save
File "/root/pyenv/lib/python3.7/site-packages/numpy/lib/npyio.py", line 525, in save
file_ctx = open(file, "wb")
FileNotFoundError: [Errno 2] No such file or directory: '/minio/kubeflow-internal/artifacts/v2/pipeline/np-test-v2/e772b3c5-6608-429f-ac7f-e352853a19f6/numpy-test/output_data/data.npy'
F0414 10:50:55.585759 25 main.go:50] Failed to execute component: exit status 1
time="2022-04-14T10:50:55.587Z" level=error msg="cannot save artifact /tmp/outputs/output_data/data" argo=true error="stat /tmp/outputs/output_data/data: no such file or directory"
Error: exit status 1
```
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5Hf8mr
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[feature] Set artifact name when using kfp dsl.importer
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[
"/assign @chensun \r\n\r\nThis will touch at least the SDK and perhaps the backend as well. More info is needed.",
"@connor-mccarthy Any updates on this?\r\n\r\nMight this be the reason that Artifacts are duplicated when imported as mentioned in #7861?\r\n\r\nIs there a work-around?",
"+1 on this!\r\n\r\nNot only is the artifact name just set to `artifact` but the importer component itself is just given the name `importer[-N]`\r\n\r\nThis makes it hard for people to understand what's going on in the pipeline at a glance:\r\n<img width=\"1242\" alt=\"image\" src=\"https://github.com/kubeflow/pipelines/assets/22200114/2ca89621-d16e-4ed5-b7ff-05011371de62\">\r\n\r\n#### Component name\r\n\r\nLooking at [`importer_node.py`](https://github.com/kubeflow/pipelines/blob/450e9108172b5a4eb76abb6647bb65661581747a/sdk/python/kfp-dsl/kfp/dsl/importer_node.py#L120-L121), it seems trivial to set a dynamic component name by adding a `task_name: str = 'importer'` function arg:\r\n\r\n```diff\r\ncomponent_spec = structures.ComponentSpec(\r\n- name='importer',\r\n+ name=task_name,\r\n```\r\n\r\n#### Artifact name\r\n\r\nIn `importer_node.py`, the artifact output key is [hard-coded](https://github.com/kubeflow/pipelines/blob/450e9108172b5a4eb76abb6647bb65661581747a/sdk/python/kfp-dsl/kfp/dsl/importer_node.py#L28) outside of the function declaration:\r\n```py\r\nOUTPUT_KEY = 'artifact'\r\n```\r\n\r\nInstead, it could also be passed as a function argument, e.g. `artifact_key: str = 'artifact'`\r\n```diff\r\n component_spec = structures.ComponentSpec(\r\n ...\r\n outputs={\r\n- OUTPUT_KEY:\r\n+ artifact_key:\r\n structures.OutputSpec(\r\n type=type_utils.create_bundled_artifact_type(\r\n artifact_class.schema_title,\r\n artifact_class.schema_version))\r\n },\r\n )\r\n```\r\n\r\n### Suggested solution\r\n\r\nUpdate the `dsl.importer` function signature in a backwards-compatible manner by adding two new optional parameters, with default values set to mirror the existing implementation:\r\n```diff\r\ndef importer(\r\n artifact_uri: Union[pipeline_channel.PipelineParameterChannel, str],\r\n artifact_class: Type[artifact_types.Artifact],\r\n+ artifact_key: str = 'artifact',\r\n+ task_name: str = 'importer',\r\n reimport: bool = False,\r\n metadata: Optional[Mapping[str, Any]] = None,\r\n) -> pipeline_task.PipelineTask:"
] | 2022-04-11T08:10:04 | 2023-08-15T10:51:21 | null |
NONE
| null |
### Feature Area
/area components
### What feature would you like to see?
I opened a [question on Stack Overflow](https://stackoverflow.com/questions/71763407/set-artifact-name-when-using-kfp-dsl-importer) about this, but since no-one answered on there, I figured that this is probably something that is not possible yet and thus rather a feature request.
When importing an artifact using the kfp `dsl.importer()` function, the imported artifact gets the default (display) name artifact. I would like to give it a custom name to make the pipeline and lineage tracking more clear.
Example code `dsl.importer()`:
```
load_dataset_step = dsl.importer(
artifact_uri=input_data_uri,
artifact_class=dsl.Dataset,
reimport=False
).set_display_name("Load Dataset")
```
Visualisation of the `dsl.importer()` step:
### What is the use case or pain point?
Giving a custom name to the imported artifact would make the pipeline and lineage tracking more clear.
### Is there a workaround currently?
I guess the workaround would be to use a custom component to load the artifact, which would allow you to edit the output artifact name.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5HdyiF
| 7,540 |
[feature] Exposing experiment name as a macro.
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[
"/cc @chensun WDYT?",
"/assign @chensun ",
"`{{workflow.annotations.*}}` and `{{workflow.name}}` aren't really part of KFP but Argo placeholders (https://argoproj.github.io/argo-workflows/variables/#global). And KFP experiments is not an Argo concept, so I don't think this is that easy to support. "
] | 2022-04-10T11:20:20 | 2022-05-12T21:59:55 | null |
NONE
| null |
### Feature Area
/area frontend
/area backend
### What feature would you like to see?
When in pipeline code I can use macros like {{workflow.annotations.pipelines.kubeflow.org/run_name}} or {{workflow.name}} to get those values, and potentially use them in some context, eg. passing to some component.
What I'd like to do is get current experiment name, that the run is assigned to. I'd like to pass it on to components for logging purposes.
Is this property exposed?
### What is the use case or pain point?
I would like to use that for consistent logging of run results. Now I need to copy and paste this value to a property in my pipeline.
### Is there a workaround currently?
Stated above - copy-pasting.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5HS_jh
| 7,529 |
support for List[str|int|float] and Dict[str,str|int|float] in py-func components
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[
"@iuiu34 Are you looking for `Dict[str, str]` and/or `Dict[str, int]`? Or `Dict[str, Union[int, str]]`?\r\n\r\nThe former is currently supported in V2, while the latter is not supported in all cases.",
"i'm looking for `Dict[str,Union[str,int,float]]` and `List[Union[str,int,float]]`.\r\n\r\nabout `Dict[str, str]` support, how it works? I'm working with kfp==1.8.11, and following code\r\n```py\r\nbase_image = 'python:3.9'\r\nfrom typing import Dict\r\n\r\n\r\n@component(base_image=base_image)\r\ndef test(a: Dict[str, str]) -> str:\r\n print(a)\r\n print(type(a))\r\n return 'pass'\r\n\r\n@pipeline(\r\n name=f'kfp-test-pipeline',\r\n description='Pipeline test.',\r\n pipeline_root=output_path\r\n)\r\ndef pipeline(\r\n a: str = 'aa',\r\n b: str = 'bb',\r\n c: str = 'cc'):\r\n args = dict(a=a, b=b, c=c)\r\n test_ = test(args)\r\n```\r\n\r\nraises error\r\n```py\r\nTypeError: Object of type PipelineParam is not JSON serializable\r\n```",
"@iuiu34 Where is the exception thrown? At compile-time? Or execution-time? If possible, could you provide a slightly more complete minimal reproducible example?",
"is at compilation time. \r\nHere a reproducible code\r\n```py\r\nfrom typing import Dict\r\n\r\nfrom kfp.v2.dsl import (component, pipeline)\r\n\r\n\r\n@component(base_image='python:3.9')\r\ndef test(a: Dict[str, str]) -> str:\r\n print(a)\r\n print(type(a))\r\n return 'pass'\r\n\r\n\r\n@pipeline(\r\n name='kfp-test-pipeline',\r\n description='Pipeline test.',\r\n pipeline_root='gs://bucket/test'\r\n)\r\ndef pipeline(\r\n a: str = 'aa',\r\n b: str = 'bb',\r\n c: str = 'cc'):\r\n args = dict(a=a, b=b, c=c)\r\n test_ = test(args)\r\n\r\n\r\nfrom kfp.v2 import compiler\r\n\r\ncompiler.Compiler().compile(pipeline_func=pipeline,\r\n package_path='tmp/kfp_pipeline.json')\r\n\r\n```\r\n\r\nraises error\r\n\r\n```py\r\nTypeError: Object of type PipelineParam is not JSON serializable\r\n```"
] | 2022-04-07T14:48:26 | 2022-04-20T08:21:57 | null |
NONE
| null |
if i want to pass a dict as an arg to a py-func component i have todo something like
```py
@component(base_image=base_image)
def test(a: str) -> str:
print(type(a)) # str
a = eval(a)
print(type(a)) # dict
return 'test'
@pipeline(
name='kfp-test-pipeline',
description='Pipeline test.',
pipeline_root=output_path
)
def pipeline(
a: str = 'aa',
b: str = 'bb',
c: str = 'cc'):
args = dict(a=a, b=b, c=c)
test_ = test(str(args))
```
Could kfp give support to Dict[str,str|int|float] and List[str|int|float]?
So `eval` is handled natively by kfp, and previous code can be simple written as
```py
from typing import Dict
@component(base_image=base_image)
def test(a: Dict[str,str]) -> str:
print(type(a)) # dict
return 'test'
@pipeline(
name='kfp-test-pipeline',
description='Pipeline test.',
pipeline_root=output_path
)
def pipeline(
a: str = 'aa',
b: str = 'bb',
c: str = 'cc'):
args = dict(a=a, b=b, c=c)
test_ = test(args)
```
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Adapt API integration test to run in full Kubeflow
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[] | 2022-04-06T23:43:01 | 2022-06-01T17:47:30 | 2022-06-01T17:47:30 |
CONTRIBUTOR
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I_kwDOB-71UM5HLTbx
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[backend] health service response should be consumed by application/json not text/plain
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[
"@hsinhoyeh Thank you and we are happy to review the patch PR from you. cc @chensun ",
"Hi, @zijianjoy I have sent my PR here https://github.com/kubeflow/pipelines/pull/7532 thanks. also cc @chensun "
] | 2022-04-06T07:07:29 | 2022-05-20T03:44:29 | 2022-05-20T03:44:29 |
CONTRIBUTOR
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)? install via kubeflow manifest with version v1.4
* KFP version: v1.7.0
* KFP SDK version: v1.7.0 (golang version under backend/api/go_http_client)
### Steps to reproduce
1. start pipeline backend api server by listening port 8888
2. sent http request with swagger generated code stub with the following code example
```
package main
import (
"github.com/go-openapi/strfmt"
"github.com/go-openapi/runtime"
healthz_client "github.com/kubeflow/pipelines/backend/api/go_http_client/healthz_client"
healthz_service "github.com/kubeflow/pipelines/backend/api/go_http_client/healthz_client/healthz_service"
)
func main() {
healthServiceClient := healthz_client.New(
client.New(
"localhost:8888",
healthz_client.DefaultBasePath,
[]string{"http"}),
strfmt.NewFormats(),
)
clientAuthInfoWriter := runtime.ClientAuthInfoWriterFunc(func(clientRequest runtime.ClientRequest, registry strfmt.Registry) error {
return nil
})
resp, err := p.HealthServiceClient.HealthzService.GetHealthz(healthz_service.NewGetHealthzParams, clientAuthInfoWriter)
if err != nil {
// should not be here
}
}
```
3. When I was sent healthz endpoint (`/apis/v1beta1/healthz`) with go_http_client(swagger generated code stubs), the generated code stub failed to decode the response with the error message `&{false} (*healthz_model.APIGetHealthzResponse) is not supported by the TextConsumer, can be resolved by supporting TextUnmarshaler interface`
the http log are displayed below:
```
GET /apis/v1beta1/healthz HTTP/1.1
Host: 127.0.0.1:8888
User-Agent: Go-http-client/1.1
Accept: application/json
Accept-Encoding: gzip
HTTP/1.1 200 OK
Content-Length: 96
Content-Type: text/plain; charset=utf-8
Date: Wed, 06 Apr 2022 06:41:40 GMT
{"commit_sha":"6ccf5bcd0b9db955c91caab85fa130714527f414", "tag_name":"1.7.0", "multi_user":true}
```
however, the message was wrapped into a json struct but the content type was specified with `text/plain`.
This triggered [APIGetHealthzResponse](https://github.com/kubeflow/pipelines/blob/master/backend/api/go_http_client/healthz_model/api_get_healthz_response.go#L16) failed to decode the response, as it failed to implement `TextUnmarshaler` interface.
The server code (on v1.7.0 as well as on master) also confirmed this https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/backend/src/apiserver/main.go#L146, without proper set the content-type, the default one will determined by [http.DetectContentType](https://cs.opensource.google/go/go/+/refs/tags/go1.18:src/net/http/sniff.go;drc=refs%2Ftags%2Fgo1.18;l=21) which will be 'text/plain'
### Expected result
the golang example mentioned earier with swagger generated stub should work well.
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
###
if this was confirmed as bug, I would like to submit a patch for this. Thanks
|
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I_kwDOB-71UM5HIrYe
| 7,523 |
[bug] cannot use component outputs in importer_node.importer metadata
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[
"/assign @chensun ",
"I was stuck on this error for hours. What worked for me is wrapping metadata arguments inside `str( )` like:\r\n```\r\n\"ports\": str(SERVING_CONTAINER_PORT),\r\n\"predict_route\": str(SERVING_PREDICT_ROUTE),\r\n\"health_route\": str(SERVING_HEALTH_ROUTE),\r\n```\r\n\r\nDon't know why though because I confirmed that the values I was passing were already strings. So it doesn't help if they are already a string. I was able to reproduce the error by just removing `str( )` so I am sure that's the solution that worked for me. Hope this helps!"
] | 2022-04-05T16:49:03 | 2022-09-12T22:13:56 | null |
NONE
| null |
### What steps did you take
I have a custom model building pipeline in Vertex AI. After my model trains, i'd like to create a Vertex AI model using ModelUploadOp.
I've noticed that the ModelUploadOp now requires you to create an UnmanagedContainerModel first, and this is where i've encountered my bug.
For a simple example, say I want to
* create a .mar file (for purpose of serving in TorchServe)
* Build a serving container
* Create an UnmanagedContainerModel
* Upload to VertexAI
A sample this pipeline is below.
```
from google_cloud_pipeline_components.v1.model import ModelUploadOp
from google_cloud_pipeline_components.types import artifact_types
from kfp.v2.components import importer_node
import kfp
from kfp.v2 import dsl
from kfp.v2 import compiler
from kfp.v2.dsl import (Artifact, Dataset, Input, InputPath, Model, Output,
OutputPath, component)
@dsl.pipeline(name="my-pipeline")
def training_pipeline(project: str, region: str):
build_mar_task = (
build_mar_op(
model_display_name=MODEL_DISPLAY_NAME,
model_version=MODEL_VERSION,
model_file=MODEL_FILE,
model_handler=MODEL_HANDLER,
extra_files=EXTRA_FILES,
model=MODEL_FILE_ARTIFACT
)
.set_caching_options(False)
.set_display_name('build mar')
)
build_container_task = (
build_container_op(
project=project,
serving_dockerfile_uri=DOCKERFILE_PATH,
serving_image_TAG=SERVE_IMAGE_TAG
)
.set_caching_options(False)
.set_display_name('build container')
)
import_unmanaged_model_task = importer_node.importer(
artifact_uri=build_mar_task.outputs['exported_mar_uri'],
artifact_class=artifact_types.UnmanagedContainerModel,
metadata={
"containerSpec": {
"imageUri": build_container_task.outputs['serving_image_uri'],
"ports": SERVING_CONTAINER_PORT,
"predict_route": SERVING_PREDICT_ROUTE,
"health_route": SERVING_HEALTH_ROUTE,
},
},
)
model_upload_task = (
ModelUploadOp(
project=project,
display_name=MODEL_DISPLAY_NAME,
unmanaged_container_model=import_unmanaged_model_task.outputs["artifact"]
)
.set_display_name("Upload model")
)
compiler.Compiler().compile(
pipeline_func=training_pipeline,
package_path="my-pipeline.json"
)
```
### What happened:
Any component output specified within the importer metadata (in this case, `imageUri`) will cause an error when the pipeline is compiled. If I try building with a hardcoded URI, no error is thrown.
```
ValueError Traceback (most recent call last)
/tmp/ipykernel_17742/3566544597.py in <module>
3 compiler.Compiler().compile(
4 pipeline_func=training_pipeline,
----> 5 package_path="my-pipeline.json"
6 )
/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in compile(self, pipeline_func, package_path, pipeline_name, pipeline_parameters, type_check)
1290 pipeline_func=pipeline_func,
1291 pipeline_name=pipeline_name,
-> 1292 pipeline_parameters_override=pipeline_parameters)
1293 self._write_pipeline(pipeline_job, package_path)
1294 finally:
/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in _create_pipeline_v2(self, pipeline_func, pipeline_name, pipeline_parameters_override)
1209
1210 with dsl.Pipeline(pipeline_name) as dsl_pipeline:
-> 1211 pipeline_func(*args_list)
1212
1213 if not dsl_pipeline.ops:
/tmp/ipykernel_17742/1441670089.py in training_pipeline(project, region, temp_location, bucket, num_epochs, bq_table, bq_record_limit, dataflow_setup_file, dataflow_runner)
99 "ports": SERVING_CONTAINER_PORT,
100 "predict_route": SERVING_PREDICT_ROUTE,
--> 101 "health_route": SERVING_HEALTH_ROUTE,
102 },
103 },
/opt/conda/lib/python3.7/site-packages/kfp/v2/components/importer_node.py in importer(artifact_uri, artifact_class, reimport, metadata)
163 artifact_type_schema = type_utils.get_artifact_type_schema(artifact_class)
164 task.importer_spec = _build_importer_spec(
--> 165 artifact_uri=artifact_uri, artifact_type_schema=artifact_type_schema, metadata=metadata)
166 task.task_spec = _build_importer_task_spec(
167 importer_base_name=task.name, artifact_uri=artifact_uri)
/opt/conda/lib/python3.7/site-packages/kfp/v2/components/importer_node.py in _build_importer_spec(artifact_uri, artifact_type_schema, metadata)
52 if metadata:
53 metadata_protobuf_struct = struct_pb2.Struct()
---> 54 metadata_protobuf_struct.update(metadata)
55 importer_spec.metadata.CopyFrom(metadata_protobuf_struct)
56
/opt/conda/lib/python3.7/site-packages/google/protobuf/internal/well_known_types.py in update(self, dictionary)
805 def update(self, dictionary): # pylint: disable=invalid-name
806 for key, value in dictionary.items():
--> 807 _SetStructValue(self.fields[key], value)
808
809 collections_abc.MutableMapping.register(Struct)
/opt/conda/lib/python3.7/site-packages/google/protobuf/internal/well_known_types.py in _SetStructValue(struct_value, value)
731 elif isinstance(value, (dict, Struct)):
732 struct_value.struct_value.Clear()
--> 733 struct_value.struct_value.update(value)
734 elif isinstance(value, (list, ListValue)):
735 struct_value.list_value.Clear()
/opt/conda/lib/python3.7/site-packages/google/protobuf/internal/well_known_types.py in update(self, dictionary)
805 def update(self, dictionary): # pylint: disable=invalid-name
806 for key, value in dictionary.items():
--> 807 _SetStructValue(self.fields[key], value)
808
809 collections_abc.MutableMapping.register(Struct)
/opt/conda/lib/python3.7/site-packages/google/protobuf/internal/well_known_types.py in _SetStructValue(struct_value, value)
736 struct_value.list_value.extend(value)
737 else:
--> 738 raise ValueError('Unexpected type')
739
740
ValueError: Unexpected type
```
### What did you expect to happen:
Expectation is that an unmanaged container model can be built using the dynamic outputs from components.
### Environment:
Using a Vertex AI notebook and deploying the pipeline into Vertex AI Pipelines.
Versions:
KFP Version: v2
kfp==1.8.12
google-cloud-pipeline-components==1.0.2
### Anything else you would like to add:
### Labels
<!-- /sdk components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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| 1,190,479,871 |
I_kwDOB-71UM5G9Uf_
| 7,512 |
kubeflow pipeline add support of postgresql
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[
"Hello @yiyuanyu17 , can you help us understand what is the reason of not being able to use mysql? And do you want to use postgresql within cluster or outside the cluster? Or are you looking for a way to configure postgresql as an alternative of cloudsql?",
"\r\n\r\n\r\n\r\n> Hello @yiyuanyu17 , can you help us understand what is the reason of not being able to use mysql? And do you want to use postgresql within cluster or outside the cluster? Or are you looking for a way to configure postgresql as an alternative of cloudsql?\r\n\r\nhello, we use kubeflow pipeline for AI model training in our platform. In the process of privatization delivery, some customers explicitly require that self built MySQL is not allowed, and the PostgreSQL provided by the customer side must be used. Therefore, our applications are modified into ORM framework to adapt to different database types. However, it is noted that the kubeflow pipeline server has not increased its support for PostgreSQL. Therefore, it proposes this issue and hopes to get the help of the community.",
"Thank you for the info, @yiyuanyu17 . I will keep this issue open so people can upvote if they are also interested in this postgresql support. People can create overlay which connects to postgresql but such support is not available in this repo yet. ",
"We would also be interested in this feature. We do a lot of on prem and disconnected/airgapped deployments. As such, Cloud Vendor hosted databases are not an option. In most scenarios it is easiest to run our own database clusters colocated on the same k8s environment as we run Kubeflow. The Crunchy Postgres experience on k8s is the best experience we've found to operate RDBMS clusters on k8s and we leverage it for other tooling. Would be nice to leverage it from Kubeflow as well, as operating MySql clusters on k8s is not as seamless an experience.",
"In our case we only have Postgres as an option for managed on prem DB. So looking for out fo the box Postgres support. @zijianjoy Can you please elaborate what creating overlay means, If that helps connect Kubeflow to postgres, I am interested to give it a try. Thanks!",
"Hi @zijianjoy,\r\n\r\nWe also strictly use PostgreSQL internally, since it's better suited for data warehousing purposes. ",
"`overlay` is a kustomize concept as described in https://kubernetes.io/docs/tasks/manage-kubernetes-objects/kustomization/. An overlay is a kubernetes resource package, it is like a variant of base KFP package. \r\n\r\nHere is a list of KFP overlay: https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/env\r\n\r\nIf you look at `platform-agnostic` folder, you will find that it is depending on mysql: https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/third-party/mysql\r\n\r\nSo if you want to introduce postgresql, what you need to do is:\r\n1. Create a postgresql folder under https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/third-party and define the postgresql resources in this folder.\r\n2. Define an overlay in https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/env which allows people to use postgresql.\r\n\r\nI would recommend testing this postgresql integration on your environment first before committing to KFP repo, because there is no guarantee/testing to verify KFP working with postgresql.",
"It would be great if kubeflow pipeline support postgres!!! \r\nFor some reason, our company also can not use MySQL. We strongly recommend the community to make the database optional @zijianjoy ",
"It's a time consuming job for us every user to implement postgresql available for pipelines. So we're eagerly waiting for someone to contribute to it.\r\n\r\nThere are already pull requests implementing postgres for kubeflow katib (https://github.com/kubeflow/katib/pull/1921), I wander if there any plan about KFP SUPPORT PG?",
"MySQL is of no doubt an excellent database, however Oracle's acquisition brought uncertainty to its future. Like the others above, I sincerely hope kubeflow/pipeline can support postgresql soon, which is license friendly, and owns lots of advanced features. ",
"Also note that google/mlmd doesn't support Postgres yet: https://github.com/google/ml-metadata/issues/26",
"As others have hinted towards here, PostgreSQL, especially with Operator Lifecycle Manager and, if wanted, being a Red-Hat-certified operator, is the way to go in an Enterprise environment that is Kubernetes-based. I wholeheartedly agree with all people who posted here. Database should not come pre-packaged with Kubeflow, as it is not a core component. Let people who really know their stuff handle things like database-ops and deployment, like e.g. Crunchy with PostgreSQL. And then use Postgres as a database for Kubeflow.\r\nSeriously, replication factor of 1, no pgbouncer proxy to improve load handling, no backup strategy .... https://github.com/kubeflow/katib/blob/9fce9dd03bc476b4e1f3d385e9692ac5cef681f4/manifests/v1beta1/components/postgres/postgres.yaml \r\nThat cannot seriously be an approach by a project that has its origins with one of the big tech firms. \r\nSame goes for air-gapped functionality support with custom docker registries, HTTP_PROXY support via env variables and custom CA configmap for PKI trust.",
"Currently we would like help from community to support PostgresQL integration. For anyone who wants to contribute making Kubeflow Pipelines runnable with PostgresSQL:\r\n\r\n1. Create a postgresql folder under https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/third-party and define the postgresql resources in this folder.\r\n2. Define an overlay in https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/env which integrates KFP with postgresql\r\n3. Change MLMD's use of database as PostgreSQL: https://github.com/google/ml-metadata/issues/26 "
] | 2022-04-02T03:22:54 | 2023-08-28T20:59:11 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
kubeflow pipeline add support of postgresql
### What is the use case or pain point?
for some case , we can not use mysql for kubeflow pipeline , hope kubeflow pipeline can add the suppoort of postgresql
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 1,189,698,641 |
I_kwDOB-71UM5G6VxR
| 7,504 |
[feature] Integrate withSequence loop with Kubeflow SDK
|
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[
"Hello @Sharathmk99 , currently this feature is not on our roadmap. Keeping this feature request open to collect more upvotes. We need a proper design if we want to support this feature.",
"@Sharathmk99, this may work as a workaround if the iterable is known at compile-time:\r\n\r\n```python\r\nfrom kfp import compiler, dsl\r\n\r\n\r\[email protected]\r\ndef print_text(msg: str):\r\n print(msg)\r\n\r\n\r\[email protected](name='pipeline-with-loops')\r\ndef my_pipeline():\r\n items = [\"a\", \"b\", \"c\"]\r\n tasks = [print_text(msg=item) for item in items]\r\n\r\n for i, task in enumerate(tasks):\r\n if i == len(tasks) - 1:\r\n continue\r\n task.after(tasks[i + 1])\r\n``` ",
"@connor-mccarthy Integration data is know runtime only from previous step.\r\nThanks for the suggestion. For now, I have added one extra step to take sequence number as input and output the list of sequence number length"
] | 2022-04-01T12:04:32 | 2022-04-08T15:55:12 | null |
NONE
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Loop feature with sequence will help to have better integration when user needs to create n number of identical steps using count number.
For example, we run hyperparameter search and need to run n number of agents. With the current implementation of ParallelFor it can't be accomplished
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Hyperparameter search and need to run n number of identical agents
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Manually create yaml similar to https://github.com/argoproj/argo-workflows/blob/master/examples/loops-sequence.yaml
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5G4wmF
| 7,503 |
[question] How to get around pulling busybox image?
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[
"+1 \r\ni have specify the base image : zhuyaguang/pipeline:v4,but it always pull busybox\r\n\r\n<img width=\"502\" alt=\"image\" src=\"https://user-images.githubusercontent.com/8857976/162148571-8ae96a66-d8fd-4bbb-af21-71aefdfcc547.png\">\r\n\r\nWhat's even weirder is that it's random \r\n\r\n```\r\ndef pass_file_op():\r\n return dsl.ContainerOp(\r\n name='pass_file',\r\n image='zhuyaguang/pipeline:v4',\r\n command=['sh', '-c'],\r\n arguments=['cat /pipelines/component/src/trainData.txt '],\r\n file_outputs={\r\n 'data': '/pipelines/component/src/trainData.txt',\r\n }\r\n )\r\n```",
"/assign @chensun ",
"\r\n\r\nI found it",
"This is resolved in KFP V2, as we do not require a cache image.\r\n\r\nIn V1, you can change this `cacheImage` to another image in your local network.",
"@connor-mccarthy How to modify `cacheImage `? I modified `manifests-1.5.0/apps/pipeline/upstream/base/installs/generic/pipeline-install-config.yaml` and re-executed` while ! kustomize build example | kubectl apply -f -; do echo \"Retrying to apply resources\"; sleep 10; done`, but it doesn't work, do I need to reinstall kubeflow after modifying the configuration?",
"I changed `manifests-1.5.0/apps/pipeline/upstream/base/installs/generic/pipeline-install-config.yaml `to the following, `gcr.io/google-containers/busybox:v1` is a tag of `gcr.io/google-containers/busybox:latest`\r\n```\r\napiVersion: v1\r\nkind: ConfigMap\r\nmetadata:\r\n name: pipeline-install-config\r\ndata:\r\n warning: |\r\n 1. Do not use kubectl to edit this configmap, because some values are used\r\n during kustomize build. Instead, change the configmap and apply the entire\r\n kustomize manifests again.\r\n 2. After updating the configmap, some deployments may need to be restarted\r\n until the changes take effect. A quick way to restart all deployments in a\r\n namespace: `kubectl rollout restart deployment -n <your-namespace>`.\r\n appName: pipeline\r\n appVersion: 1.8.1\r\n dbHost: mysql\r\n dbPort: \"3306\"\r\n mlmdDb: metadb\r\n cacheDb: cachedb\r\n pipelineDb: mlpipeline\r\n bucketName: mlpipeline\r\n ## defaultPipelineRoot: Optional. Default pipeline root in v2 compatible mode.\r\n ## https://www.kubeflow.org/docs/components/pipelines/sdk/v2/v2-compatibility/\r\n ##\r\n ## If the field is not set, kfp-launcher configmaps won't be created and \r\n ## v2 compatible mode defaults to minio://mlpipeline/v2/artifacts as pipeline\r\n ## root.\r\n ##\r\n ## When not in Kubeflow Pipelines multi-user mode, the config works as you\r\n ## would normally expect.\r\n ##\r\n ## In Kubeflow Pipelines multi-user mode, the config creates default\r\n ## kfp-launcher configmaps in each user's namespace. Users can edit the\r\n ## kfp-launcher configmap's defaultPipelineRoot field afterwards to configure\r\n ## namespace-specific default pipeline root. The namespace specific changes in\r\n ## kfp-launcher configmap won't be overridden by pipeline-install-config.\r\n ##\r\n ## Caveat: when you update the config from a non-empty value, only new\r\n ## namespaces get the updated config by default. Owners of existing namespaces\r\n ## must delete the kfp-launcher configmap to get the new default config value.\r\n ##\r\n ## Examples:\r\n ## defaultPipelineRoot: minio://mlpipeline/v2/artifacts\r\n ## defaultPipelineRoot: gs://your-bucket/path/to/artifacts\r\n ## defaultPipelineRoot: s3://your-bucket/path/to/artifacts\r\n ##\r\n ## V2 Compatible Mode Feature stage:\r\n ## [Beta](https://github.com/kubeflow/pipelines/blob/master/docs/release/feature-stages.md#beta)\r\n defaultPipelineRoot: \"\"\r\n ## autoUpdatePipelineDefaultVersion: States if the pipeline version\r\n ## should be updated by defult for a versioned pipeline or not when a new\r\n ## version is uploaded. This sets the deployment wide definition.\r\n autoUpdatePipelineDefaultVersion: \"true\"\r\n ## cronScheduleTimezone: States the timezone which should be used for\r\n ## the cron scheduler. If not specified the local timezone of the\r\n ## cluster will be used. Valid values are UTC, Local or values according to\r\n ## the IANA Time Zone database, such as \"America/New_York\" and \"Asia/Shanghai\".\r\n ## Feature stage:\r\n ## [Alpha](https://github.com/kubeflow/pipelines/blob/master/docs/release/feature-stages.md#alpha)\r\n cronScheduleTimezone: \"UTC\"\r\n ## cacheImage is the image that the mutating webhook will use to patch\r\n ## cached steps with. Will be used to echo a message announcing that \r\n ## the cached step result will be used. If not set it will default to \r\n ## 'gcr.io/google-containers/busybox'\r\n cacheImage: \"gcr.io/google-containers/busybox:v1\"\r\n ## cacheNodeRestrictions the dummy container runing if output is cached\r\n ## will run with the same affinity and node selector as the default pipeline\r\n ## step. This is defaulted to 'false' to allow the pod to be scheduled on \r\n ## any node and avoid defaulting to specific nodes. Allowed values are:\r\n ## 'false' and 'true'.\r\n cacheNodeRestrictions: \"false\"\r\n ## ConMaxLifeTime will set the connection max lifetime for MySQL\r\n ## this is very important to setup when using external databases.\r\n ## See this issue for more details: https://github.com/kubeflow/pipelines/issues/5329\r\n ## Note: this value should be a string that can be parsed by `time.ParseDuration`.\r\n ## If this value doesn't include a unit abbreviation, the units will be assumed\r\n ## to be nanoseconds.\r\n ConMaxLifeTime: \"120s\"\r\n\r\n```"
] | 2022-04-01T06:03:51 | 2022-05-10T06:03:07 | 2022-04-14T22:53:35 |
NONE
| null |
I'm now using kubeflow pipeline in a local environment which has no network I have wrote a pipeline with only one components, I have build the image for it but the pipeline always fail, because it fails to pull the busybox image from internet(gcr.io/google-containers/busybox) .
I have two questions:
1. why does kubeflow pipeline always pull the image "busybox" ?
2. how can I solve it ?
I have pull busybox manually but it doesn't work, I guess the pipeline needs the latest busybox image, so it will pull everytime
---
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5G01Xq
| 7,494 |
[feature] allow upload_pipeline_version to create a pipeline with initial version
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[
"We also just ran into this problem: Every pipeline we create ends up with a \"pipeline_name\" version tag.",
"Found a hack to get around this until it is fixed properly\r\n\r\n```python\r\n kfp.compiler.Compiler().compile(pipeline_func, pipeline_path)\r\n\r\n pipeline = client.upload_pipeline(pipeline_path, pipeline_name=pipeline_name)\r\n pipeline_id = pipeline.id\r\n # delete the initial un-versioned \"version\"\r\n client.pipelines.delete_pipeline_version(pipeline_id)\r\n\r\n # create the versioned \"version\"\r\n pipeline_version = client.upload_pipeline_version(\r\n pipeline_path, pipeline_version_name=f\"{pipeline_name}:{version}\", pipeline_id=pipeline_id\r\n )\r\n pipeline_version_id = pipeline_version.id\r\n```"
] | 2022-03-31T15:21:16 | 2022-09-09T12:51:00 | null |
NONE
| null |
### Feature Area
/area backend
/area sdk
### What feature would you like to see?
My understanding is that at the moment, pipelines must be created using the method `upload_pipeline`.
If you create a new pipeline with `upload_pipeline_version` and tag it, for example, as v1.0.0:
```
client.upload_pipeline_version(
pipeline_package_path="my-pipeline.yaml",
pipeline_version_name="v1.0.0",
pipeline_name="my-pipeline",
description="my-pipeline description",
)
```
An ApiException (400) is raised:
```
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'x-powered-by': 'Express', 'date': 'Thu, 31 Mar 2022 15:00:15 GMT', 'content-length': '736', 'content-type': 'text/plain; charset=utf-8', 'x-envoy-upstream-service-time': '21', 'server': 'envoy'})
HTTP response body: {"error_message":"Please specify a pipeline id when creating versions.","error_details":"Please specify a pipeline id when creating versions.\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:171\nnet/http.HandlerFunc.ServeHTTP\n\t/usr/local/go/src/net/http/server.go:2007\ngithub.com/gorilla/mux.(*Router).ServeHTTP\n\t/go/pkg/mod/github.com/gorilla/[email protected]/mux.go:210\nnet/http.serverHandler.ServeHTTP\n\t/usr/local/go/src/net/http/server.go:2802\nnet/http.(*conn).serve\n\t/usr/local/go/src/net/http/server.go:1890\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357"}
```
### What is the use case or pain point?
We should be able to tag all pipeline versions, including the first one.
Right now `upload_pipeline` tags the initial version with the name of the pipeline.
**Pain points:**
1. Two different functions must be used to create and maintain a pipeline.
2. It would be nice to have the pipeline versions tagged from the start:
_Having:_
my-pipeline
- v3.0.0
- v2.1.0
- v2.0.1
- v2.0.0
- v.1.0.0
_rather than:_
my-pipeline
- v3.0.0
- v2.1.0
- v2.0.1
- v2.0.0
- my-pipeline
### Is there a workaround currently?
No, but pain points can be ignored.
---
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
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I_kwDOB-71UM5GvoUj
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[feature] Make log level configurable in the SDK
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[] | 2022-03-30T19:24:51 | 2022-07-29T11:50:04 | 2022-07-29T11:50:04 |
CONTRIBUTOR
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Add a `level` or `log_level` param to the `Client` class that defaults to `INFO`, and set the root log level accordingly in the constructor. Something like:
```python
levels_whitelist = ["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"]
if log_level not in levels_whitelist:
raise Exception(f"log_level must be one of {levels_whitelist}")
logging.getLogger().setLevel(getattr(logging, log_level))
```
I'd be more than happy to submit the PR. Already have it running and working locally.
### Is there a workaround currently?
No.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5Gtnh6
| 7,486 |
[Question] Converting TFX Pipeline (.py) to Kubeflow Pipeline (.YAML) for Local Deployment (using UI)
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[
"I don't know if this works for TFX (I'm totally new with Kubeflow), but I think you need to either write code in your python file such that it creates a YAML file itself, or you run a compile command.\r\n\r\nThe code alternative looks like this for me:\r\n```python\r\n# Some of these imports can probably be omitted\r\nimport kfp\r\nfrom kfp.v2 import dsl\r\nfrom kfp.v2.dsl import component\r\nfrom kfp.v2.compiler import Compiler\r\nfrom kfp.v2.dsl import (\r\n Input,\r\n Output,\r\n Artifact,\r\n Dataset,\r\n)\r\n\r\n# I have omitted code for pipeline steps, but that would be here\r\n\r\n# Then we have our pipeline definition\r\[email protected](\r\n name='my-pipeline',\r\n)\r\ndef my_pipeline(url: str):\r\n # Some pipeline code here\r\n web_downloader_task = web_downloader_op(url=url)\r\n\r\n# This is the important part\r\nkfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(\r\n pipeline_func=my_pipeline,\r\n package_path='pipeline.yaml')\r\n```\r\nRunning that code yields a `yaml` file I can upload.\r\n\r\n\r\nThe [compile with terminal command](https://www.kubeflow.org/docs/components/pipelines/tutorials/build-pipeline/#choose-and-compile-a-pipeline) option would look something like this:\r\n```sh\r\ndsl-compile --py [path/to/python/file] --output [path/to/output/tar.gz]\r\n```\r\n\r\nI assume it will be something like this for TFX as well, but possibly with other commands.\r\n\r\nI've has a positive experience with Kubeflows slack community so I can recommend [heading over there](https://www.kubeflow.org/docs/about/community/) if you want to discuss this more.",
"@nicolaiskogheim @valentindbdg No, thats not how you do it. TFX has it's own set of tools for compiling a pipeline into a kfp pipeline def. See this example: https://github.com/tensorflow/tfx/blob/master/tfx/examples/penguin/penguin_pipeline_kubeflow.py#L401"
] | 2022-03-30T12:09:17 | 2022-04-14T10:23:15 | null |
NONE
| null |
Hello,
I created a TFX pipeline that I would like to orchestrate with Kubeflow Pipeline UI to do a **local deployment** using `kind`.
I installed and launched the Kubeflow Pipeline UI, but I do not know how to upload my TFX pipeline (.py).
From what I understand I need to find a way to "convert" my TFX pipeline (.py) into a Kubeflow pipeline (YAML file).
Is there a way to do this locally, on my computer?
Thank you in advance for your help!
|
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I_kwDOB-71UM5Gs_HL
| 7,485 |
[question] who create the pipeline pods
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[
"I read somewhere that it is a pod that creates your pods, but for all I know it could be Custom Resource Definition stuff. But Kubeflow Pipelines runs on Argo Workflows as the workflow engine, so you might want to Google in that direction.\r\nOtherwise I recommend you hop onto the [Kubeflow slack](https://www.kubeflow.org/docs/about/community/). I've had a positive experience there.",
"When using Argo, it is the Argo controller that spins up pods and monitors them while executing the DAG. ",
"Hello @zhuyaguang , please confirm if the answers from @jszendre sound good to you. Feel free to reopen this issue if not."
] | 2022-03-30T09:59:07 | 2022-04-07T22:40:05 | 2022-04-07T22:40:04 |
NONE
| null |
when we run the pipeline, in the namespace "kubeflow-user-example-com" will create pods to exec the pipeline.
my question is ,who create it ? There is no deploy corresponding to it.
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I_kwDOB-71UM5Go_Tw
| 7,481 |
[frontend] artifacts not compiled in case of an afterwards error
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[
"Hi @iuiu34, could you share more on your compiler mode and version for this code snippet and possibly share more code? Thanks. ",
"I run it in google-cloud vertex. Using google-cloud-aiplatform. But seems to me more a kfp general problem. Or is not the case?\r\n\r\nkfp==1.8.11\r\ngoogle-cloud-aiplatform==1.10.0\r\nfrom google.cloud import aiplatform\r\n\r\n```py\r\nfrom kfp.v2.dsl import component, Output, Artifact\r\n@component\r\ndef test(\r\n artifact_url: Output[Artifact]\r\n):\r\n artifact_url.uri = 'gs://test/bucket/uri'\r\n raise ValueError\r\n\r\n@pipeline(\r\n name=f'{image_name}-pipeline',\r\n description='An example pipeline that performs arithmetic calculations.',\r\n pipeline_root=bucket\r\n )\r\n def kfp_pipeline():\r\n test()\r\n\r\nprint('kfp')\r\npipeline_filename = 'tmp/kfp_pipeline.json'\r\ncompiler.Compiler().compile(pipeline_func=pipeline,\r\n package_path=pipeline_filename)\r\n\r\naiplatform.init()\r\njob = aiplatform.PipelineJob(\r\n display_name='test',\r\n location='europe-west4',\r\n template_path=pipeline_filename,\r\n pipeline_root=bucket,\r\n labels={'test_env': 'test_env'}\r\n )\r\n\r\nclient, _ = google.auth.default()\r\njob.run(service_account=client.service_account_email)\r\n```"
] | 2022-03-29T16:53:19 | 2022-04-01T19:16:28 | null |
NONE
| null |
given the following component
```py
from kfp.v2.dsl import component, Output, Artifact
@component
def test(
artifact_url: Output[Artifact]
):
artifact_url.uri = 'gs://test/bucket/uri'
raise ValueError
```
artifact_url will not be compiled. Event though, the uri declaration is before the code error.
questions
a) would be possible to compile it?
b) would be possible to compile it at execution time, before the component execution finishes?
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Failed to start pipeline
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[
"Not an issue of KFP"
] | 2022-03-29T11:48:21 | 2022-03-29T12:11:20 | 2022-03-29T12:11:19 |
NONE
| null |
### Environment
Standalone KFP
Bootstrapper fails when there is python package installed with -e
```
[D 06:36:56.914] Parsing Arguments.....
[I 06:36:56.916] 'model-0329063649':'pre' - starting operation
[I 06:36:56.916] 'model-0329063649':'pre' - Installing packages
Traceback (most recent call last):
File "/jupyter-work-dir/bootstrapper.py", line 652, in <module>
main()
File "/jupyter-work-dir/bootstrapper.py", line 635, in main
OpUtil.package_install(user_volume_path=input_params.get('user-volume-path'))
File "/jupyter-work-dir/bootstrapper.py", line 524, in package_install
current_packages = cls.package_list_to_dict("requirements-current.txt")
File "/jupyter-work-dir/bootstrapper.py", line 571, in package_list_to_dict
package_name, package_version = line.strip('\n').split(sep="==")
ValueError: not enough values to unpack (expected 2, got 1)
```
Here is the list of packages installed in the docker
```
# pip freeze
certifi==2021.10.8
joblib==1.1.0
minio==7.1.5
# Editable install with no version control (model-utils==0.0.4)
-e /model_utils
numpy==1.22.3
pandas==1.4.1
python-dateutil==2.8.2
pytz==2022.1
scikit-learn==1.0.2
scipy==1.8.0
six==1.16.0
threadpoolctl==3.1.0
urllib3==1.26.9
```
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I_kwDOB-71UM5GlMsa
| 7,478 |
ModuleNotFoundError: No module named 'kfp.deprecated'
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[
"+1",
"i fix it by replace \"kfp.deprecated\" to \"kfp\".\r\n\r\ni think it caused by this PR \r\nhttps://github.com/kubeflow/pipelines/pull/7291",
"This is intended since our samples are now based off KFP v2 (main branch). `sdk/release-1.8` contains the v1 specific samples and naming. Please reopen if you have more issues with this. Plan is here: https://github.com/kubeflow/pipelines/issues/7238",
"I don't understand what you mean. \r\nShouldn't following the guide not generate errors? Because I did everything that was in the guide as far as I recall.",
"It installed `Pipelines Version: [1.8.1]` following the guide. I.e. I didn't choose some specific version to install.",
"I solved this issue by launching `git checkout 1.8.13` in the repo I cloned from Github"
] | 2022-03-29T00:51:24 | 2022-07-21T12:53:35 | 2022-03-31T22:56:49 |
NONE
| null |
Pipelines Version: [1.8.1]
```
$ pip list | grep kfp
kfp 1.8.11
kfp-pipeline-spec 0.1.14
kfp-server-api 1.8.1
```
Following this tutorial:
https://www.kubeflow.org/docs/components/pipelines/tutorials/build-pipeline/#choose-and-compile-a-pipeline
After running this:
dsl-compile --py ${DIR}/sequential.py --output ${DIR}/sequential.tar.gz
I get this error:
```
Traceback (most recent call last):
File "/Users/andriy/miniconda3/envs/mlpipeline/bin/dsl-compile", line 8, in <module>
sys.exit(main())
File "/Users/andriy/miniconda3/envs/mlpipeline/lib/python3.7/site-packages/kfp/compiler/main.py", line 138, in main
mode,
File "/Users/andriy/miniconda3/envs/mlpipeline/lib/python3.7/site-packages/kfp/compiler/main.py", line 108, in compile_pyfile
__import__(os.path.splitext(filename)[0])
File "pipelines/samples/core/sequential/sequential.py", line 17, in <module>
from kfp.deprecated import dsl, compiler
ModuleNotFoundError: No module named 'kfp.deprecated'
```
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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I_kwDOB-71UM5GkCAo
| 7,477 |
[sdk] Error with python component with uppercase letters in parameter names
|
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[
"Hi @micahjsmith, we do check for namings due to Kubernetes requirements (https://kubernetes.io/docs/concepts/overview/working-with-objects/names/) and https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/. Let me know if this explains your questions. ",
"Hi @ji-yaqi, thanks for the explanation! This doesn't address the bug in the sdk. Either (1) this should be a clearly documented limitation in the decorator docstring, (2) the decorator should raise an error if invalid parameter names are used or (3) the pipeline compilation should resolve the mixed case situation.\r\n\r\nWould you accept a PR with either resolution (1) or (2) above?"
] | 2022-03-28T18:52:09 | 2022-04-05T14:20:18 | 2022-04-04T21:27:18 |
NONE
| null |
### Environment
* KFP SDK version: 1.8.11
### Steps to reproduce
A component with an uppercase letter in a parameter name cannot be called using keyword arguments
```
import kfp
@kfp.components.create_component_from_func
def comp(P: list) -> None:
print(f'P: {repr(P)}')
@kfp.dsl.pipeline()
def pipeline():
comp(P=[])
kfp.compiler.Compiler().compile(pipeline, '/tmp/pipeline.yml')
```
This gives an error:
```
kfp-1.8.11-py3-none-any.whl/kfp/compiler/compiler.py in compile(self, pipeline_func, package_path, type_check, pipeline_conf)
1176 pipeline_func=pipeline_func,
1177 pipeline_conf=pipeline_conf,
-> 1178 package_path=package_path)
1179 finally:
1180 kfp.TYPE_CHECK = type_check_old_value
kfp-1.8.11-py3-none-any.whl/kfp/compiler/compiler.py in _create_and_write_workflow(self, pipeline_func, pipeline_name, pipeline_description, params_list, pipeline_conf, package_path)
1227 workflow = self._create_workflow(pipeline_func, pipeline_name,
1228 pipeline_description, params_list,
-> 1229 pipeline_conf)
1230 self._write_workflow(workflow, package_path)
1231 _validate_workflow(workflow)
kfp-1.8.11-py3-none-any.whl/kfp/compiler/compiler.py in _create_workflow(self, pipeline_func, pipeline_name, pipeline_description, params_list, pipeline_conf)
1003
1004 with dsl.Pipeline(pipeline_name) as dsl_pipeline:
-> 1005 pipeline_func(*args_list, **kwargs_dict)
1006
1007 pipeline_conf = pipeline_conf or dsl_pipeline.conf # Configuration passed to the compiler is overriding. Unfortunately, it's not trivial to detect whether the dsl_pipeline.conf was ever modified.
<ipython-input-11-45f810bbf964> in pipeline()
2 def pipeline():
3 comp(
----> 4 P=[],
5 )
TypeError: Comp() got an unexpected keyword argument 'P'
```
### Expected result
The pipeline should compile successfully and `[]` should be bound to the parameter `P`
### Materials and Reference
Current workaround is to make sure all parameters are lowercase, or use position arguments to call rather than keyword arguments. Somehow the `create_component_from_func` decorator is changing the signature of the function to use a lowercase parameter name (undocumented). See also possibly related https://github.com/kubeflow/pipelines/issues/5683#issuecomment-845248098
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5Gh_RZ
| 7,476 |
[feature] Compare tensorboards output from selected runs
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[
"@zijianjoy for viz",
"The issue can be reproduced using a locally deployed KFP infra (with minikube), following documentation under `manifests/kustomize`. \r\nhttps://github.com/kubeflow/pipelines/tree/master/manifests/kustomize#envplatform-agnostic-install-on-any-kubernetes-cluster\r\n\r\nWe compiled the following pipeline using the latest KFP SDK \r\nhttps://github.com/kubeflow/pipelines/blob/1.8.1/samples/core/visualization/tensorboard_minio.py\r\n\r\n```\r\ndsl-compile --py tensorboard_minio.py --output tensorboard_minio_pipeline.yaml\r\n```\r\nFor each version of the KFP infra, the generated YAML file was uploaded and two runs were created from it. \r\n\r\n### KFP 1.5.1\r\n\r\n### KFP 1.7.0\r\n\r\n",
"Thank you for reporting this bug, can you help me validate that the Tensorboard has disappeared from individual run's visualization too?\r\n\r\nOn the KFP UI, navigate to `Runs`, select a run with Tensorboard metrics, then click the pipeline step which has Tensorboard metrics on the Pipeline graph. It will open the `side panel` for you, then select `Visualization` to check whether Tensorboard exists. \r\n\r\nFor reference, we don't see much change from the comparison page recently: https://github.com/kubeflow/pipelines/blob/master/frontend/src/pages/Compare.tsx",
"cc @jiyongjung0 ",
"Hello, in individual runs with selected tensorboard output works fine. Viewer created and works fine. \r\n\r\nBTW I am using V1_LEGACY mode for pipeline creation",
"I also tried with `V2_COMPATIBLE` compilation mode and got the same result in KFP 1.7.0. \r\n\r\n",
"In both `V1` and `V2_COMPATIBLE` versions, there's no tensorboard in pipeline output page.\r\n\r\n\r\nHowever tensorboard can be found in the visualization component task\r\n\r\n",
"Could it be some of the changes, e.g. #5731 #5963 #5961 #5999 \r\n",
"The compare-run page is trying to read `metric` field from ApiRun: https://github.com/kubeflow/pipelines/blob/1c3e2768e6177d5d6e3f4b8eff8fafb9a3b76c1f/frontend/src/lib/RunUtils.ts#L141. However, it is actually a backend change which has switched from ReportMetrics API to use MLMD to store visualization. Because of that, the compare-run page stopped working on metrics visualization. We are going to address this as part of KFPv2 effort, but the functionality will probably stay broken for v1. \r\n\r\n\r\ncc @Bobgy to correct me if I made any mistake in the statement.",
"cc @chensun for visibility",
"@zijianjoy thanks for getting the root cause! \r\nTensorboard metrics comparison across runs is a really powerful feature that we'd love to have. Is there any way to get a quick fix for v1? ",
"I am not super familiar with MetricsReporting part in the backend, I will let @chensun comments on how to support such fix. ",
"Any update on this issue?",
"Currently we are looking for help to implement this feature on KFPv2."
] | 2022-03-28T11:45:02 | 2022-08-30T05:32:44 | null |
NONE
| null |
### Feature Area
/area frontend
### What feature would you like to see?
In kubeflow v1.3 was such feature. On bare metal kubeflow deployment (with local volume mounting for tensorboard, more here [#4208](https://github.com/kubeflow/pipelines/issues/4208)) I had a possibility selected several runs, click compare and in `Metrics` section there was combined tensorboard artifact for viewing. Now this feature apparently removed.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
To be able to view several runs logs simultaniosly in one tensorboard pod
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
There was, but apparently it is possible only to compare scalar metrics
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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| 1,182,818,396 |
I_kwDOB-71UM5GgGBc
| 7,475 |
[backend] Pipeline fails when adding Outputs
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[] | null |
[
"The error was that I was passing an int instead of a string to f.write() ..."
] | 2022-03-28T03:16:17 | 2022-03-28T17:43:44 | 2022-03-28T17:43:44 |
NONE
| null |
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Deployed latest Kubeflow version following the current master instructions on the [manifest](https://github.com/kubeflow/manifests) page.
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
I'm afraid I cant find the version in "the left side navigation of user interface".
Could it be `build version dev_local`?
<img width="1884" alt="Screen Shot 2022-03-27 at 11 09 09 PM" src="https://user-images.githubusercontent.com/29357459/160319982-f9634470-7702-4c62-9408-5bfc7ecd1dcb.png">
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.6.3
kfp-pipeline-spec 0.1.13
kfp-server-api 1.6.0
### Steps to reproduce
Create the simplest pipeline possible:
```python
@component
def convert_str_int(
input_str: str,
output_file: Output[Dataset],
) -> int:
print(f"Converting string {input_str} to int ...")
output_int = int(input_str)
with open(output_file.path, 'w') as f:
f.write(output_int)
return output_int
# Define a pipeline and create a task from a component:
@dsl.pipeline(
name='extremely-simple-pipeline',
)
def extremely_simple_pipeline(
input_str: str,
):
convert_str_int_task = convert_str_int(
input_str=input_str,
)
arguments = {
"input_str": "10",
}
client.create_run_from_pipeline_func(
extremely_simple_pipeline,
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE,
# You can optionally override your pipeline_root when submitting the run too:
# pipeline_root='gs://my-pipeline-root/example-pipeline',
arguments=arguments,
)
```
### Expected result
Have a pipeline that runs
### Actual result
Pipeline fails. It doesn't even execute the code inside the component.
If I remove the Output, it works.
I've been trying to follow the instructions in the official documentation, and I don't know what I'm missing.
Pod logs:
```
time="2022-03-28T03:03:32.844Z" level=info msg="capturing logs" argo=true
Traceback (most recent call last):
File "/tmp/tmp.lSqglRpd6S", line 828, in <module>
executor_main()
File "/tmp/tmp.lSqglRpd6S", line 822, in executor_main
function_to_execute=function_to_execute)
File "/tmp/tmp.lSqglRpd6S", line 549, in __init__
artifacts_list[0])
File "/tmp/tmp.lSqglRpd6S", line 562, in _make_output_artifact
os.makedirs(os.path.dirname(artifact.path), exist_ok=True)
File "/usr/local/lib/python3.7/posixpath.py", line 156, in dirname
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
F0328 03:03:35.287326 20 main.go:56] Failed to execute component: exit status 1
time="2022-03-28T03:03:35.342Z" level=error msg="cannot save artifact /tmp/outputs/Output/data" argo=true error="stat /tmp/outputs/Output/data: no such file or directory"
time="2022-03-28T03:03:35.342Z" level=error msg="cannot save artifact /tmp/outputs/output_file/data" argo=true error="stat /tmp/outputs/output_file/data: no such file or directory"
Error: exit status 1
```
---
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
|
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I_kwDOB-71UM5GfjZ0
| 7,474 |
[sdk] Error 400 authenticating via Google IAP
|
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[
"Hi @votti, feel free to send a PR and we will take a look. If it involves an interface change, we would need a design. Thank you!",
"@ji-yaqi Have you all had time to review this related PR? https://github.com/kubeflow/pipelines/pull/7526",
"Hi @votti.\r\n\r\nWe have implemented a workaround for OOB in #8262. It should be included in the next SDK releases for v2 and v1.8.\r\n\r\nYou can also build it from **master** branch if you want to test earlier:\r\n```bash\r\nmkdir testkfp && cd testkfp\r\ngit clone [email protected]:kubeflow/pipelines\r\ncd pipelines/sdk/python\r\nrm -r ./dist/\r\npython3 setup.py bdist_wheel\r\n```\r\n\r\nThis will create a wheel in **pipelines/sdk/python/dist** folder. You can create a new virtual environment to install the wheel:\r\n```bash\r\ncd ./dist\r\npython3 -m venv testenv\r\n. ./testenv/bin/activate\r\npip install ./kfp-2.0.0b4-py3-none-any.whl # filename could vary \r\n```\r\n\r\nTo test it, run thin in python (replace parameters below):\r\n```python\r\nimport kfp\r\nclient = kfp.Client(host='https://<KF_NAME>.endpoints.<PROJECT>.cloud.goog/pipeline',\r\n client_id='<AAAAAAAAAAAAAAAAAAAAAA>.apps.googleusercontent.com',\r\n other_client_id='<BBBBBBBBBBBBBBBBBBB>.apps.googleusercontent.com',\r\n other_client_secret='<CCCCCCCCCCCCCCCCCCCC>')\r\nclient.list_pipelines()\r\n```\r\n\r\nLet me know if it works."
] | 2022-03-27T21:58:58 | 2022-09-19T17:54:29 | 2022-09-15T23:27:26 |
NONE
| null |
### Environment
* KFP version:
?
* KFP SDK version:
1.8.11
* All dependencies version:
kfp 1.8.11
kfp-pipeline-spec 0.1.14
kfp-server-api 1.8.1
### Steps to reproduce
I followed the steps described in the documentation to connect to Kubeflow Pipelines in a full Kubeflow deployment with authentication through IAP:
https://www.kubeflow.org/docs/distributions/gke/pipelines/authentication-sdk/#connecting-to-kubeflow-pipelines-in-a-full-kubeflow-deployment
Following the Oauth link from the SDK:
_3. To connect to Kubeflow Pipelines public endpoint, initiate SDK client like the following:_
```
import kfp
client = kfp.Client(host='https://<KF_NAME>.endpoints.<PROJECT>.cloud.goog/pipeline',
client_id='<AAAAAAAAAAAAAAAAAAAAAA>.apps.googleusercontent.com',
other_client_id='<BBBBBBBBBBBBBBBBBBB>.apps.googleusercontent.com',
other_client_secret='<CCCCCCCCCCCCCCCCCCCC>')
```
I get an Error 400:
### Expected result
I should get an authentication screen and then a code.
### Materials and Reference
This error seems to a deprecation of the copy-paste workflow for oauth authentification at the google side, as discussed here:
https://stackoverflow.com/questions/71318804/google-oauth-2-0-failing-with-error-400-invalid-request-for-some-client-id-but/71327990#71327990
I also implemented the workaround discussed here: https://stackoverflow.com/a/71331562
essentially "Replace urn:ietf:wg:oauth:2.0:oob with http://localhost:1/"
It is not sufficient to just change the `redirect_uri` parameter in url returned in the vanilla 1.8.11. A change of this parameter in the source code is required as well.
This seems to work in my quick POC patch of KFP v1.8.11: https://github.com/votti/pipelines/tree/fix_redirect_uri_1.8.11
Installable via: `pip install "git+https://github.com/votti/pipelines@fix_redirect_uri_1.7#subdirectory=sdk/python"`
--
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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I_kwDOB-71UM5GWHu0
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[bug] connect to kfp method KF_PIPELINES_SA_TOKEN_PATH not working
|
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[
"[Temporary workaround](https://github.com/kubeflow/kubeflow/issues/6411#issuecomment-1080029132)",
"just use following code, you will get pipeline info, no more additional params\r\n```\r\nimport kfp\r\nclient = kfp.Client()\r\nprint(client.list_experiments())\r\n```"
] | 2022-03-25T00:46:50 | 2022-04-26T10:02:56 | null |
NONE
| null |
### What steps did you take
I deployed a kubeflow in multi-user mode.
and now I try to connect to KFP with https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/
I use the PodDefault method, and use the variable of KF_PIPELINES_SA_TOKEN_PATH.
### What happened:
But try failed.
and I clone the source code, but I can not see KF_PIPELINES_SA_TOKEN_PATH in the source code.
### What did you expect to happen:
KF_PIPELINES_SA_TOKEN_PATH Is this PodDefault mothod still available?
### Environment:
KFP version: 1.2
KFP SDK version: kfp-1.8.11
### Labels
<!-- /area sdk -->
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I_kwDOB-71UM5GUubB
| 7,461 |
[feature] Programmatically retrieve logs via the SDK
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[
"Hey all, any updates on this? "
] | 2022-03-24T17:48:47 | 2022-08-11T18:35:49 | null |
CONTRIBUTOR
| null |
### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
It would be great if there was a way to programmatically retrieve container logs from the SDK so that users are not dependent on navigating to the GUI to access logs.
I wrote a method to that effect. It extracts the token from the instantiated `Client` and uses it to make an authenticated request to the k8s and minio endpoints of the KFP API, analogous to what the front-end does (I kind of reverse engineered it), then (optionally) pretty prints the output, and returns a data structure that contains all of the logs broken down by source and component.
I'd like to contribute it upstream if it seems useful, just following due process with an issue first.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Retrieving logs from a pipeline without needing to navigate to the GUI.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
Not via the SDK.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. We prioritize fulfilling features with the most π.
|
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I_kwDOB-71UM5GTw6Z
| 7,460 |
[bug] documentation jupyter notebook file missing for google-cloud-component
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[] | 2022-03-24T14:19:38 | 2022-03-25T14:43:29 | 2022-03-25T14:43:29 |
NONE
| null |
In google-cloud-components documentation [link](https://cloud.google.com/vertex-ai/docs/pipelines/gcpc-list) for operator HyperparameterTuningJobRunOp [link](
https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.0/google_cloud_pipeline_components.v1.hyperparameter_tuning_job.html#google_cloud_pipeline_components.v1.hyperparameter_tuning_job.HyperparameterTuningJobRunOp) it redirects "For example usage" to
https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/v1/hyperparameter_tuning_job/hp_tuning_job_sample.ipynb
but this file doesn't exist in
https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/v1/hyperparameter_tuning_job
same behavior for the other components (also i don't know why google calls them operators instead of components)
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I_kwDOB-71UM5GP4Wd
| 7,457 |
[components] google_cloud_pipeline_components JsonArray PipelineParam not resolved/serializable in component
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[
"After further investigation, it seems kfp.v2 does not support PipelineParam json serialization (as implemented here for v1 https://github.com/kubeflow/pipelines/pull/2212). Any plans on supporting this in v2?",
"scratch that, I have no idea what is wrong here\r\n\r\nhere is why I was thinking so:\r\nusing compiler v2 doesn't work (`Object of type PipelineParam is not JSON serializable`), but v1 does\r\n```\r\nfrom kfp.v2 import compiler\r\nfrom kfp import compiler as compiler_v1\r\nfrom kfp import components\r\nfrom kfp.dsl import PipelineParam\r\n\r\ndef consume_list_p(list_param: list) -> int:\r\n pass\r\n\r\nconsume_list = components.create_component_from_func(consume_list_p)\r\n\r\ndef pip():\r\n task = consume_list([1, 2, 3, PipelineParam(\"aaa\"), 4, 5, 6])\r\n\r\n# doesn't work\r\n# compiler.Compiler().compile(\r\n# pipeline_func=pip,\r\n# package_path=\"test.json\",\r\n# )\r\n\r\ncompiler_v1.Compiler().compile(\r\n pipeline_func=pip,\r\n package_path=\"test.yaml\",\r\n)\r\n```"
] | 2022-03-23T19:25:33 | 2022-03-24T22:34:19 | null |
NONE
| null |
### Environment
* KFP version:
Vertex AI on GCP
* KFP SDK version:
kfp = "^1.8.11"
* All dependencies version:
google-cloud-pipeline-components = "^1.0.1"
### Steps to reproduce
```
from kfp.v2.dsl import component
from google_cloud_pipeline_components.experimental.dataflow import DataflowPythonJobOp
def pip() -> None:
@component
def get_input(input: str) -> str:
return input
inp = get_input("test")
DataflowPythonJobOp(
python_module_path="gs://whatever/script.py",
temp_location="gs://whatever/tmp",
project="whatever",
args=[
"--input_table", inp.output,
]
)
```
Gives this error: `Object of type PipelineParam is not JSON serializable` (but Lists are supposed to be serializable - https://github.com/kubeflow/pipelines/pull/1945)
inp.output does have `.to_struct()`, and if I use it (`"--input-table", inp.output.to_struct()`) it goes through, although the serialization is passed to the component and not the resolved output value (these are the arguments to the component:
```
--project; whatever; --location; us-central1; --python_module_path; gs://whatever/script.py; --temp_location; gs://whatever/tmp; --requirements_file_path; ; --args; ["--input_table", "{{pipelineparam:op=get-input;name=Output}}"];
```
)
JsonArray seems like an alias to List in the sdk code, which is why I think it might be a sdk issue, but it also might be the specific component that is not setup correctly, or me not understanding the serialization correctly. What do you think?
### Expected result
args array is serialized and input_table has value "test" inside component
### Materials and Reference
- Component YAML: https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/experimental/dataflow/python_job/component.yaml
---
Impacted by this bug? Give it a π. We prioritise the issues with the most π.
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