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https://api.github.com/repos/kubeflow/pipelines/issues/8688 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8688/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8688/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8688/events | https://github.com/kubeflow/pipelines/issues/8688 | 1,537,094,194 | I_kwDOB-71UM5bnjIy | 8,688 | [chore] Investigate/implement automation of KFP SDK release note generation | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-01-17T22:05:13 | 2023-08-27T07:42:10 | null | 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?
KFP SDK uses [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/). Can we automate the creation of release notes to (1) ensure that they comprehensively document all SDK changes and (2) eliminate the need to ask OSS contributors to update the release notes by hand in [RELEASE.md](https://github.com/kubeflow/pipelines/blob/master/sdk/RELEASE.md).
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-01-17T18:29:00 | 2023-08-28T16:21:40 | null | 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?
With MLMD upgrade to 1.4.0, we are able to list multi-layer lineage graph using the new MLMD API call. As a result, we can introduce this feature on KFP UI.
<!-- Provide a description of this feature and the user experience. -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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"Hi @MatthewRalston , \r\n\r\nFirst of all `dsl.ContainerOp` has been deprecated as a user interface, you should have seen a deprecation warning on it.\r\nHowever, I think the real culprit for the error is `str(fastq1).rstrip(\".gz\")`, this is code not containerized. You can either wrap the code in a lightweight c... | 2023-01-17T13:27:32 | 2023-02-02T23:56:41 | 2023-02-02T23:56:41 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
Minikube v1.28.0
kubernetes 1.22.2
Kustomize v3.2.0
kubectl v1.25.5
Manifests v1.6.1
* How do you deploy Kubeflow Pipelines (KFP)?
Locally, on minikube, via manifests v1.6.1.
I've also experimented with Argoflow on my OS, but it's not relevant here.
<!-- 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 1.8.17
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
Create pipeline.py from the following.
```python
#!/bin/env python
import os
import sys
import argparse
import kfp.dsl as dsl
import kfp.components as comp
import kfp
import logging
global logger
logger = None
def get_root_logger(level):
levels=[logging.WARNING, logging.INFO, logging.DEBUG]
if level < 0 or level > 2:
raise TypeError("{0}.get_root_logger expects a verbosity between 0-2".format(__file__))
logging.basicConfig(level=levels[level], format="%(levelname)s: %(asctime)s %(funcName)s L%(lineno)s| %(message)s", datefmt="%Y/%m/%d %I:%M:%S")
root_logger = logging.getLogger()
return root_logger
components_dir = os.path.join(os.path.dirname(__file__), "components")
#gunzip = comp.load_component_from_file(os.path.join(components_dir, "gunzip.yaml"))
#is_unzip_needed = comp.load_component_from_file(os.path.join(components_dir, "is_unzip_needed.yaml"))
def gunzip(infile:str, outfile:str):
""" Infile like 'path/to/example.txt.gz', outfile like 'path/to/example.txt' """
return dsl.ContainerOp(
name='gunzip',
image='bitnami/minideb:latest',
command=[
'/bin/zcat $0 > input.txt ',
'|| ',
'mv $0 input.txt'
],
file_outputs={
'output': 'input.txt'
}
)
@dsl.pipeline(
name='kubeflow-barebone-demo',
description='kubeflow demo with minimal setup'
)
def pipeline(fastq1:str):
# Step 1: training component
gunzip1 = gunzip(infile=fastq1, outfile = str(fastq1).rstrip(".gz")).set_cpu_request('1').set_cpu_limit('1').set_memory_request('512Mi').set_memory_limit('512Mi')
if __name__ == "__main__":
logger = get_root_logger(2)
kfp.compiler.Compiler().compile(rnaseq_pipeline, 'pipeline.yaml')
```
<!--
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
I expect that my 'gunzip' container should run, given that the executable listed can be verified to be installed in that container (alpine:latest or bitnami/minideb:latest)
However, I am observing the following in the dashboard logs.
```bash
failed to find name in PATH: exec: "echo $0": executable file not found in $PATH
```
<!-- What should the correct behavior be? -->
The correct behavior should be to locate or understand the shell builtin 'exec'.
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### 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 👍. | {
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"The Error comes out because you failed to access to kfp client.\r\nTry like this\r\n```\r\nimport requests\r\n\r\nUSERNAME = \"user@example.com\"\r\nPASSWORD = \"12341234\" \r\nNAMESPACE = \"kubeflow-user-example-com\"\r\nHOST = \"http://127.0.0.1:8080\" # your istio-ingressgateway pod ip:8080\r\n\r\nsession = req... | 2023-01-16T18:32:28 | 2023-08-28T15:35:22 | 2023-08-28T15:35:21 | NONE | null | ### Environment
* KFP version:
Kubeflow 1.5
* KFP SDK version:
KFP v2.0.0b10
* All dependencies version:
kfp 2.0.0b10
kfp-pipeline-spec 0.1.17
kfp-server-api 2.0.0a6
### Steps to reproduce
Tried upgrading from KFP 1.8 to 2.0 for higher version kubernetes sdk support. Tried copying the example from [here](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/)
```python3
import kfp
from kfp import compiler
from kfp import dsl
@dsl.component
def addition_component(num1: int, num2: int) -> int:
return num1 + num2
@dsl.pipeline(name='addition-pipeline')
def my_pipeline(a: int=1, b: int=2, c: int = 10):
add_task_1 = addition_component(num1=a, num2=b)
add_task_2 = addition_component(num1=add_task_1.output, num2=c)
cmplr = compiler.Compiler()
cmplr.compile(my_pipeline, package_path='my_pipeline.yaml')
client=kfp.Client()
client.create_run_from_pipeline_package('my_pipeline.yaml',arguments={"a":1,"b":2})
```
When I give it arguments I get the error `ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'date': 'Mon, 16 Jan 2023 18:20:24 GMT', 'content-length': '190', 'x-envoy-upstream-service-time': '0', 'server': 'envoy'})
HTTP response body: {"error":"json: cannot unmarshal number into Go value of type map[string]json.RawMessage","code":3,"message":"json: cannot unmarshal number into Go value of type map[string]json.RawMessage"}`
And without arguments I get the error `ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'date': 'Mon, 16 Jan 2023 18:30:38 GMT', 'content-length': '548', 'x-envoy-upstream-service-time': '1', 'server': 'envoy'})
HTTP response body: {"error":"Validate create run request failed.: InvalidInputError: Invalid IR spec format.: invalid character 'c' looking for beginning of value","code":3,"message":"Validate create run request failed.: InvalidInputError: Invalid IR spec format.: invalid character 'c' looking for beginning of value","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Invalid IR spec format.","error_details":"Validate create run request failed.: InvalidInputError: Invalid IR spec format.: invalid character 'c' looking for beginning of value"}]}`
### Expected result
Pipeline compiles and runs.
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8680 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8680/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8680/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8680/events | https://github.com/kubeflow/pipelines/issues/8680 | 1,534,951,739 | I_kwDOB-71UM5bfYE7 | 8,680 | What is Difference of pipelines between V1 and V2??? | {
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"Hi @kimkihoon0515 \r\n\r\n> Does V2 still supports argo workflow or not??\r\n\r\nKFP v2 still supports argo workflow.\r\n\r\n> And for now V1 compiler compiles pipeline into yaml file but in V2 the compiler only supports json file format.\r\n> Are you guys planning to support a function that supports to be compile... | 2023-01-16T13:34:09 | 2023-08-28T07:42:20 | null | NONE | null | Does V2 still supports argo workflow or not??
And for now V1 compiler compiles pipeline into yaml file but in V2 the compiler only supports json file format.
Are you guys planning to support a function that supports to be compiled as a yaml file??
Plus what is pipeline Spec? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8676 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8676/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8676/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8676/events | https://github.com/kubeflow/pipelines/issues/8676 | 1,533,811,531 | I_kwDOB-71UM5bbBtL | 8,676 | [feature] conda-forge feedstock github repository for google-cloud-pipeline-components | {
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"Happy to help or to give more context on this FR",
"just some context, there are already 62 google-cloud python packages available in conda-forge channel\r\n\r\nhttps://github.com/orgs/conda-forge/repositories?q=google-cloud-&type=all&language=&sort=\r\n**62 results for all repositories matching google-cloud- so... | 2023-01-15T13:17:57 | 2023-08-28T07:42:22 | 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. -->
Would like to have google-cloud-pipeline-components python package in the conda-forge channel.
There is no conda forge feedstock available yet [link](https://github.com/orgs/conda-forge/repositories?q=google-cloud-&type=all&language=&sort=)
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Mamba is a much better python package manager than what we can get with pip/pypi which works for example with conda-forge channel. Most of the open source libraries and gpc python lib (cloud storage, logging, hyperparameter, veretx ai ..) exist in the conda-forge channel like the kfp python sdk [link](https://anaconda.org/conda-forge/kfp) with the following conda-forge feedstock [link](https://github.com/conda-forge/kfp-feedstock).
When a conda-forge feedstock is created for google-cloud-pipeline-components, I will be happy to do PR for new version as I did for kfp sdk but this is is a google/gcp package I guess the conda-forge feedstock repository need to be created by google/gcp as well asthe approval of the PR to avoid malicious actor to use it no ? Happy to help but never create a conda-forge feedstock from scratch
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
The package exist in pypi [link](https://pypi.org/project/google-cloud-pipeline-components/)
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"Hello @kimkihoon0515 , can you deploy Kubeflow Pipelines 2.0.0-alpha versions like this one? https://github.com/kubeflow/pipelines/releases/tag/2.0.0-alpha.6.\r\n\r\nAlso, please use KFP SDK version which is 2.0.0-beta like this one: https://github.com/kubeflow/pipelines/releases/tag/2.0.0b10",
"@zijianjoy Hey ... | 2023-01-14T09:42:34 | 2023-01-19T23:40:33 | 2023-01-19T23:40:33 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
I deployed Kubeflow using this command
```
minikube start --driver=docker --kubernetes-version=1.23.14 --memory=12g --cpus=4
```
* KFP version:
1.8.18
### Steps to reproduce
I'm trying to get confusion matrix metric output by using kfp v2 like this
```
@component(
packages_to_install=['scikit-learn'],
base_image='python:3.9'
)
def iris_sgdclassifier(test_samples_fraction: float, metrics: Output[ClassificationMetrics]):
from sklearn import datasets, model_selection
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
iris_dataset = datasets.load_iris()
train_x, test_x, train_y, test_y = model_selection.train_test_split(
iris_dataset['data'], iris_dataset['target'], test_size=test_samples_fraction)
classifier = SGDClassifier()
classifier.fit(train_x, train_y)
predictions = model_selection.cross_val_predict(classifier, train_x, train_y, cv=3)
metrics.log_confusion_matrix(
['Setosa', 'Versicolour', 'Virginica'],
confusion_matrix(train_y, predictions).tolist() # .tolist() to convert np array to list.
)
```
and it shows me a confusion matrix output in "Visualizations" tab.
But nothing shows up in "Input/Output" tab.
### Expected result
Actually I'm trying to compare confusion matrixes of several runs which is possible on kfp v2.
<img width="749" alt="image" src="https://user-images.githubusercontent.com/63439911/212465776-cbeb5807-4294-4dac-91ba-6c0c52ec750e.png">
the log says
```
launcher.go:560] Local filepath "/minio/mlpipeline/v2/artifacts/pipeline/v2-metrics/58c160ea-4ef3-49c8-a08a-0e1c0347a031/iris-sgdclassifier/metrics" does not exist
```
I think that log comes out because theres no output file in the component. But idk how to do it.
### Materials and reference
https://www.kubeflow.org/docs/components/pipelines/v1/sdk-v2/run-comparison/
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8672 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8672/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8672/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8672/events | https://github.com/kubeflow/pipelines/issues/8672 | 1,532,822,877 | I_kwDOB-71UM5bXQVd | 8,672 | [sdk] Notebook Executor pipeline component does not cause the pipeline to fail when a notebook execution fails | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-01-13T19:48:16 | 2023-08-28T07:42:25 | 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: 1.8.12
* `google-cloud-pipeline-components 1.0.4`
<!-- 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 -->
`kfp 1.8.12`
`kfp-pipeline-spec 0.1.14`
`kfp-server-api 1.8.1`
### 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.
--> I encountered this error when using the kubeflow pipelines sdk with Vertex AI Pipelines.
Run a pipeline which uses the notebook executor Google Cloud pipeline component. When the notebook execution raises an error, the pipeline will continue to run until stopped manually. This is due to a bug in executor.py that results in an infinite loop if the notebook job fails with an error.
### Expected result
<!-- What should the correct behavior be? -->
When the notebook throws an error, the pipeline should stop running and return an error message.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
components/google-cloud/google_cloud_pipeline_components/container/experimental/notebooks/executor.py
https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/container/experimental/notebooks/executor.py#L220
By changing Line 220 to the following:
`if job_state.JobState(custom_job_state) in _STATES_COMPLETED:`
I was able to get the expected behavior.
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"follower... | null | [] | 2023-01-11T08:28:12 | 2023-01-17T18:46:44 | 2023-01-17T18:46:44 | 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?
<!-- Provide a description of this feature and the user experience. -->
The SDK currently requires `PyYAML>=5.3,<6`. I would like the SDK to accept higher versions - `>=6` specifically.
### What is the use case or pain point?
Would like to use kfp SDK from Google Cloud Composer. However, the latest Google Cloud Composer comes with `PyYAML==6.0` as per [Cloud Composer version list](https://cloud.google.com/composer/docs/concepts/versioning/composer-versions)
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
There are some workarounds but I think it's no problem for the SDK to accept newer PyYAML versions. I double-checked the [change list of PyYAML 6.0](https://github.com/yaml/pyyaml/blob/6.0/CHANGES#L7-L18). I believe those changes have no impact to kfp SDK.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8661 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8661/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8661/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8661/events | https://github.com/kubeflow/pipelines/issues/8661 | 1,526,100,273 | I_kwDOB-71UM5a9nEx | 8,661 | [sdk] `wait_for_run_completion` occasionally hangs forever in `RunServiceApi.get_run` call | {
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"@jli Thanks for reaching out. Can you provide how you observe the run status (GetRun API call)? Also, have you experienced this issue from UI as well? Can you provide a few screenshot of your logging?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed i... | 2023-01-09T18:27:50 | 2023-08-28T07:42:27 | null | CONTRIBUTOR | null | ### Environment
* KFP version: 1.7.1
* KFP SDK version: 1.8.12
* All dependencies version:
```
kfp 1.8.12
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.3
```
(I know we're on an older version of the kfp sdk, but it doesn't look like the relevant code has changed much since then.)
### Steps to reproduce
- Launch a long-running run on KFP
- Create a few processes that call `kfp.Client.wait_for_run_completion` with INFO logging enabled[1]
- After ~10-15 minutes, I observe that ~2 out of 3 wait calls are hanging after the RunServiceApi.get_run call. The hanging continues even after the run completes.
[1] enable INFO logging to see when `wait_for_run_completion` makes a new call
```python
logging.basicConfig(format="%(asctime)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S", force=True)
logging.getLogger().setLevel(logging.INFO)
```
I also added more logging calls before and after the `get_run` API call here: https://github.com/kubeflow/pipelines/blob/f4588f31128d268974bdffb16e487ea170706011/sdk/python/kfp/client/client.py#L1330
### Expected result
`wait_for_run_completion` should continue polling until the run completes.
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8660 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8660/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8660/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8660/events | https://github.com/kubeflow/pipelines/issues/8660 | 1,525,743,316 | I_kwDOB-71UM5a8P7U | 8,660 | Contributing to pipeline CICD for ppc64le arch | {
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"@pranavpandit1 We suggest you to pull the changes from our website and make your own CICD pipeline, because our current testing infra in Google Cloud (GKE), and we don't have the access to IBM Power. Feel free to reopen this if you have any idea or suggestion.",
"@jlyaoyuli \r\n we can add support for ppc64le ... | 2023-01-09T14:50:34 | 2023-06-16T13:13:00 | null | NONE | null | Hello,
My team is working on a request of "Porting Kubeflow to IBM Power (ppc64le)"
For the same purpose, we want to contribute to CICD for the pipeline repository for ppc64le architecture.
Kindly provide the details of how to access existing CI builds, files, logs, etc.
for more details, reference the link [Kubeflow Umbrella issue](https://github.com/kubeflow/kubeflow/issues/6684) | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8654 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8654/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8654/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8654/events | https://github.com/kubeflow/pipelines/issues/8654 | 1,520,574,513 | I_kwDOB-71UM5aoiAx | 8,654 | [feature] Add filter for finished_at runs | {
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"followers_url": "https://api.git... | null | [] | 2023-01-05T11:20:36 | 2023-01-15T19:01:00 | 2023-01-15T19:01:00 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area sdk
### What feature would you like to see?
Protocol buffer filtering for the `finished_at` field in runs.
### What is the use case or pain point?
We are looking to archive runs that finished over 90 days ago. While we have a workaround (see below), if we have a filter (like you do currently for the `created_at` field), it would mean that our request payloads are substantially smaller.
### Is there a workaround currently?
Our current approach is to get all non-archived runs (using a predicate filter), then iterate over the result to compare the `datetime` of the `finished_at` field to the `datetime` from 90 days ago. We then archive those runs that completed 90 days ago.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8650 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8650/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8650/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8650/events | https://github.com/kubeflow/pipelines/issues/8650 | 1,518,810,483 | I_kwDOB-71UM5ahzVz | 8,650 | [sdk] No longer possible to compile components that use PipelineTaskFinalStatus | {
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"Closed due to replication of #8649"
] | 2023-01-04T11:29:26 | 2023-01-05T23:51:24 | 2023-01-05T23:51:24 | CONTRIBUTOR | null | ### Environment
* KFP version: N/A
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 2.0.0b10
<!-- 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: $pip list | grep kfp -->
```
kfp 2.0.0b10
kfp-pipeline-spec 0.1.17
kfp-server-api 2.0.0a6
```
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
In version 1.x.x it was possible to compile components that uses `PipelineTaskFinalStatus`, e.g using:
```python
from kfp.v2.dsl import component, PipelineTaskFinalStatus, pipeline, ExitHandler
from kfp.components import load_component_from_file
@component(output_component_file='example.yaml')
def example(status: PipelineTaskFinalStatus):
print(status)
loaded_component = load_component_from_file('example.yaml')
@pipeline
def example_pipeline():
with ExitHandler(loaded_component()):
pass
```
In version 2.0.0bx we expected this to still be possible, e.g with
```python
from kfp.dsl import component, PipelineTaskFinalStatus, pipeline, ExitHandler
from kfp.compiler import Compiler
from kfp.components import load_component_from_file
@component
def example(status: PipelineTaskFinalStatus):
print(status)
Compiler().compile(example, 'example.yaml')
loaded_component = load_component_from_file('example.yaml')
@pipeline
def example_pipeline():
with ExitHandler(loaded_component()):
pass
```
However, this is no longer possible for what seems like a variety of reasons. First of all the sdk explicitly forbids it in
https://github.com/kubeflow/pipelines/blob/fdf3ee7b68b2293d08e14c389b0dab9a57854e2a/sdk/python/kfp/compiler/pipeline_spec_builder.py#L333-L338
I tried commenting out the above check, which does enable us to compile valid component yaml without further changes:
```yaml
# PIPELINE DEFINITION
# Name: example
# Inputs:
# status: dict
components:
comp-example:
executorLabel: exec-example
inputDefinitions:
parameters:
status:
isOptional: true
parameterType: STRUCT
deploymentSpec:
executors:
exec-example:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- example
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.0.0-beta.10'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
python3 -m kfp.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef example(status: PipelineTaskFinalStatus):\n print(status)\n\
\n"
image: python:3.7
pipelineInfo:
name: example
root:
dag:
tasks:
example:
cachingOptions:
enableCache: true
componentRef:
name: comp-example
inputs:
parameters:
status:
componentInputParameter: status
taskInfo:
name: example
inputDefinitions:
parameters:
status:
isOptional: true
parameterType: STRUCT
schemaVersion: 2.1.0
sdkVersion: kfp-2.0.0-beta.10
```
Unfortunately, the input type of the `status` parameter is converted to `STRUCT` in the process, so loading in the component yaml results in a component that fails at runtime because the `status` argument is not provided by the backend as expected.
### Expected result
<!-- What should the correct behavior be? -->
It should be possible to compile components that use `PipelineTaskFinalStatus` for feature parity with version 1.x.x
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8649 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8649/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8649/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8649/events | https://github.com/kubeflow/pipelines/issues/8649 | 1,518,810,123 | I_kwDOB-71UM5ahzQL | 8,649 | [sdk] No longer possible to compile components that use `PipelineTaskFinalStatus` | {
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"I'm impacted by this too, and not clear what the workaround is either. Is there a standard way I'm not finding in Vertex to report exit status?",
"This is on our radar to fix, @berniecamus. No update/workaround yet.",
"This issue has been automatically marked as stale because it has not had recent activity. It... | 2023-01-04T11:29:09 | 2023-08-28T15:35:47 | 2023-08-28T15:35:46 | CONTRIBUTOR | null | ### Environment
* KFP version: N/A
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 2.0.0b10
<!-- 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: $pip list | grep kfp -->
```
kfp 2.0.0b10
kfp-pipeline-spec 0.1.17
kfp-server-api 2.0.0a6
```
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
In version 1.x.x it was possible to compile components that uses `PipelineTaskFinalStatus`, e.g using:
```python
from kfp.v2.dsl import component, PipelineTaskFinalStatus, pipeline, ExitHandler
from kfp.components import load_component_from_file
@component(output_component_file='example.yaml')
def example(status: PipelineTaskFinalStatus):
print(status)
loaded_component = load_component_from_file('example.yaml')
@pipeline
def example_pipeline():
with ExitHandler(loaded_component()):
pass
```
In version 2.0.0bx we expected this to still be possible, e.g with
```python
from kfp.dsl import component, PipelineTaskFinalStatus, pipeline, ExitHandler
from kfp.compiler import Compiler
from kfp.components import load_component_from_file
@component
def example(status: PipelineTaskFinalStatus):
print(status)
Compiler().compile(example, 'example.yaml')
loaded_component = load_component_from_file('example.yaml')
@pipeline
def example_pipeline():
with ExitHandler(loaded_component()):
pass
```
However, this is no longer possible for what seems like a variety of reasons. First of all the sdk explicitly forbids it in
https://github.com/kubeflow/pipelines/blob/fdf3ee7b68b2293d08e14c389b0dab9a57854e2a/sdk/python/kfp/compiler/pipeline_spec_builder.py#L333-L338
I tried commenting out the above check, which does produce valid component yaml:
```yaml
# PIPELINE DEFINITION
# Name: example
# Inputs:
# status: dict
components:
comp-example:
executorLabel: exec-example
inputDefinitions:
parameters:
status:
isOptional: true
parameterType: STRUCT
deploymentSpec:
executors:
exec-example:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- example
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.0.0-beta.10'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
python3 -m kfp.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef example(status: PipelineTaskFinalStatus):\n print(status)\n\
\n"
image: python:3.7
pipelineInfo:
name: example
root:
dag:
tasks:
example:
cachingOptions:
enableCache: true
componentRef:
name: comp-example
inputs:
parameters:
status:
componentInputParameter: status
taskInfo:
name: example
inputDefinitions:
parameters:
status:
isOptional: true
parameterType: STRUCT
schemaVersion: 2.1.0
sdkVersion: kfp-2.0.0-beta.10
```
Unfortunately, the input type of the `status` parameter is converted to `STRUCT` in the process, so loading in the component yaml results in a component that fails at runtime because the `status` argument is not provided by the backend as expected.
### Expected result
<!-- What should the correct behavior be? -->
It should be possible to compile components that use `PipelineTaskFinalStatus` for feature parity with version 1.x.x
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8648 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8648/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8648/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8648/events | https://github.com/kubeflow/pipelines/issues/8648 | 1,518,787,063 | I_kwDOB-71UM5ahtn3 | 8,648 | [sdk] jsonschema dependency pinning | {
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"Moving back a little in time seems to be fine -- the following runs without an issue\r\n\r\n```\r\nFROM mambaorg/micromamba:jammy as build-stage\r\n\r\nRUN micromamba install --name base --channel conda-forge \\\r\n \"python >=3.10\" \\\r\n \"kfp >=1.8\" \\\r\n \"jupyterlab 3.2.*\" \\\r\n \"jupyterlab_... | 2023-01-04T11:16:25 | 2023-08-28T07:42:31 | null | NONE | null | ### Environment
* KFP SDK version: `>=1.8`
### Steps to reproduce
In the `requirements.in` file, `jsonschema >=3.0.1,<4`. A lot of other packages (e.g. [jupyterlab, via jupyterlab_server](https://github.com/conda-forge/jupyterlab_server-feedstock/blob/main/recipe/meta.yaml#L27)) have moved to `jsonschema >=4`
### Expected result
It should be possible to install kfp in the same environment as jupyterlab.
#### Attempt 1
Here is a docker file where the environment is created successfully, but `jupterlab_server` is broken because it didn't pin `jsonschema` properly.
```
FROM mambaorg/micromamba:jammy as build-stage
RUN micromamba install --name base --channel conda-forge \
"python >=3.10" \
"kfp >=1.8" \
"jupyterlab >=3.5"
ARG MAMBA_DOCKERFILE_ACTIVATE=1 # (otherwise python will not be found)
RUN python -c "import kfp; print(kfp.__version__)"
RUN python -c "import jupyterlab_server; print(jupyterlab_server.__version__)"
```
`docker build --progress plain --no-cache -f Dockerfile .`
<details>
<summary>Docker build output</summary>
```bash
#1 [internal] load build definition from debugging.Dockerfile
#1 sha256:72a7d0078219e5aabc2b20905f1eda6c042b0871faff96192fd91696fe22ae06
#1 transferring dockerfile: 54B done
#1 DONE 0.0s
#2 [internal] load .dockerignore
#2 sha256:6f6399f5781af6f88a59e1b8fb6df788f99d62320b583d83e37211ae3e5917b2
#2 transferring context: 2B done
#2 DONE 0.0s
#3 [internal] load metadata for docker.io/mambaorg/micromamba:jammy
#3 sha256:70f396090a53ab9b6a069cc5d6d0672a0924b076e550e08e2f4c414e429987cc
#3 DONE 1.1s
#4 [1/4] FROM docker.io/mambaorg/micromamba:jammy@sha256:3e3d2899397fb031d57d4269a19f7700d4eb72e802d1d1b8f6054acd93b6fe47
#4 sha256:4a94cba5ef489da4fe85e3887c63904a40cb788f5ac27ff5dff6a4f3998815af
#4 CACHED
#5 [2/4] RUN micromamba install --name base --channel conda-forge "python >=3.10" "kfp >=1.8" "jupyterlab >=3.5"
#5 sha256:3855277fd177e022a0fd9b8478652f3563e2e3be3cbdb8c93c2e6b34728fe389
#5 0.276
#5 0.276 __
#5 0.276 __ ______ ___ ____ _____ ___ / /_ ____ _
#5 0.276 / / / / __ `__ \/ __ `/ __ `__ \/ __ \/ __ `/
#5 0.276 / /_/ / / / / / / /_/ / / / / / / /_/ / /_/ /
#5 0.276 / .___/_/ /_/ /_/\__,_/_/ /_/ /_/_.___/\__,_/
#5 0.276 /_/
#5 0.276
#5 24.28
#5 24.28 Transaction
#5 24.28
#5 24.28 Prefix: /opt/conda
#5 24.28
#5 24.28 Updating specs:
#5 24.28
#5 24.28 - python[version='>=3.10']
#5 24.28 - kfp[version='>=1.8']
#5 24.28 - jupyterlab[version='>=3.5']
#5 24.28
#5 24.28
#5 24.31 Package Version Build Channel Size
#5 24.31 ────────────────────────────────────────────────────────────────────────────────────────────────────
#5 24.31 Install:
#5 24.31 ────────────────────────────────────────────────────────────────────────────────────────────────────
#5 24.31
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#5 24.31 + _openmp_mutex 4.5 2_gnu conda-forge/linux-64 24kB
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#5 24.31 + future 0.18.2 pyhd8ed1ab_6 conda-forge/noarch 365kB
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#5 24.31 + libstdcxx-ng 12.2.0 h46fd767_19 conda-forge/linux-64 4MB
#5 24.31 + libuuid 2.32.1 h7f98852_1000 conda-forge/linux-64 28kB
#5 24.31 + libzlib 1.2.13 h166bdaf_4 conda-forge/linux-64 66kB
#5 24.31 + markupsafe 2.1.1 py311hd4cff14_2 conda-forge/linux-64 26kB
#5 24.31 + matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge/noarch 12kB
#5 24.31 + mistune 2.0.4 pyhd8ed1ab_0 conda-forge/noarch 69kB
#5 24.31 + multidict 6.0.4 py311h2582759_0 conda-forge/linux-64 57kB
#5 24.31 + nbclassic 0.4.8 pyhd8ed1ab_0 conda-forge/noarch 8MB
#5 24.31 + nbclient 0.7.2 pyhd8ed1ab_0 conda-forge/noarch 62kB
#5 24.31 + nbconvert 7.2.7 pyhd8ed1ab_0 conda-forge/noarch 8kB
#5 24.31 + nbconvert-core 7.2.7 pyhd8ed1ab_0 conda-forge/noarch 199kB
#5 24.31 + nbconvert-pandoc 7.2.7 pyhd8ed1ab_0 conda-forge/noarch 6kB
#5 24.31 + nbformat 5.7.1 pyhd8ed1ab_0 conda-forge/noarch 101kB
#5 24.31 + ncurses 6.3 h27087fc_1 conda-forge/linux-64 1MB
#5 24.31 + nest-asyncio 1.5.6 pyhd8ed1ab_0 conda-forge/noarch 10kB
#5 24.31 + notebook 6.5.2 pyha770c72_1 conda-forge/noarch 273kB
#5 24.31 + notebook-shim 0.2.2 pyhd8ed1ab_0 conda-forge/noarch 15kB
#5 24.31 + oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge/noarch 92kB
#5 24.31 + openssl 3.0.7 h0b41bf4_1 conda-forge/linux-64 3MB
#5 24.31 + packaging 22.0 pyhd8ed1ab_0 conda-forge/noarch 40kB
#5 24.31 + pandoc 2.19.2 h32600fe_1 conda-forge/linux-64 31MB
#5 24.31 + pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge/noarch 12kB
#5 24.31 + parso 0.8.3 pyhd8ed1ab_0 conda-forge/noarch 71kB
#5 24.31 + pexpect 4.8.0 pyh1a96a4e_2 conda-forge/noarch 49kB
#5 24.31 + pickleshare 0.7.5 py_1003 conda-forge/noarch 9kB
#5 24.31 + pip 22.3.1 pyhd8ed1ab_0 conda-forge/noarch 2MB
#5 24.31 + platformdirs 2.6.2 pyhd8ed1ab_0 conda-forge/noarch 17kB
#5 24.31 + prometheus_client 0.15.0 pyhd8ed1ab_0 conda-forge/noarch 51kB
#5 24.31 + prompt-toolkit 3.0.36 pyha770c72_0 conda-forge/noarch 272kB
#5 24.31 + protobuf 3.20.2 py311ha362b79_1 conda-forge/linux-64 369kB
#5 24.31 + psutil 5.9.4 py311hd4cff14_0 conda-forge/linux-64 497kB
#5 24.31 + ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge/noarch 17kB
#5 24.31 + pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge/noarch 15kB
#5 24.31 + pyasn1 0.4.8 py_0 conda-forge/noarch 54kB
#5 24.31 + pyasn1-modules 0.2.7 py_0 conda-forge/noarch 61kB
#5 24.31 + pycparser 2.21 pyhd8ed1ab_0 conda-forge/noarch 103kB
#5 24.31 + pydantic 1.10.4 py311h2582759_0 conda-forge/linux-64 2MB
#5 24.31 + pygments 2.14.0 pyhd8ed1ab_0 conda-forge/noarch 824kB
#5 24.31 + pyjwt 2.6.0 pyhd8ed1ab_0 conda-forge/noarch 21kB
#5 24.31 + pyopenssl 23.0.0 pyhd8ed1ab_0 conda-forge/noarch 127kB
#5 24.31 + pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge/noarch 81kB
#5 24.31 + pyrsistent 0.19.3 py311h2582759_0 conda-forge/linux-64 124kB
#5 24.31 + pysocks 1.7.1 pyha2e5f31_6 conda-forge/noarch 19kB
#5 24.31 + python 3.11.0 ha86cf86_0_cpython conda-forge/linux-64 38MB
#5 24.31 + python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge/noarch 246kB
#5 24.31 + python-fastjsonschema 2.16.2 pyhd8ed1ab_0 conda-forge/noarch 247kB
#5 24.31 + python-json-logger 2.0.4 pyhd8ed1ab_0 conda-forge/noarch 13kB
#5 24.31 + python-kubernetes 18.20.0 pyhd8ed1ab_0 conda-forge/noarch 484kB
#5 24.31 + python_abi 3.11 3_cp311 conda-forge/linux-64 6kB
#5 24.31 + pytz 2022.7 pyhd8ed1ab_0 conda-forge/noarch 186kB
#5 24.31 + pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge/noarch 32kB
#5 24.31 + pyyaml 5.4.1 py311hd4cff14_4 conda-forge/linux-64 212kB
#5 24.31 + pyzmq 24.0.1 py311ha4b6469_1 conda-forge/linux-64 587kB
#5 24.31 + readline 8.1.2 h0f457ee_0 conda-forge/linux-64 298kB
#5 24.31 + requests 2.28.1 pyhd8ed1ab_1 conda-forge/noarch 55kB
#5 24.31 + requests-oauthlib 1.3.1 pyhd8ed1ab_0 conda-forge/noarch 22kB
#5 24.31 + requests-toolbelt 0.10.1 pyhd8ed1ab_0 conda-forge/noarch 43kB
#5 24.31 + rich 12.6.0 pyhd8ed1ab_0 conda-forge/noarch 175kB
#5 24.31 + rsa 4.9 pyhd8ed1ab_0 conda-forge/noarch 30kB
#5 24.31 + send2trash 1.8.0 pyhd8ed1ab_0 conda-forge/noarch 18kB
#5 24.31 + setuptools 65.6.3 pyhd8ed1ab_0 conda-forge/noarch 634kB
#5 24.31 + shellingham 1.5.0.post1 pyhd8ed1ab_0 conda-forge/noarch 14kB
#5 24.31 + simplejson 3.18.1 py311h2582759_0 conda-forge/linux-64 135kB
#5 24.31 + six 1.16.0 pyh6c4a22f_0 conda-forge/noarch 14kB
#5 24.31 + sniffio 1.3.0 pyhd8ed1ab_0 conda-forge/noarch 14kB
#5 24.31 + soupsieve 2.3.2.post1 pyhd8ed1ab_0 conda-forge/noarch 35kB
#5 24.31 + stack_data 0.6.2 pyhd8ed1ab_0 conda-forge/noarch 26kB
#5 24.31 + strip-hints 0.1.8 py_0 conda-forge/noarch 20kB
#5 24.31 + tabulate 0.9.0 pyhd8ed1ab_1 conda-forge/noarch 36kB
#5 24.31 + termcolor 2.1.1 pyhd8ed1ab_0 conda-forge/noarch 10kB
#5 24.31 + terminado 0.17.1 pyh41d4057_0 conda-forge/noarch 21kB
#5 24.31 + tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge/noarch 23kB
#5 24.31 + tk 8.6.12 h27826a3_0 conda-forge/linux-64 3MB
#5 24.31 + tomli 2.0.1 pyhd8ed1ab_0 conda-forge/noarch 16kB
#5 24.31 + tornado 6.2 py311hd4cff14_1 conda-forge/linux-64 885kB
#5 24.31 + traitlets 5.8.0 pyhd8ed1ab_0 conda-forge/noarch 98kB
#5 24.31 + typer 0.7.0 pyhd8ed1ab_0 conda-forge/noarch 58kB
#5 24.31 + typing-extensions 4.4.0 hd8ed1ab_0 conda-forge/noarch 9kB
#5 24.31 + typing_extensions 4.4.0 pyha770c72_0 conda-forge/noarch 30kB
#5 24.31 + tzdata 2022g h191b570_0 conda-forge/noarch 108kB
#5 24.31 + uritemplate 3.0.1 py_0 conda-forge/noarch 16kB
#5 24.31 + urllib3 1.26.13 pyhd8ed1ab_0 conda-forge/noarch 111kB
#5 24.31 + wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge/noarch 34kB
#5 24.31 + webencodings 0.5.1 py_1 conda-forge/noarch 12kB
#5 24.31 + websocket-client 1.4.2 pyhd8ed1ab_0 conda-forge/noarch 44kB
#5 24.31 + wheel 0.38.4 pyhd8ed1ab_0 conda-forge/noarch 33kB
#5 24.31 + wrapt 1.14.1 py311hd4cff14_1 conda-forge/linux-64 61kB
#5 24.31 + xz 5.2.6 h166bdaf_0 conda-forge/linux-64 418kB
#5 24.31 + yaml 0.2.5 h7f98852_2 conda-forge/linux-64 89kB
#5 24.31 + yarl 1.8.2 py311hd4cff14_0 conda-forge/linux-64 98kB
#5 24.31 + zeromq 4.3.4 h9c3ff4c_1 conda-forge/linux-64 360kB
#5 24.31 + zipp 3.11.0 pyhd8ed1ab_0 conda-forge/noarch 16kB
#5 24.31
#5 24.31 Summary:
#5 24.31
#5 24.31 Install: 174 packages
#5 24.31
#5 24.31 Total download: 132MB
#5 24.31
#5 24.31 ────────────────────────────────────────────────────────────────────────────────────────────────────
#5 24.31
#5 24.31
#5 24.31 Confirm changes: [Y/n]
#5 24.31 Transaction starting
#5 55.40 Linking _libgcc_mutex-0.1-conda_forge
#5 55.40 Linking python_abi-3.11-3_cp311
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#5 55.42 Linking libgomp-12.2.0-h65d4601_19
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#5 55.43 Linking libuuid-2.32.1-h7f98852_1000
#5 55.43 Linking libsodium-1.0.18-h36c2ea0_1
#5 55.44 Linking openssl-3.0.7-h0b41bf4_1
#5 55.51 Linking libffi-3.4.2-h7f98852_5
#5 55.51 Linking bzip2-1.0.8-h7f98852_4
#5 55.51 Linking yaml-0.2.5-h7f98852_2
#5 55.52 Linking ncurses-6.3-h27087fc_1
#5 59.34 Linking libcrc32c-1.1.2-h9c3ff4c_0
#5 59.34 Linking xz-5.2.6-h166bdaf_0
#5 59.38 Linking libzlib-1.2.13-h166bdaf_4
#5 59.39 Linking libnsl-2.0.0-h7f98852_0
#5 59.39 Linking zeromq-4.3.4-h9c3ff4c_1
#5 59.40 Linking readline-8.1.2-h0f457ee_0
#5 59.41 Linking pandoc-2.19.2-h32600fe_1
#5 59.41 Linking libsqlite-3.40.0-h753d276_0
#5 59.41 Linking libprotobuf-3.20.2-h6239696_0
#5 59.44 Linking tk-8.6.12-h27826a3_0
#5 59.54 Linking tzdata-2022g-h191b570_0
#5 59.63 Linking python-3.11.0-ha86cf86_0_cpython
#5 60.54 Linking wheel-0.38.4-pyhd8ed1ab_0
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#5 61.03 Linking pip-22.3.1-pyhd8ed1ab_0
#5 61.12 Linking blinker-1.5-pyhd8ed1ab_0
#5 61.12 Linking pyjwt-2.6.0-pyhd8ed1ab_0
#5 61.12 Linking pycparser-2.21-pyhd8ed1ab_0
#5 61.16 Linking pytz-2022.7-pyhd8ed1ab_0
#5 61.25 Linking backports-1.0-pyhd8ed1ab_3
#5 61.25 Linking soupsieve-2.3.2.post1-pyhd8ed1ab_0
#5 61.26 Linking pyparsing-3.0.9-pyhd8ed1ab_0
#5 61.27 Linking pysocks-1.7.1-pyha2e5f31_6
#5 61.27 Linking certifi-2022.12.7-pyhd8ed1ab_0
#5 61.27 Linking future-0.18.2-pyhd8ed1ab_6
#5 61.33 Linking dataclasses-0.8-pyhc8e2a94_3
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#5 61.35 Linking shellingham-1.5.0.post1-pyhd8ed1ab_0
#5 61.35 Linking colorama-0.4.6-pyhd8ed1ab_0
#5 61.36 Linking zipp-3.11.0-pyhd8ed1ab_0
#5 61.36 Linking parso-0.8.3-pyhd8ed1ab_0
#5 61.37 Linking json5-0.9.5-pyh9f0ad1d_0
#5 61.38 Linking mistune-2.0.4-pyhd8ed1ab_0
#5 61.39 Linking pandocfilters-1.5.0-pyhd8ed1ab_0
#5 61.39 Linking defusedxml-0.7.1-pyhd8ed1ab_0
#5 61.40 Linking webencodings-0.5.1-py_1
#5 61.40 Linking python-fastjsonschema-2.16.2-pyhd8ed1ab_0
#5 61.41 Linking pure_eval-0.2.2-pyhd8ed1ab_0
#5 61.41 Linking executing-1.2.0-pyhd8ed1ab_0
#5 61.42 Linking ptyprocess-0.7.0-pyhd3deb0d_0
#5 61.42 Linking charset-normalizer-2.1.1-pyhd8ed1ab_0
#5 61.43 Linking idna-3.4-pyhd8ed1ab_0
#5 61.44 Linking flit-core-3.8.0-pyhd8ed1ab_0
#5 61.47 Linking termcolor-2.1.1-pyhd8ed1ab_0
#5 61.47 Linking cachetools-4.2.4-pyhd8ed1ab_0
#5 61.48 Linking attrs-22.2.0-pyh71513ae_0
#5 61.48 Linking python-json-logger-2.0.4-pyhd8ed1ab_0
#5 61.49 Linking tabulate-0.9.0-pyhd8ed1ab_1
#5 61.49 Linking docstring_parser-0.15-pyhd8ed1ab_0
#5 61.50 Linking cloudpickle-2.2.0-pyhd8ed1ab_0
#5 61.50 Linking typing_extensions-4.4.0-pyha770c72_0
#5 61.51 Linking click-8.1.3-unix_pyhd8ed1ab_2
#5 61.51 Linking absl-py-1.3.0-pyhd8ed1ab_0
#5 61.52 Linking six-1.16.0-pyh6c4a22f_0
#5 61.53 Linking pygments-2.14.0-pyhd8ed1ab_0
#5 61.59 Linking backcall-0.2.0-pyh9f0ad1d_0
#5 61.60 Linking decorator-5.1.1-pyhd8ed1ab_0
#5 61.60 Linking nest-asyncio-1.5.6-pyhd8ed1ab_0
#5 61.60 Linking entrypoints-0.4-pyhd8ed1ab_0
#5 61.61 Linking traitlets-5.8.0-pyhd8ed1ab_0
#5 61.62 Linking websocket-client-1.4.2-pyhd8ed1ab_0
#5 61.63 Linking prometheus_client-0.15.0-pyhd8ed1ab_0
#5 61.64 Linking sniffio-1.3.0-pyhd8ed1ab_0
#5 61.64 Linking tomli-2.0.1-pyhd8ed1ab_0
#5 61.65 Linking packaging-22.0-pyhd8ed1ab_0
#5 61.65 Linking send2trash-1.8.0-pyhd8ed1ab_0
#5 61.66 Linking ipython_genutils-0.2.0-py_1
#5 61.67 Linking pickleshare-0.7.5-py_1003
#5 61.67 Linking strip-hints-0.1.8-py_0
#5 61.68 Linking babel-2.11.0-pyhd8ed1ab_0
#5 61.87 Linking backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0
#5 61.88 Linking beautifulsoup4-4.11.1-pyha770c72_0
#5 61.89 Linking httplib2-0.21.0-pyhd8ed1ab_0
#5 61.89 Linking commonmark-0.9.1-py_0
#5 61.90 Linking pyasn1-modules-0.2.7-py_0
#5 61.93 Linking rsa-4.9-pyhd8ed1ab_0
#5 61.93 Linking importlib-metadata-6.0.0-pyha770c72_0
#5 61.94 Linking jedi-0.18.2-pyhd8ed1ab_0
#5 62.44 Linking tinycss2-1.2.1-pyhd8ed1ab_0
#5 62.44 Linking pexpect-4.8.0-pyh1a96a4e_2
#5 62.46 Linking typing-extensions-4.4.0-hd8ed1ab_0
#5 62.46 Linking asttokens-2.2.1-pyhd8ed1ab_0
#5 62.47 Linking pyu2f-0.1.5-pyhd8ed1ab_0
#5 62.48 Linking fire-0.4.0-pyh44b312d_0
#5 62.49 Linking python-dateutil-2.8.2-pyhd8ed1ab_0
#5 62.50 Linking jupyterlab_pygments-0.2.2-pyhd8ed1ab_0
#5 62.52 Linking comm-0.1.2-pyhd8ed1ab_0
#5 62.52 Linking matplotlib-inline-0.1.6-pyhd8ed1ab_0
#5 62.53 Linking anyio-3.6.2-pyhd8ed1ab_0
#5 62.54 Linking bleach-5.0.1-pyhd8ed1ab_0
#5 62.56 Linking wcwidth-0.2.5-pyh9f0ad1d_2
#5 62.56 Linking rich-12.6.0-pyhd8ed1ab_0
#5 62.58 Linking async-timeout-4.0.2-pyhd8ed1ab_0
#5 62.59 Linking platformdirs-2.6.2-pyhd8ed1ab_0
#5 62.59 Linking stack_data-0.6.2-pyhd8ed1ab_0
#5 62.60 Linking prompt-toolkit-3.0.36-pyha770c72_0
#5 62.66 Linking typer-0.7.0-pyhd8ed1ab_0
#5 62.66 Linking ipython-8.8.0-pyh41d4057_0
#5 62.76 Linking psutil-5.9.4-py311hd4cff14_0
#5 62.78 Linking debugpy-1.6.4-py311ha362b79_0
#5 62.94 Linking multidict-6.0.4-py311h2582759_0
#5 62.95 Linking frozenlist-1.3.3-py311hd4cff14_0
#5 62.96 Linking pyrsistent-0.19.3-py311h2582759_0
#5 62.97 Linking wrapt-1.14.1-py311hd4cff14_1
#5 62.98 Linking simplejson-3.18.1-py311h2582759_0
#5 63.00 Linking pyzmq-24.0.1-py311ha4b6469_1
#5 63.06 Linking markupsafe-2.1.1-py311hd4cff14_2
#5 63.06 Linking tornado-6.2-py311hd4cff14_1
#5 63.09 Linking pyyaml-5.4.1-py311hd4cff14_4
#5 63.10 Linking cffi-1.15.1-py311h409f033_3
#5 63.11 Linking protobuf-3.20.2-py311ha362b79_1
#5 63.12 Linking pydantic-1.10.4-py311h2582759_0
#5 63.14 Linking jupyter_core-5.1.2-py311h38be061_0
#5 63.15 Linking yarl-1.8.2-py311hd4cff14_0
#5 63.16 Linking brotlipy-0.7.0-py311hd4cff14_1005
#5 63.16 Linking cryptography-39.0.0-py311h9b4c7bb_0
#5 63.20 Linking google-crc32c-1.1.2-py311h98db957_4
#5 63.21 Linking argon2-cffi-bindings-21.2.0-py311hd4cff14_3
#5 63.21 Linking aiosignal-1.3.1-pyhd8ed1ab_0
#5 63.21 Linking jsonschema-3.2.0-pyhd8ed1ab_3
#5 63.22 Linking deprecated-1.2.13-pyh6c4a22f_0
#5 63.23 Linking uritemplate-3.0.1-py_0
#5 63.23 Linking jinja2-3.1.2-pyhd8ed1ab_1
#5 63.24 Linking terminado-0.17.1-pyh41d4057_0
#5 63.25 Linking googleapis-common-protos-1.57.0-pyhd8ed1ab_3
#5 63.28 Linking jupyter_client-7.4.8-pyhd8ed1ab_0
#5 63.30 Linking oauthlib-3.2.2-pyhd8ed1ab_0
#5 63.34 Linking pyopenssl-23.0.0-pyhd8ed1ab_0
#5 63.35 Linking google-resumable-media-1.3.3-pyh6c4a22f_0
#5 63.37 Linking argon2-cffi-21.3.0-pyhd8ed1ab_0
#5 63.37 Linking nbformat-5.7.1-pyhd8ed1ab_0
#5 63.39 Linking jupyter_events-0.5.0-pyhd8ed1ab_0
#5 63.40 Linking jupyter_server_terminals-0.4.3-pyhd8ed1ab_0
#5 63.41 Linking kfp-pipeline-spec-0.1.17-pyhd8ed1ab_0
#5 63.41 Linking ipykernel-6.19.4-pyh210e3f2_0
#5 63.44 Linking urllib3-1.26.13-pyhd8ed1ab_0
#5 63.46 Linking nbclient-0.7.2-pyhd8ed1ab_0
#5 63.47 Linking requests-2.28.1-pyhd8ed1ab_1
#5 63.48 Linking kfp-server-api-1.8.4-pyhd8ed1ab_0
#5 63.50 Linking nbconvert-core-7.2.7-pyhd8ed1ab_0
#5 63.55 Linking requests-oauthlib-1.3.1-pyhd8ed1ab_0
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#5 63.58 Linking nbconvert-pandoc-7.2.7-pyhd8ed1ab_0
#5 63.58 Linking jupyter_server-2.0.6-pyhd8ed1ab_0
#5 63.63 Linking nbconvert-7.2.7-pyhd8ed1ab_0
#5 63.63 Linking notebook-shim-0.2.2-pyhd8ed1ab_0
#5 63.64 Linking jupyterlab_server-2.16.6-pyhd8ed1ab_0
#5 63.66 Linking nbclassic-0.4.8-pyhd8ed1ab_0
#5 63.97 Linking notebook-6.5.2-pyha770c72_1
#5 64.01 Linking jupyterlab-3.5.2-pyhd8ed1ab_0
#5 64.12 Linking aiohttp-3.8.3-py311hd4cff14_1
#5 64.14 Linking google-auth-1.35.0-pyh6c4a22f_0
#5 64.15 Linking google-auth-httplib2-0.1.0-pyhd8ed1ab_1
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#5 64.29 Linking google-api-python-client-1.12.8-pyhd3deb0d_0
#5 64.30 Linking google-cloud-storage-1.30.0-pyh9f0ad1d_0
#5 64.30 Linking kfp-1.8.14-pyhd8ed1ab_0
#5 65.65 Transaction finished
#5 DONE 67.0s
#6 [3/4] RUN python -c "import kfp; print(kfp.__version__)"
#6 sha256:d26d993ae6308c1c74fae85095e667ae0e756b1b5b67798d4d3f4c3270dc397e
#6 1.468 1.8.14
#6 DONE 1.7s
#7 [4/4] RUN python -c "import jupyterlab_server; print(jupyterlab_server.__version__)"
#7 sha256:9658a8dc9ad79f099f3bf704f1e72fbbf2bfdec8d42489868625cfa38c078749
#7 1.257 Traceback (most recent call last):
#7 1.257 File "<string>", line 1, in <module>
#7 1.257 File "/opt/conda/lib/python3.11/site-packages/jupyterlab_server/__init__.py", line 5, in <module>
#7 1.257 from .app import LabServerApp
#7 1.257 File "/opt/conda/lib/python3.11/site-packages/jupyterlab_server/app.py", line 6, in <module>
#7 1.258 from jupyter_server.extension.application import ExtensionApp, ExtensionAppJinjaMixin
#7 1.258 File "/opt/conda/lib/python3.11/site-packages/jupyter_server/extension/application.py", line 14, in <module>
#7 1.258 from jupyter_server.serverapp import ServerApp
#7 1.259 File "/opt/conda/lib/python3.11/site-packages/jupyter_server/serverapp.py", line 33, in <module>
#7 1.259 from jupyter_events.logger import EventLogger
#7 1.259 File "/opt/conda/lib/python3.11/site-packages/jupyter_events/__init__.py", line 3, in <module>
#7 1.260 from .logger import EVENTS_METADATA_VERSION, EventLogger
#7 1.260 File "/opt/conda/lib/python3.11/site-packages/jupyter_events/logger.py", line 19, in <module>
#7 1.260 from .schema_registry import SchemaRegistry
#7 1.260 File "/opt/conda/lib/python3.11/site-packages/jupyter_events/schema_registry.py", line 3, in <module>
#7 1.260 from .schema import EventSchema
#7 1.260 File "/opt/conda/lib/python3.11/site-packages/jupyter_events/schema.py", line 6, in <module>
#7 1.260 from jsonschema.protocols import Validator
#7 1.260 ModuleNotFoundError: No module named 'jsonschema.protocols'
#7 ERROR: executor failed running [/usr/local/bin/_dockerfile_shell.sh python -c "import jupyterlab_server; print(jupyterlab_server.__version__)"]: exit code: 1
------
> [4/4] RUN python -c "import jupyterlab_server; print(jupyterlab_server.__version__)":
------
executor failed running [/usr/local/bin/_dockerfile_shell.sh python -c "import jupyterlab_server; print(jupyterlab_server.__version__)"]: exit code: 1
```
</details>
#### Attempt 2
Here is a docker file where I have fixed the problem with `Attempt 1` by pinning to the fixed version of `jupyterlab_server` too. Now the environment doesn't build.
```
FROM mambaorg/micromamba:jammy as build-stage
RUN micromamba install --name base --channel conda-forge \
"python >=3.10" \
"kfp >=1.8" \
"jupyterlab >=3.5" \
"jupyterlab_server >=2.17"
```
`docker build --progress plain --no-cache -f Dockerfile .`
<details>
<summary>Docker build output</summary>
```bash
#1 [internal] load build definition from debugging.Dockerfile
#1 sha256:3a0dc4a0c95482ec6cc84124298afe0c2ebae9294c277bc91274793bf36aac2c
#1 transferring dockerfile: 465B done
#1 DONE 0.0s
#2 [internal] load .dockerignore
#2 sha256:c1c4fbf0b0e6bb3ce897f5e9c1fd0615466b85f3bacdae64fd25220cad1efed2
#2 transferring context: 2B done
#2 DONE 0.0s
#3 [internal] load metadata for docker.io/mambaorg/micromamba:jammy
#3 sha256:70f396090a53ab9b6a069cc5d6d0672a0924b076e550e08e2f4c414e429987cc
#3 DONE 2.1s
#4 [1/4] FROM docker.io/mambaorg/micromamba:jammy@sha256:3e3d2899397fb031d57d4269a19f7700d4eb72e802d1d1b8f6054acd93b6fe47
#4 sha256:4a94cba5ef489da4fe85e3887c63904a40cb788f5ac27ff5dff6a4f3998815af
#4 CACHED
#5 [2/4] RUN micromamba install --name base --channel conda-forge "python >=3.10" "kfp >=1.8" "jupyterlab >=3.5" "jupyterlab_server >=2.17"
#5 sha256:756e7ff99e52e8c6d7b872a5482a4c4db23a3c34732d11392cc3313276149405
#5 0.232
#5 0.232 __
#5 0.232 __ ______ ___ ____ _____ ___ / /_ ____ _
#5 0.232 / / / / __ `__ \/ __ `/ __ `__ \/ __ \/ __ `/
#5 0.232 / /_/ / / / / / / /_/ / / / / / / /_/ / /_/ /
#5 0.232 / .___/_/ /_/ /_/\__,_/_/ /_/ /_/_.___/\__,_/
#5 0.232 /_/
#5 0.232
#5 19.09 error libmamba Could not solve for environment specs
#5 19.09 Encountered problems while solving:
#5 19.09 - package kfp-1.8.9-pyhd8ed1ab_0 requires jsonschema >=3.0.1,<4, but none of the providers can be installed
#5 19.09
#5 19.09 The environment can't be solved, aborting the operation
#5 19.09
#5 19.13 critical libmamba Could not solve for environment specs
#5 ERROR: executor failed running [/usr/local/bin/_dockerfile_shell.sh micromamba install --name base --channel conda-forge "python >=3.10" "kfp >=1.8" "jupyterlab >=3.5" "jupyterlab_server >=2.17"]: exit code: 1
------
> [2/4] RUN micromamba install --name base --channel conda-forge "python >=3.10" "kfp >=1.8" "jupyterlab >=3.5" "jupyterlab_server >=2.17":
------
executor failed running [/usr/local/bin/_dockerfile_shell.sh micromamba install --name base --channel conda-forge "python >=3.10" "kfp >=1.8" "jupyterlab >=3.5" "jupyterlab_server >=2.17"]: exit code: 1
```
</details>
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8641 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8641/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8641/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8641/events | https://github.com/kubeflow/pipelines/issues/8641 | 1,517,061,835 | I_kwDOB-71UM5abIbL | 8,641 | Pipeline：<lambda>() missing 1 required positional argument: 'y' | {
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] | closed | false | null | [] | null | [
"Hi @leoliang1988, it looks like this error message comes from the function `model.fit()`, where it requires more input arguments than you provided. Maybe the `DummyClassifier`'s documentation can provide more info?"
] | 2023-01-03T08:17:30 | 2023-01-05T23:47:11 | 2023-01-05T23:47:10 | NONE | null |
Hello,here is my model:
model = Pipeline([
('scaler',FunctionTransformer(StandardScaler().fit_transform, validate=True)),
('dummy', FunctionTransformer(lambda X, y: dummy_fit_transform(X, y), validate=False)),
('reshape', FunctionTransformer(reshape_to_3d, validate=True)),
('bidirectional', KerasClassifier(build_fn=create_model, epochs=10, batch_size=64))
])
def dummy_fit_transform(X, y):
model = DummyClassifier(strategy='most_frequent')
model.fit(X, y)
return model.predict(X)
def create_model():
model = Sequential([
Bidirectional(LSTM(128)),
Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def reshape_to_3d(X):
return np.reshape(X, (X.shape[0], 1, X.shape[1]))
The question is that when I run : model.fit(x_train, y_train)
the system notice me :
TypeError: () missing 1 required positional argument: 'y'
please tell me how to fix it,thank you very much!
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/8627 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8627/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8627/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8627/events | https://github.com/kubeflow/pipelines/issues/8627 | 1,508,877,981 | I_kwDOB-71UM5Z76ad | 8,627 | [sdk] dsl.Condition throws Attribute error on valid condition in kfp.compiler.compiler_utilsL131 | {
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"u... | closed | false | null | [] | null | [
"`str(fastq1).endswith(\".gz\")` is not a valid condition to `dsl.Condition`.\r\n`dsl.Condition` expects two operants and an operator:\r\n\r\nhttps://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/pipelines/#dslcondition"
] | 2022-12-23T04:40:38 | 2022-12-30T00:03:54 | 2022-12-30T00:03:54 | NONE | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
Minikube v1.28.0
kubernetes 1.22.2
Kustomize v3.2.0
kubectl v1.25.5
Manifests v1.6.1
* 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.
2.0.0-beta.9
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 -->
kfp 2.0.0b9
kfp-pipeline-spec 0.1.16
kfp-server-api 2.0.0a6
### 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.
-->
All you have to do is specify a `dsl.Condition` with a Python condition, and an attribute error is thrown when called on the 'bool' evaluated from the Python condition.
```python
@dsl.container_component
def gunzip(infile:dsl.InputPath(str), outfile:dsl.OutputPath(str)):
return dsl.ContainerSpec(
image='bitnami/minideb:latest',
command=[
'zcat '
'$1 ',
'> ',
'$2 ',
'||',
'mv $1 $2'
],
args=[infile, outfile]
)
@dsl.pipeline(
name='kubeflow-barebone-demo',
description='kubeflow demo with minimal setup'
)
def rnaseq_pipeline(fastq1:str):
# Step 1: checking file extension(s) for gzip compression
with dsl.Condition(str(fastq1).endswith(".gz"), 'fastq1-needs-gunzip'):
gunzip1 = gunzip(infile=fastq1)
```
#### Output
```python
Traceback (most recent call last):
File "./pipeline.py", line 51, in <module>
def rnaseq_pipeline(fastq1:dsl.Input[dsl.Dataset]):
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/components/pipeline_context.py", line 67, in pipeline
return component_factory.create_graph_component_from_func(func)
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/components/component_factory.py", line 527, in create_graph_component_from_func
name=component_name,
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/components/graph_component.py", line 69, in __init__
pipeline_outputs=pipeline_outputs,
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/compiler/pipeline_spec_builder.py", line 1549, in create_pipeline_spec
root_group)
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/compiler/compiler_utils.py", line 146, in get_condition_channels_for_tasks
_get_condition_channels_for_tasks_helper(root_group, [])
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/compiler/compiler_utils.py", line 144, in _get_condition_channels_for_tasks_helper
group, new_current_conditions_channels)
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/kfp/compiler/compiler_utils.py", line 131, in _get_condition_channels_for_tasks_helper
if isinstance(group.condition.left_operand,
AttributeError: 'bool' object has no attribute 'left_operand'
```
### Expected result
<!-- What should the correct behavior be? -->
I'm not sure what was intended by the `left_operand` call, but I expected more documentation on how to use a conditional in Kubeflow pipelines.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8626 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8626/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8626/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8626/events | https://github.com/kubeflow/pipelines/issues/8626 | 1,508,525,416 | I_kwDOB-71UM5Z6kVo | 8,626 | [bug] kfp does not allow introspection of PipelineParam values for debugging. | {
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] | closed | false | null | [] | null | [
"This is expected behavior. See [comment](https://github.com/kubeflow/pipelines/issues/8203#issuecomment-1366043593) for a relevant information.\r\n\r\nYou can achieve this by swapping out the arbitrary Python code in `dsl.Condition` for a task that does the check:\r\n\r\n```\r\nis_gz_task = is_gz(path=infile)\r\nw... | 2022-12-22T20:48:50 | 2022-12-27T18:51:21 | 2022-12-27T17:04:42 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
```bash
Minikube v1.28.0
kubernetes 1.22.2
Kustomize v3.2.0
kubectl v1.25.5
Manifests v1.6.1
kfp 1.8.17
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
```
* 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/. -->
I deploy it locally on a minikube cluster.
* KFP version:
kfp 1.8.17
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.8.17
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### 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.
-->
I expect to be able to use Python logic or properly use the `kfp.dsl.Condition` constructor to provide conditional logic (i.e. whether or not to decompress an input file) within a `@kfp.dsl.pipeline` pipeline. Instead, I cannot introspect `PipelineParam` objects in the pipeline! How do I do conditional validations? Seems like an essential function for a production grade pipeline. I'm sure it's user error and documentation.
```python
#!/bin/env python
import os
import sys
import argparse
import kfp.dsl as dsl
import kfp.components as comp
import kfp
components_dir = os.path.join(os.path.dirname(__file__), "components")
#gunzip = comp.load_component_from_file(os.path.join(components_dir, "gunzip.yaml"))
@dsl.pipeline(
name='kubeflow-barebone-demo',
description='kubeflow demo with minimal setup'
)
def rnaseq_pipeline(fastq1: str) -> bool:
# Step 1: training component
gunzip = dsl.ContainerOp(
name='gunzip',
images='bitnami/minideb:latest',
command=[
'gunzip',
fastq1
],
file_outputs={'fastq1': fastq1.rstrip(".gz")}
)
with dsl.Condition(os.path.splitext(fastq)[1] is ".gz", name='gunzip_is_needed'):
gunzip1 = gunzip(fastq)
# I'd really like to view and print the components of gunzip1.outputs, or even understand when gunzip1.output
# can be used as a convenient shortcut, or even debug what the expected key value should be. I'm getting KeyErrors before the step is complete.
print("GUNZIPPED")
with dsl.Condition(os.path.splitext(infile)[1] is not ".gz", name='is_gunzip_not_needed'):
print("NOT GUNZIPPED")
if __name__ == "__main__":
logger = get_root_logger(2)
kfp.compiler.Compiler().compile(rnaseq_pipeline, 'pipeline.yaml')
```
### Expected result
<!-- What should the correct behavior be? -->
The desired behavior is either the proper way to create a function that can launch individual pipeline steps, checking outputs for certain desired behaviors in the path only. I'm trying to debug some very basic code, and I don't understand the desired `kfp` implementation semantics/syntax. Thanks for your help.
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### 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 -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8624 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8624/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8624/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8624/events | https://github.com/kubeflow/pipelines/issues/8624 | 1,507,111,121 | I_kwDOB-71UM5Z1LDR | 8,624 | [frontend] brace-editor search module not imported or included in webpack | {
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"@Linchin You moved this issue to \"Needs More Info\" but I'm not sure what additional information would be necessary. What else is required to move forward with this?",
"Pinging @jlyaoyuli and @chensun to see if I can get more context around the \"Needs More Info\" status",
"@Linchin @jlyaoyuli @chensun I'm st... | 2022-12-22T01:12:08 | 2023-08-10T15:37:11 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
* Minikube v1.28.0
* kubernetes 1.21.14
* Kustomize v3.2.0
* kubectl v1.26.0
* Manifests v1.6.1
* KFP version: dev_local from latest master (2.0.0-alpha6?)
### Steps to reproduce
Open any page in UI which uses brace-editor (e.g. Pipelines -> [any pipeline] -> "YAML" tab) and press CTRL+F to search. No search box opens, and Javascript console logs the following (at least in Chrome; unsure of logs in other browser but search box still doesn't open in Edge or FF):
```
> GET http://192.168.0.202:8080/pipeline/ext-searchbox.js net::ERR_ABORTED 404 (Not Found)
> Refused to execute script from 'http://192.168.0.202:8080/pipeline/ext-searchbox.js' because its MIME type ('text/html') is not executable, and strict MIME type checking is enabled.
```
Trying to load the referenced page gives `Cannot GET /pipeline/ext-searchbox.js`.
### Expected result
The brace-editor search box should open. This is necessary as the view is buffered, meaning normal browser search functionality cannot find strings that are not currently visible (e.g. require scrolling to view).
### Materials and Reference
This bug was reported for another library that uses brace-editor [here](https://github.com/fxmontigny/ng2-ace-editor/issues/53), and the suggested fix is to import the search module when the brace-editor is used.
I've found three places where brace is used in the pipelines codebase:
* frontend/src/components/DetailsTable.tsx
* frontend/src/components/viewers/VisualizationCreator.tsx
* frontend/src/pages/PipelineDetails.tsx
I made the following change to each file as a test:
```diff
import 'brace/ext/language_tools';
+import 'brace/ext/searchbox';
```
After this, the JS console no longer logs an error, and the search box successfully appears:
If this is an acceptable approach I can submit a pull request with those changes.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8605 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8605/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8605/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8605/events | https://github.com/kubeflow/pipelines/issues/8605 | 1,504,198,785 | I_kwDOB-71UM5ZqECB | 8,605 | [bug] Max retries exceeded with connection refused | {
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"Update in status. I can verify the `istio-ingressgateway` internal address (1.2.3.4 here) but the external IP is still wonky.\r\n```bash\r\nistio-ingressgateway NodePort 1.2.3.4 <none> 1234:2345/TCP,80:987/TCP,443:789/TCP,5678:6789/TCP,91234:92234/TCP 15h\r\n\r\n```",
"If I use the port described... | 2022-12-20T08:53:06 | 2023-01-17T13:15:06 | 2023-01-17T13:15:06 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Minikube v1.28.0
kubernetes 1.22.2
Kustomize v3.2.0
kubectl v1.25.5
Manifests v1.6.1
### Manifests were installed on a running minikube instance:
```bash
# After clone..
while ! kustomize build example | minikube kubectl -- apply -f -; do echo "Retrying to apply resources"; sleep 10; done
```
### Minikube started with compatible version of kubernetes and kustomize according to v1.6.1 Manifests `README.md`
```
minikube start --driver=docker --kubernetes-version=v1.22.2 --cpus=10 --memory=40gb --disk-size=50gb --container-runtime=containerd --wait-timeout=6m
# Then verify that pods are running with
minikube kubectl -- get pods -n kubeflow --all
```
* KFP version:
dev_local ????
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```bash
kfp 1.8.17
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
```
### 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? -->
The expected behavior is that the `kfp` Python client (v1.8.17) is able to connect with the following basic invocation in a Kubeflow Pipelines Python script, with `@dsl` declarations adorning the pipeline function called next.
```python
kubeflow_host='192.168.111.222'
kubeflow_port=443
client = kfp.Client(host="{0}:{1}/pipeline".format(kubeflow_host, kubeflow_port), namespace="kubeflow")
client.create_run_from_pipeline_func(my_func, arguments={ ... some params })
```
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
#### Screenshot of KFP Version running the pipelines dashboard I am able to access with `minikube tunnel`
Screenshot showing dev_local kfp version, running on localhost through `minikube tunnel`
```python
py", line 788, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/home/matt/.pyenv/versions/miniconda3-4.7.12/lib/python3.7/site-packages/urllib3/util/retry.py", line 592, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='127.0.0.1', port=443): Max retries exceeded with url: /pipeline/apis/v1beta1/experiments?filter=%7B%22predicates%22%3A+%5B%7B%22op%22%3A+1%2C+%22key%22%3A+%22name%22%2C+%22stringValue%22%3A+%22Default%22%7D%5D%7D&resource_reference_key.type=NAMESPACE&resource_reference_key.id=default (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fa5a794f050>: Failed to establish a new connection: [Errno 111] Connection refused'))
```
### 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! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8602 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8602/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8602/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8602/events | https://github.com/kubeflow/pipelines/issues/8602 | 1,503,522,406 | I_kwDOB-71UM5Zne5m | 8,602 | [bug] component BigqueryQueryJobOp throws error "The KMS key does not contain a location" on Vertex AI | {
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```
extract_output = BigqueryQueryJobOp(
project=bq_project,
location=bq_location,
query=get_query()
)
```
Error message:
> RuntimeError: The BigQuery job https://www.googleapis.com/bigquery/v2/projects/xxx/jobs/job_XXXXXXXYTxuWce7PjLzOHK1tEZ?location=EU failed. Error: {'errorResult': {'reason': 'invalid', 'message': 'The KMS key does not contain a location.'}, 'errors': [{'reason': 'invalid', 'message': 'The KMS key does not contain a location.'}], 'state': 'DONE'}
Let me know if you need additional information | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8600 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8600/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8600/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8600/events | https://github.com/kubeflow/pipelines/issues/8600 | 1,503,480,284 | I_kwDOB-71UM5ZnUnc | 8,600 | SDK: Drop support for Python 3.7 in Q2/Q3 2023 | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-12-19T19:43:06 | 2023-08-28T07:42:33 | null | COLLABORATOR | null | Python 3.7 will reach its EOL on June 23, 2023: https://devguide.python.org/versions/#supported-versions
We plan to drop Python 3.7 support in KFP SDK around the same time.
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"This was a regression in the Pipeline frontend, it has been fixed in #8343, but has not been part of a release yet. If this feature is a must have, a custom image of the Pipeline frontend can be [built](https://github.com/kubeflow/pipelines/blob/master/developer_guide.md).\r\n\r\nSimilar issue for more context: ht... | 2022-12-19T19:01:45 | 2023-02-08T23:58:41 | 2023-02-08T23:58:40 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Cloud: AWS, EKS.
* KFP version:
Kubeflow - 1.6.1
KFP - 1.8.13
### Steps to reproduce
No graph to show error (even though the pipeline successfully ran)
- Currently, our pipeline is for our offline training architecture and kubeflow is running on top of AWS Elastic Kubernetes Service.
- It seems the problem of "No graph to show" occurs when we have too many number of pods running for our pipeline.
- The num_games parameter is used to determine how many games we should use to train our models. At num_games=500, we are able to successfully view the graphs as shown in the expected results section.
- But, beyond that at num_games=750 and 1000, we are unable to see the pipeline graph and get a "No graph to show" in the Kubeflow UI.
- This is our current kubeflow pipeline for our offline training at num_games=100. We are currently utilizing the ParallelFor method to parallelize and batch our preprocessing process. For each batch, **10 games are processed**, thus for num_games=100 there are 10 batches for preprocessing and within each ParallelFor there are a total of 3 components being run.
- The number of pods is therefore linear to the num_games we are training on. With num_games = 100, there are 10 batches and in each batch there are 3 components/pod, so we can assume there are at least 30 pods that have been initialized.
num_games = 500, 50x3 = 150 + 10 (constant pods) = 160 pods initialized
num_games = 750, 75x3 = 235 + 10 (constant pods) = 245 pods initialized
num_games = 1000, 100x3 = 300 + 10 (constant pods) = 310 pods initialized
### Expected result
- The expected result should be to show the graph of the kubeflow pipeline
- At a training session with num_games = 500, the graph is displayed correctly in the Kubeflow UI and pass that at 750 games or 1000 games, no graph is shown.
### Materials and Reference
https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ParallelFor
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https://api.github.com/repos/kubeflow/pipelines/issues/8596 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8596/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8596/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8596/events | https://github.com/kubeflow/pipelines/issues/8596 | 1,501,840,996 | I_kwDOB-71UM5ZhEZk | 8,596 | [sdk] How to apply PVCs to pipeline tasks when using KFP SDK v2 | {
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"@yshimizu37 PVC is not supported yet in SDK v2. It's on our roadmap to be supported once [platform-specific runtime support](https://docs.google.com/document/d/10Cx-B18V6gR35VOmTe8_8gB67srOFF_un7NN8qXP1T4/edit#heading=h.x9snb54sjlu9) is ready. (Join [kubeflow-discuss](https://groups.google.com/g/kubeflow-discuss) ... | 2022-12-18T09:53:55 | 2023-08-28T15:34:57 | 2023-08-28T15:34:57 | NONE | null | ### Environment
* KFP version:
I could not find out my KFP version.
Not sure this is a valuable information, but I am using Kubeflow v1.6(manually installed).
* KFP SDK version:
2.0.0b8
* All dependencies version:
kfp 2.0.0b8
kfp-pipeline-spec 0.1.16
kfp-server-api 2.0.0a6
### Steps to reproduce
Here is my source code.
```python
@dsl.component
def data_preprocessing():
# do some data tasks
@dsl.pipeline(name="AI Training Run")
def ai_training_run(
epochs: int=1,
batch_size: int=64,
learning_rate: float=0.001,
dataset_pvc_name: str="dataset",
) -> Model:
dataset_dir = "/mnt/dataset"
# proprocessing dataset
preprocess_data_task = data_preprocessing()
preprocess_data_task.apply(
onprem.mount_pvc(dataset_pvc_name, 'datavol', dataset_dir)
).set_display_name('Data preprocessing')
```
It could be compiled successfully when I was using KFP 1.8.17.
```sh
$ python3 ai-training-run.py
/opt/deepops/env/lib/python3.8/site-packages/kfp/compiler/compiler.py:79: UserWarning: V2_COMPATIBLE execution mode is at Beta quality. Some pipeline features may not work as expected.
warnings.warn('V2_COMPATIBLE execution mode is at Beta quality.'
```
But in KFP 2.0.0b8, It doesn't work.
```sh
$ python3 ai-training-run.py
/opt/deepops/env/lib/python3.8/site-packages/kfp/deprecated/dsl/type_utils.py:28: FutureWarning: Module kfp.dsl.type_utils is deprecated and will be removed in KFP v2.0. Please use from kfp.components.types.type_utils instead.
warnings.warn(
Traceback (most recent call last):
File "ai-training-run.py", line 58, in <module>
def ai_training_run(
File "/opt/deepops/env/lib/python3.8/site-packages/kfp/components/pipeline_context.py", line 67, in pipeline
return component_factory.create_graph_component_from_func(func)
File "/opt/deepops/env/lib/python3.8/site-packages/kfp/components/component_factory.py", line 501, in create_graph_component_from_func
return graph_component.GraphComponent(
File "/opt/deepops/env/lib/python3.8/site-packages/kfp/components/graph_component.py", line 56, in __init__
pipeline_outputs = pipeline_func(*args_list)
File "ai-training-run.py", line 89, in ai_training_run
preprocess_data_task.apply(
AttributeError: 'PipelineTask' object has no attribute 'apply'
```
I know 'PipelineTask' class does not have 'apply' method anymore, but am curious that are there other ways to mount PVCs to pipeline tasks?
### Expected result
finding out how to mount PVCs to pipeline tasks.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8592 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8592/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8592/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8592/events | https://github.com/kubeflow/pipelines/issues/8592 | 1,500,850,560 | I_kwDOB-71UM5ZdSmA | 8,592 | [sdk] TabularDatasetCreateOp Fails to Validate Labels because they are a string not dict | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-12-16T20:53:12 | 2023-08-29T07:41:51 | null | NONE | null | TabularDatasetCreateOp Fails to Validate Labels because they are a string not dict. This fails whether or not the labels are passed in. The underlying function `TabularDataset.create` works without issues.
### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
Vertex AI Pipelines
* 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. -->
Vertex AI Pipelines
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
google-cloud-pipeline-components==1.0.30
kfp==1.8.17
kfp-pipeline-spec==0.1.16
kfp-server-api==1.8.5
### 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.
-->
```python
@dsl.pipeline(name='test')
def my_pipeline():
dataset_create_op = TabularDatasetCreateOp(
project=PROJECT_ID,
display_name="beans",
bq_source = f"bq://aju-dev-demos.beans.beans1",
labels = {'test': 'hi'} # Same result if this is uncommented
)
from kfp.v2 import compiler
compiler.Compiler().compile(
pipeline_func=my_pipeline, package_path=TEMPLATE_PATH
)
job = aip.PipelineJob(
display_name=DISPLAY_NAME,
template_path=TEMPLATE_PATH,
pipeline_root=PIPELINE_ROOT,
)
job.run()
```
Job fails with the following logs:
```console
...
--method.labels
{"test": "hi"}
...
File "/opt/python3.7/lib/python3.7/site-packages/google/cloud/aiplatform/datasets/tabular_dataset.py", line 122, in create
utils.validate_labels(labels)
File "/opt/python3.7/lib/python3.7/site-packages/google/cloud/aiplatform/utils/__init__.py", line 242, in validate_labels
for k, v in labels.items():
AttributeError: 'str' object has no attribute 'items'
```
### Expected result
<!-- What should the correct behavior be? -->
The dataset should be created. This command works without any issue so it seems like the issue is in `google-cloud-pipeline-components` not the underlying datasets library.
```python
from google.cloud.aiplatform.datasets.tabular_dataset import TabularDataset
TabularDataset.create(
display_name='beans',
bq_source="bq://aju-dev-demos.beans.beans1",
labels = {'test': 'hi'}
)
```
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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"@telpirion, can you verify whether you get the same result using the latest versions of KFP SDK v1 and GCPC v1? If so, can you provide a minimal reproducible example?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Than... | 2022-12-16T19:43:50 | 2023-08-29T07:41:52 | null | NONE | null | ## Environment
* KFP version:
Vertex Pipelines
* KFP SDK version:
v1.8.12
### Steps to reproduce
+ Pass the output of another operation to the ModelBatchPredictOp, subscript into the `output` property
+ Compile the pipeline
### Expected result
Pipeline compiles
### Actual result
Receive error "PipelineParams object is not iterable"
### Materials and Reference
Related: #2206
---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-12-16T19:32:18 | 2023-08-29T07:41:54 | null | NONE | null | ## Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Vertex AI Workbench
* KFP version:
Vertex Pipelines
* KFP SDK version:
1.8.12
## Repro
When trying to create a `ModelBatchPredictOp` in my pipeline, I get a `gcs_source_uris` `KeyError`. After running `help(ModelBatchPredictOp)`, I see that the documentation for this component is incorrect.
[Incorrect docs page](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.30/google_cloud_pipeline_components.v1.batch_predict_job.html#google_cloud_pipeline_components.v1.batch_predict_job.ModelBatchPredictOp)
Errors:
- `gcs_source_uris` should be `gcs_source`
- `gcs_destination_output_uri_prefix` should be `gcs_destination_prefix` | {
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"u... | closed | false | null | [] | null | [] | 2022-12-16T11:10:05 | 2022-12-19T18:48:48 | 2022-12-19T18:48:48 | CONTRIBUTOR | null | ### Environment
* KFP version: 2.0.0b8
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 2.0.0b8
<!-- 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: 2.0.0b8
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
When compiling a component that uses `PipelineTaskFinalStatus`, only the given parameter type is special cased during type checking, leading to an exception:
```python
from kfp.dsl import component, PipelineTaskFinalStatus
from kfp.compiler import Compiler
@component
def test(status: PipelineTaskFinalStatus):
pass
Compiler().compile(test, package_path='test.yaml') # TypeError: Enum value for ParameterTypeEnum must be an int, but got <class 'NoneType'> None.
```
### Expected result
It should be possible to compile components that use `PipelineTaskFinalStatus` to yaml.
### 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! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8581 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8581/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8581/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8581/events | https://github.com/kubeflow/pipelines/issues/8581 | 1,498,781,827 | I_kwDOB-71UM5ZVZiD | 8,581 | [feature] User defined Init and Wait container resources in Pipeline Spec | {
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"node_id": "MDU6TGFiZWwxMTM2MTEwMDM3"... | open | false | null | [] | null | [
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-12-15T16:58:46 | 2023-08-29T07:41:56 | 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?
We’re looking into ways to provide resource requests and limits for each pipeline’s init and wait containers for efficient compute utilization.
### What is the use case or pain point?
I’m aware that Kubeflow currently allows for the modification of ‘init’ and ‘wait’ container limits via a global configmap (`workflow-controller-configmap`), but we’d prefer to provide these resources for each pipeline separately because each pipeline may have different resource requirements when handling artifacts. It appears that ‘Argo workflows’ allows for resource modification via the [PodSpecPatch](https://github.com/argoproj/argo-workflows/discussions/9203), but Kubeflow pipelines do not.
### Is there a workaround currently?
Currently, we rely on global configuration parameters in the `workflow-controller-configmap` to tune the 'init' and 'wait' container resource requirements, but these do not scale well across pipelines.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8580 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8580/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8580/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8580/events | https://github.com/kubeflow/pipelines/issues/8580 | 1,498,434,779 | I_kwDOB-71UM5ZUEzb | 8,580 | [bug] component VertexNotificationEmailOp fails when there are 2-level chained conditions | {
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"/assign @chongyouquan ",
"Got the same issue. We should have a fix for Vertex AI on GCP. The fix will need 3 weeks to be deployed.\r\nhttps://issuetracker.google.com/issues/274611724",
"On my side, it is now working in europe-west4. Look like GCP team fully deployed the fix",
"This issue has been automatical... | 2022-12-15T13:26:04 | 2023-08-29T07:41:58 | null | NONE | null | Message Error:
<_InactiveRpcError of RPC that terminated with:
status = StatusCode.INTERNAL
details = "Internal error encountered."
debug_error_string = "{"created":"@1671108949.152751032","description":"Error received from peer ipv4:142.250.125.95:443","file":"src/core/lib/surface/call.cc","file_line":966,"grpc_message":"Internal error encountered.","grpc_status":13}"
>
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"@divjyotsinghanzx, `google-cloud-pipeline-components` is not yet compatible with `kfp==2.x.x` yet. This will work as expected once `google-cloud-pipeline-components` supports KFP v2.\r\n\r\ncc @chongyouquan "
] | 2022-12-15T05:20:20 | 2022-12-15T23:52:27 | 2022-12-15T23:52:27 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? : Creating Vertex AI pipeline, however the bug is while importing `google_cloud_pipeline_components`
* KFP version: `2.0.0b8`
### Steps to reproduce
Executing the following:
`from google_cloud_pipeline_components.experimental.custom_job.utils import create_custom_training_job_op_from_component`
produces following error:
```
File ~/pipelines/training_pipeline.py:4
----> 4 from google_cloud_pipeline_components.experimental.custom_job.utils import (
5 create_custom_training_job_op_from_component,
6 )
File ~/.venv/lib/python3.9/site-packages/google_cloud_pipeline_components/experimental/custom_job/utils.py:23
20 import tempfile
21 from typing import Callable, Dict, Optional, Sequence
---> 23 from google_cloud_pipeline_components.aiplatform import utils
24 from kfp import components
25 from kfp.components import structures
File ~/.venv/lib/python3.9/site-packages/google_cloud_pipeline_components/aiplatform/__init__.py:171
163 ModelDeployOp = load_component_from_file(
164 os.path.join(
165 os.path.dirname(__file__), 'endpoint/deploy_model/component.yaml'))
167 ModelUndeployOp = load_component_from_file(
168 os.path.join(
169 os.path.dirname(__file__), 'endpoint/undeploy_model/component.yaml'))
--> 171 ModelBatchPredictOp = load_component_from_file(
172 os.path.join(os.path.dirname(__file__), 'batch_predict_job/component.yaml'))
174 ModelUploadOp = load_component_from_file(
175 os.path.join(
176 os.path.dirname(__file__), 'model/upload_model/component.yaml'))
178 EndpointCreateOp = load_component_from_file(
179 os.path.join(
180 os.path.dirname(__file__), 'endpoint/create_endpoint/component.yaml'))
File ~/.venv/lib/python3.9/site-packages/kfp/components/yaml_component.py:91, in load_component_from_file(file_path)
75 """Loads a component from a file.
76
77 Args:
(...)
88 components.load_component_from_file('~/path/to/pipeline.yaml')
89 """
90 with open(file_path, 'r') as component_stream:
---> 91 return load_component_from_text(component_stream.read())
File ~/.venv/lib/python3.9/site-packages/kfp/components/yaml_component.py:70, in load_component_from_text(text)
60 def load_component_from_text(text: str) -> YamlComponent:
61 """Loads a component from text.
62
63 Args:
(...)
67 Component loaded from YAML.
68 """
69 return YamlComponent(
---> 70 component_spec=structures.ComponentSpec.load_from_component_yaml(text),
71 component_yaml=text)
File ~/.venv/lib/python3.9/site-packages/kfp/components/structures.py:824, in ComponentSpec.load_from_component_yaml(cls, component_yaml)
821 if is_v1:
822 v1_component = v1_components._load_component_spec_from_component_text(
823 component_yaml)
--> 824 return cls.from_v1_component_spec(v1_component)
825 else:
826 return ComponentSpec.from_pipeline_spec_dict(json_component)
File ~/.venv/lib/python3.9/site-packages/kfp/components/structures.py:607, in ComponentSpec.from_v1_component_spec(cls, v1_component_spec)
601 container = component_dict['implementation']['container']
602 command = [
603 placeholders.maybe_convert_v1_yaml_placeholder_to_v2_placeholder(
604 command, component_dict=component_dict)
605 for command in container.get('command', [])
606 ]
--> 607 args = [
608 placeholders.maybe_convert_v1_yaml_placeholder_to_v2_placeholder(
609 command, component_dict=component_dict)
610 for command in container.get('args', [])
611 ]
612 env = {
613 key:
614 placeholders.maybe_convert_v1_yaml_placeholder_to_v2_placeholder(
615 command, component_dict=component_dict)
616 for key, command in container.get('env', {}).items()
617 }
618 container_spec = ContainerSpecImplementation.from_container_dict({
619 'image': container['image'],
620 'command': command,
621 'args': args,
622 'env': env
623 })
File ~/.venv/lib/python3.9/site-packages/kfp/components/structures.py:608, in <listcomp>(.0)
601 container = component_dict['implementation']['container']
602 command = [
603 placeholders.maybe_convert_v1_yaml_placeholder_to_v2_placeholder(
604 command, component_dict=component_dict)
605 for command in container.get('command', [])
606 ]
607 args = [
--> 608 placeholders.maybe_convert_v1_yaml_placeholder_to_v2_placeholder(
609 command, component_dict=component_dict)
610 for command in container.get('args', [])
611 ]
612 env = {
613 key:
614 placeholders.maybe_convert_v1_yaml_placeholder_to_v2_placeholder(
615 command, component_dict=component_dict)
616 for key, command in container.get('env', {}).items()
617 }
618 container_spec = ContainerSpecImplementation.from_container_dict({
619 'image': container['image'],
...
223 )
225 # check that there is no illegal state found recursively
226 if isinstance(self.then, ConcatPlaceholder):
ValueError: Cannot use IfPresentPlaceholder within ConcatPlaceholder when `then` and `else_` arguments to IfPresentPlaceholder are lists. Please use a single element for `then` and `else_` only.
```
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8570 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8570/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8570/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8570/events | https://github.com/kubeflow/pipelines/issues/8570 | 1,494,680,592 | I_kwDOB-71UM5ZFwQQ | 8,570 | [bug] KFP V2 does not support Volumes | {
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"Thanks, @StefanoFioravanzo. This is on our roadmap for before June 2023.\r\n\r\nWhile volumes are a general concept/feature, the implementation is typically platform-specific. For example, you'd configure a volume on Kubernetes differently than you would on GCP. KFP v2, however, compiles pipelines to a platform-ag... | 2022-12-13T16:47:07 | 2023-07-05T17:37:25 | 2023-07-05T17:07:47 | MEMBER | null | ### TL;DR
Kubeflow Pipelines V2 does not support V1 `VolumeOp` and `PipelineVolume` to manipulate volume objects during the run and mount them on pipeline steps.
Kubeflow Pipelines V2 doesn't seem to have a roadmap commitment to support volumes before GA in June 2023.
### Context
Kubeflow Pipelines V1 provides the [`ResourceOp`](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/manipulate-resources/#resourceop) construct to define arbitrary K8s resources as a pipeline step. [`VolumeOp`](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/manipulate-resources/#volumeop) is an extension of `ResourceOp` and allows provisioning and mounting PVCs.
### Problem
Kubeflow Pipelines V2 gets rid of those constructs (There is no `ResourceOp` or `VolumeOp` among the public attributes and objects of the V2 DSL https://github.com/kubeflow/pipelines/blob/4bb57e6723b7a5c2eb685536e5a293aea87bd3a1/sdk/python/kfp/dsl/__init__.py#L17-L47).
Not having a `ResourceOp` is ok since one can emulate its behavior with a custom component, but KFP execution engine seems to miss the ability to declare a volume mount in the step's Pod in the first place. We are concerned that adding volume support may not just be a matter of extending the V2 DSL because of how the new execution model is designed.
In the [Kubeflow Pipelines (KFP) v2 System Design](https://docs.google.com/document/d/1fHU29oScMEKPttDA1Th1ibImAKsFVVt2Ynr4ZME05i0) document a paragraph states the following
> In this design, we support gcs, s3, minio object stores using their clients. However, a caveat is that the approach doesn't scale to support more cloud providers or custom solutions. After Kubernetes standardizes on CSI, I'd hope there will be more momentum on ReadWriteMany volume. Especially, volume drivers backed by object stores like csi-gcs, csi-s3 and azure-blob-csi may become a good fit as pipeline storages. We will continuously evaluate the space and consider supporting volumes as an alternative later.
As stated in the comments, K8s has standardized on CSI since January 2019.
We haven't seen other comments or design proposals targeting this missing functionality.
### Proposed Next Steps
This is a fundamental breaking change from KFP V1 and may not find a straightforward resolution for many people relying on volumes in their production pipelines.
We propose first-class support for volumes as a non-negotiable feature that needs to be part of KFP V2 before it becomes GA.
We would love to hear some feedback from the Google folks who are contributing to this design and discuss together a way forward. We would be happy to step in and help solve this issue.
@zijianjoy @james-jwu @chensun @connor-mccarthy @elikatsis
/area backend
/area sdk
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8565 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8565/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8565/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8565/events | https://github.com/kubeflow/pipelines/issues/8565 | 1,492,396,464 | I_kwDOB-71UM5Y9Cmw | 8,565 | [backend] Cache Server referencing the wrong file in secret webhook-server-tls | {
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"NOTE: I deployed Kubeflow v1.6.0 and this problem did not appear. The problem appears to be a regression due to the updated version (in v.1.6.1) of the image used in CacheServer deployment.",
"Any update on this bug? Is the tls.crt the file that that cache-server is looking for?",
"I was affected by this as ... | 2022-12-12T17:33:32 | 2023-08-29T07:41:59 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
KFP was deployed via Manifests with Kubeflow v1.6.1 onto K8S 1.24.8 on baremetal with Ubuntu 22.04 host OS.
* 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. -->
See the below problem description for info on what images were used
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
See the below problem description.
### 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.
-->
```
git clone https://github.com/kubeflow/manifests.git
pushd manifests
git checkout tags/v1.6.1
export CD_REGISTRATION_FLOW=true
while ! ../kustomize build example | kubectl $APPLY -f -; do echo "Retrying to $APPLY resources"; sleep 10; done
git switch -
```
CacheServer (Server Pod) fails to execute properly. See the logs:
```
2022/12/12 17:19:41 Initing client manager....
2022/12/12 17:19:41 Database created
2022/12/12 17:19:42 execution_caches
2022/12/12 17:19:42 open /etc/webhook/certs/cert.pem: no such file or directory
```
The file in question should be served up via a volume mount:
```
spec:
containers:
- name: server
image: gcr.io/ml-pipeline/cache-server:2.0.0-alpha.5
args:
- '--db_driver=$(DBCONFIG_DRIVER)'
- '--db_host=$(DBCONFIG_HOST_NAME)'
- '--db_port=$(DBCONFIG_PORT)'
- '--db_name=$(DBCONFIG_DB_NAME)'
- '--db_user=$(DBCONFIG_USER)'
- '--db_password=$(DBCONFIG_PASSWORD)'
- '--namespace_to_watch=$(NAMESPACE_TO_WATCH)'
volumeMounts:
- name: webhook-tls-certs
readOnly: true
mountPath: /etc/webhook/certs
volumes:
- name: webhook-tls-certs
secret:
secretName: webhook-server-tls
defaultMode: 420
```
Secret webhook-server-tls:
```
apiVersion: v1
kind: Secret
metadata:
name: webhook-server-tls
namespace: kubeflow
type: kubernetes.io/tls
data:
ca.crt: <hidden>
tls.crt: <hidden>
tls.key: <hidden>
```
As you can see, there is no `cert.pem` file
### Expected result
<!-- What should the correct behavior be? -->
The correct behavior should be that the Cache Server is properly configured to consume the TLS certificate resources that its configured to consume.
### 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! -->
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"u... | open | false | null | [] | null | [
"This is currently be design, `set_display_name` doesn't support runtime value an this moment.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-12-12T04:05:52 | 2023-08-29T07:42:01 | null | NONE | null | ### Environment
* KFP version:
kfp==1.8.16
* KFP SDK version:
kfp 1.8.16
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
```
def pipeline(countries, start, end, model_types):
get_countriest_task = get_countriest_op(countries=countries)
with dsl.ParallelFor(get_countriest_task.output) as country_code:
download_data_task = download_data_op(
country_code=country_code, start_date=start, end_date=end
).set_display_name(str(country_code))
```
### Expected result
I expect in the UI, each component's name is corresponding to the `country_code`.
### Materials and reference
Code for download_data_op:
```
def get_countriest(countries:str) -> str:
import json
countries= countries.split(",")
return json.dumps(countries)
```
### Actual result
<!-- Please include labels below by uncommenting them to help us better triage issues -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8558 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8558/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8558/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8558/events | https://github.com/kubeflow/pipelines/issues/8558 | 1,486,627,621 | I_kwDOB-71UM5YnCMl | 8,558 | [sdk] Component parameters with a default value of `None` are incorrectly interpreted as required when compiling to yaml | {
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"/assign @connor-mccarthy ",
"Hi, @suned. https://github.com/kubeflow/pipelines/pull/8572 should resolve this issue, I believe."
] | 2022-12-09T11:55:22 | 2023-03-21T17:48:33 | 2023-03-21T17:48:32 | CONTRIBUTOR | null | ### Environment
* KFP version: 2.0.0b8
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 2.0.0b8
<!-- 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 2.0.0b8
* kfp-pipeline-spec 0.1.16
* kfp-server-api 2.0.0a6
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Example:
```python
from typing import Optional
from kfp.dsl import component
from kfp.compiler import Compiler
@component
def example(optional_param: Optional[str] = None):
pass
Compiler().compile(example, 'example.yaml')
```
Output:
```yaml
# example.yaml
components:
comp-example:
executorLabel: exec-example
inputDefinitions:
parameters:
optional_param:
parameterType: STRING
deploymentSpec:
executors:
exec-example:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- example
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.0.0-beta.8'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
python3 -m kfp.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef example(optional_param: Optional[str] = None):\n pass\n\n"
image: python:3.7
pipelineInfo:
name: example
root:
dag:
tasks:
example:
cachingOptions:
enableCache: true
componentRef:
name: comp-example
inputs:
parameters:
optional_param:
componentInputParameter: optional_param
taskInfo:
name: example
inputDefinitions:
parameters:
optional_param:
parameterType: STRING
schemaVersion: 2.1.0
sdkVersion: kfp-2.0.0-beta.8
```
### Expected result
`optional_param` in the above example should have a default value under the `defaultValue` key in the output yaml.
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8555 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8555/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8555/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8555/events | https://github.com/kubeflow/pipelines/issues/8555 | 1,485,860,530 | I_kwDOB-71UM5YkG6y | 8,555 | [Bazel support] <Will support bazel build for Kubeflow> | {
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"Thanks, @xushaoxiao. We've supported bazel in the past, but it slowed down development velocity and required extra effort to maintain. This is not something we are looking to support at this time.\r\n\r\nHere is a bit of context: https://github.com/kubeflow/pipelines/issues/3250\r\n\r\ncc @chensun "
] | 2022-12-09T03:00:13 | 2022-12-15T23:58:27 | 2022-12-15T23:58:26 | NONE | null | ### Bazel build is good
Whether to consider supporting bazel build ?
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https://api.github.com/repos/kubeflow/pipelines/issues/8554 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8554/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8554/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8554/events | https://github.com/kubeflow/pipelines/issues/8554 | 1,485,641,884 | I_kwDOB-71UM5YjRic | 8,554 | Behavior of set_retry() - component marked as failed, pipeline marked as success | {
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"@hydeta, just to confirm: are you running this on the Kubeflow Pipelines open source BE? If so, which version (both KFP SDK and KFP BE versions)?\r\n\r\ncc @gkcalat ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank... | 2022-12-09T00:15:51 | 2023-08-29T07:42:03 | null | NONE | null | Hello,
I am using the method `set_retry()` and occasionally I am seeing instances where the component is marked as a failure but the pipeline will still be marked as a success. How does this occur? Is it the case where if the component fails but succeeds on subsequent tries then the component will still be marked as a failure but the pipeline will succeed? Or is the component truly failing and the pipeline is proceeding when it should instead fail?
Thanks. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8552 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8552/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8552/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8552/events | https://github.com/kubeflow/pipelines/issues/8552 | 1,485,616,850 | I_kwDOB-71UM5YjLbS | 8,552 | MySQL logs disk full error | {
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"/cc @jlyaoyuli "
] | 2022-12-08T23:53:35 | 2022-12-09T03:51:11 | 2022-12-09T03:51:11 | COLLABORATOR | null | We noticed on some KFP cluster listing run failed consistently with 524 timeout error.
Looking at the pod logs, metadata-writer and persistence-agent are in crash loop with error failed to connect to mysql.
Checking mysql pod logs, it's flooded with
> `disk is full writing './binlog.~rec~'`
Did some research, looks like MySQL 8.0 changed the default log expiration from 10 days to 30 days:
https://dba.stackexchange.com/questions/233048/my-disk-is-full-of-binlog-files | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8546 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8546/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8546/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8546/events | https://github.com/kubeflow/pipelines/issues/8546 | 1,483,062,768 | I_kwDOB-71UM5YZb3w | 8,546 | [backend] <Wrong Parameter loading for google_cloud_pipeline_components.aiplatform.TabularDatasetCreateOp in v1.0.29> | {
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"/assign @chongyouquan ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-12-07T23:10:01 | 2023-08-29T07:42:05 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
We use the Vertex AI managed notebook to create KFP and call the "google_cloud_pipeline_components.aiplatform.TabularDatasetCreateOp" component to create a Vertex AI dataset.
* KFP version:
V2
* KFP SDK version:
google-cloud-pipeline-components-1.0.29
### Steps to reproduce
Under google-cloud-pipeline-components-1.0.29.
Use simple pipelineJob to create the TabularDatasetCreateOp with normal "project" and "location" as parameters. This will raise the error.
Did some research and found:
The error is found in container execution. The remote_runner did not correctly pass the "project" and "location" as Str; they are recognized as "Dict()" here https://github.com/kubeflow/pipelines/blob/217cbd9e6e0c79316bb98c005e3154cea08afb71/components/google-cloud/google_cloud_pipeline_components/container/aiplatform/remote_runner.py#L211
### Expected result
This may be caused by the most recent change in the labeling part. Because we still have the correct results in the 1.0.27 version
### Materials and Reference
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8527 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8527/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8527/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8527/events | https://github.com/kubeflow/pipelines/issues/8527 | 1,477,298,221 | I_kwDOB-71UM5YDcgt | 8,527 | [feature] Documentation around specifying pod configuration for components (e.g. volume mounts/pod affinity etc) in v2 | {
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"/assign @connor-mccarthy ",
"Hi, @elibixby. Please see a description of plans to support this in KFP SDK v2 here: https://github.com/kubeflow/pipelines/issues/8570#issuecomment-1349057570",
"Do I read this correctly that the plan is to be platform agnostic even outside of Kubernetes? I'm really skeptical that ... | 2022-12-05T19:42:27 | 2023-08-29T07:42:07 | null | NONE | null | ### Feature Area
/area samples
### What feature would you like to see?
It's unclear from documentation to what extent you should be able to configure the pod that run a given task in v2. In v1 the [BaseOp](https://github.com/kubeflow/pipelines/blob/1.8.16/sdk/python/kfp/dsl/_container_op.py#L810) class seems to suggest that most aspects of a pod are configurable within the pipeline but this functionality seems to completely disappear in v2 with the move to `ComponentSpec` which seems to have no such configuration fields. Is this still possible? Some documentation on customizing pod fields would help here.
### What is the use case or pain point?
Many steps for my use case require specific pod affinity, tolerances, and premounted volumes etc. It is unclear how to do that in the context of v2 pipelines.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8524 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8524/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8524/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8524/events | https://github.com/kubeflow/pipelines/issues/8524 | 1,474,265,308 | I_kwDOB-71UM5X34Dc | 8,524 | [feature] Support different ServiceAccounts for each component (least privilege) | {
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"Hello @agilgur5 , thank you so much for bringing this up and offering to contribution. If you can write a design doc about how you plan to implement the per-component service account feature, and get it reviewed by Pipelines Working Group (Join the Pipelines Community Meeting), we can review and approve it for ena... | 2022-12-03T22:53:24 | 2023-07-06T00:12:31 | 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. -->
I would like to be able to configure different k8s `ServiceAccount`s for each component in my pipeline.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Main examples are for integrations:
1. Let's say I want to run the Spark Operator in one of my components, then I need to give that component RBAC access to the `SparkApplication` CR. _But_, I don't want to allow my other components access to `SparkApplication` CRs.
2. Let's say I want one of my components to access secrets from Vault's CSI Driver, then I need to give that component RBAC access to PSPs for CSI (in older k8s versions). _But_, I don't want to allow my other components access to CSI.
Basically, limiting to **least privilege** RBAC per component.
In secured environments with a component catalog, an example of usage here would be that anything that requires RBAC has to go through the component catalog -- i.e. any integrations that require RBAC are through secured components.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
All components must be given the same `ServiceAccount` with RBAC allowed for all. So if I only have _one_ component that needs to integrate with Spark, _all_ components are given access to Spark.
This does not fit least privilege security paradigms, causing many components to have extra unnecessary permissions, widening the attack surface area unnecessarily.
## Additional Details
Argo Workflows does currently allow different `ServiceAccount`s per Workflow, and there exists a workaround to allow different `ServiceAccount`s per step: https://github.com/argoproj/argo-workflows/issues/2053. So this is already possible in Argo Workflows, it just needs support in Kubeflow Pipelines.
I would be more than happy to contribute this work as it's critical to a few teams that I work with. I may also contribute to an official feature in Argo for this as well (so that the workaround is not necessary)
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8513 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8513/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8513/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8513/events | https://github.com/kubeflow/pipelines/issues/8513 | 1,469,973,409 | I_kwDOB-71UM5XngOh | 8,513 | [feature] Export VertexModels in python function based components in Vertex Pipelines. | {
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"/assign @connor-mccarthy ",
"@wardVD, thanks for your question. Can you let me know which version of the KFP SDK you're using? If it's `kfp==1.x.x` or `kfp<2.0.0b5` this is expected.\r\n\r\nFor `kfp>=2.0.0b5`, the `VertexModel` symbol will be defined, but the GCPC library is not yet compatible with KFP v2 (this ... | 2022-11-30T17:15:38 | 2023-08-30T07:41:50 | null | NONE | null | ### Feature Area
I wanted to create a custom component from a python function that creates a VertexModel artifact when running with Vertex Pipelines.
I thought it could be as simple as something like this (dummy code):
```
from google_cloud_pipeline_components.types.artifact_types import VertexModel
@component(
base_image="python:3.9-slim",
)
def ModelUploadCustomOp(
model_name: str,
model: Output[VertexModel],
):
model.uri = <MODEL_URI_STRING>
```
The compilation step works, but running gives me the following error:
```
model: Output[VertexModel],
NameError: name 'VertexModel' is not defined
```
Is there a way on how to create a python function based component that creates a VertexModel artifact in Vertex Pipelines?
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8502 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8502/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8502/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8502/events | https://github.com/kubeflow/pipelines/issues/8502 | 1,467,179,111 | I_kwDOB-71UM5Xc2Bn | 8,502 | [feature] S3 Integration for Artifact Storage (IRSA) | {
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"Hi @zijianjoy @jlyaoyuli Can you please ~~tag Jo Li/people from frontend side to~~ review this proposal\r\n\r\ncc @chensun, @james-jwu",
"@jlyaoyuli ",
"Hi @gkcalat, I believe @ryansteakley is interested to contribute this functionality. Shall we go ahead with the PR?\r\n@jlyaoyuli let us know if you have any ... | 2022-11-28T22:27:53 | 2023-03-10T18:23:29 | 2023-03-10T18:23:29 | CONTRIBUTOR | null | ## S3 Integration for Artifact Storage (IRSA)
As of Kubeflow pipelines 2.0-alpha6 release, using IRSA for artifact storage in S3 is not fully supported(details later in the doc). There has been a long-running thread with high-interest for this functionality in the[ kubeflow pipelines github](https://github.com/kubeflow/pipelines/issues/3405). These proposal aims to address it.
To support using IRSA with S3, we want to add the[ AWS JS SDK credentials](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/modules/_aws_sdk_credential_providers.html) module to the frontend container image and update the[ aws-helper](https://github.com/kubeflow/pipelines/blob/4bb57e6723b7a5c2eb685536e5a293aea87bd3a1/frontend/server/aws-helper.ts) to use credentialProviderChain function from the AWS JS SDK.
**Please read the following [design document](https://docs.google.com/document/d/1kWbenzENgj2SE8oKshiu_MMzZuBqfZ8Iv1LQSmwJD7E/edit?usp=sharing&resourcekey=0-DxxoE1CFqJkUeSYpEBKfmg) to better understand the reasoning and use-case behind this feature**.
---
/area frontend
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https://api.github.com/repos/kubeflow/pipelines/issues/8500 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8500/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8500/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8500/events | https://github.com/kubeflow/pipelines/issues/8500 | 1,466,545,203 | I_kwDOB-71UM5XabQz | 8,500 | [bug] Issue with ImageDatasetCreateOp | {
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"Currently Google Cloud Pipeline Component hasn't been migrated to KFP SDK V2, so it is a work-in-progress.\r\n\r\n/cc @connor-mccarthy \r\n\r\n"
] | 2022-11-28T14:37:40 | 2022-12-15T07:56:42 | 2022-12-08T23:52:40 | NONE | null | ### Environment
* KFP SDK version:
2.0.0b4
### Steps to reproduce
Run https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb
No issues on `kfp==1.8.13`
Issue:
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8499 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8499/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8499/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8499/events | https://github.com/kubeflow/pipelines/issues/8499 | 1,464,518,232 | I_kwDOB-71UM5XSsZY | 8,499 | [feature] Filter splits not possible for AutoMLTrainingJobOp | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-11-25T13:01:21 | 2023-08-30T07:41:52 | null | NONE | null | ### Feature Area
I noticed that the `component.yaml` file for AutoMLTrainingJobOp mentions data filters in the description, but the parameters for filter splits are not yet present:
https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/aiplatform/automl_training_job/automl_image_training_job/component.yaml
This unfortunately does not allow to train an AutoML model by defining the training/validation/test splits when they are already defined in the dataset itself.
This is possible with the Google Cloud Vertex SDK: https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/training_jobs.py#L5332
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8498 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8498/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8498/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8498/events | https://github.com/kubeflow/pipelines/issues/8498 | 1,464,357,609 | I_kwDOB-71UM5XSFLp | 8,498 | How do I use an external variable, an external function, or an external class inside a pipeline component? | {
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"The Containerized Python Components approach is what you're looking for: https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#2-containerized-python-components.\r\n\r\nPlease feel free to re-open this you have any other questions."
] | 2022-11-25T10:43:58 | 2022-12-02T00:06:04 | 2022-12-02T00:06:03 | NONE | null | I defined a pipeline component in com.py like this:
@component(base_image="my-image")
def com1():
a = 1
def func1(aa):
pass
def func2():
pass
x = func1(a)
y = func2()
return com1
I want to move a, func1 and func2 outside from com1, because the com1 is too long, Too long code leads to poor readability.
So I want to define like this:
a = 1
def func1(aa):
pass
def func2():
pass
@component(base_image="my-image")
def com1():
x = func1(a)
y = func2()
return com1
but when I compiler it to yaml file , the yaml file could not find a, func1 and func2.
So, how should I do ? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8497 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8497/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8497/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8497/events | https://github.com/kubeflow/pipelines/issues/8497 | 1,464,356,741 | I_kwDOB-71UM5XSE-F | 8,497 | How do I use an external variable, an external function, or an external class inside a pipeline component? | {
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} | [] | closed | false | null | [] | null | [] | 2022-11-25T10:43:12 | 2022-11-26T06:45:48 | 2022-11-26T06:45:48 | NONE | null | I defined a pipeline component in com.py like this:
@component(base_image="my-image")
def com1():
a = 1
def func1(aa):
pass
def func2():
pass
x = func1(a)
y = func2()
return com1
I want to move a, func1 and func2 outside from com1, because the com1 is too long, Too long code leads to poor readability.
So I want to define like this:
a = 1
def func1(aa):
pass
def func2():
pass
@component(base_image="my-image")
def com1():
x = func1(a)
y = func2()
return com1
but when I compiler it to yaml file , the yaml file could not find a, func1 and func2.
So, how should I do ? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8495 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8495/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8495/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8495/events | https://github.com/kubeflow/pipelines/issues/8495 | 1,463,129,321 | I_kwDOB-71UM5XNZTp | 8,495 | [bug] BigqueryCreateModelJob component fails when query includes double quotes (") | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-11-24T10:33:46 | 2023-08-30T07:41:54 | null | NONE | null | ### Environment
google-cloud-pipeline-components = "1.0.26"
gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.26
* How do you deploy Kubeflow Pipelines (KFP)?
Running on Vertex AI Pipelines
* https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
n/a
* KFP SDK version:
kfp = "^1.8.14"
### Steps to reproduce
install google-cloud-pipeline-components = "1.0.26"
create a pipeline with the component
```
create_model_tast = BigqueryCreateModelJobOp(
project = project_id,
location = location,
query = 'CREATE OR REPLACE MODEL `dev_models.model_tfidf_v1`
OPTIONS
( MODEL_TYPE="LOGISTIC_REG",
input_label_cols=["category"]
) AS
SELECT body, category FROM
`bigquery-public-data.bbc_news.fulltext` LIMIT 1000')
```
Error:
```
File "/opt/python3.7/lib/python3.7/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting ',' delimiter: line 1 column 203 (char 202)
```
Workaround:
Replaced all double quotes (") with single quotes (')
Suggested solution:
Escape double quotes before parsing the query to json
### Expected result
Create model query to run successfully like when using the BigQuery UI to execute the same query / string
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8491 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8491/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8491/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8491/events | https://github.com/kubeflow/pipelines/issues/8491 | 1,460,653,686 | I_kwDOB-71UM5XD852 | 8,491 | [feature] kfp.component to accept tuples/lists of component outputs as args | {
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"Hi @sradc. Thanks for suggesting this. We definitely want to enable expression of more complex topologies like the ones you show.\r\n\r\nRight now we're working on three separate designs/implementations related to this feature request:\r\n- Support for lists of artifacts\r\n- Support for fan-in from `dsl.ParallelF... | 2022-11-22T22:39:20 | 2023-02-10T16:02:56 | 2023-02-10T12:12:30 | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
The ability to pass in a list/tuple of `component.output` as an arg to a component. E.g. so that examples like the following work:
```python
from kfp.v2 import compiler, dsl
@dsl.component
def my_transformer_op(item: str) -> str:
return item + "_transformed"
@dsl.component
def my_aggregator_op(args: list) -> str:
return " ".join(args)
@dsl.pipeline("aggtest", "agg test")
def dynamic_pipeline():
transformed_vals = []
for x in ["a", "b", "c"]:
transformed_vals.append(my_transformer_op(x))
my_aggregator_op([x.output for x in transformed_vals])
compiler.Compiler().compile(pipeline_func=dynamic_pipeline, package_path="my_pipeline.yaml")
```
```
TypeError: Object of type PipelineParam is not JSON serializable
```
(Looking at [this](https://stackoverflow.com/a/63219053) stackoverflow post, seems this kind of thing was possible in v1; but seems it's not possible in v2?)
### What is the use case or pain point?
For aggregating results, e.g. see [this](https://sidsite.com/posts/kubeflow-hyperparam-opt/) example of hyperparameter optimization, and the screenshots attached below.
### Is there a workaround currently?
*Yes*. There are workarounds. For example, I created a hacky workaround, that modifies the source code of a function, to expand args annotated with PseudoList into actual args of the function, and then add lines to the function def to collect these into tuples with the original name of the arg. (Using ast/inspect.)
Source code here: https://github.com/sradc/kubeflow_hyperparam_opt_example/blob/b3ef4d7e01055e27011a4d1311cf9adccf37869e/pseudo_tuple_component.py
Example of using it here (there's also an example in blog post linked above): https://github.com/sradc/kubeflow_hyperparam_opt_example/blob/41bc48e31407f79fbbcd07b39ea82586a5a71562/pseudo_tuple_example.ipynb
Will also reproduce the example here:
```python
from kfp.v2 import compiler, dsl
from pseudo_tuple_component import PseudoTuple, pseudo_tuple_component
MY_LIST = ["a", "b", "c"]
PIPELINE_NAME = "pseudo-tuple-example"
@dsl.component
def my_transformer_op(item: str) -> str:
return item + "_transformed"
@pseudo_tuple_component(globals_=globals(), locals_=locals())
def my_aggregator_op(args: PseudoTuple(len(MY_LIST), str)) -> str:
return " ".join(args)
@dsl.pipeline("aggtest", "agg test")
def dynamic_pipeline():
transformed_vals = []
for x in MY_LIST:
transformed_vals.append(my_transformer_op(x).output)
my_aggregator_op(*transformed_vals)
compiler.Compiler().compile(pipeline_func=dynamic_pipeline, package_path=f"{PIPELINE_NAME}.json")
```
To show more explicitely how the function is modified:
```python
from pseudo_tuple_component import expand_PseudoTuple_annotated_args_to_str
def my_aggregator_op(args: PseudoTuple(len(MY_LIST), str)) -> str:
return " ".join(args)
new_source_code_for_func = expand_PseudoTuple_annotated_args_to_str(
my_aggregator_op, globals_=globals(), locals_=locals()
)
print(new_source_code_for_func)
```
```
def my_aggregator_op(args_0: str, args_1: str, args_2: str) -> str:
args = (args_0, args_1, args_2)
return ' '.join(args)
```
---
This is related to issues:
- https://github.com/kubeflow/pipelines/issues/1933
- https://github.com/kubeflow/pipelines/issues/3412
---
This is the kind of thing you could do (taken from [here](https://sidsite.com/posts/kubeflow-hyperparam-opt/), using my workaround):
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8490 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8490/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8490/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8490/events | https://github.com/kubeflow/pipelines/issues/8490 | 1,460,581,612 | I_kwDOB-71UM5XDrTs | 8,490 | [frontend] Image reference specifying tag with digest should work | {
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"Hi @doctapp,\r\nCan you share what client are you using and how you submitted the job?",
"Hi @gkcalat,\r\nusing Python 3.10.6, google_cloud_aiplatform 1.18.3 and kfp 1.8.16.\r\n\r\nAs for how the image is set:\r\n```\r\n@dsl.component\r\ndef some_func(...): ...\r\n...\r\nsome_fun.component_spec.implementation.co... | 2022-11-22T21:34:00 | 2022-12-16T14:32:24 | 2022-12-15T23:39:45 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? Google Vertex AI
* KFP version: 1.8.6 (Python 3.10.6)
### Steps to reproduce
Use image reference containing both tag and digest, e.g., `gcr.io/some-project-id/some-product:0.0.1@sha256:0123456789abcedf0123456789abcedf0123456789abcedf0123456789abcedf`
### Expected result
Pipeline should run but receiving `google.api_core.exceptions.InvalidArgument: 400 Invalid image URI gcr.io/some-project-id/some-product@sha256:0123456789abcedf0123456789abcedf0123456789abcedf0123456789abcedf` instead.
### Materials and Reference
Using either tag (e.g., `gcr.io/some-project-id/some-product:0.0.1`) or digest (e.g., `gcr.io/some-project-id/some-product@sha256:0123456789abcedf0123456789abcedf0123456789abcedf0123456789abcedf`) works.
Expected image reference including both tag and digest is very useful for identifying correct images which is complex with just using digest.
Example stack trace:
```
Traceback (most recent call last):
File "/home/user/product/venv/lib/python3.10/site-packages/google/api_core/grpc_helpers.py", line 72, in error_remapped_callable
return callable_(*args, **kwargs)
File "/home/user/product/venv/lib/python3.10/site-packages/grpc/_channel.py", line 946, in __call__
return _end_unary_response_blocking(state, call, False, None)
File "/home/user/product/venv/lib/python3.10/site-packages/grpc/_channel.py", line 849, in _end_unary_response_blocking
raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INVALID_ARGUMENT
details = "Invalid image URI gcr.io/some-project-id/some-product@sha256:0123456789abcedf0123456789abcedf0123456789abcedf0123456789abcedf."
debug_error_string = "UNKNOWN:Error received from peer ipv4:172.217.13.106:443 {grpc_message:"Invalid image URI gcr.io/some-project-id/some-product@sha256:0123456789abcedf0123456789abcedf0123456789abcedf0123456789abcedf.", grpc_status:3, created_time:"2022-11-22T00:00:00.000000000+00:00"}"
>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/user/product/venv/lib/python3.10/site-packages/google/api_core/grpc_helpers.py", line 74, in error_remapped_callable
raise exceptions.from_grpc_error(exc) from exc
google.api_core.exceptions.InvalidArgument: 400 Invalid image URI gcr.io/some-project-id/some-product@sha256:0123456789abcedf0123456789abcedf0123456789abcedf0123456789abcedf.
```
Thanks!
---
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"#8864 "
] | 2022-11-18T20:23:15 | 2023-05-18T00:01:17 | 2023-05-18T00:00:34 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
KFP standalone
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
Self built based on #8480
<!-- 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
Since this bug only appeared after PR #8480, maybe it's more reasonable to investigate after it is merged.
Here are the steps:
1. Create an experiment using v2 api
2. Archive this experiment
3. On the frontend, the archived experiment still appears in the active experiments list. It's not present in the archived experiments list
4. However, if you click into this archived experiment, the button says "Restore", which means FE acknowledges that the experiment is archived.
5. This might be because the v2 api uses a different `StorageState` that is generated from the v2 proto
<!--
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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8460 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8460/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8460/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8460/events | https://github.com/kubeflow/pipelines/issues/8460 | 1,450,779,480 | I_kwDOB-71UM5WeSNY | 8,460 | [pipelines]why user can see all pipelines from other member | {
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"Hi @hellobiek , namespace for pipeline is working in progress. \r\nhttps://github.com/kubeflow/pipelines/issues/4197",
"@jlyaoyuli thank you. ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contri... | 2022-11-16T03:32:27 | 2023-08-30T07:41:55 | null | NONE | null | kubeflow 1.5
kfp 1.8.12
why user can see all pipelines from other member? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8459 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8459/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8459/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8459/events | https://github.com/kubeflow/pipelines/issues/8459 | 1,450,779,375 | I_kwDOB-71UM5WeSLv | 8,459 | [sdk] Unable to use api/v2alpha1/pipeline_spec.proto with bazel and py_proto_library() intact | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-11-16T03:32:20 | 2023-08-30T07:41:57 | null | NONE | null | ### Environment
* KFP version: 1.8.13
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* All dependencies version:
* kfp==1.8.13
* kfp-pipeline-spec==0.1.16
* kfp-server-api==1.8.4
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Based on [kfp-pipeline-repro-codes-8459](https://github.com/Writtic/kfp-pipeline-repro-codes-8459)
```bash
bazel run //tools:gen_proto
python main.py
```
### Expected result
Using `py_proto_library()` from com_google_protobuf, I hope to use the part of pipeline_spec.proto on [kubeflow/pipelines](https://github.com/kubeflow/pipelines) for reusability and integrity.
However, Protobuf descriptor in `kfp-pipeline-spec` package has different package path from actual protobuf file path, so I can't use as it is.
Due to this, I add the dependancy of [kubeflow/pipelines](https://github.com/kubeflow/pipelines) and bazel patches, build file for [kubeflow/pipelines](https://github.com/kubeflow/pipelines).
As a result, I can get [pipeline_config_pb2.py](reproduce/proto/pipeline_config_pb2.py) with gen_proto.sh which is simply copy `*_pb2.py` file from bazel runtime environment.
But `pipeline_config_pb2.py` refers to `kfp.pipeline_spec` which is in `kfp-pipeline-spec` python package in python runtime eventually.
And even the protobuf file of `kfp.pipeline_spec` has no subpackage path.
This bring some problem like below.
- Protobuf file, [pipeline_config.proto](reproduce/proto/pipeline_config.proto), should use `import "pipeline_spec.proto";`
- `pb2.py` file, [pipeline_config_pb2.py](reproduce/proto/pipeline_config_pb2.py), should use `from kfp.pipeline_spec import pipeline_spec` because of `kfp-pipeline-spec`
### Materials and Reference
- Github Repository: [kfp-pipeline-repro-codes-8459](https://github.com/Writtic/kfp-pipeline-repro-codes-8459)
<!-- 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 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/8451 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8451/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8451/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8451/events | https://github.com/kubeflow/pipelines/issues/8451 | 1,449,261,523 | I_kwDOB-71UM5WYfnT | 8,451 | kfp component can not access gpu (on premise) | {
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"Hello @TranThanh96 , \r\nHere is the instruction of enabling GPU: https://www.kubeflow.org/docs/distributions/gke/pipelines/enable-gpu-and-tpu/\r\nMaybe you were missing `gpu_op.add_node_selector_constraint()`"
] | 2022-11-15T06:52:26 | 2022-12-01T23:53:54 | 2022-12-01T23:53:54 | NONE | null | ### Environment
ubuntu 20-04
cluster k3s: 1 node gpu: https://www.kubeflow.org/docs/components/pipelines/v1/installation/localcluster-deployment/#1-setting-up-a-cluster-on-k3s
deployed gpu-operator
### Steps to reproduce
this is code check:
```
import kfp
from kfp import dsl
def gpu_smoking_check_op():
return dsl.ContainerOp(
name='check',
image='tensorflow/tensorflow:latest-gpu',
command=['sh', '-c'],
arguments=['nvidia-smi']
).set_gpu_limit(1)
@dsl.pipeline(
name='GPU smoke check',
description='smoke check as to whether GPU env is ready.'
)
def gpu_pipeline():
gpu_smoking_check = gpu_smoking_check_op()
if __name__ == '__main__':
arguments = {}
client.create_run_from_pipeline_func(
gpu_pipeline,
arguments=arguments)
```
and this is result: sh: 1: nvidia-smi: not found
all pods are running:
nvidia-docker check ok:
How can I access gpu on kfp? | {
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"This is by design. KFP v2 is currently in progress, and we're not ready to claim GA yet. \r\nThe 2.0 alpha version of KFP included in Kubeflow 1.6 fully supports KFP v1 feature with the addition of v2 feature in alpha phase. Users can continue using KFP SDK 1.8.* with Kubeflow 1.6 for v1 features. Alternatively, t... | 2022-11-15T04:58:53 | 2022-11-21T23:52:39 | 2022-11-21T23:52:32 | MEMBER | null | Currently, the latest version of the KFP SDK (`2.0.0b6`) is [marked as a "pre-release" on PyPI](https://pypi.org/project/kfp/#history).
This is problematic for 2 main reasons:
1. If a user runs `pip install kfp` they will get `1.8.14`, which will not include 2.0.0 features
1. Kubeflow 1.6 [includes Kubeflow Pipelines `v2.0.0-alpha5`](https://www.kubeflow.org/docs/releases/kubeflow-1.6/#component-versions) | {
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"Hi @stobias123 , thank you for reaching out. Currently KFP v2 is in the alpha phase, and the fix is what we are currently working on.",
"Hello @stobias123, we are happy to share that the logs feature is already included in our latest release (https://github.com/kubeflow/pipelines/releases/tag/2.0.0-rc.1). Please... | 2022-11-15T03:51:47 | 2023-05-24T17:21:01 | 2023-05-24T17:21:01 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
* AWS Manifests.
https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
gcr.io/ml-pipeline/frontend:2.0.0-alpha.5
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
Run this pipeline.
https://github.com/kubeflow/pipelines/blob/master/samples/v2/lightweight_python_functions_v2_pipeline/lightweight_python_functions_v2_pipeline.py
After fixing a few parameter naming issues, it runs, and fails without showing why. (In my case it was b/c of bucket issues, but i have to go to logs)
### Expected result
See logs and understand why it fails.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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"TL;DR: This should have been resolved now. \r\n\r\nLast week, we updated these two images, which we shouldn't have referenced via `latest` from the beginning:\r\nhttps://github.com/kubeflow/pipelines/blob/6b3b4df3d2a0dae0d10195e3d2fea76ed478af0c/backend/src/v2/compiler/argocompiler/argo.go#L110-L111\r\nThe change ... | 2022-11-13T21:34:40 | 2023-03-28T17:24:58 | 2022-11-14T20:42:51 | NONE | null | `###` Environment
kfp v2 SDK
kfp version 2.0.0b6
kfp-pipeline-spec version 0.1.16
kfp-server-api version 2.0.0a6
### Steps to reproduce
We are trying to run a simple example from the [docs](https://www.kubeflow.org/docs/components/pipelines/v1/sdk-v2/python-function-components/) in order to test kfp v2 sdk in kubeflow namespace as shown below:
```
import kfp
import kfp.dsl as dsl
#from kfp.v2.dsl import component
from kfp.dsl import component
@component
def add(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
return a + b
@dsl.pipeline(
name='addition-pipeline',
description='An example pipeline that performs addition calculations.',
# pipeline_root='gs://my-pipeline-root/example-pipeline'
)
def add_pipeline(a: float = 1, b: float = 7):
add_task = add(a=a, b=b)
if __name__ == '__main__':
# Compiling the pipeline
kfp.compiler.Compiler().compile(pipeline_func=add_pipeline, package_path='pipeline1.yaml')
```
Once we try and submit this pipeline, we see that system-dag-driver pod ran successfully. However, the system-container-driver pod terminates with an error:
`I1113 20:31:38.515160 27 main.go:213] output ExecutorInput:{
"inputs": {
"parameterValues": {
"parallelism": "2"
}
},
"outputs": {
"parameters": {
"Output": {
"outputFile": "/tmp/kfp/outputs/Output"
}
},
"outputFile": "/tmp/kfp_outputs/output_metadata.json"
}
}
F1113 20:31:38.515178 27 main.go:74] KFP driver: driver.Container(pipelineName=pipeline/stress-test-v2, runID=caf03c46-c9db-4398-afb9-cea0821a3c51, task="get-loop-args", component="comp-get-loop-args", dagExecutionID=1085086, componentSpec) failed: failure while getting executionCache: failed to list tasks: rpc error: code = Unimplemented desc = unknown method ListTasksV1 for service api.TaskService`
Also, another issuing we are facing is, when we submit the pipeline from the kubeflow UI, we are getting an error `Cannot get MLMD objects from Metadata store. Cannot find context with {"typeName":"system.PipelineRun": Unknown Content-type received.`
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https://api.github.com/repos/kubeflow/pipelines/issues/8445 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8445/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8445/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8445/events | https://github.com/kubeflow/pipelines/issues/8445 | 1,446,516,138 | I_kwDOB-71UM5WOBWq | 8,445 | Kubeflow pipeline is displayed as single line json instead of formatted YAML file with KFP 1.8.5 | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-11-12T16:31:48 | 2023-08-30T07:41:58 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
KFP is deployed on EKS cluster (1.22) with S3+RDS using following procedure:
https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/env/aws
* KFP version:
1.8.5
### Steps to reproduce
After upgrading KFP from 1.7.1 to 1.8.5 upgrade we noticed that uploading a pipeline.yaml from Kubeflow dashboard shows up as json as a result it is just displayed in single line (instead of formatted yaml) making it hard to read.
This was working fine with older version (1.7.1+eks1.21)
NOTE: This issue is also seen with the fresh deployment of KFP v1.8.5
### Expected result
Pipeline YAML should appear as a formatted text with the line breaks instead of single like json file as with the older versions
Impacted by this bug? Give it a 👍
 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8436 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8436/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8436/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8436/events | https://github.com/kubeflow/pipelines/issues/8436 | 1,440,967,563 | I_kwDOB-71UM5V42uL | 8,436 | Publish conformance test guidelines to the KF community | {
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"Will continue driving KF conformance through a project in KF repo: https://github.com/orgs/kubeflow/projects/52"
] | 2022-11-08T22:01:25 | 2022-11-08T22:06:20 | 2022-11-08T22:05:59 | CONTRIBUTOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8435 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8435/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8435/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8435/events | https://github.com/kubeflow/pipelines/issues/8435 | 1,440,965,753 | I_kwDOB-71UM5V42R5 | 8,435 | KFP conformance test design | {
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"Design doc: https://docs.google.com/document/d/1_til1HkVBFQ1wCgyUpWuMlKRYI4zP1YPmNxr75mzcps/edit"
] | 2022-11-08T21:59:53 | 2022-11-08T22:01:01 | 2022-11-08T22:01:01 | CONTRIBUTOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8429 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8429/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8429/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8429/events | https://github.com/kubeflow/pipelines/issues/8429 | 1,438,319,937 | I_kwDOB-71UM5VuwVB | 8,429 | Need to replace Minlo with s3 as an alternative for artifact storage in kubeflow | {
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"Hi @SoujanyaSan, thank you for bringing up the request. We have a similar feature request here #7878. Please see updates in this request."
] | 2022-11-07T12:55:00 | 2022-11-10T23:54:37 | 2022-11-10T23:54:37 | 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?
Need to have pipeline manifests,metrics and logs of each pipeline node should be exported to s3 from ml pipelines and also need to retireve from the same for the visualization in the kubeflow
### What is the use case or pain point?
Facing difficulty in disintegrating minio from kubeflow(as by default kubeflow ships with Minlo)
### 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! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8427 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8427/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8427/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8427/events | https://github.com/kubeflow/pipelines/issues/8427 | 1,437,252,717 | I_kwDOB-71UM5Vqrxt | 8,427 | [Question] How to save GPU costs in kubeflow pipelines? | {
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"Hi @r-matsuzaka, thank you for your question! We offer component level support for node selection. See reference here: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.BaseOp.add_node_selector_constraint\r\nPlease let us know if you have any more questions."
] | 2022-11-06T01:40:52 | 2022-11-10T23:47:43 | 2022-11-10T23:47:42 | NONE | null | Excuse me, is it possible to run each step on different node?
For example, in this case `cifar-preproc` runs on CPU node but `cifar-train` runs on GPU node.
To save costs, I want to use GPUs only when they are needed to save costs.
https://cloud.google.com/blog/topics/developers-practitioners/scalable-ml-workflows-using-pytorch-kubeflow-pipelines-and-vertex-pipelines?hl=en | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8424 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8424/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8424/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8424/events | https://github.com/kubeflow/pipelines/issues/8424 | 1,436,779,088 | I_kwDOB-71UM5Vo4JQ | 8,424 | [bug] Kubeflow pipeline doesn't pick-up metrics from file /mlpipeline-metrics.json | {
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"Hi @crbl1122, the correct name for the metrics file should be `mlpipeline-ui-metadata.json`. See reference here: https://www.kubeflow.org/docs/components/pipelines/v1/sdk/output-viewer/#v1-sdk-writing-out-metadata-for-the-output-viewers\r\nPlease let us know if you have any questions. Also, if you upgrade to kfp v... | 2022-11-05T02:35:14 | 2022-11-11T12:41:02 | 2022-11-10T23:50:32 | NONE | null | ### 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 1.8.14
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
I've ensured that both the metrics artifact is correctly named: "mlpipeline-metrics" and the file saved in the root of the container: "mlpipeline-metrics.json". Still the Kubeflow pipeline doesn't display the metrics.
I have seen this problem reported by others but seems to be a bug because no solution was documented. I am testing the metrics display in Kubeflow UI for a pipeline running on GCP. I use the reusable component method not python function based components. The component is containerized and pushed to artifact registry and I reference it through YAML specification.
```
def produce_metrics(
mlpipeline_metrics):
accuracy = 0.9
metrics = {
'metrics': [{
'name': 'accuracy-score', #
'numberValue': accuracy, #
'format': "PERCENTAGE", #
}]
}
# save to mlpipeline-metrics.json file in the root
with open('/mlpipeline-metrics.json', 'w') as f:
json.dump(metrics, f)
# save to artifact path
with open(mlpipeline_metrics + '.json', 'w') as f:
json.dump(metrics, f)
def main_fn(arguments):
training_table_bq = arguments.training_table
validation_table_bq = arguments.validation_table
schema_dict = generate_schema(training_table_bq)
target_name = arguments.target_name
gcs_model_path = arguments.gcs_model_path
mlpipeline_metrics = arguments.mlpipeline_metrics
# run train evaluate
gcs_model_path = train_evaluate(training_table_bq, validation_table_bq, schema_dict, target_name, gcs_model_path, mlpipeline_metrics)
return gcs_model_path
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = "train evaluate model")
parser.add_argument("--training_table", type = str, help = 'Name of the input training table')
parser.add_argument("--validation_table", type = str, help = 'Name of the input validation table')
parser.add_argument("--target_name", type = str, help = 'Name of the target variable')
parser.add_argument("--gcs_model_path", type = str, help = 'output directory where the model is saved')
# parser.add_argument('--ui_metadata_output_path',
# type=str,
# default='/mlpipeline-ui-metadata.json',
# help='Local output path for the file containing UI metadata JSON structure.')
parser.add_argument('--mlpipeline_metrics',
type=str,
required = False,
default='/mlpipeline-metrics.json',
help='Local output path for the file containing metrics JSON structure.')
args = parser.parse_args()
```
### 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. -->
### 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 -->
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8420 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8420/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8420/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8420/events | https://github.com/kubeflow/pipelines/issues/8420 | 1,435,040,964 | I_kwDOB-71UM5ViPzE | 8,420 | [sdk] bad generation of components path when buliding with target image in windows | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-11-03T17:39:38 | 2023-08-31T07:42:19 | null | NONE | null | ### Environment
Windows 10 with python 3.9 running in a venv
* KFP SDK version: 1.8.14
* All dependencies version:
kfp 1.8.14
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
Hello there,
Im having some problems regarding the building of python-based components in containers. Im trying to pack a set of components in the same image.
### Steps to reproduce
My folder structure looks like this
```
src/generic_components/
|-- component_metadata/
|--- download_from_azure.yaml
|--- download_from_ftp.yaml
|--- visualize_dataset.yaml
|-- download/
|-- download_from_azure/
|--- download_from_azure_comp.py
|--- test/
|--- download_from_ftp/
|--- download_from_ftp_comp.py
|--- test/
|-- visualization/
|--- visualize_dataset/
|--- visualize_dataset_comp.py
|--- test/
|-- utils/
|--- modules that are used in the _comp files
```
All of them use the same target_image and the same base_image.
So I'm using:
`kfp components build src/generic_components/ --component-filepattern */*/*_comp.py`
Then, both my kfp_config.ini and my dockerfile are generated using windows path instead of linux path (my base_image is based on linux) so I have:
**kfp_config.ini**
```
[Components]
download_from_azure = download\download_from_azure_comp.py
....
```
**dockerfile**
```
# Generated by KFP.
FROM python:3.8
WORKDIR \usr\local\src\kfp\components
COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
RUN pip install --no-cache-dir kfp==1.8.14
COPY . .
```
This ended up creating the docker image which first folder is:
`'/usrlocalsrckfpcomponents/'`
And looking for the component in:
`'./download\download_from_azure\'`
### Expected result
So, I'm bypassing the kfp command. Im generating the component.yaml file by running the script, creating my own kfp_config.ini and my own dockerfile and building/pushing the image myself. This way everything is working, as expected in kubeflow. I can have several components in the same image, maintained in the same project and with shared modules between them.
I expected that kfp components build already generate this with the correct path agnostic from the platform.
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8418 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8418/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8418/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8418/events | https://github.com/kubeflow/pipelines/issues/8418 | 1,434,958,638 | I_kwDOB-71UM5Vh7su | 8,418 | [bug] <Component container doesn't start> | {
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"Hi @crbl1122, can you please share how you define the component YAML? Is `/src/components/program.py` coming from the YAML definition?",
"Hi @chensun I realize that I overlooked the discrepance in path definition with the YAML files. Was in error thinking that the reference to 'program.py' is made in Dockerfile ... | 2022-11-03T16:36:34 | 2022-11-04T04:43:26 | 2022-11-04T04:42:28 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant.
I am testing a yaml specified component in a GCP Kubeflow pipeline. The problem is that the component container doesn't start because is searching the "program.py" test module on a different path than the one specified in Dockerfile as working directory and where the module actually is placed. Please observe that the path reported in the error: " "/src/components/program.py" is different than the one from Dockerfile "/pipelines/component/src".
`the replica workerpool0-0 exited with a non-zero status of 127. Termination reason: ContainerCannotRun.
Termination log message: failed to create shim: OCI runtime create failed: runc create failed: unable to start container process: exec: "/src/components/program.py": stat /src/components/program.py: no such file or directory: unknown
FROM python:3.9
RUN mkdir -p /pipelines/component/src
WORKDIR /pipelines/component/src
COPY program.py /pipelines/component/src/program.py
COPY input.txt /pipelines/component/src/input.txt
ENTRYPOINT ["python", "program.py"]`
-->
* 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 1.8.14
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### 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.
-->
I am testing a yaml specified component in a GCP Kubeflow pipeline. The problem is that the component container doesn't start because is searching the "program.py" test module on a different path than the one specifies in Dockerfile as working directory and where the module actually is placed. Please observe that the path reported in the error: " "/src/components/program.py" is different than the one from Dockerfile "/pipelines/component/src".
`the replica workerpool0-0 exited with a non-zero status of 127. Termination reason: ContainerCannotRun.
Termination log message: failed to create shim: OCI runtime create failed: runc create failed: unable to start container process: exec: "/src/components/program.py": stat /src/components/program.py: no such file or directory: unknown
FROM python:3.9
RUN mkdir -p /pipelines/component/src
WORKDIR /pipelines/component/src
COPY program.py /pipelines/component/src/program.py
COPY input.txt /pipelines/component/src/input.txt
ENTRYPOINT ["python", "program.py"]`
### 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. -->
### 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 -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8417 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8417/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8417/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8417/events | https://github.com/kubeflow/pipelines/issues/8417 | 1,434,942,596 | I_kwDOB-71UM5Vh3yE | 8,417 | [bug] <Kubeflow Component container doesn't start > | {
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<!-- I am testing a container based component specified with YAML file. While launching the pipeline in GCP, the container doesn't start because it cannot find the program.py module. I observe that it is searching program.py module on a different path than the one specified in Dockerfile as working directory and where the module actually is placed. I do not understand why is searching the module on a totally different path?
The error message. Please note that path inside container "/src/components/program.py" from error, is different than the one specified in Dockerfile: " /pipelines/component/src".
`The replica workerpool0-0 exited with a non-zero status of 127. Termination reason: ContainerCannotRun.
Termination log message: failed to create shim: OCI runtime create failed: runc create failed: unable to start container process: exec: "/src/components/program.py": stat /src/components/program.py: no such file or directory: unknown
## Dockerfile
FROM python:3.9
RUN mkdir -p /pipelines/component/src
WORKDIR /pipelines/component/src
COPY program.py /pipelines/component/src/program.py
COPY input.txt /pipelines/component/src/input.txt
ENTRYPOINT ["python", "program.py"]` -->
* 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 1.8.14
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### 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. -->
### 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 -->
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8413 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8413/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8413/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8413/events | https://github.com/kubeflow/pipelines/issues/8413 | 1,433,442,497 | I_kwDOB-71UM5VcJjB | 8,413 | [bug] Compiling a component raises AttributeError: 'NoneType' object has no attribute '_set_metadata' | {
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"u... | closed | false | null | [] | null | [
"solved installing V2 through `pip install kfp --pre`"
] | 2022-11-02T16:28:04 | 2022-11-03T14:39:28 | 2022-11-03T14:39:28 | NONE | null | Following the official documentation on Kubeflow V2 (link below), I'm trying to compile a simple component which just prints a string, but it raises an `AttributeError` which I don't understand. What's going on?
### Environment
Windows 11
Python 3.10.1
kfp 1.8.14
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
```
from kfp import dsl
@dsl.component
def printer():
print('----o----')
from kfp import compiler
cmplr = compiler.Compiler()
cmplr.compile(printer, package_path='printer.yaml')
----o----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\gtu\AppData\Local\Programs\Python\Python310\lib\site-packages\kfp\compiler\compiler.py", line 1175, in compile
self._create_and_write_workflow(
File "C:\Users\gtu\AppData\Local\Programs\Python\Python310\lib\site-packages\kfp\compiler\compiler.py", line 1227, in _create_and_write_workflow
workflow = self._create_workflow(pipeline_func, pipeline_name,
File "C:\Users\gtu\AppData\Local\Programs\Python\Python310\lib\site-packages\kfp\compiler\compiler.py", line 1005, in _create_workflow
pipeline_func(*args_list, **kwargs_dict)
File "C:\Users\gtu\AppData\Local\Programs\Python\Python310\lib\site-packages\kfp\dsl\_component.py", line 118, in _component
container_op._set_metadata(component_meta)
AttributeError: 'NoneType' object has no attribute '_set_metadata'
```
### Expected result
Generation of file `printer.yaml`.
### Materials and reference
https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/#compiling
### Labels
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8411 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8411/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8411/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8411/events | https://github.com/kubeflow/pipelines/issues/8411 | 1,431,932,301 | I_kwDOB-71UM5VWY2N | 8,411 | Pipelines WG Roadmap for KF 1.7 | {
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"cc @jbottum @chensun @james-jwu ",
"/Priority P1",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-11-01T19:00:34 | 2023-08-31T07:42:21 | null | COLLABORATOR | null | This is for tracking the progress for KFP deliverables in Kubeflow 1.7:
- [ ] Enter KFP v2 beta phase
- [ ] Introduce KFP APIv2 package which is a new standard for communicating with KFP server.
- [ ] Complete Sub-DAG visualization on KFPv2 UI.
- [ ] [KFPv2 Documentation](https://www.kubeflow.org/docs/components/pipelines/v2/)
- [ ] [Tentative] Status IR in Backend Run API.
- [ ] SDK
- [ ] Support parallelism setting in ParallelFor
- [ ] Support Pipeline as a component and exit handler.
- [ ] Support [dynamic importer metadata](https://github.com/kubeflow/pipelines/pull/7660)
- [ ] Authoring Pipelines with a list of artifacts.
- [ ] Support for parallel-for fan-in.
- [ ] Multiple Condition fan-in.
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https://api.github.com/repos/kubeflow/pipelines/issues/8408 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8408/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8408/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8408/events | https://github.com/kubeflow/pipelines/issues/8408 | 1,431,708,642 | I_kwDOB-71UM5VViPi | 8,408 | [backend] ML Metadata writer errors with - Received message larger than max | {
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"followers_url": "https://api.githu... | null | [
"Hi @Sharathmk99, I'm not sure if it's a good idea to allow configure the limit, there likely will be other system limitation, for example, at DB storage layer. \r\nCan you help explain a bit your use case, why did you get such large content for artifacts?",
"@chensun I was not facing this issue initially, but st... | 2022-11-01T16:19:50 | 2023-08-31T07:42:23 | null | NONE | null | ### Environment
ML Metadata writes errors with below exception. Looks like we need to change default `max_receive_message_length` from 4194304 to bigger number.
Error:
```
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/ml_metadata/metadata_store/metadata_store.py", line 199, in _call_method
response.CopyFrom(grpc_method(request, timeout=self._grpc_timeout_sec))
File "/usr/local/lib/python3.7/site-packages/grpc/_channel.py", line 946, in __call__
return _end_unary_response_blocking(state, call, False, None)
File "/usr/local/lib/python3.7/site-packages/grpc/_channel.py", line 849, in _end_unary_response_blocking
raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.RESOURCE_EXHAUSTED
details = "Received message larger than max (4765896 vs. 4194304)"
debug_error_string = "{"created":"@1667318328.637859534","description":"Received message larger than max (4765896 vs. 4194304)","file":"src/core/ext/filters/message_size/message_size_filter.cc","file_line":206,"grpc_status":8}"
```
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
On-premises
* KFP version: 1.7.0
<!-- 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 -->
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8406 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8406/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8406/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8406/events | https://github.com/kubeflow/pipelines/issues/8406 | 1,431,618,888 | I_kwDOB-71UM5VVMVI | 8,406 | [feature] Improved User Isolation in Kubeflow Pipelines | {
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"color": "cb03cc",
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... | open | false | null | [] | null | [
"/priority p1",
"I think the there are three main tasks. \r\nFrom here https://github.com/kubeflow/kubeflow/issues/6662 the listed main problems are \r\n\r\n1. \"The pipeline UI allows reading other peoples artifacts. The artifact proxy in the user namespace is insecure and obsolete. In the UI you can just get th... | 2022-11-01T15:25:55 | 2023-07-20T09:23:02 | 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?
Authenticated and Authorized Users should be isolated by namespaces and should not have access to other users artifacts, unless authorized. The solution should be handled in frontend, backend, object storage and sdk.
### What is the use case or pain point?
The current implementation allows users to access other users artifacts, this is a big security risk and a feature that limits enterprise adoption.
### Is there a workaround currently?
Distributions are doing their own workarounds or enterprise customers need to deploy separate clusters for different users, which is unefficient.
This is a Roadmap Item for Kubeflow 1.7 requested by the 1.7 Release Team.
@zijianjoy @juliusvonkohout @StefanoFioravanzo @jbottum @annajung @kimwnasptd
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8399 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8399/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8399/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8399/events | https://github.com/kubeflow/pipelines/issues/8399 | 1,428,577,321 | I_kwDOB-71UM5VJlwp | 8,399 | [sdk] Cannot put metrics | {
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"Hi @r-matsuzaka, sorry about the confusion, but KFP SDK 1.6.3 is not to be paired with KF manifest 1.6. To try out the v2 features, can you upgrade to KFP SDK 2.0.0b6? Thanks!",
"@chensun \r\nHi. Thank you for your answer.\r\nExcuse me but I set up kubeflow by running the following and choose the Docker image fr... | 2022-10-30T03:26:21 | 2022-11-05T07:45:16 | 2022-11-03T22:42:16 | NONE | null | ### Environment
* Ubuntu 20.04.5 LTS
* kubernetes
```
$ kubectl version --short
Client Version: v1.21.14
Server Version: v1.21.14
$ minikube version
minikube version: v1.27.1
$ kustomize version --short
3.2.0
```
* kubeflow/manifests
v1.6-branch
* KFP version:
```
$ pip list | grep kfp
kfp 1.6.3
kfp-pipeline-spec 0.1.16
kfp-server-api 1.6.0
```
### Steps to reproduce
```python
import os
import kfp
from kfp.v2 import dsl
from kfp.v2.dsl import (
component,
Output,
ClassificationMetrics,
Metrics,
)
@component(
packages_to_install=['sklearn'],
base_image='python:3.9'
)
def iris_sgdclassifier(test_samples_fraction: float, metrics: Output[ClassificationMetrics]):
from sklearn import datasets, model_selection
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
iris_dataset = datasets.load_iris()
train_x, test_x, train_y, test_y = model_selection.train_test_split(
iris_dataset['data'], iris_dataset['target'], test_size=test_samples_fraction)
classifier = SGDClassifier()
classifier.fit(train_x, train_y)
predictions = model_selection.cross_val_predict(classifier, train_x, train_y, cv=3)
metrics.log_confusion_matrix(
['Setosa', 'Versicolour', 'Virginica'],
confusion_matrix(train_y, predictions).tolist() # .tolist() to convert np array to list.
)
@dsl.pipeline(
name='metrics-visualization-pipeline')
def metrics_visualization_pipeline():
iris_sgdclassifier_op = iris_sgdclassifier(test_samples_fraction=0.3)
import requests
HOST = "http://12.324.678.910:80" # please change this value
USERNAME = "user@example.com"
PASSWORD = "12341234"
NAMESPACE = "kubeflow-user-example-com"
session = requests.Session()
response = session.get(HOST)
print(response)
print(response.url)
headers = {
"Content-Type": "application/x-www-form-urlencoded",
}
data = {"login": USERNAME, "password": PASSWORD}
session.post(response.url, headers=headers, data=data)
session_cookie = session.cookies.get_dict()["authservice_session"]
client = kfp.Client(
host=f"{HOST}/pipeline",
cookies=f"authservice_session={session_cookie}",
namespace=NAMESPACE,
)
client.create_run_from_pipeline_func(
metrics_visualization_pipeline, arguments={})
```
### Expected result
I should see confusion matrix as described in https://www.kubeflow.org/docs/components/pipelines/v1/sdk/output-viewer/
But I got this error.
### Materials and Reference
- https://www.kubeflow.org/docs/components/pipelines/v1/sdk/output-viewer/
- https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/test/metrics_visualization_v2.py
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8396 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8396/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8396/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8396/events | https://github.com/kubeflow/pipelines/issues/8396 | 1,426,627,478 | I_kwDOB-71UM5VCJuW | 8,396 | [bug] component pull image from docker local registry | {
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"Hi @TranThanh96, I think pulling from local docker registry is out of scope for KFP, but a generic K8s issue. Maybe you can search for k8s tutorials on if this is possible and how to configure it."
] | 2022-10-28T03:58:08 | 2022-11-03T22:44:38 | 2022-11-03T22:44:38 | NONE | null | I am using kubeflow deploy on k3AI.
I have a local docker registry on ip: 1.2.3.4
then I create a component and push into 1.2.3.4/hello-world:v0.0.1
I have a pipeline that uses the component above:
```
name: hello world
description: hello world
metadata:
annotations:
author: ThanhTM
inputs:
- {name: input, type: String, description: 'data'}
implementation:
container:
image: 1.2.3.4/hello-world:v0.0.1
command: [python3, /pipelines/component/src/main.py]
args: [
--input, {inputValue: data_uri_s3}, # input
]
```
but error keep happening:
This step is in Pending state with this message: ImagePullBackOff: Back-off pulling image "1.2.3.4/hello-world:v0.0.1"
Any tutorial to tackle this issue? thank you | {
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"node_id": "MDU6TGFiZWwyMTU3NjM0MjA0"... | open | false | null | [] | null | [
"@amrobbins, can you check to see if you have the same issue in KFP v2? If so, can you please send a reproducible example?",
"Yes, I'll be migrating over to kubeflow 2 and I'll provide an updates. Thanks.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be clo... | 2022-10-27T18:46:45 | 2023-09-01T07:42:13 | 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?
I would like kubeflow to pass op output via a file transparently to the user.
### What is the use case or pain point?
I have a need to pass a list of batches of skus as output from an op, but some stores have enough sku's that this exceeds the maximum number of bytes that can be returned by an op, as shown below:
The reference to the list is then passed to a parallel for so that a pod is launched for each batch of skus.
### Is there a workaround currently?
I am not aware of a workaround. I tried to write the sku's into a file instead of returning a list, however it doesn't seem like I can iterate over the contents of the file using a parallel for, so I don't have a way of dynamically determining the batches of skus and launching ops for each.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"@amrobbins, I suggest using KFP v2. Parallel for loops are represented as subDAGs in v2, which enables the UI to handle them layer-by-layer."
] | 2022-10-26T17:02:52 | 2022-10-27T22:47:12 | 2022-10-27T22:47:11 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
I followed the instructs here: https://www.kubeflow.org/docs/distributions/gke/deploy/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. -->
v1.5.0
### Steps to reproduce
1. Create a pipeline that includes a parallel for loop
2. Increase the number of pods that the parallel for launches until the issue is observed.
The issue appear as in this screenshot
<!--
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? -->
The graph would eventually display. This was the case for me prior to upgrading to v1.5.0.
### 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! -->
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2022-10-25T14:40:49 | 2023-09-02T07:42:21 | null | NONE | null | ### 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: [1.0.4](https://www.github.com/kubeflow/pipelines/commit/b604c6171244cc1cd80bfdc46248eaebf5f985d6)
### 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.
-->
Using the kubeflow API, create a recurring run for any existing pipeline with the job name being blank. In the UI, you will not be able to select this job to modify or delete it.
### Expected result
<!-- What should the correct behavior be? -->
I should be able to click on the row containing the recurring run (job) even if the name is blank.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area frontend
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"@tobiasimbus thank you for your contribution!\r\nWe will keep this in backlog until the next release."
] | 2022-10-25T07:29:32 | 2023-05-04T22:57:08 | null | NONE | null | ### Environment
KFP version:1.8.5
Critical OS CVEs reported by the anchore scanning service: CVE-2022-37434
### Steps to reproduce
`sudo docker run anchore/grype gcr.io/ml-pipeline/frontend:1.8.5 --only-fixed | grep Critical`
### Materials and reference
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8385 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8385/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8385/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8385/events | https://github.com/kubeflow/pipelines/issues/8385 | 1,418,758,270 | I_kwDOB-71UM5UkIh- | 8,385 | [sdk] Containerized Python Component module not found error | {
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"Hi @connor-mccarthy I am trying to follow the docs and I ended up with the same error as #8353 how can I help to close this issue?",
"I can reproduce it with even one just module:\r\n\r\nworking directory:\r\n\r\n```bash\r\n├── main.py\r\n├── poetry.lock\r\n├── poetry.toml\r\n├── pyproject.toml\r\n└── utils.py\r... | 2022-10-21T19:14:59 | 2023-08-16T12:33:02 | null | MEMBER | null | There is a bug when building a containerized Python component that happens (at least) in the case when the longest path of the import graph ending at the component involves >2 modules.
### Environment
KFP SDK 2.0.0-beta.6
### Steps to reproduce
For example:
```python
# component.py
from module_one import one
from kfp import dsl
@dsl.component
def comp(): ...
```
```python
# module_one.py
from module_two import two
one = 1
```
```python
# module_two.py
two = 2
```
Then: `kfp component build .`
You get a `No module named` error.
### Expected result
Should build without an error.
### Materials and Reference
Related: https://github.com/kubeflow/pipelines/issues/8353 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8382 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8382/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8382/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8382/events | https://github.com/kubeflow/pipelines/issues/8382 | 1,418,197,168 | I_kwDOB-71UM5Uh_iw | 8,382 | [feature] Allow users to skip `docker build` in `kfp components build` | {
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"The feature request makes sense to me. In terms of implementation, have you considered adding support for different engines? https://github.com/kubeflow/pipelines/blob/1f5d8ff39759d1b305807939a90f3f58f4befb78/sdk/python/kfp/deprecated/cli/components.py#L72-L76",
"Hi Chen! Thanks for your quick response!\r\n\r\n... | 2022-10-21T11:31:54 | 2022-10-31T10:18:35 | 2022-10-26T15:58:36 | 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. -->
Building containerised components using the Python DSL, and packaging them using `kfp components build` is a really nice user experience - you get the benefits of the Python DSL without some of the drawbacks of "lightweight" components (e.g. having to install dependencies at runtime).
However, there are some scenarios where building container images using Docker is not an option, for example:
- Some organisations do not allow Docker to be installed on Data Scientists' laptops (very common for enterprise users)
- Some CI/CD platforms do not support docker-in-docker, and use other mechanisms (such as kaniko) for building container images
This feature would allow the user to skip the actual container image build, but still perform the other functions in `kfp components build` (generation of the different files). The user would then be responsible for building the container images using their preferred mechanism (e.g. Cloud Build, Kaniko)
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Currently not all users are able to use the `kfp components build` command as they cannot run Docker (locally or in CI/CD)
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Current workarounds:
- Roll your own containerised components without the convenience of the `kfp components build` command
- I suppose you could mock the `docker` library as is done in the tests? But this is quite hacky!
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8381 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8381/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8381/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8381/events | https://github.com/kubeflow/pipelines/issues/8381 | 1,415,375,551 | I_kwDOB-71UM5UXOq_ | 8,381 | [bug] GKE preemption inconsistently treated as WorkflowNodeError and WorkflowNodeFailed | {
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"Hello @pjo256 , looks like it is an Argo question, can you create the issue in their repository and link it here? Thank you so much!",
"Created https://github.com/argoproj/argo-workflows/issues/9880"
] | 2022-10-19T17:55:11 | 2022-10-21T15:19:10 | null | NONE | null | Hello! Unsure if this should be filed with Argo, but we're seeing some inconsistency with error-handling for preemptions. For some context, we run a large number of jobs with preemptible node pools in GKE. We typically don't want to retry application failures, but we always want to retry preemptions. We added a retry policy for `OnError`, and successfully triggered this policy when manually pre-empting nodes (e.g. deleting a node in the node pool).
When looking at Argo workflows in the cluster, this was recorded as a `WorkflowNodeError` with a `pod deleted` error message and triggered our retry policy. During actual pre-emption, we see `WorkflowNodeFailed` - `Pod was terminated due to imminent node shutdown` - this does not trigger our `OnError` retry policy.
Typical application failures show as `WorkflowNodeFailed` and do not trigger our retry policy as expected.
On a related note, it would be useful if we could inspect the workflow status before retrying, or conditionally retry for a subset of failure reasons - not sure if this is already possible.
### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? GCP AI Platform Managed Pipelines on GKE
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.8.5
<!-- 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. -->
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Expected result
Node pre-emption should trigger the `OnError` retry policy.
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
<!-- 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 -->
---
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw... | open | false | null | [] | null | [
"Please bring i18n support to Kubeflow community (For example: https://github.com/kubeflow/kubeflow/issues/6665).\r\n\r\nMy feedback is that it will bring difficulty to maintain the i18n up-to-date in long term. Therefore I suggest making i18n an extension than contributing to the main repos."
] | 2022-10-19T15:55:19 | 2022-10-20T22:51:18 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Include i18n support for Kubeflow Pipelines to allow multilingual support.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Offering kubeflow pipelines in languages other than English will help non-english native users with the kubeflow pipelines UI.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
We host Kubeflow Pipelines at Statistics Canada, we're required to offer a french translation of the frontend, which we use our own [fork](https://github.com/StatCan/kubeflow-pipelines) to implement using i18next.
One major issue is keeping the fork synced with the upstream code base, as we try to keep our hosting of Kubeflow up to date.
A solution which would benefit the upstream project, our team, and other users would be to merge the i18n work to the upstream repo. I've already seen [a PR for i18n ](https://github.com/kubeflow/pipelines/pull/8137) which shows that other users of kubeflow are interested in internationalization of Pipelines.
Our fork's current state is 2.0.0-alpha.3 which should integrate with KF1.6 successfully. We are currently implementing the i18n for that release.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8379 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8379/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8379/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8379/events | https://github.com/kubeflow/pipelines/issues/8379 | 1,414,360,940 | I_kwDOB-71UM5UTW9s | 8,379 | Please provide information on how to generate or get API_Keys to us the pipeline endpoints in the documentation | {
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"Hello @JV-DHL , what do you mean by API_KEY and why do you need it? Closing this issue for now since I don't think it is related to KFP at the moment based on current information.",
"I need the API_KEY to use the endpoints like run a pipeline , archive an experiment , get run details etc. Which can be done using... | 2022-10-19T06:15:46 | 2022-10-21T04:47:17 | 2022-10-20T22:44:55 | NONE | null | I request you to please provide information on how to generate the API_KEY to use the kubeflow pipelines endpoints in the documentation | {
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"Hello @amrobbins , I think your request is very similar to `ParallelFor` functionality, and you can define pipeline run inside a pipeline run in KFPv2. Closing this issue now and feel free to reopen it if it doesn't fulfill your requirement.\r\n\r\nReferences:\r\n1. Docs on ParallelFor: https://www.kubeflow.org/do... | 2022-10-17T21:25:50 | 2023-02-19T18:49:36 | 2022-10-20T22:42:32 | 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?
Responded to all of these below.
### What is the use case or pain point?
Responded to all of these below.
### Is there a workaround currently?
For my use-case I need to create demand forecasts each night for each of the sku's that belong to a few hundred online stores. If I were to try to run this as a single pipeline, it would fail because it would surpass the size limits for the graph, but also it would be unwieldy due to its size.
To work around this problem, my pipeline has 2 parameters named num_shards and selected_shard. I then use use modulus with the hash of the store's name to shard the jobs. This has gotten me by for a while but with a recent acquisition I now have so many stores that I would need to maintain maybe 50 shards.
So my feature request is to build shard support into kubeflow. The way I am thinking about it, I would specify how many shards that a pipeline should be broken into and then kubeflow would create that many pipeline jobs, each one getting passed its shard number. At the top level, a sharded job would look like a normal pipeline run, but when I click on it, there is another level of page that would show maybe just a simple box representing each shard, with status of passed, failed or running. Then I could click on the box to drill down into the shard's graph to inspect the job.
<!-- Without this feature, how do you accomplish your task today? -->
I am considering trying to get 50 shards manually set up, but it will by kinda messy for this job. I expect that I will need to use a set up like this for a number of features so it's hard to imagine that I'll be able to keep scaling this way for other future jobs that I will need to run for the online stores that we support.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8370 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8370/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8370/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8370/events | https://github.com/kubeflow/pipelines/issues/8370 | 1,409,784,569 | I_kwDOB-71UM5UB5r5 | 8,370 | [sdk] Protobuf 4 Support | {
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"/assign @connor-mccarthy ",
"Any progress on this? New packages are starting to depend on protobuf 4, making dependency resolution impossible.",
"We plan to release at least one version of `kfp==2.x.x` that depends on protobuf 3 before bumping to depend on protobuf 4.",
"Any ETA here?",
"bumping this up, a... | 2022-10-14T19:49:47 | 2023-09-06T06:39:21 | null | NONE | null | ### Environment
The latest `kfp` version pins `protobuf < 4.0`:
https://github.com/kubeflow/pipelines/blob/71c8e77d8a583c629e6f83dcd629fee8133f6d2a/sdk/python/requirements.in#L27
What would it take for `kfp` to support `protobuf==4.23`? It looks like Python's protobuf package went from `3.20 -> 4.21` and other packages are now starting to require `protobuf > 4`.
The list of breaking changes is available at https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8366 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8366/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8366/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8366/events | https://github.com/kubeflow/pipelines/issues/8366 | 1,409,705,068 | I_kwDOB-71UM5UBmRs | 8,366 | [backend] e2e test flakiness due to regression on v1 caching | {
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https://github.com/kubeflow/pipelines/blob/65ee01a436758e0f7ca4741e517452c02182ec03/samples/core/recursion/recursion.py#L49-L50
Example run results:
https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/8354/kubeflow-pipeline-e2e-test/1580250814025306112
```
STEP TEMPLATE PODNAME DURATION MESSAGE
● recursive-loop-pipeline-2q672 recursive-loop-pipeline
├─✔ flip-coin flip-coin recursive-loop-pipeline-2q672-2911149689 7s
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1450500999 3s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-4208770976 6s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-906617105 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3449589790 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3599713631 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2157693544 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1678845337 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3451272790 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3682909207 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3689557328 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3638286081 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1308976046 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2740954799 3s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2458413912 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-4041224585 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2080960678 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2436650855 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-4253650176 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3661157489 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2305072254 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2227129919 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2711823560 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2085747065 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2345187766 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3268679415 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3551208624 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3411284065 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2249539982 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1748291983 4s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1446557880 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2369874281 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2625115526 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2721038535 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3615791712 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-776749137 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1875536862 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1362749599 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2914464296 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3165717209 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1084307990 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2109524055 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1156597520 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2903817793 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3088352366 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3461388015 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1389615128 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1712861385 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1411227238 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1239254695 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3752137408 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-4021074865 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-534689086 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-579256959 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-4212266120 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3603108025 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-2620779126 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-1910118711 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-171526256 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2859045793 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3404074830 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-920673231 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-814696056 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2741399721 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-4138003782 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3921474311 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-3899151136 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3092523921 2s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-4064315038 2s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-3852097759 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1269973480 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-4275018777 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1842151638 1s
└─● condition-2 condition-2
└─● graph-flip-component-1 graph-flip-component-1
├─✔ print print recursive-loop-pipeline-2q672-2946413463 1s
├─✔ flip-coin-2 flip-coin-2 recursive-loop-pipeline-2q672-1685612752 2s
└─● condition-2 condition-2
....
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"How do you get the endpoiint for V2??",
"This request was based on the v1 api. I just started following the proposal for the v2 api and am curious about whether this feature will be included in v2. I know the proposal contains a plan to drop resource references, but will filtering by job id still be possible?"
] | 2022-10-14T18:11:11 | 2022-11-15T04:21:38 | 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?
Add the ability to select runs by job ID in multi-user mode when calling the get runs api endpoint
### What is the use case or pain point?
It is very difficult to gather all runs associated with a single job without this filter
### Is there a workaround currently?
The runs api has to be called without this filter and then the results have to be parsed one by one to determine if they are associated with the given job.
I would be happy to work on contributing this feature if the community thinks it would be helpful and aligned with the overall design path for kubeflow pipelines
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8358 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8358/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8358/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8358/events | https://github.com/kubeflow/pipelines/issues/8358 | 1,407,527,664 | I_kwDOB-71UM5T5Srw | 8,358 | [sdk] Unable to retrieve runs / experiments / recurring jobs in pipeline component (500 error) | {
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"HI @Lejboelle!\r\nIt seems that you might have wrong client initialization. Please, follow [these instructions](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/).",
"I fixed the issue - it wasn't the client initialization since the defaults point to the correct host and credentials.\r\nI di... | 2022-10-13T10:11:55 | 2022-10-14T10:06:30 | 2022-10-14T10:06:29 | NONE | null | ### Environment
* KFP version:
2.0.0-alpha.5 (Kubeflow 1.6.1)
* KFP SDK version:
1.8.14
### Steps to reproduce
My main goal is to retrieve a job name or job id in a recurring run. I tried the PIPELINE_JOB_ID_PLACEHOLDER from the v2 dsl, but that didn't work. Then I tried creating a component that uses the KFP SDK to retrieve the job id, however, it is not possible to even retrieve a list of runs, even though, the token is mounted.
`from kfp import dsl, compiler
from kfp.components import func_to_container_op
import kubernetes.client as k8s
`
`def _print(namespace: str):
from kfp import Client
client = Client()
try:
print("runs:")
print(client.list_runs(namespace=namespace))
print()
except Exception as e:
print("failed to get runs")
print(e)
try:
print("pipelines")
print(client.list_pipelines())
except Exception:
print("failed to get pipelines")
try:
print("experiments")
print(client.list_experiments(namespace=namespace))
print()
except Exception as e:
print("failed to get experiments")
print(e)
print_op = func_to_container_op(_print, packages_to_install=["kfp==1.8.14"])
@dsl.pipeline(
name="test-pipeline",
)
def test_pipeline():
vol_sat = k8s.V1ServiceAccountTokenProjection(audience="pipeline.kubeflow.org", expiration_seconds=7200, path="token")
vol_proj = k8s.V1ProjectedVolumeSource(sources=[k8s.V1VolumeProjection(service_account_token=vol_sat)])
vol = k8s.V1Volume(name="volume-kf-pipeline-token", projected=vol_proj)
task = print_op(namespace="{{workflow.namespace}}")
task.add_pvolumes({"/var/run/secrets/kubeflow/pipelines": vol})
`
### Expected result
The 'list_pipelines' works as expected:
`{'next_page_token': None, 'pipelines': [...], 'total_size': 3}`
But the other return:
`(500)
Reason: Internal Server Error
HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'date': 'Thu, 13 Oct 2022 09:52:55 GMT', 'x-envoy-upstream-service-time': '4', 'server': 'istio-envoy', 'x-envoy-decorator-operation': 'ml-pipeline.kubeflow.svc.cluster.local:8888/*', '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., Authentication failure: Unauthenticated: Review.Status.Authenticated is false: Failed to authenticate token review]\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:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).canAccessRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:400\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:192\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581\nFailed to authorize with namespace resource reference.\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:194\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581","code":13,"message":"Internal error: [Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header., Authentication failure: Unauthenticated: Review.Status.Authenticated is false: Failed to authenticate token review]\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:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).canAccessRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:400\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:192\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581\nFailed 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reference.\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:194\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal error: [Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header., Authentication failure: Unauthenticated: Review.Status.Authenticated is false: Failed to authenticate token review]\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:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).canAccessRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:400\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:192\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581\nFailed 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references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).canAccessRun\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:400\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:192\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581\nFailed to authorize with namespace resource reference.\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:287\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).ListRuns\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:194\ngithub.com/kubeflow/pipelines/backend/api/go_client._RunService_ListRuns_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2198\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_ListRuns_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2200\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1581"}]}`
Also tried manually loading the credentials using kfp.auth.ServiceAccountTokenVolumeCredentials(path=None) and input those to the client constructor.
When running a notebook using an access-ml-pipeline poddefault, there are no issues, and I'm able to list runs, experiments, etc.
Not sure if this is a bug or a design choice.
Is there no way to retrieve the job id / job name in a recurring run?
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8356 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8356/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8356/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8356/events | https://github.com/kubeflow/pipelines/issues/8356 | 1,406,862,021 | I_kwDOB-71UM5T2wLF | 8,356 | [frontend] No metrics shown in Run Output tab | {
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"The metrics is not shown along side each run in the **Experiments** tab either:\r\n\r\n\r\n<img width=\"1440\" alt=\"Screen Shot 2022-10-12 at 3 33 16 PM\" src=\"https://user-images.githubusercontent.com/22306485/195460346-01cb5f5c-db9a-4395-9181-b0d66e96c825.png\">\r\n",
"This has been fixed in the recent patch... | 2022-10-12T22:08:23 | 2023-06-19T18:14:23 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
I installed it with the full kubeflow deployment. Kubeflow 1.6, installed with [juju](https://charmed-kubeflow.io/docs/install)
* KFP version: **build version dev_local** (this shows on bottom of KFP UI left sidenav)
### Steps to reproduce
Running this minimal pipeline that just saves a metrics
```
from kfp.components import OutputPath, create_component_from_func
import kfp.dsl as dsl
import kfp
@dsl.pipeline(
name='kubeflow-metric-demo')
def metric_pipeline():
produce_metrics_op = create_component_from_func(
produce_metrics,
base_image='python:3.7',
packages_to_install=[],
output_component_file='component.yaml',
)
produce_metrics_op()
def produce_metrics(
mlpipeline_metrics_path: OutputPath('Metrics')
):
import json
metrics = {
'metrics': [{
'name': 'accuracy',
'numberValue': 0.9,
'format': "RAW"
}]
}
with open(mlpipeline_metrics_path, 'w') as f:
json.dump(metrics, f)
print("metrics path")
print(mlpipeline_metrics_path)
if __name__ == "__main__":
kfp.compiler.Compiler().compile(metric_pipeline, 'metric_pipeline.yaml')
```
### Expected result
I expect the metrics to be shown in the "Run output" tab in the UI. However, nothing is there as you can see in the image.
<img width="1192" alt="Screen Shot 2022-10-12 at 3 03 14 PM" src="https://user-images.githubusercontent.com/22306485/195456680-858247f8-9f61-4283-a85d-964c71831de0.png">
However, the metrics did show up at the "output artifacts" section of the component
<img width="1189" alt="Screen Shot 2022-10-12 at 3 07 34 PM" src="https://user-images.githubusercontent.com/22306485/195457044-ea0def8b-0d41-48b5-9734-8927e70b5faf.png">
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. | {
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"Hi @casassg ! Thank you for bringing this up.\r\nKFP v1 backend does not support retry op. This is on our roadmap in KFP v2, but it is not available yet.",
"@gkcalat note that RetryRun is part of KFP v1. Its an already existing feature, it just breaks under the above scenario"
] | 2022-10-12T20:04:56 | 2022-10-31T21:37:51 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow 1.4 on GKE. Custom Cluster
* KFP version:
1.7.0
* KFP SDK version:
1.8.11
### Steps to reproduce
- Rough code:
```python
import kfp
from kfp.components import create_component_from_func
@create_component_from_func
def wait_for_n_seconds(seconds: int):
import time
time.sleep(seconds)
raise Exception("Finished Sleeping")
@kfp.dsl.pipeline()
def my_waiting_pipeline():
wait_for_n_seconds(60)
c = kfp.Client()
conf = kfp.dsl.PipelineConf().set_ttl_seconds_after_finished(60)
result = c.create_run_from_pipeline_func(pipeline_func=my_waiting_pipeline, pipeline_conf=conf, arguments={})
result.wait_for_run_completion()
import time
time.sleep(20)
c.runs.retry_run(result.run_id)
result.wait_for_run_completion()
```
- Running this code results in pipeline being stuck in running state forever
### Expected result
- On retry it should attempt again to execute pipeline and then it will fail
### Materials and Reference
Important logs from workflow-controller pod:
```
workflow-controller
time="2022-10-12T19:51:04.079Z" level=info msg="Queueing Failed workflow gcasassaez/my-waiting-pipeline-v96t7 for delete in 1m0s"
2022-10-12 13:51:05.139 MDT
workflow-controller
time="2022-10-12T19:51:05.139Z" level=info msg="Queueing Failed workflow gcasassaez/my-waiting-pipeline-v96t7 for delete in 59s"
2022-10-12 13:51:05.140 MDT
workflow-controller
time="2022-10-12T19:51:05.139Z" level=info msg="Queueing Failed workflow gcasassaez/my-waiting-pipeline-v96t7 for delete in 59s"
2022-10-12 13:51:33.100 MDT
workflow-controller
time="2022-10-12T19:51:33.100Z" level=info msg="Processing workflow" namespace=gcasassaez workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:33.101 MDT
workflow-controller
time="2022-10-12T19:51:33.101Z" level=info msg="All of node my-waiting-pipeline-v96t7.wait-for-n-seconds dependencies [] completed" namespace=gcasassaez workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:33.102 MDT
workflow-controller
time="2022-10-12T19:51:33.102Z" level=info msg="Pod node my-waiting-pipeline-v96t7-2776729111 initialized Pending" namespace=gcasassaez workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:33.532 MDT
workflow-controller
time="2022-10-12T19:51:33.532Z" level=info msg="Created pod: my-waiting-pipeline-v96t7.wait-for-n-seconds (my-waiting-pipeline-v96t7-2776729111)" namespace=gcasassaez workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:33.549 MDT
workflow-controller
time="2022-10-12T19:51:33.549Z" level=info msg="Workflow update successful" namespace=gcasassaez phase=Running resourceVersion=2355618548 workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:43.075 MDT
workflow-controller
time="2022-10-12T19:51:43.075Z" level=info msg="Processing workflow" namespace=gcasassaez workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:43.076 MDT
workflow-controller
time="2022-10-12T19:51:43.076Z" level=info msg="Updating node my-waiting-pipeline-v96t7-2776729111 status Pending -> Running" namespace=gcasassaez workflow=my-waiting-pipeline-v96t7
2022-10-12 13:51:43.093 MDT
workflow-controller
time="2022-10-12T19:51:43.093Z" level=info msg="Workflow update successful" namespace=gcasassaez phase=Running resourceVersion=2355619126 workflow=my-waiting-pipeline-v96t7
2022-10-12 13:52:05.000 MDT
workflow-controller
time="2022-10-12T19:52:05.000Z" level=info msg="Deleting TTL expired workflow 'gcasassaez/my-waiting-pipeline-v96t7'"
```
Argo Workflows GC controller which does not check if the updated workflow should be GCed or not: https://github.com/argoproj/argo-workflows/blob/39b7f91392c4c0a0a7c167b5ad7c89b1382df68d/workflow/gccontroller/gc_controller.go#L201
Not sure if there is a way to clear the queue on Argo or wether this is something we can do from RetryRun which avoids this type of issues.
---
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"**Update**\r\n\r\nI upgraded to `kfp.__version__ = 2.0.0b5` and it fixe the error of \"ERROR: No such command 'component'.\" and it matches the KFP API SDK.\r\n\r\nI still see the behavior of `ERROR: No KFP components found in file ...` for any module in the folder that does not contain a component definition. I ... | 2022-10-12T14:46:48 | 2022-10-21T19:16:05 | 2022-10-13T21:04:04 | NONE | null | `kfp.__version__ = 1.8.14`
Trying the instructions at [2. Containerized Python components](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#2-containerized-python-components):
- command `kfp component build ...` results in error: "Error: No such command 'component'."
- KFP API SDK Reference for [kfp compontent build](https://kubeflow-pipelines.readthedocs.io/en/master/source/cli.html#kfp-component-build)
- changing `kfp component` to `kfp components` seem to get pass the command issue
- using `kfp components build` throw errors for `*.py` files that do not contain components: `ERROR: No KFP components found in file ...`
- the other `*.py` file are modules imported by the file.py with component definitions using relative imports. This seems to be
exactly how the example is laid out [doc example](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#2-containerized-python-components)
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* All dependencies version:
kfp 1.8.11
kfp-pipeline-spec 0.1.13
kfp-server-api 1.7.1
### Steps to reproduce
add the same name to dsl.Condition in sample code https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/DSL%20-%20Control%20structures/DSL%20-%20Control%20structures.py
### Expected result
successfully compiled
### Materials and Reference
sample code:
```python
#!/usr/bin/env python3
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# %% [markdown]
# # DSL control structures tutorial
# Shows how to use conditional execution and exit handlers.
# %%
from typing import NamedTuple
import kfp
from kfp import dsl
from kfp.components import func_to_container_op, InputPath, OutputPath
# %% [markdown]
# ## Conditional execution
# You can use the `with dsl.Condition(task1.outputs["output_name"] = "value"):` context to execute parts of the pipeline conditionally
# %%
@func_to_container_op
def get_random_int_op(minimum: int, maximum: int) -> int:
"""Generate a random number between minimum and maximum (inclusive)."""
import random
result = random.randint(minimum, maximum)
print(result)
return result
@func_to_container_op
def flip_coin_op() -> str:
"""Flip a coin and output heads or tails randomly."""
import random
result = random.choice(['heads', 'tails'])
print(result)
return result
@func_to_container_op
def print_op(message: str):
"""Print a message."""
print(message)
@dsl.pipeline(
name='Conditional execution pipeline',
description='Shows how to use dsl.Condition().'
)
def flipcoin_pipeline():
flip = flip_coin_op()
with dsl.Condition(flip.output == 'heads'):
random_num_head = get_random_int_op(0, 9)
with dsl.Condition(random_num_head.output > 5):
print_op('heads and %s > 5!' % random_num_head.output)
with dsl.Condition(random_num_head.output <= 5):
print_op('heads and %s <= 5!' % random_num_head.output)
with dsl.Condition(flip.output == 'tails'):
random_num_tail = get_random_int_op(10, 19)
with dsl.Condition(random_num_tail.output > 15):
print_op('tails and %s > 15!' % random_num_tail.output)
with dsl.Condition(random_num_tail.output <= 15):
print_op('tails and %s <= 15!' % random_num_tail.output)
# Submit the pipeline for execution:
#kfp.Client(host=kfp_endpoint).create_run_from_pipeline_func(flipcoin_pipeline, arguments={})
# %% [markdown]
# ## Exit handlers
# You can use `with dsl.ExitHandler(exit_task):` context to execute a task when the rest of the pipeline finishes (succeeds or fails)
# %%
@func_to_container_op
def fail_op(message):
"""Fails."""
import sys
print(message)
sys.exit(1)
@dsl.pipeline(
name='Conditional execution pipeline with exit handler',
description='Shows how to use dsl.Condition() and dsl.ExitHandler().'
)
def flipcoin_exit_pipeline():
exit_task = print_op('Exit handler has worked!')
with dsl.ExitHandler(exit_task):
flip = flip_coin_op()
with dsl.Condition(flip.output == 'heads', name='test'):
random_num_head = get_random_int_op(0, 9)
with dsl.Condition(random_num_head.output > 5, name='test'):
print_op('heads and %s > 5!' % random_num_head.output)
with dsl.Condition(random_num_head.output <= 5, name='test'):
print_op('heads and %s <= 5!' % random_num_head.output)
with dsl.Condition(flip.output == 'tails', name='test'):
random_num_tail = get_random_int_op(10, 19)
with dsl.Condition(random_num_tail.output > 15, name='test'):
print_op('tails and %s > 15!' % random_num_tail.output)
with dsl.Condition(random_num_tail.output <= 15, name='test'):
print_op('tails and %s <= 15!' % random_num_tail.output)
with dsl.Condition(flip.output == 'tails', name='test'):
fail_op(message="Failing the run to demonstrate that exit handler still gets executed.")
if __name__ == '__main__':
# Compiling the pipeline
kfp.compiler.Compiler().compile(flipcoin_exit_pipeline, __file__ + '.yaml')
```
Error message:
```
Traceback (most recent call last):
File "/Users/tobychen/Desktop/kipeline/kfp_generator/report_bug.py", line 123, in <module>
kfp.compiler.Compiler().compile(flipcoin_exit_pipeline, __file__ + '.yaml')
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1180, in compile
self._create_and_write_workflow(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1232, in _create_and_write_workflow
workflow = self._create_workflow(pipeline_func, pipeline_name,
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1063, in _create_workflow
workflow = self._create_pipeline_workflow(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 791, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 726, in _create_dag_templates
inputs, outputs = self._get_inputs_outputs(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 295, in _get_inputs_outputs
self._get_uncommon_ancestors(op_groups, opsgroup_groups, upstream_op, op)
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 190, in _get_uncommon_ancestors
raise ValueError(op2.name + ' does not exist.')
ValueError: print-op-2 does not exist.
```
In some other scenarios using the same name on multiple condition ops give KeyError:
```
Traceback (most recent call last):
File "/Users/tobychen/Desktop/kipeline/kfp_generator/algorithm_readiness/easy_generator.py", line 656, in <module>
kfp.compiler.Compiler().compile(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1180, in compile
self._create_and_write_workflow(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1232, in _create_and_write_workflow
workflow = self._create_workflow(pipeline_func, pipeline_name,
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1063, in _create_workflow
workflow = self._create_pipeline_workflow(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 791, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 726, in _create_dag_templates
inputs, outputs = self._get_inputs_outputs(
File "/Users/tobychen/opt/anaconda3/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 316, in _get_inputs_outputs
for group_name in op_groups[op.name][::-1]:
KeyError: 'create-pv-3'
```
---
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8350 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8350/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8350/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8350/events | https://github.com/kubeflow/pipelines/issues/8350 | 1,404,994,998 | I_kwDOB-71UM5TvoW2 | 8,350 | Pipeline running on GKE, Is there any way to access GPU from local machine? | {
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} | [] | open | false | null | [] | null | [
"Hi @MLHafizur!\r\nCould you please clarify what means \"local machine\" and what purposes would GPU access serve?"
] | 2022-10-11T17:31:43 | 2022-10-13T22:48:14 | null | NONE | null | Pipeline running on GKE, Is there any way to access GPU from local machine? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8348 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8348/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8348/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8348/events | https://github.com/kubeflow/pipelines/issues/8348 | 1,404,352,292 | I_kwDOB-71UM5TtLck | 8,348 | [bug] visualization keep showing nothing ? | {
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"I have this problem, too.",
"@TranThanh96 @Kokkini Please take a look at the workaround on this issue: https://github.com/awslabs/kubeflow-manifests/issues/117",
"> @TranThanh96 @Kokkini Please take a look at the workaround on this issue: [awslabs/kubeflow-manifests#117](https://github.com/awslabs/kubeflow-man... | 2022-10-11T09:52:49 | 2022-10-13T05:11:48 | 2022-10-13T05:11:48 | NONE | null | ### Environment
Kubeflow 1.6 on AWS
### Steps to reproduce
my code:
```
import argparse
import json
from pathlib import Path
from collections import namedtuple
def main(args):
metadata = {
'outputs' : [{
'type': 'web-app',
'storage': 'inline',
'source': '<h1>Hello, World!</h1>',
}]
}
print('>> meta data: ', metadata)
output = namedtuple('HelloWorldOutput', ['echo', 'mlpipeline_ui_metadata'])
# return output(name + ': Hello, Wolrd!', json.dumps(metadata))
Path(args.mlpipeline_ui_metadata_path).parent.mkdir(parents=True, exist_ok=True)
with open(args.mlpipeline_ui_metadata_path, 'w') as metadata_file:
json.dump(metadata, metadata_file)
return
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Argparse for com 1')
parser.add_argument('--mlpipeline_ui_metadata_path', type=str, help='mlpipeline_ui_metadata_path, log output ')
args = parser.parse_args()
main(args)
```
my component:
```
name: download data
description: download data from s3 bucket
metadata:
annotations:
author: ThanhTM
outputs:
- {name: mlpipeline_ui_metadata_path, type: String, description: 'mlpipeline_ui_metadata_path visualize'}
implementation:
container:
image: 80************03.dkr.ecr.us-east-1.amazonaws.com/cnndha@sha256:dc54c86e43ac49d3a543c3c0df725c715abfa4e06c928d449537c3454eb25de9
command: [python3, /pipelines/component/src/main.py]
args: [
--mlpipeline_ui_metadata_path, {outputPath: mlpipeline_ui_metadata_path}
]
```
my code create pipeline:
```
visual_component = kfp.components.load_component_from_file('component.yaml')
@kfp.dsl.pipeline(
name="demo pipeline",
description="hi"
)
def my_pipeline(
):
download_data_step = visual_component()
arguments = {}
client.create_run_from_pipeline_func(my_pipeline, arguments=arguments)
```
and my result:
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https://api.github.com/repos/kubeflow/pipelines/issues/8344 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8344/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8344/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8344/events | https://github.com/kubeflow/pipelines/issues/8344 | 1,402,996,790 | I_kwDOB-71UM5ToAg2 | 8,344 | [ScheduledWorkflow] Recurring Runs still scheduled disabled ScheduledWorkflow | {
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] | open | false | null | [] | null | [] | 2022-10-10T11:09:47 | 2022-10-10T11:09:47 | null | NONE | null | ### Environment
I deploy a kubeflow 1.5 system, and when I using "Recurring Runs" for my etl work in kubeflow.
and "Recurring Runs" runs in the following timezone "0 30 1 ? * 1-5"
I disabled a "Recurring Runs" 7 days ago. when I enabled the "Recurring Runs" today.
I suppose in the passed 7 days, the "Recurring Runs" should not be rescheduled.
the system also run the disabled timing "Recurring Runs"
* How do you deploy Kubeflow Pipelines (KFP)?
kubeflow 1.5
cache-deployer:1.8.1
metadata-envoy:1.8.1
metadata-writer:1.8.1
api-server:1.8.1
frontend:1.8.1
persistenceagent:1.8.1
scheduledworkflow:1.8.1
viewer-crd-controller:1.8.1
visualization-server:1.8.1
ml_metadata_store_server:1.5.0
argoexec:v3.2.3-license-compliance
workflow-controller:v3.2.3-license-compliance
* KFP version:
1.8.1
* KFP SDK version:
1.8.12
### Steps to reproduce
set a "Recurring Runs", set to run it in cron format for every minute. then disable it.
after a few minutes later, enable it.
then you will find some runs in the disabled time will still run.
### Expected result
some runs in the disabled time not run.
### Materials and reference
createdAt: "2022-10-10T05:16:41Z"
finishedAt: "2022-10-10T05:38:03Z"
index: 4
name: cplf2m4p5l-4-1993319229
namespace: xisc01
phase: Succeeded
**scheduledAt: "2022-10-04T01:30:00Z"**
**startedAt: "2022-10-10T05:16:41Z"**
uid: 92f4bf3c-fd75-40a7-be7b-b627a704d650
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8339 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8339/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8339/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8339/events | https://github.com/kubeflow/pipelines/issues/8339 | 1,400,456,760 | I_kwDOB-71UM5TeUY4 | 8,339 | [sdk] launcher component is not up to date | {
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"followers_url": "https://api.githu... | null | [] | 2022-10-07T00:22:06 | 2022-10-13T22:43:08 | null | NONE | null | There has been PRs to fix the delete function in launch_tfjob but the image has not been updated in the component
This PR https://github.com/kubeflow/pipelines/issues/7984 fixes the launch_crd.py, but the component here:
https://github.com/kubeflow/pipelines/blob/master/components/kubeflow/launcher/component.yaml
references the image `nikenano/launchernew:latest`
This image is over 2 years old and still has the bug: https://hub.docker.com/r/nikenano/launchernew/tags
From the repo, the build_image.sh script reference the local image as `ml-pipeline-kubeflow-tfjob`, and the corresponding image `gcr.io/ml-pipeline/ml-pipeline-kubeflow-tfjob:1.8.5` does not work either; it does not have the code packaged installed under `/ml` properly, it has `/ml/src/launch_tfjob.py` and `/ml/common/launch_crd.py`
When running it I get:
```
docker run -it gcr.io/ml-pipeline/ml-pipeline-kubeflow-tfjob:1.8.5
python: can't open file '/ml/launch_tfjob.py': [Errno 2] No such file or directory
```
running the launch_tfjob.py from the /ml/src/ dir does not work either because the launch_crd is not in the right folder structure.
```bash
docker run -it --entrypoint /usr/local/bin/python gcr.io/ml-pipeline/ml-pipeline-kubeflow-tfjob:1.8.5 /ml/src/launch_tfjob.py
Traceback (most recent call last):
File "/ml/src/launch_tfjob.py", line 22, in <module>
import launch_crd
ModuleNotFoundError: No module named 'launch_crd'
```
The image needs to be rebuilt and pushed, and the component.yaml needs to be updated with the latest image.
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8338 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8338/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8338/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8338/events | https://github.com/kubeflow/pipelines/issues/8338 | 1,400,252,927 | I_kwDOB-71UM5Tdin_ | 8,338 | [backend] Silent failure for Rest API connection issue | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | open | false | null | [] | null | [] | 2022-10-06T20:20:53 | 2022-10-06T22:48:14 | null | NONE | null | This is not a bug _per se_, but falls into the category of improvements. The current setup of the [Rest API](https://github.com/kubeflow/pipelines/blob/master/backend/api/python_http_client/kfp_server_api/rest.py)'s RestClientObject has a method called `request` that does not return an error in the case of a connection error.
The only instance where it handles the error is SSLError. However, there are a number of additional connection errors that should throw errors instead of providing empty results (which can be misinterpreted by the end user.)
Additional errors which can be handled are listed here: https://urllib3.readthedocs.io/en/stable/reference/urllib3.exceptions.html#urllib3.exceptions.HTTPError
This became particularly evident when I tried to follow [these instructions](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/#standalone-kubeflow-pipelines-subfrom-outside-clustersub) to access Kubeflow Pipelines from outside my AWS cluster. In that case, running the following failed to return an error, even though it did not receive a result via GET from the server:
```
client = kfp.Client(host=f"{KUBEFLOW_ENDPOINT}/pipeline", cookies=auth_session["session_cookie"])
print(client.list_experiments())
```
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
N/A
* KFP version:
1.3, but current master branch contains this issue
* KFP SDK version:
N/A
### Steps to reproduce
From off-server on AWS:
```
import kfp
KUBEFLOW_ENDPOINT = "http://localhost:8080"
KUBEFLOW_USERNAME = "user@example.com"
KUBEFLOW_PASSWORD = "12341234"
auth_session = get_istio_auth_session(
url=KUBEFLOW_ENDPOINT,
username=KUBEFLOW_USERNAME,
password=KUBEFLOW_PASSWORD
)
client = kfp.Client(host=f"{KUBEFLOW_ENDPOINT}/pipeline", cookies=auth_session["session_cookie"])
print(client.list_experiments())
```
### Expected result
Either a list of experiments on the server, or an error message stating the reason it failed to connect.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8332 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8332/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8332/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8332/events | https://github.com/kubeflow/pipelines/issues/8332 | 1,398,070,538 | I_kwDOB-71UM5TVN0K | 8,332 | ml-pipeline unable to connect to mysql in kubefloq 1.6 | {
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"Hi @psheorangithub!\r\n\r\nCould you please clarify where and how did you deploy Kubeflow 1.6? I see some placeholders in the yaml manifests. Were they replaced before applying?",
"@psheorangithub Did you resolve the issue? it seems I have the same ",
"@gkcalat I have the same issue, when I change minio to use... | 2022-10-05T16:33:04 | 2023-02-04T00:54:54 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Deployed pipelines as part of kubeflow 1.6
* KFP version:
2.0.0-alpha.3
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 -->
### Steps to reproduce
Deploy kubeflow manifest 1.6
### Expected result
ml-pipeline pod should come up
### Materials and reference
Below is ml-pipeline anc mysql deployment manifest.
ml-pipeline:
---------------
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: ml-pipeline
app.kubernetes.io/component: ml-pipeline
app.kubernetes.io/name: kubeflow-pipelines
application-crd-id: kubeflow-pipelines
{{- toYaml .Values.labels | nindent 4 }}
name: ml-pipeline
namespace: kubeflow
annotations:
{{- toYaml .Values.annotations | nindent 4 }}
spec:
selector:
matchLabels:
app: ml-pipeline
app.kubernetes.io/component: ml-pipeline
app.kubernetes.io/name: kubeflow-pipelines
application-crd-id: kubeflow-pipelines
template:
metadata:
annotations:
cluster-autoscaler.kubernetes.io/safe-to-evict: 'true'
{{- toYaml .Values.annotations | nindent 8 }}
labels:
app: ml-pipeline
app.kubernetes.io/component: ml-pipeline
app.kubernetes.io/name: kubeflow-pipelines
application-crd-id: kubeflow-pipelines
{{- toYaml .Values.labels | nindent 8 }}
spec:
containers:
- env:
- name: KUBEFLOW_USERID_HEADER
value: kubeflow-userid
- name: KUBEFLOW_USERID_PREFIX
value: ''
- name: AUTO_UPDATE_PIPELINE_DEFAULT_VERSION
valueFrom:
configMapKeyRef:
key: autoUpdatePipelineDefaultVersion
name: pipeline-install-config
- name: POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
- name: OBJECTSTORECONFIG_SECURE
value: "true"
- name: MINIO_SERVICE_SERVICE_HOST
value: '{{ .Values.s3.host }}'
- name: MINIO_SERVICE_SERVICE_PORT
value: '{{ .Values.s3.port | quote }}'
- name: OBJECTSTORECONFIG_BUCKETNAME
valueFrom:
configMapKeyRef:
key: bucketName
name: pipeline-install-config
- name: DBCONFIG_USER
valueFrom:
secretKeyRef:
key: username
name: mysql-secret
- name: DBCONFIG_PASSWORD
valueFrom:
secretKeyRef:
key: password
name: mysql-secret
- name: DBCONFIG_DBNAME
valueFrom:
configMapKeyRef:
key: pipelineDb
name: pipeline-install-config
- name: DBCONFIG_HOST
valueFrom:
configMapKeyRef:
key: dbHost
name: pipeline-install-config
- name: DBCONFIG_PORT
valueFrom:
configMapKeyRef:
key: dbPort
name: pipeline-install-config
- name: DBCONFIG_CONMAXLIFETIME
valueFrom:
configMapKeyRef:
key: ConMaxLifeTime
name: pipeline-install-config
- name: OBJECTSTORECONFIG_ACCESSKEY
valueFrom:
secretKeyRef:
key: accesskey
name: mlpipeline-ceph-s3-artifact
- name: OBJECTSTORECONFIG_SECRETACCESSKEY
valueFrom:
secretKeyRef:
key: secretkey
name: mlpipeline-ceph-s3-artifact
envFrom:
- configMapRef:
name: pipeline-api-server-config-f4t72426kt
image: gcr.io/ml-pipeline/api-server:2.0.0-alpha.3
imagePullPolicy: IfNotPresent
livenessProbe:
exec:
command:
- wget
- -q
- -S
- -O
- '-'
- http://localhost:8888/apis/v1beta1/healthz
initialDelaySeconds: 3
periodSeconds: 5
timeoutSeconds: 2
name: ml-pipeline-api-server
ports:
- containerPort: 8888
name: http
- containerPort: 8887
name: grpc
readinessProbe:
exec:
command:
- wget
- -q
- -S
- -O
- '-'
- http://localhost:8888/apis/v1beta1/healthz
initialDelaySeconds: 3
periodSeconds: 5
timeoutSeconds: 2
resources:
requests:
cpu: 250m
memory: 500Mi
startupProbe:
exec:
command:
- wget
- -q
- -S
- -O
- '-'
- http://localhost:8888/apis/v1beta1/healthz
failureThreshold: 12
periodSeconds: 5
timeoutSeconds: 2
volumeMounts:
- name: ca-bundles
mountPath: /etc/ssl/certs/ca-certificates.crt
subPath: ca-certificates.crt
serviceAccountName: ml-pipeline
volumes:
- name: ca-bundles
configMap:
name: ca-bundles
defaultMode: 420
mysql:
--------------
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: mysql
application-crd-id: kubeflow-pipelines
name: mysql
namespace: kubeflow
spec:
selector:
matchLabels:
app: mysql
application-crd-id: kubeflow-pipelines
strategy:
type: Recreate
template:
metadata:
labels:
app: mysql
application-crd-id: kubeflow-pipelines
spec:
containers:
- args:
- --ignore-db-dir=lost+found
- --datadir
- /var/lib/mysql
env:
- name: MYSQL_ROOT_PASSWORD
valueFrom:
secretKeyRef:
key: password
name: mysql-secret
image: gcr.io/ml-pipeline/mysql:5.7-debian
name: mysql
ports:
- containerPort: 3306
name: mysql
resources:
requests:
cpu: 100m
memory: 800Mi
volumeMounts:
- mountPath: /var/lib/mysql
name: mysql-persistent-storage
serviceAccountName: mysql
volumes:
- name: mysql-persistent-storage
persistentVolumeClaim:
claimName: mysql-pv-claim
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
No errors in ml-pipeline logs
-----------
ml-pipeline-5fc565dfc4-2zljp 1/2 Running 55 3h27m
bash-3.2$ k logs ml-pipeline-5fc565dfc4-2zljp -n kubeflow
I1005 16:29:47.455100 8 client_manager.go:160] Initializing client manager
I1005 16:29:47.455255 8 config.go:57] Config DBConfig.ExtraParams not specified, skipping
bash-3.2$ k logs ml-pipeline-5fc565dfc4-2zljp -n kubeflow -p
I1005 16:28:17.581634 7 client_manager.go:160] Initializing client manager
I1005 16:28:17.581844 7 config.go:57] Config DBConfig.ExtraParams not specified, skipping
bash-3.2$
mysql shows below errors:
-----------------------
2022-10-05T12:53:21.615125Z 0 [Note] - '::' resolves to '::';
2022-10-05T12:53:21.615171Z 0 [Note] Server socket created on IP: '::'.
2022-10-05T12:53:21.617003Z 0 [Warning] Insecure configuration for --pid-file: Location '/var/run/mysqld' in the path is accessible to all OS users. Consider choosing a different directory.
2022-10-05T12:53:21.635095Z 0 [Note] Event Scheduler: Loaded 0 events
2022-10-05T12:53:21.635645Z 0 [Note] mysqld: ready for connections.
Version: '5.7.38' socket: '/var/run/mysqld/mysqld.sock' port: 3306 MySQL Community Server (GPL)
2022-10-05T12:56:07.554978Z 7 [Note] Aborted connection 7 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T12:57:37.555598Z 9 [Note] Aborted connection 9 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T12:59:07.557053Z 11 [Note] Aborted connection 11 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T13:00:37.551263Z 13 [Note] Aborted connection 13 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T13:02:07.552976Z 15 [Note] Aborted connection 15 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T13:03:37.547383Z 17 [Note] Aborted connection 17 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T13:07:57.543144Z 19 [Note] Aborted connection 19 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T13:14:37.538061Z 21 [Note] Aborted connection 21 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
2022-10-05T13:16:07.554355Z 23 [Note] Aborted connection 23 to db: 'mlpipeline' user: 'root' host: '127.0.0.6' (Got an error reading communication packets)
<!-- /area samples -->
<!-- /area components -->
---
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Impacted by this bug? Give it a 👍. | {
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"u... | closed | false | null | [] | null | [] | 2022-10-03T14:48:12 | 2022-10-05T09:00:07 | 2022-10-05T09:00:07 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? **Full Kubeflow Deployement on OCI**
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: **1.6**
<!-- 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==0.1.16
kfp-server-api==1.6.0
### 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.
-->
- Create a jupyter notebook without a volume attached to it. Create a pipeline using the `components.func_to_container_op` with the following specifications:
- Pipeline First step: Download and Process and Store Data. Data path is passed as an output of the first to different pipeline step using _filePath_
- Pipeline Second Step: Error is thrown because the file references from the first step are not found
### Expected result
- Expected the second step to be able to fetch the data as it has been downloaded and processed.
<!-- 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. -->
<img width="1174" alt="Screen Shot 2022-10-03 at 6 47 34 PM" src="https://user-images.githubusercontent.com/61509694/193607018-dc21dc22-cbe1-4c7e-9601-fb79f0760ca7.png">
---
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"@vstrimaitis, can you please share the YAML that is generated?",
"The compiler doesn't allow me to generate a YAML file, but here's the JSON output for the very first example:\r\n\r\n```json\r\n{\r\n \"pipelineSpec\": {\r\n \"components\": {\r\n \"comp-read-data\": {\r\n \"executorLabel\": \"exec... | 2022-10-03T10:52:31 | 2022-10-11T10:57:51 | null | NONE | null | ### Environment
* How do you deploy Kubeflow Pipelines (KFP)? Vertex AI Pipelines on GCP
* KFP version: Don't know - whatever Vertex AI uses 😁
* KFP SDK version: `1.8.12`
### Steps to reproduce
Define the following pipeline:
```python
from kfp.v2 import dsl
from kfp.v2.dsl import Output, Artifact, Input
@dsl.component
def write_data(destination: Output[Artifact]):
assert destination.path is not None
with open(destination.path, "w") as f:
f.write("test")
@dsl.component
def read_data(data1: Input[Artifact], data2: Input[Artifact]):
assert data1.path is not None
assert data2.path is not None
with open(data1.path, "r") as f:
print(f"data1:\n{f.read()}")
with open(data2.path, "r") as f:
print(f"data2:\n{f.read()}")
@dsl.pipeline(name="pipeline-bug-test")
def my_pipeline():
writer = write_data()
reader = read_data(data1=writer.output, data2=writer.output)
```
Run it on Vertex AI. It will fail because either `data1.path` or `data2.path` will be `None` in the `read_data` step.
### Expected result
The pipeline should run successfully.
### Materials and reference
I also tried the following pipeline with the same result (probably because caching is enabled):
```python
@dsl.pipeline(name="pipeline-bug-test")
def my_pipeline():
writer1 = write_data()
writer2 = write_data()
reader = read_data(data1=writer1.output, data2=writer2.output)
```
I also tried this approach, which worked fine:
```python
@dsl.component
def write_data(text: str, destination: Output[Artifact]):
assert destination.path is not None
with open(destination.path, "w") as f:
f.write(text)
# ...
@dsl.pipeline(name="pipeline-bug-test")
def my_pipeline():
writer1 = write_data(text="abc")
writer2 = write_data(text="def")
reader = read_data(data1=writer1.output, data2=writer2.output)
```
Overall it seems like if you try to pass an output from component `A` into multiple inputs of component `B`, only one of those inputs is actually being passed in.
We first encountered this when using components defined using the YAML spec, in which case one of the placeholders is not replaced (e.g. `{{$.inputs.artifacts['Train data path'].path}}` is passed into the component for one of the inputs whereas `/gcs/my-bucket/...` is passed in for the other input)
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
I'm actually not sure where the source of the bug is, so I'm not adding any labels for now.
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8327 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8327/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8327/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8327/events | https://github.com/kubeflow/pipelines/issues/8327 | 1,393,732,872 | I_kwDOB-71UM5TEq0I | 8,327 | saving pickle file | {
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"@riyaj8888 Would you mind providing the code that get exception?"
] | 2022-10-02T11:29:28 | 2022-10-06T22:39:41 | null | NONE | null | Hi ,
I am trying to write pickle file of my trained sklearn model , but i am unable to do it.
I am defining output_pickle_file:OutputPath(str) for saving it but not able to save using this.
So i tried using BinaryFile but this tym i am able to save it but after saving ,tried to load it but i am getting following error.
`TypeError: expected str, bytes or os.PathLike object, not _io.BufferedWriter`
does anyone know why ?
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https://api.github.com/repos/kubeflow/pipelines/issues/8318 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8318/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8318/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8318/events | https://github.com/kubeflow/pipelines/issues/8318 | 1,389,973,836 | I_kwDOB-71UM5S2VFM | 8,318 | [backend] Duplicate volumes are found when running on ACI | {
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"Hi @abishek-murali, thank you for reporting the bug. Maybe you can try upgrading the SDK to the version 1.8.14 and see if the error still exist. ",
"@Linchin I upgraded to 1.8.14, and the error still exists"
] | 2022-09-28T21:38:17 | 2022-09-30T21:09:08 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? [Standalone deployment](https://www.kubeflow.org/docs/components/pipelines/v1/installation/standalone-deployment/)
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.8.5 (Kubeflow 1.6.0)
<!-- 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: 1.2.0 (and 1.8.14)
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
- Running kubeflow job on AKS with virtual node enabled. Trying to run the containers using Azure container instances (ACI)
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
Logs
```
api call to https://management.azure.com/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.ContainerInstance/containerGroups/xxxx?api-version=2021-07-01: got HTTP response status code 400 error code "DuplicateVolumes": Duplicate volumes 'kube-api-access-vn5q4' are found.
```
### Expected result
<!-- What should the correct behavior be? -->
- Run container using ACI
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
Known [limitations](https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Flearn.microsoft.com%2Fen-us%2Fazure%2Faks%2Fvirtual-nodes%23known-limitations&data=05%7C01%7Cabishek.murali%40canvass.io%7C8966891c9a864c4459bf08daa1838d8e%7C97332a9ca51844d9892b98b1e9b24b29%7C0%7C0%7C637999883635197847%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Tgf43N7KROmJ2bfCvZxPzJbCrk5Umfup4beRpd86VWo%3D&reserved=0) about ACI
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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