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] | open | false | null | [] | null | [] | "2023-09-13T09:40:05" | "2023-09-13T09:40:08" | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
KFP V1 supported setting the image pull policy [link](https://github.com/kubeflow/pipelines/blob/4bee3d8dc2ee9c33d87e1058bac2a94d899dd4a5/sdk/python/kfp/deprecated/dsl/_container_op.py#L518)
This feature is currently not available in KFP V2
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
We used to set image pull policy to always in V1 to avoid cases when outdated images are pulled.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
No current workaround.
---
<!-- 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/9976 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9976/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9976/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9976/events | https://github.com/kubeflow/pipelines/issues/9976 | 1,892,166,555 | I_kwDOB-71UM5wyCub | 9,976 | [bug] (Big) Integer overflow in python components | {
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] | open | false | null | [] | null | [] | "2023-09-12T10:15:29" | "2023-09-12T10:15:29" | null | NONE | null | /kind bug
### Environment
* How do you deploy Kubeflow Pipelines (KFP)? </br>
Using kubeflow sdk
* KFP version: </br>
1.7
* KFP SDK version: </br>
1.8.17
* Python base image used: </br>
Image tag: python:3.8-slim </br>
Image sha: sha256:7672e07ca8cd61d8520be407d6a83d45e6d37faf26bb68b91a2fa8ab89a7798f </br>
Image ID: 1b0f3f18921c
### Steps to reproduce
#### Issue:
An integer parameter (Only certain numbers) when tried to be printed using python function component, kfp mutates the input and prints a different output.
Eg:
#### Scenario1:
Input parameter value: 20230807093130621
</br>
Printed value: 20230807093130620
#### Scenario2:
Input parameter value: 20230807093130621
</br>
Added number: 10
</br>
Printed value: 20230807093130630
### Expected result
#### Scenario1:
Input parameter value: 20230807093130621
</br>
Expected value: 20230807093130621
#### Scenario2:
Input parameter value: 20230807093130621
</br>
Added number: 10
</br>
Expected value: 20230807093130631
### Pipeline yaml definition
```
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: integer-overflow-test-pipeline-
annotations:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
pipelines.kubeflow.org/pipeline_compilation_time: '2023-09-11T20:57:57.297958'
pipelines.kubeflow.org/pipeline_spec: >-
{"inputs": [{"default": "20230807093130621", "name": "x", "optional":
true, "type": "Integer"}, {"default": "10", "name": "num_to_add",
"optional": true, "type": "Integer"}], "name":
"integer_overflow_test_pipeline"}
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
spec:
entrypoint: integer-overflow-test-pipeline
templates:
- name: add-num-to-integer
container:
args:
- '--x'
- '{{inputs.parameters.x}}'
- '--num'
- '{{inputs.parameters.num_to_add}}'
command:
- sh
- '-ec'
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- >
def add_num_to_integer(x, num = 10):
added_num = x + num
print(added_num)
import argparse
_parser = argparse.ArgumentParser(prog='Add num to integer',
description='')
_parser.add_argument("--x", dest="x", type=int, required=True,
default=argparse.SUPPRESS)
_parser.add_argument("--num", dest="num", type=int, required=False,
default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = add_num_to_integer(**_parsed_args)
image: 'python:3.8-slim'
inputs:
parameters:
- name: num_to_add
- name: x
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: 'true'
annotations:
pipelines.kubeflow.org/component_spec: >-
{"implementation": {"container": {"args": ["--x", {"inputValue":
"x"}, {"if": {"cond": {"isPresent": "num"}, "then": ["--num",
{"inputValue": "num"}]}}], "command": ["sh", "-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" >
\"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", "def
add_num_to_integer(x, num = 10):\n added_num = x + num\n
print(added_num)\n\nimport argparse\n_parser =
argparse.ArgumentParser(prog='Add num to integer',
description='')\n_parser.add_argument(\"--x\", dest=\"x\", type=int,
required=True,
default=argparse.SUPPRESS)\n_parser.add_argument(\"--num\",
dest=\"num\", type=int, required=False,
default=argparse.SUPPRESS)\n_parsed_args =
vars(_parser.parse_args())\n\n_outputs =
add_num_to_integer(**_parsed_args)\n"], "image":
"python:3.8-slim"}}, "inputs":
[{"name": "x", "type": "Integer"}, {"default": "10", "name": "num",
"optional": true, "type": "Integer"}], "name": "Add num to integer"}
pipelines.kubeflow.org/component_ref: '{}'
pipelines.kubeflow.org/arguments.parameters: >-
{"num": "{{inputs.parameters.num_to_add}}", "x":
"{{inputs.parameters.x}}"}
pipelines.kubeflow.org/max_cache_staleness: P0D
- name: integer-overflow-test-pipeline
inputs:
parameters:
- name: num_to_add
- name: x
dag:
tasks:
- name: add-num-to-integer
template: add-num-to-integer
arguments:
parameters:
- name: num_to_add
value: '{{inputs.parameters.num_to_add}}'
- name: x
value: '{{inputs.parameters.x}}'
- name: print-integer
template: print-integer
arguments:
parameters:
- name: x
value: '{{inputs.parameters.x}}'
- name: print-integer
container:
args:
- '--x'
- '{{inputs.parameters.x}}'
command:
- sh
- '-ec'
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- >
def print_integer(x):
print(x)
import argparse
_parser = argparse.ArgumentParser(prog='Print integer',
description='')
_parser.add_argument("--x", dest="x", type=int, required=True,
default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = print_integer(**_parsed_args)
image: 'python:3.8-slim'
inputs:
parameters:
- name: x
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: 'true'
annotations:
pipelines.kubeflow.org/component_spec: >-
{"implementation": {"container": {"args": ["--x", {"inputValue":
"x"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf
\"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\"
\"$@\"\n", "def print_integer(x):\n print(x)\n\nimport
argparse\n_parser = argparse.ArgumentParser(prog='Print integer',
description='')\n_parser.add_argument(\"--x\", dest=\"x\", type=int,
required=True, default=argparse.SUPPRESS)\n_parsed_args =
vars(_parser.parse_args())\n\n_outputs =
print_integer(**_parsed_args)\n"], "image":
"python:3.8-slim"}}, "inputs":
[{"name": "x", "type": "Integer"}], "name": "Print integer"}
pipelines.kubeflow.org/component_ref: '{}'
pipelines.kubeflow.org/arguments.parameters: '{"x": "{{inputs.parameters.x}}"}'
pipelines.kubeflow.org/max_cache_staleness: P0D
arguments:
parameters:
- name: x
value: '20230807093130621'
- name: num_to_add
value: '10'
serviceAccountName: <Intentionally redacted>
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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] | open | false | null | [] | null | [] | "2023-09-12T09:43:48" | "2023-09-12T13:01:04" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines as part of a full Kubeflow deployment provides all Kubeflow components and more integration with each platform
Kubeflow is deployed on top of AWS EKS cluster.
* KFP version: v2
* KFP SDK version: Issue identified in UI, possible source in backend.
### Steps to reproduce
1. In Kubeflow UI Pipeline Runs section (using "[Tutorial] Data passing in python components" pipeline run) click on any of pipeline completed steps.
2. Click on Input artifact s3 url (s3://kf-artifacts-store-..../) and open input artifact and that works fine, i.e. one can see contents of artifact.
3. BUT when click on output artifacts "main-logs" s3 url one gets HTTP 403 Forbidden error.
### Expected result
Both input and output artifact contents are visible in preview and when clicking on S3 url.
### Materials and Reference
Additional observations:
- Issue so far is observed for s3 objects with extension `.log`.
- s3 objects with extension `.tgz` are opened without a problem.
---
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"I've been impacted by this change as well and I think it's a behavior change in the latest version of KFP 2.1.3 only. I was able to get my pipeline running by using KFP 2.0.1.\r\n\r\nI think the \"issue\" might be coming from the following commit https://github.com/kubeflow/pipelines/pull/9886/commits/80881c12ee2a23688cd7bd5e6eeb330228949cf8 that added `--no-deps` to the pip install command.",
"@Lothiraldan Thank you, that was indeed the problem! Everything works as expected when I downgrade to KFP 2.0.1. I do wonder why this change was implemented since it makes it seemingly impossible to properly install packages via 'packages_to_install'. I'll keep this open for now since I'm curious if it's intended behavior or a bug.",
"Having the same issue, feels like a crazy \"feature\" to require manually listing every single transitive dependency before your component can run 😅\r\n\r\nI think the intention behind the PR was to remove the compile time dependencies of `kfp` for the runtime environment, which makes sense. However, because `kfp=={version} --no-deps` is appended to the `packages_to_install` list, all user-specified dependencies get the same `--no-deps` treatment... definitely feels like a regression!"
] | "2023-09-12T08:07:41" | "2023-09-13T11:09:14" | null | NONE | null | ### Environment
#### Relevant package versions
kfp==2.1.3
google-cloud-pipeline-components==2.3.1
google-cloud-aiplatform==1.32.0
kfp-pipeline-spec== 0.2.2
kfp-server-api == 2.0.1
### Steps to reproduce
After upgrading to KFP V2, Initializing a component as follows doesn't work:
```python
from kfp.dsl import component
@component(
base_image="python:3.10",
packages_to_install=["google-cloud-aiplatform"],
)
def my_function():
from google.cloud import aiplatform
```
This gives the error `ModuleNotFoundError: No module named 'google.api_core'`, while in KFP V1 (< 2.0), this worked without any issues. It seems to be an issue with 'packages_to_install'. Adding 'google_api_core' to the 'packages_to_install' then throws the error `ModuleNotFoundError: No module named 'grpc'`, adding that gives `RuntimeError: Please install the official package with: pip install grpcio`, adding that gives `ModuleNotFoundError: No module named 'google.rpc'`, and finally, adding that gives `ERROR: No matching distribution found for google.rpc`. It seems that the 'packages_to_install' do not install their dependencies correctly, or there is an issue with 'google-cloud-aiplatform- and KFP V2.
### Expected result
The 'packages_to_install' are installed correctly and can be imported within the component. With the following versions, and the exact same code, this behavior does work:
kfp==1.8.14
google-cloud-pipeline-components==1.0.26
google-cloud-aiplatform==1.18.3
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"> set gpu resources (.set_gpu_limit())\r\n\r\n`set_gpu_limit()` has been renamed to [`set_accelerator_limit`](https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/source/dsl.html?h=set_acc#kfp.dsl.PipelineTask.set_accelerator_limit) so that is covers both GPU and TPU (on Vertex). Meanwhile, `set_gpu_limit` still exists but gives you a deprecation warning and calls into `set_accelerator_limit` under the hood.\r\n\r\n> mount hostPath (.add_volume(), add_volume_mount())\r\n\r\nVolume mount has been moved into an extension package: \r\nhttps://www.kubeflow.org/docs/components/pipelines/v2/platform-specific-features/\r\n\r\n> .set_security_context()\r\n\r\nKFP v2 doesn't have this feature parity yet. My advice would be submitting a separate feature request with the description of your use case.",
"@chensun Is there a way to use data stored on Nas??\r\n"
] | "2023-09-11T07:20:36" | "2023-09-12T01:35:55" | null | NONE | null | Hello,
we have upgraded our Kubeflow Pipelines to version 2.0.1. We are now having a lot of trouble rewriting our code from version 1.8. We are missing the following features (maybe we just didn't find them in v2):
- set gpu resources (.set_gpu_limit())
- mount hostPath (.add_volume(), add_volume_mount())
- .set_security_context()
We want to train on multiple gpus, so we need to set gpu and cpu resources, connect network data storage, and so on.
Is Kubeflow Pipelines v2 good for us? Are these features planned in version 2? Or should we still use Kubeflow Pipelines 1.8? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9962 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9962/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9962/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9962/events | https://github.com/kubeflow/pipelines/issues/9962 | 1,887,425,701 | I_kwDOB-71UM5wf9Sl | 9,962 | [backend] Persistence Agent failing kubeflow-pipeline-mkp-test (2) | {
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] | open | false | null | [] | null | [] | "2023-09-08T10:52:36" | "2023-09-08T10:57:54" | null | MEMBER | null | ### Steps to reproduce
Having in mind issue: [Persistence agent failing kubeflow-pipeline-mkp-test](https://github.com/kubeflow/pipelines/issues/9904) that appeared after changes in the Persistence agent manifests files - I guess that the mkp-test may fail again after the merge of [this](https://github.com/kubeflow/pipelines/pull/9957) PR as it also has changed the manifests files.
At the moment there is no automatic way to move/sync changes done in main manifest files to the manifests in the marketplace helm chart that is why I suggest someone validate the state of the `mkp-test` and apply the changes done in the above PR in the helm chart if needed.
Please close this issue if that is not the case.
cc: @zijianjoy, @chensun
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"@fclesio, I think the best solution for what you are trying to achieve is to compose the tasks together into a sequential DAG using a pipeline. Please take a look at the docs on [data passing and task dependencies](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines/pipeline-basics/#data-passing-and-task-dependencies) and feel free to re-open if there are outstanding issues.",
"Thanks for the answer @connor-mccarthy, but I am afraid that I express myself with a lack of detail. \r\n\r\nFor a single pipeline it's clear that we can set those dependencies; however, if we have multiple and _independent_ pipelines that needs to have some sequencing, that solution does not work. \r\n\r\nOn top of that, let's say that we have a Pipeline (DAG) with almost 1000 lines, if we have 5 similar ones with the same size we're talking about to have a single pipeline with 5000 lines. \r\n\r\nBeing more specific: there is some component that can triggers another pipeline/DAG like [Airflow has the triggerDAGrun](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/operators/trigger_dagrun/index.html)? ",
"I see, @fclesio. Thanks for the detail. KFP doesn't provide this sort of functionality directly, unfortunately."
] | "2023-09-06T20:53:45" | "2023-09-08T14:47:16" | "2023-09-07T22:50:17" | NONE | null | Thanks once again for the great library.
I have a ML pipeline that consists in 3 tasks: ETL > Pre-Processing > Modelling and Deploy.
Since those tasks are sequential, I created 3 pipelines and I used the [kfp.client.run_pipeline ](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L688C9-L688C21) to run each one sequentially.
One thing that I noticed is that the client only _fires_ the pipeline and if the API returns 200 marks it as a success, regardless of the pipeline runtime length.
In my case the run_pipeline did actually fired up all pipelines, but all of them ran at the same time (not desirable behaviour).
There some way to have some kind of "wait status" that only return success if the pipeline called runs until the end?
Thanks once again.
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https://api.github.com/repos/kubeflow/pipelines/issues/9958 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9958/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9958/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9958/events | https://github.com/kubeflow/pipelines/issues/9958 | 1,882,400,753 | I_kwDOB-71UM5wMyfx | 9,958 | chore(frontend): Refactor the class component to functional component | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9956 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9956/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9956/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9956/events | https://github.com/kubeflow/pipelines/issues/9956 | 1,879,683,257 | I_kwDOB-71UM5wCbC5 | 9,956 | [bug] Tensorboard gives "No dashboards are active for the current data set." when passing minio path. | {
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<!-- 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. -->
1.8.1
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
1.8.19
### 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.
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Create a function to check logging of scalars:
def foo(runid) ->NamedTuple("EvaluationOutput", [('mlpipeline_ui_metadata', 'UI_metadata')]):
import subprocess
subprocess.run(["pip","install","tensorboardX==2.5"])
from tensorboardX import SummaryWriter
from minio import Minio
log_dir = " /tensorboard_logs/"
writer = SummaryWriter(log_dir)
for i in range(20):
# Log loss to TensorBoard
acc = (i+2)**2/(i+1)**2 + i-1
writer.add_scalar("Accuracy",acc, i+1)
writer.flush()
from pipelines.utils.minio_utils import upload_local_directory_to_minio
upload_local_directory_to_minio(client, bucket_name, " /tensorboard_logs/","foo_logs")
metadata = {
'outputs': [{
'type': 'tensorboard',
'source': f'http://<Minio_IP>:<Minio_Port>/minio/datasets/foo_logs',
}]
}
out_tuple = namedtuple("EvaluationOutput", ["mlpipeline_ui_metadata"])
return out_tuple(json.dumps(metadata))
### Expected result
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Tensorboard should show a graph for the scalars written to events file. The issue is not with the events file as it works for a locally deployed tensorboard server.
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"**In Kfp 2.0.1 retry on policy is not present.**\r\n\r\n**Old Code**\r\n```\r\n def set_retry(self,\r\n num_retries: int,\r\n policy: Optional[str] = None,\r\n backoff_duration: Optional[str] = None,\r\n backoff_factor: Optional[float] = None,\r\n backoff_max_duration: Optional[str] = None):\r\n \"\"\"Sets the number of times the task is retried until it's declared\r\n failed.\r\n\r\n Args:\r\n num_retries: Number of times to retry on failures.\r\n policy: Retry policy name.\r\n backoff_duration: The time interval between retries. Defaults to an\r\n immediate retry. In case you specify a simple number, the unit\r\n defaults to seconds. You can also specify a different unit, for\r\n instance, 2m (2 minutes), 1h (1 hour).\r\n backoff_factor: The exponential backoff factor applied to\r\n backoff_duration. For example, if backoff_duration=\"60\"\r\n (60 seconds) and backoff_factor=2, the first retry will happen\r\n after 60 seconds, then after 120, 240, and so on.\r\n backoff_max_duration: The maximum interval that can be reached with\r\n the backoff strategy.\r\n \"\"\"\r\n if policy is not None and policy not in ALLOWED_RETRY_POLICIES:\r\n raise ValueError('policy must be one of: %r' %\r\n (ALLOWED_RETRY_POLICIES,))\r\n\r\n self.num_retries = num_retries\r\n self.retry_policy = policy\r\n self.backoff_factor = backoff_factor\r\n self.backoff_duration = backoff_duration\r\n self.backoff_max_duration = backoff_max_duration\r\n return self\r\n```\r\n\r\n**New Code**\r\n```\r\ndef set_retry(self,\r\n num_retries: int,\r\n backoff_duration: Optional[str] = None,\r\n backoff_factor: Optional[float] = None,\r\n backoff_max_duration: Optional[str] = None) -> 'PipelineTask':\r\n\r\n Args:\r\n num_retries : Number of times to retry on failure.\r\n backoff_duration: Number of seconds to wait before triggering a retry. Defaults to ``'0s'`` (immediate retry).\r\n backoff_factor: Exponential backoff factor applied to ``backoff_duration``. For example, if ``backoff_duration=\"60\"`` (60 seconds) and ``backoff_factor=2``, the first retry will happen after 60 seconds, then again after 120, 240, and so on. Defaults to ``2.0``.\r\n backoff_max_duration: Maximum duration during which the task will be retried. Maximum duration is 1 hour (3600s). Defaults to ``'3600s'``.\r\n\r\n Returns:\r\n Self return to allow chained setting calls.\r\n \"\"\"\r\n self._task_spec.retry_policy = structures.RetryPolicy(\r\n max_retry_count=num_retries,\r\n backoff_duration=backoff_duration,\r\n backoff_factor=backoff_factor,\r\n backoff_max_duration=backoff_max_duration,\r\n )\r\n return self\r\n```\r\n\r\nFYI @chensun ",
"retry-random-failures-xgqdv-1470953391 0/2 Completed 0 69s\r\nretry-random-failures-xgqdv-2197860257 0/2 Error 0 59s\r\nretry-random-failures-xgqdv-2342167526 0/2 Completed 0 69s\r\nretry-random-failures-xgqdv-3239894960 0/2 Error 0 58s\r\nretry-random-failures-xgqdv-611022813 0/2 Completed 0 79s"
] | "2023-08-31T12:31:13" | "2023-09-02T13:17:07" | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
* KFP version: 2.0.0
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 2.0.1
### Steps to reproduce
You can run the following code to reproduce the issue.
```
from kfp import dsl
@dsl.component
def random_failure_op(exit_codes: str):
"""A component that fails randomly."""
import random
import sys
exit_code = int(random.choice(exit_codes.split(",")))
print(exit_code)
sys.exit(exit_code)
@dsl.pipeline(
name="retry-random-failures",
description="The pipeline includes two steps which fail randomly. It shows how to use ContainerOp(...).set_retry(...).",
)
def retry_random_failures():
op1 = random_failure_op(exit_codes="0,1,2,3").set_retry(10)
op2 = random_failure_op(exit_codes="0,1").set_retry(5)
```
### Expected result
The component should retry on failure but it never does not even once. In the pipeline spec I also see the following policy
```
retryPolicy:
backoffDuration: 0s
backoffFactor: 2
backoffMaxDuration: 3600s
maxRetryCount: 10
```
### Materials and reference
This is the documentation I referred - https://kubeflow-pipelines.readthedocs.io/en/latest/source/dsl.html#kfp.dsl.PipelineTask.set_retry
### 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/9949 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9949/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9949/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9949/events | https://github.com/kubeflow/pipelines/issues/9949 | 1,875,251,131 | I_kwDOB-71UM5vxg-7 | 9,949 | Deprecated setup.cfg used in pyyaml@5.4.1 version not compatible with Python versions 3.10 and above | {
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"This is fixed. See [requirements](https://github.com/kubeflow/pipelines/blob/3b8cea060fc3088520666fea26e6452bda2fdb15/sdk/python/requirements.in#L22)."
] | "2023-08-31T10:34:16" | "2023-09-07T22:36:55" | "2023-09-07T22:36:54" | NONE | null | ### Description
`pip install -r requirements.txt` fails on Python 3.10 and 3.11 due to deprecated setup.cfg in pyyaml 5.4.1 using `license_file` instead of `license_files` . According to PyYaml this problem was fixed and recommended version is 6.0.1.
Fixing this issue may require to update requirements to accept `pyyaml>=5.4.1`.
### Materials and reference
https://github.com/yaml/pyyaml/issues/724
### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? Private fork
* KFP version: 1.8.19
* KFP SDK version: 1.8.22
### Steps to reproduce
```
$ pip install -r requirements.txt
Collecting PyYAML==5.4.1 (from -r requirements.txt (line 43))
Using cached .../PyYAML-5.4.1.tar.gz (175 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [62 lines of output]
.../python3.9/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg`
!!
********************************************************************************
The license_file parameter is deprecated, use license_files instead.
By 2023-Oct-30, you need to update your project and remove deprecated calls
or your builds will no longer be supported.
See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
********************************************************************************
!!
...
AttributeError: cython_sources
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
```
### Expected result
`pip install -r requirements.txt` installs all dependencies successfully.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9942 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9942/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9942/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9942/events | https://github.com/kubeflow/pipelines/issues/9942 | 1,871,704,455 | I_kwDOB-71UM5vj_GH | 9,942 | [feature] Add support for Habana device cards | {
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] | open | false | null | [] | null | [] | "2023-08-29T13:40:47" | "2023-08-29T13:40:50" | null | NONE | null | ### Feature Area
/area sdk
/area samples
### What feature would you like to see?
I would like to have the option to choose if I would like to use Nvidia gpu cards or Habana pci cards.
The only difference is with the requests to change it from `nvidia.com/gpu` to `habana.ai/gaudi`
At the backend we are running our device plugin and other stuff to manage the driver.
### What is the use case or pain point?
Currently I cannot work on Habana device cards. With this feature I would be able to train models with their solution.
### Is there a workaround currently?
We have custom YAMLs to run on the training-operator (TF jobs, Pytorch, etc), with specific mounts. but we would like to add it also in the UI, notebooks, etc.
---
If you can help me with how to do it, that would be great :)
I am happy to submit that feature request of course.
<!-- Don't delete message below to encourage users to support your feature request! -->
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"You need first to create an experiment and than use it's id in run, passing it as a parameter.",
"> \r\n\r\nPlease provide advice on how to create an [experiment](https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v2beta1-experiments-post) using [Kubeflow API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/) as the error is **Job has no experiment** .I'm literally stuck and need assistance to move on. Also how to create [Job](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-jobs-post).",
"Just check the API specification for the case:\r\nhttps://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-experiments-post",
"> Just check the API specification for the case: https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-experiments-post\r\n\r\ncan you please give me one example on creation of [Experiment ](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-experiments-post)using kubeflow API"
] | "2023-08-29T07:41:43" | "2023-09-07T22:47:11" | null | NONE | null | I am following tutorial of Kubeflow Pipelines API: [[Kubeflow](https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/api-pipelines/)] & [Kubeflow Pipelines API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/)
I've installed Kubeflow on a MicroK8s and started experimenting with the Kubeflow Pipelines API, I was able to upload a pipeline to the central dashboard by using [kubeflow upload API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/), but when I tried utilizing the [Kubeflow RUN API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs-post), I was unable to establish a RUN. As RUN_ID is showing as null but in the [document](https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/api-pipelines/), by running this command curl ${SVC}/apis/v1beta1/runs/${RUN_ID} | jq it is showing the value of RUN_ID.Can anyone offer assistance.
I tried several various approaches, but they were unsuccessful, I was literally struck at my work regarding this issue can anyone please help on this issue..
RUN_ID=$((
curl -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs
\-d @- \<\< EOF
{
"name":"${PIPELINE_NAME}\_run",
"pipeline_spec":{
"pipeline_id":"${PIPELINE_ID}"
}
}
EOF
) | jq -r .run.id)
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 388 100 277 100 111 23083 9250 --:--:-- --:--:-- --:--:-- 32333
$ echo $RUN_ID
null
$ curl -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs
\-d @- \<\< EOF
{
"name":"${PIPELINE_NAME}\_run",
"pipeline_spec":{
"pipeline_id":"${PIPELINE_ID}"
}
}
EOF
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https://api.github.com/repos/kubeflow/pipelines/issues/9940 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9940/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9940/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9940/events | https://github.com/kubeflow/pipelines/issues/9940 | 1,870,687,285 | I_kwDOB-71UM5vgGw1 | 9,940 | [bug] Always 404 on kfp sdk | {
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"Which version of the KFP BE do you have deployed?\r\n\r\ncc @chensun"
] | "2023-08-29T00:25:14" | "2023-09-07T22:44:39" | null | NONE | null | I was using kfp==2.0.0b12 and everything was working great. But as soon as i got into 2.0.1 I am always getting 404 when trying to use methods from client, like this:
`kfp_server_api.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'x-powered-by': 'Express', 'content-security-policy': "default-src 'none'", 'x-content-type-options': 'nosniff', 'content-type': 'text/html; charset=utf-8', 'content-length': '170', 'date': 'Mon, 28 Aug 2023 23:52:33 GMT', 'x-envoy-upstream-service-time': '2', 'server': 'istio-envoy'})
HTTP response body: <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Error</title>
</head>
<body>
<pre>Cannot GET /pipeline/apis/v2beta1/pipelines</pre>
</body>
</html>`
I am now using kfp==2.0.1 kfp-pipeline-spec==0.2.2 kfp-server-api==2.0.1 and I am creating the client this way:
`session = requests.Session()
response = session.get(HOST)
headers = {
"Content-Type": "application/x-www-form-urlencoded",
}
data = {"login": USERNAME, "password": PASSWORD}
session.post(response.url, headers=headers, data=data)
return session.cookies.get_dict()["authservice_session"]
client = Client(host=f"{HOST}/pipeline",
cookies=f"authservice_session={session_cookie}",
namespace=NAMESPACE)
return client
`
It used to work great, but now using kfp sdk 2.0.1 if I try something like list_pipelines() I am always getting the error.
What am I doing wrong here?
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9937 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9937/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9937/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9937/events | https://github.com/kubeflow/pipelines/issues/9937 | 1,869,601,448 | I_kwDOB-71UM5vb9qo | 9,937 | [feature] Make Persistence Agent use SA Token when calling KF APIServer endpoints | {
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"/assign @difince "
] | "2023-08-28T11:48:07" | "2023-09-07T23:49:34" | "2023-09-07T23:49:34" | 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?
Currently, when the Persistence Agent (PA) calls KFPipeline APIs - **readArtifacts** and **ReportMetrics** - It authenticates as a user.
See:
https://github.com/kubeflow/pipelines/blob/110e0824812883b74c73b26603a78d8cc00548d5/backend/src/agent/persistence/client/pipeline_client.go#L196-L204
The proper way for service-to-service authentication/authorization is the use of a Service Account Token. PA SA [token](https://github.com/kubeflow/pipelines/blob/110e0824812883b74c73b26603a78d8cc00548d5/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml#L39-L41C19) has been already introduced by this [PR](https://github.com/kubeflow/pipelines/pull/9699) .
My suggestion is to use the PA SA token for **readArtifacts** and **ReportMetrics** as well.
Thus the communication between PA and KFPipeline Api service will be done in the correct way. The code will become clearer/shorter and the unnecessary [requests](https://github.com/kubeflow/pipelines/blob/110e0824812883b74c73b26603a78d8cc00548d5/backend/src/agent/persistence/client/kubernetes_core.go#L33-L46) to the kubeapi will be removed.
### What is the use case or pain point?
### Is there a workaround currently?
---
<!-- 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/9933 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9933/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9933/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9933/events | https://github.com/kubeflow/pipelines/issues/9933 | 1,867,215,948 | I_kwDOB-71UM5vS3RM | 9,933 | module 'kfp.components' has no attribute 'create_component_from_func' | {
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"For KFP SDK v2, please use the `@dsl.component` decorator. See [documentation on Lightweight Python Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/lightweight-python-components/) for more information."
] | "2023-08-25T15:02:38" | "2023-09-07T22:42:16" | "2023-09-07T22:42:15" | NONE | null | ### Environment
I am trying to build the Kubeflow pipeline. i am using version 2.0.1.
I am getting this error:
module 'kfp.components' has no attribute 'create_component_from_func'
And along i i followed the document of KFPv2 there are some code already written in docs for the testing it has already created some function and pipelines I copy paste all things from docs for test then I compiled then it's created yaml file I simply upload in kubeflow ui which install in a cluster
But I am getting this error :
Cannot get MLMD object from meta store
again and again I getting same error also it has shown a pop-up and says:
Unkown content type recived
Is there any other way to import this or this is a version error?
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https://api.github.com/repos/kubeflow/pipelines/issues/9924 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9924/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9924/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9924/events | https://github.com/kubeflow/pipelines/issues/9924 | 1,863,477,869 | I_kwDOB-71UM5vEmpt | 9,924 | [backend] Cannot inject environment variables and volumes into components with the KFP CLI | {
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* How did you deploy Kubeflow Pipelines (KFP)?
Full Kubeflow Deployment
* KFP version: 1.7.0 (we are using Kubeflow 1.7.0)
* KFP SDK version: 2.0.1
### Steps to reproduce
The injection of environment variables and secrets into components is not working correctly with kfpv2. Please look below for the python code and the generated yaml.
We could use volumes and environment variables in KFPv1 but when updating to kfpv2 and Kubeflow 1.7 the (updated) code does not work anymore. The Pipeline yaml looks fine from our perspective but in Kubernetes the pipeline component pod does not get the required configurations. (volume mount, env)
### Expected result
The Kubernetes Pod has access to the volumes and environment variables and we can use them inside a Kubeflow pipeline component.
### Materials and Reference
We are using this to add the secret in the kfp cli.
```
def add_gsc_access(task):
task.set_env_variable(
name="GOOGLE_APPLICATION_CREDENTIALS", value="/var/secrets/google/key.json"
)
kubernetes.use_secret_as_volume(
task,
secret_name="pubsub-key",
mount_path="/var/secrets/google",
)
```
Generated Pipeline YAML:
```
components:
comp-download-from-gcs:
executorLabel: exec-download-from-gcs
inputDefinitions:
parameters:
gcs_path:
parameterType: STRING
outputDefinitions:
artifacts:
data:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
...
deploymentSpec:
executors:
exec-download-from-gcs:
container:
command:
- ...
```
---
<!-- 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/9906 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9906/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9906/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9906/events | https://github.com/kubeflow/pipelines/issues/9906 | 1,857,198,876 | I_kwDOB-71UM5uspsc | 9,906 | [frontend] No Logs tab in UI | {
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"Hi @Saurav-D,\r\n\r\nLogs are available in KFP 2.0.0. It is expected to be included in Kubeflow 1.8 (releasing in 2023 Q4)"
] | "2023-08-18T19:33:17" | "2023-08-24T22:48:53" | "2023-08-24T22:48:52" | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
AWS, Terraform, Vanilla
* KFP version:
KFP 1.7
pipeline: 2.0.0-alpha.7
### Steps to reproduce
Run any pipeline
### Expected result
Logs tab with logs. Right now, I just see Input/Output and Task Details. Also, I don't see pod ID.
### 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/9904 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9904/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9904/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9904/events | https://github.com/kubeflow/pipelines/issues/9904 | 1,856,240,751 | I_kwDOB-71UM5uo_xv | 9,904 | [backend] Persistence agent failing `kubeflow-pipeline-mkp-test` | {
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"### Exploration\r\n\r\nI granted `roles/iam.serviceAccountTokenCreator` to the GCE default service account: https://cloud.google.com/iam/docs/service-account-permissions#token-creator-role, and retried the mkt-test. If this permission unblocks the test, then we will need to do one of the followings:\r\n\r\n1. Make SA token requirement configurable (which means it can become optional), it allows us to exclude volume token projection for KFP standalone mode. We still need to clearly document the permission needed in the full kubeflow mode.\r\n2. Develop a solution for full Kubeflow to make workload identity association for the persistence-agent-sa, or the default-editor which is configured by default in full Kubeflow: https://googlecloudplatform.github.io/kubeflow-gke-docs/docs/pipelines/authentication-pipelines/#cluster-setup-to-use-workload-identity-for-full-kubeflow. Update all existing clusters to grant this additional permission. Put up a warning to all users about this breaking change and how should they unblock the KFP deployment. Update documentations for KFP standalone and full Kubeflow.",
"Hi @zijianjoy Thanks for reporting this issue. \r\nWhat is the result - does your change unblock the test? If not the root cause could be some kustomize file I missed ?! \r\nI would like to clearly state that in the current (the new) implementation, PA( Persistence Agent) authentication is not related to the multiuser mode at all. No matter the multiuser mode, PA is the only one that has the permissions and the right identity to call these two endpoints (no matter the namespace the reported workflow belongs to). \r\nFrom the log message, it looks like in GCE the SA token hasn't been created. As I do not have any knowledge of GCE .. I will totally count on your decision and if really needed I can work to make PA auth configurable .. \r\n",
"Yes, in a normal kubeflow there is a serviceaccount\r\n\r\n```\r\n volumeMounts:\r\n - mountPath: /var/run/secrets/kubeflow/tokens\r\n name: persistenceagent-sa-token\r\n serviceAccountName: ml-pipeline-persistenceagent\r\n volumes:\r\n - name: persistenceagent-sa-token\r\n projected:\r\n sources:\r\n - serviceAccountToken:\r\n path: persistenceagent-sa-token\r\n expirationSeconds: 3600\r\n audience: pipelines.kubeflow.org\r\n```\r\n\r\nno such file looks like a GKE specific issue.\r\n\r\nCan you check manually whether `/var/run/secrets/kubeflow/tokens` exists in the pod? `ls -lah` recursively for that directory tree might help\r\n\r\n\"volume token projection\" is also used in other parts of Kubeflow and should be there by default on a kubernetes cluster.",
"Thank you both @difince and @juliusvonkohout for the help. \r\n\r\nThe IAM role grant doesn't make a difference in terms of the error message. Persistence agent is in crashloop as well.\r\n\r\nI added the volume mount sections mentioned by @juliusvonkohout to a released KFP persistence agent's yaml file. After manually adding the volume mount, the SA token exists.\r\n\r\nNext step is to try using a persistence agent image that is built by `kubeflow-pipeline-mkp-test` in a self-deployed cluster to see if it works.",
"Current suspect:\r\n\r\nWe haven't upgrade the mkp manifest for persistence-agent in https://github.com/kubeflow/pipelines/blob/master/manifests/gcp_marketplace/chart/kubeflow-pipelines/templates/pipeline.yaml#L532C1-L563. It might be the reason causing this failure.",
"Update: PR https://github.com/kubeflow/pipelines/pull/9908 is passing `mkp-test`."
] | "2023-08-18T07:49:37" | "2023-08-21T20:08:43" | "2023-08-21T20:08:43" | COLLABORATOR | null | ### Environment
Currently `/test kubeflow-pipeline-mkp-test` is failing on HEAD.
### Steps to reproduce
In https://github.com/kubeflow/pipelines/commit/cb18d00bbbaed9cd77fc50dce739ed62c72b2356#diff-7df17678529866306ef6665ffbeeda377a1e869aca24856f7cf8a99891877fe8, service account token projection is introduced to enable Auth. However, it is failing the `kubeflow-pipeline-mkp-test` test because persistent agent is unable to find the service account token. Error message as below:
cc @difince @chensun
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9893 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9893/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9893/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9893/events | https://github.com/kubeflow/pipelines/issues/9893 | 1,855,183,517 | I_kwDOB-71UM5uk9qd | 9,893 | [bug] Can't increase/attach shared memory to pipeline task in kfp sdk v2, e.g. PyTorch training fails. | {
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"Hi @hsteude!\r\n\r\nYou can specify memory requests using SDK: `.set_memory_request()` and `.set_memory_limit()`: https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/source/dsl.html?h=set_memory#kfp.dsl.PipelineTask.set_memory_request",
"Hi @gkcalat and thanks for your answer!\r\n\r\nUnfortunately, using `.set_memory_request()` and `.set_memory_limit()` doesn't address the actual issue I am facing. These methods set the general memory allocation for the container, not the *shared memory*, which is the limiting factor here. PyTorch's DataLoader uses shared memory when `num_workers > 0`, and it's this shared memory that gets exhausted.\r\n\r\nThe example above still fails when using the methods to increase memory requests and limits.\r\n\r\nCould this issue be reopened since the problem still persists even when applying the suggested memory settings?\r\n\r\nThanks a lot in advance!\r\n\r\nCheers,\r\nHenrik\r\n"
] | "2023-08-17T15:01:45" | "2023-09-06T11:48:12" | "2023-08-24T22:54:22" | CONTRIBUTOR | null | ### Environment
* How do you deploy Kubeflow Pipelines (KFP)?
Manifests
* KFP version:
2.0.0
* KFP SDK version:
2.0.1
### Steps to reproduce
Create and run a simple PyTorch (lightning) training pipeline:
```python
from kfp import dsl
from kfp.client import Client
# define pytorch (lightning) training component
@dsl.component(
packages_to_install=["torch==2.0.0", "pytorch_lightning"],
base_image="python:3.9",
)
def pytorch_training(num_workers: int, input_size: int, batch_size: int):
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
import pytorch_lightning as pl
class ExampleDataset(Dataset):
def __init__(self, size: int = 1000, number_samples: int = 100):
self.x = torch.randn(number_samples, size, size)
self.size = 1000
def __len__(self):
return self.x.shape[0]
def __getitem__(self, idx):
return self.x[idx, :, :]
class ExampleModel(pl.LightningModule):
def __init__(self, inputsize: int):
super(ExampleModel, self).__init__()
self.linear = nn.Linear(inputsize, inputsize)
def forward(self, x):
return self.linear(x)
def training_step(self, batch, batch_idx):
x = batch
loss = F.mse_loss(self(x), x)
return loss
def configure_optimizers(self):
return torch.optim.SGD(self.parameters(), lr=0.1)
dataset = ExampleDataset(size=input_size, number_samples=100)
train_loader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size)
model = ExampleModel(inputsize=input_size)
trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, train_loader)
# define pipeline
@dsl.pipeline
def shared_memory_pipeline(
num_workers: int = 0, input_size: int = 1000, batch_size: int = 100
):
pytorch_training_task = pytorch_training(
num_workers=num_workers, input_size=input_size, batch_size=batch_size
)
# compile and run pipeline
client = Client()
client.create_run_from_pipeline_func(
shared_memory_pipeline,
arguments=dict(num_workers=5, input_size=1_000, batch_size=100),
experiment_name="shared-memory",
enable_caching=True,
)
```
### Expected result
Pipelne gets compiled and executed without errors.
### Materials and reference
* This is the error I get:
```
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/usr/local/lib/python3.9/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/usr/local/lib/python3.9/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/usr/local/lib/python3.9/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/usr/local/lib/python3.9/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/usr/local/lib/python3.9/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 503) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
```
* There is a workaround in skd v1: https://github.com/kubeflow/pipelines/issues/6880
* Default values for shared memory for containerd and Docker is 64MD. Thus, the pipeline above works fine, if a batch is small enough (e.g. for batch_size=1). It will also run just fine if the number of workers for the Data Loader is set to 0 and no shared memory is required.
### Labels
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"/assign @difince ",
"Fixed by https://github.com/kubeflow/pipelines/pull/9892"
] | "2023-08-17T14:04:00" | "2023-08-17T17:09:24" | "2023-08-17T17:09:24" | MEMBER | null | ### Environment
The issue is introduced by [PR](https://github.com/kubeflow/pipelines/pull/9699)
1. `DefaultTokenRefresherInterval` of 1 hour is initialized in nanoseconds (3600000000000) see here:
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/backend/src/agent/persistence/main.go#L68
2. But the following line of code transforms it to just seconds (3600) ( done by `DefaultTokenRefresherInterval.Seconds()`)
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/backend/src/agent/persistence/main.go#L154
3. The problem comes when we create the TokenRefresher which uses `time.Duration()`.
`time.Duration` expects nanoseconds - > because of **2.** the passed interval is a smaller number and this makes the Ticker executes much more often than it should.
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/backend/src/agent/persistence/main.go#L103
* How do you deploy Kubeflow Pipelines (KFP)?
locally
* KFP version: latest https://github.com/kubeflow/pipelines/commit/cb18d00bbbaed9cd77fc50dce739ed62c72b2356
### 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
The ticker to execute every hour ( set as a default) .
### Materials and reference
**Side note:** I would like the input parameter to be in seconds, keeping it consistent with the persistence agent deployment yaml file where this value is provided in seconds.
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml#L49
Future enhancement could be to keep these two values in sync by an env variable.
### 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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The listruns query is also very slow (seconds for large run_details tables).
It must also be migrated to joins or something other more performant
Already this small database leads to 15 seconds per listruns api call
It regressed from 5 to 15 seconds with Kubeflow 1.5 -> 1.7.
```
mysql> SELECT
-> namespace,
-> COUNT(*) AS entry_count,
-> SUM(LENGTH(CAST(namespace AS CHAR))) AS column_size
-> FROM
-> run_details
-> GROUP BY
-> namespace
-> ORDER BY
-> column_size DESC;
+------------------------------------------------+-------------+-------------+
| namespace | entry_count | column_size |
+------------------------------------------------+-------------+-------------+
| namespace-xxx | 93931 | 1033241 |
| namespace-yyy | 22645 | 362320 |
| namespace-zzz | 16700 | 317300 |
| namespace-www | 9894 | 187986
````
### Steps to reproduce
Just let users run a lot of pipelines per year, e.g. 200,000 and see how slow the listruns query becomes.
### Expected result
Listruns in less than one second for 200,000 runs, which is a very small database task
### Materials and Reference
Impacted by this bug? Give it a 👍. | {
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* How did you deploy Kubeflow Pipelines (KFP)? Kubeflow 1.7/1.8
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0alpha7, but its also in the master branch
<!-- 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: irrelevant, since it is a mlpipeline-ui issue
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
This image here shows Bobs S3 artifacts produced by a pipeline run (e.g. tensorflow model)
Sadly the KFP UI allows Alice to read Bobs artifacts content
But how?
If Alice spies the S3 artifact location from Bob, as seen at the bottom of the image,
She can just can remove the yellow namespace parameter and the UI server will skip permission checks
So you still need to know the S3/GCS path which you can for example get from an insecure (that is the default in KFP) not-disabled ML-metadata
### Expected result
The namespace parameter of the readartifact api call should not be user-configurable.
That should have been achieved for the APIserver in another PR from me and @difince https://github.com/difince/pipelines/blob/d0424bf86de5217d41dd03b50f91ac5ec3489df3/backend/src/apiserver/server/run_server.go#L313
But with mlpipeline-ui you can circumvent the permission check in the apiserver as seen on the screenshot.
You can just remove the namespace parameter and the artifact-proxy mode is disabled.
So mlpipeline-ui must set the namespace parameter based on the namespace dropdown selection for an authenticated user and not expose the namespace parameter to the user in the URL.
https://github.com/kubeflow/pipelines/issues/8074 is also important to secure the apiserver in the future and
in the long term i would really like to get rid of the artifactproxy altogether as stated in https://github.com/kubeflow/pipelines/issues/8406#issuecomment-1643579605 and https://github.com/kubeflow/pipelines/issues/4790#issuecomment-1643574994 but that is for another day.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
This has been discussed in the last KFP meeting (16th August 2023) and acknowledged by @zijianjoy on a technical level.
From the Kubecon presentation https://static.sched.com/hosted_files/kccnceu2023/f8/Hardening%20Kubeflow%20Security%20for%20Enterprise%20Environments%20-%20Julius%20von%20Kohout%2C%20DPDHL%20%26%20Diana%20Dimitrova%20Atanasova%2C%20VMware.pdf
---
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"The frontend test failure seems to be flaky. \r\n\r\nIn another run, it failed on basic sample tests:\r\n```\r\nintegration-test-q5mqw-2112930252: Traceback (most recent call last):\r\nintegration-test-q5mqw-2112930252: File \"exit_handler.py\", line 26, in <module>\r\nintegration-test-q5mqw-2112930252: @component(kfp_package_path=_KFP_PACKAGE_PATH)\r\nintegration-test-q5mqw-2112930252: TypeError: component() got an unexpected keyword argument 'kfp_package_path'\r\nintegration-test-q5mqw-2392976712: /usr/local/lib/python3.7/dist-packages/kfp/dsl/_container_op.py:1268: FutureWarning: Please create reusable components instead of constructing ContainerOp instances directly. Reusable components are shareable, portable and have compatibility and support guarantees. Please see the documentation: https://www.kubeflow.org/docs/pipelines/sdk/component-development/#writing-your-component-definition-file The components can be created manually (or, in case of python, using kfp.components.create_component_from_func or func_to_container_op) and then loaded using kfp.components.load_component_from_file, load_component_from_uri or load_component_from_text: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_file\r\nintegration-test-q5mqw-2392976712: category=FutureWarning,\r\n```\r\nhttps://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9699/kubeflow-pipeline-e2e-test/1691812763929677824"
] | "2023-08-16T16:54:10" | "2023-08-17T09:33:39" | "2023-08-17T09:33:39" | COLLABORATOR | null |
https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9873/kubeflow-pipeline-e2e-test/1691525546791407616
```
integration-test-g8t6t-369894966: [0-0] AssertionError in "deploy helloworld sample run.shows a 4-node static graph"
integration-test-g8t6t-369894966: AssertionError [ERR_ASSERTION]: should have a 4-node graph, instead has: 0
integration-test-g8t6t-369894966: at Context.<anonymous> (/src/helloworld.spec.js:70:5)
integration-test-g8t6t-369894966: at processTicksAndRejections (node:internal/process/task_queues:96:5)
integration-test-g8t6t-369894966: 19:49:32.603 WARN [SeleniumSpanExporter$1.lambda$export$3] - {"traceId": "2ae031a384cadb8eb7f70e9a187d622b","eventTime": 1692128972602956682,"eventName": "HTTP request execution complete","attributes": {"http.flavor": 1,"http.handler_class": "org.openqa.selenium.remote.http.Route$PredicatedRoute","http.host": "127.0.0.1:4444","http.method": "POST","http.request_content_length": "8346","http.scheme": "HTTP","http.status_code": 404,"http.target": "\u002fsession\u002fd0c75f008d8950640953b1914f2f233a\u002fexecute\u002fsync","http.user_agent": "webdriver\u002f8.3.2"}}
integration-test-g8t6t-369894966:
integration-test-g8t6t-369894966: 19:49:33.711 WARN [SeleniumSpanExporter$1.lambda$export$3] - {"traceId": "72fe6381777527734f56dd38817f7a8e","eventTime": 1692128973710578070,"eventName": "HTTP request execution complete","attributes": {"http.flavor": 1,"http.handler_class": "org.openqa.selenium.remote.http.Route$PredicatedRoute","http.host": "127.0.0.1:4444","http.method": "POST","http.request_content_length": "8346","http.scheme": "HTTP","http.status_code": 404,"http.target": "\u002fsession\u002fd0c75f008d8950640953b1914f2f233a\u002fexecute\u002fsync","http.user_agent": "webdriver\u002f8.3.2"}}
integration-test-g8t6t-369894966:
integration-test-g8t6t-369894966: Handling connection for 3000
integration-test-g8t6t-369894966: Handling connection for 3000
integration-test-g8t6t-369894966: 19:50:20.670 INFO [LocalSessionMap.lambda$new$0] - Deleted session from local Session Map, Id: d0c75f008d8950640953b1914f2f233a
integration-test-g8t6t-369894966: 19:50:20.672 INFO [GridModel.release] - Releasing slot for session id d0c75f008d8950640953b1914f2f233a
integration-test-g8t6t-369894966: 19:50:20.675 INFO [SessionSlot.stop] - Stopping session d0c75f008d8950640953b1914f2f233a
integration-test-g8t6t-369894966: [0-0] FAILED in chrome - file:///helloworld.spec.js
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<!-- 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?
Allow dataflow to be launched with a fixed python and beam sdk version.
Apache Beam sdk was locked within `google_cloud_pipeline_components.container.v1.dataflow.dataflow_launcher`
to apache_beam[gcp]<2.34.0 via [this commit](https://github.com/kubeflow/pipelines/commit/63724207bceb189a7e1a78a9b8b374fefc14135e#diff-0feaf0e5009fa36aa80e31cd4e896cced929633a334e425add6e75663ef27983R28) .
Beam is apparently launched with whatever current python and beam_sdk is in this container.
Can we launch dataflow jobs with a fixed python and beam sdk?
At least upgrade to a more recent sdk.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
The locked sdk within DataflowPythonJobOp is quite outdated and is missing features. It starts dataflow
with a fixed old apache beam runtime
### Is there a workaround currently?
- Create a completly custom pipeline component with own docker image which pins exact python version and apache_beam
version.
or
- use FlexTemplate component which allows to specify custom dockerfiles
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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] | open | false | null | [] | null | [] | "2023-09-13T09:40:05" | "2023-09-13T09:40:08" | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
KFP V1 supported setting the image pull policy [link](https://github.com/kubeflow/pipelines/blob/4bee3d8dc2ee9c33d87e1058bac2a94d899dd4a5/sdk/python/kfp/deprecated/dsl/_container_op.py#L518)
This feature is currently not available in KFP V2
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
We used to set image pull policy to always in V1 to avoid cases when outdated images are pulled.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
No current workaround.
---
<!-- 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/9976 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9976/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9976/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9976/events | https://github.com/kubeflow/pipelines/issues/9976 | 1,892,166,555 | I_kwDOB-71UM5wyCub | 9,976 | [bug] (Big) Integer overflow in python components | {
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] | open | false | null | [] | null | [] | "2023-09-12T10:15:29" | "2023-09-12T10:15:29" | null | NONE | null | /kind bug
### Environment
* How do you deploy Kubeflow Pipelines (KFP)? </br>
Using kubeflow sdk
* KFP version: </br>
1.7
* KFP SDK version: </br>
1.8.17
* Python base image used: </br>
Image tag: python:3.8-slim </br>
Image sha: sha256:7672e07ca8cd61d8520be407d6a83d45e6d37faf26bb68b91a2fa8ab89a7798f </br>
Image ID: 1b0f3f18921c
### Steps to reproduce
#### Issue:
An integer parameter (Only certain numbers) when tried to be printed using python function component, kfp mutates the input and prints a different output.
Eg:
#### Scenario1:
Input parameter value: 20230807093130621
</br>
Printed value: 20230807093130620
#### Scenario2:
Input parameter value: 20230807093130621
</br>
Added number: 10
</br>
Printed value: 20230807093130630
### Expected result
#### Scenario1:
Input parameter value: 20230807093130621
</br>
Expected value: 20230807093130621
#### Scenario2:
Input parameter value: 20230807093130621
</br>
Added number: 10
</br>
Expected value: 20230807093130631
### Pipeline yaml definition
```
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: integer-overflow-test-pipeline-
annotations:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
pipelines.kubeflow.org/pipeline_compilation_time: '2023-09-11T20:57:57.297958'
pipelines.kubeflow.org/pipeline_spec: >-
{"inputs": [{"default": "20230807093130621", "name": "x", "optional":
true, "type": "Integer"}, {"default": "10", "name": "num_to_add",
"optional": true, "type": "Integer"}], "name":
"integer_overflow_test_pipeline"}
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
spec:
entrypoint: integer-overflow-test-pipeline
templates:
- name: add-num-to-integer
container:
args:
- '--x'
- '{{inputs.parameters.x}}'
- '--num'
- '{{inputs.parameters.num_to_add}}'
command:
- sh
- '-ec'
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- >
def add_num_to_integer(x, num = 10):
added_num = x + num
print(added_num)
import argparse
_parser = argparse.ArgumentParser(prog='Add num to integer',
description='')
_parser.add_argument("--x", dest="x", type=int, required=True,
default=argparse.SUPPRESS)
_parser.add_argument("--num", dest="num", type=int, required=False,
default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = add_num_to_integer(**_parsed_args)
image: 'python:3.8-slim'
inputs:
parameters:
- name: num_to_add
- name: x
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: 'true'
annotations:
pipelines.kubeflow.org/component_spec: >-
{"implementation": {"container": {"args": ["--x", {"inputValue":
"x"}, {"if": {"cond": {"isPresent": "num"}, "then": ["--num",
{"inputValue": "num"}]}}], "command": ["sh", "-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" >
\"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", "def
add_num_to_integer(x, num = 10):\n added_num = x + num\n
print(added_num)\n\nimport argparse\n_parser =
argparse.ArgumentParser(prog='Add num to integer',
description='')\n_parser.add_argument(\"--x\", dest=\"x\", type=int,
required=True,
default=argparse.SUPPRESS)\n_parser.add_argument(\"--num\",
dest=\"num\", type=int, required=False,
default=argparse.SUPPRESS)\n_parsed_args =
vars(_parser.parse_args())\n\n_outputs =
add_num_to_integer(**_parsed_args)\n"], "image":
"python:3.8-slim"}}, "inputs":
[{"name": "x", "type": "Integer"}, {"default": "10", "name": "num",
"optional": true, "type": "Integer"}], "name": "Add num to integer"}
pipelines.kubeflow.org/component_ref: '{}'
pipelines.kubeflow.org/arguments.parameters: >-
{"num": "{{inputs.parameters.num_to_add}}", "x":
"{{inputs.parameters.x}}"}
pipelines.kubeflow.org/max_cache_staleness: P0D
- name: integer-overflow-test-pipeline
inputs:
parameters:
- name: num_to_add
- name: x
dag:
tasks:
- name: add-num-to-integer
template: add-num-to-integer
arguments:
parameters:
- name: num_to_add
value: '{{inputs.parameters.num_to_add}}'
- name: x
value: '{{inputs.parameters.x}}'
- name: print-integer
template: print-integer
arguments:
parameters:
- name: x
value: '{{inputs.parameters.x}}'
- name: print-integer
container:
args:
- '--x'
- '{{inputs.parameters.x}}'
command:
- sh
- '-ec'
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- >
def print_integer(x):
print(x)
import argparse
_parser = argparse.ArgumentParser(prog='Print integer',
description='')
_parser.add_argument("--x", dest="x", type=int, required=True,
default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = print_integer(**_parsed_args)
image: 'python:3.8-slim'
inputs:
parameters:
- name: x
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.17
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: 'true'
annotations:
pipelines.kubeflow.org/component_spec: >-
{"implementation": {"container": {"args": ["--x", {"inputValue":
"x"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf
\"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\"
\"$@\"\n", "def print_integer(x):\n print(x)\n\nimport
argparse\n_parser = argparse.ArgumentParser(prog='Print integer',
description='')\n_parser.add_argument(\"--x\", dest=\"x\", type=int,
required=True, default=argparse.SUPPRESS)\n_parsed_args =
vars(_parser.parse_args())\n\n_outputs =
print_integer(**_parsed_args)\n"], "image":
"python:3.8-slim"}}, "inputs":
[{"name": "x", "type": "Integer"}], "name": "Print integer"}
pipelines.kubeflow.org/component_ref: '{}'
pipelines.kubeflow.org/arguments.parameters: '{"x": "{{inputs.parameters.x}}"}'
pipelines.kubeflow.org/max_cache_staleness: P0D
arguments:
parameters:
- name: x
value: '20230807093130621'
- name: num_to_add
value: '10'
serviceAccountName: <Intentionally redacted>
```
---
<!-- 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/9975 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9975/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9975/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9975/events | https://github.com/kubeflow/pipelines/issues/9975 | 1,892,107,655 | I_kwDOB-71UM5wx0WH | 9,975 | [backend] Pipeline run's input artifacts in S3 are accessible but output artifacts are not accessible (403 Error) | {
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] | open | false | null | [] | null | [] | "2023-09-12T09:43:48" | "2023-09-12T13:01:04" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines as part of a full Kubeflow deployment provides all Kubeflow components and more integration with each platform
Kubeflow is deployed on top of AWS EKS cluster.
* KFP version: v2
* KFP SDK version: Issue identified in UI, possible source in backend.
### Steps to reproduce
1. In Kubeflow UI Pipeline Runs section (using "[Tutorial] Data passing in python components" pipeline run) click on any of pipeline completed steps.
2. Click on Input artifact s3 url (s3://kf-artifacts-store-..../) and open input artifact and that works fine, i.e. one can see contents of artifact.
3. BUT when click on output artifacts "main-logs" s3 url one gets HTTP 403 Forbidden error.
### Expected result
Both input and output artifact contents are visible in preview and when clicking on S3 url.
### Materials and Reference
Additional observations:
- Issue so far is observed for s3 objects with extension `.log`.
- s3 objects with extension `.tgz` are opened without a problem.
---
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"I've been impacted by this change as well and I think it's a behavior change in the latest version of KFP 2.1.3 only. I was able to get my pipeline running by using KFP 2.0.1.\r\n\r\nI think the \"issue\" might be coming from the following commit https://github.com/kubeflow/pipelines/pull/9886/commits/80881c12ee2a23688cd7bd5e6eeb330228949cf8 that added `--no-deps` to the pip install command.",
"@Lothiraldan Thank you, that was indeed the problem! Everything works as expected when I downgrade to KFP 2.0.1. I do wonder why this change was implemented since it makes it seemingly impossible to properly install packages via 'packages_to_install'. I'll keep this open for now since I'm curious if it's intended behavior or a bug.",
"Having the same issue, feels like a crazy \"feature\" to require manually listing every single transitive dependency before your component can run 😅\r\n\r\nI think the intention behind the PR was to remove the compile time dependencies of `kfp` for the runtime environment, which makes sense. However, because `kfp=={version} --no-deps` is appended to the `packages_to_install` list, all user-specified dependencies get the same `--no-deps` treatment... definitely feels like a regression!"
] | "2023-09-12T08:07:41" | "2023-09-13T11:09:14" | null | NONE | null | ### Environment
#### Relevant package versions
kfp==2.1.3
google-cloud-pipeline-components==2.3.1
google-cloud-aiplatform==1.32.0
kfp-pipeline-spec== 0.2.2
kfp-server-api == 2.0.1
### Steps to reproduce
After upgrading to KFP V2, Initializing a component as follows doesn't work:
```python
from kfp.dsl import component
@component(
base_image="python:3.10",
packages_to_install=["google-cloud-aiplatform"],
)
def my_function():
from google.cloud import aiplatform
```
This gives the error `ModuleNotFoundError: No module named 'google.api_core'`, while in KFP V1 (< 2.0), this worked without any issues. It seems to be an issue with 'packages_to_install'. Adding 'google_api_core' to the 'packages_to_install' then throws the error `ModuleNotFoundError: No module named 'grpc'`, adding that gives `RuntimeError: Please install the official package with: pip install grpcio`, adding that gives `ModuleNotFoundError: No module named 'google.rpc'`, and finally, adding that gives `ERROR: No matching distribution found for google.rpc`. It seems that the 'packages_to_install' do not install their dependencies correctly, or there is an issue with 'google-cloud-aiplatform- and KFP V2.
### Expected result
The 'packages_to_install' are installed correctly and can be imported within the component. With the following versions, and the exact same code, this behavior does work:
kfp==1.8.14
google-cloud-pipeline-components==1.0.26
google-cloud-aiplatform==1.18.3
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https://api.github.com/repos/kubeflow/pipelines/issues/9970 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9970/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9970/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9970/events | https://github.com/kubeflow/pipelines/issues/9970 | 1,889,838,578 | I_kwDOB-71UM5wpKXy | 9,970 | Usage of Kubeflow Pipelines v2 | {
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"> set gpu resources (.set_gpu_limit())\r\n\r\n`set_gpu_limit()` has been renamed to [`set_accelerator_limit`](https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/source/dsl.html?h=set_acc#kfp.dsl.PipelineTask.set_accelerator_limit) so that is covers both GPU and TPU (on Vertex). Meanwhile, `set_gpu_limit` still exists but gives you a deprecation warning and calls into `set_accelerator_limit` under the hood.\r\n\r\n> mount hostPath (.add_volume(), add_volume_mount())\r\n\r\nVolume mount has been moved into an extension package: \r\nhttps://www.kubeflow.org/docs/components/pipelines/v2/platform-specific-features/\r\n\r\n> .set_security_context()\r\n\r\nKFP v2 doesn't have this feature parity yet. My advice would be submitting a separate feature request with the description of your use case.",
"@chensun Is there a way to use data stored on Nas??\r\n"
] | "2023-09-11T07:20:36" | "2023-09-12T01:35:55" | null | NONE | null | Hello,
we have upgraded our Kubeflow Pipelines to version 2.0.1. We are now having a lot of trouble rewriting our code from version 1.8. We are missing the following features (maybe we just didn't find them in v2):
- set gpu resources (.set_gpu_limit())
- mount hostPath (.add_volume(), add_volume_mount())
- .set_security_context()
We want to train on multiple gpus, so we need to set gpu and cpu resources, connect network data storage, and so on.
Is Kubeflow Pipelines v2 good for us? Are these features planned in version 2? Or should we still use Kubeflow Pipelines 1.8? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9962 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9962/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9962/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9962/events | https://github.com/kubeflow/pipelines/issues/9962 | 1,887,425,701 | I_kwDOB-71UM5wf9Sl | 9,962 | [backend] Persistence Agent failing kubeflow-pipeline-mkp-test (2) | {
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] | open | false | null | [] | null | [] | "2023-09-08T10:52:36" | "2023-09-08T10:57:54" | null | MEMBER | null | ### Steps to reproduce
Having in mind issue: [Persistence agent failing kubeflow-pipeline-mkp-test](https://github.com/kubeflow/pipelines/issues/9904) that appeared after changes in the Persistence agent manifests files - I guess that the mkp-test may fail again after the merge of [this](https://github.com/kubeflow/pipelines/pull/9957) PR as it also has changed the manifests files.
At the moment there is no automatic way to move/sync changes done in main manifest files to the manifests in the marketplace helm chart that is why I suggest someone validate the state of the `mkp-test` and apply the changes done in the above PR in the helm chart if needed.
Please close this issue if that is not the case.
cc: @zijianjoy, @chensun
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"@fclesio, I think the best solution for what you are trying to achieve is to compose the tasks together into a sequential DAG using a pipeline. Please take a look at the docs on [data passing and task dependencies](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines/pipeline-basics/#data-passing-and-task-dependencies) and feel free to re-open if there are outstanding issues.",
"Thanks for the answer @connor-mccarthy, but I am afraid that I express myself with a lack of detail. \r\n\r\nFor a single pipeline it's clear that we can set those dependencies; however, if we have multiple and _independent_ pipelines that needs to have some sequencing, that solution does not work. \r\n\r\nOn top of that, let's say that we have a Pipeline (DAG) with almost 1000 lines, if we have 5 similar ones with the same size we're talking about to have a single pipeline with 5000 lines. \r\n\r\nBeing more specific: there is some component that can triggers another pipeline/DAG like [Airflow has the triggerDAGrun](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/operators/trigger_dagrun/index.html)? ",
"I see, @fclesio. Thanks for the detail. KFP doesn't provide this sort of functionality directly, unfortunately."
] | "2023-09-06T20:53:45" | "2023-09-08T14:47:16" | "2023-09-07T22:50:17" | NONE | null | Thanks once again for the great library.
I have a ML pipeline that consists in 3 tasks: ETL > Pre-Processing > Modelling and Deploy.
Since those tasks are sequential, I created 3 pipelines and I used the [kfp.client.run_pipeline ](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L688C9-L688C21) to run each one sequentially.
One thing that I noticed is that the client only _fires_ the pipeline and if the API returns 200 marks it as a success, regardless of the pipeline runtime length.
In my case the run_pipeline did actually fired up all pipelines, but all of them ran at the same time (not desirable behaviour).
There some way to have some kind of "wait status" that only return success if the pipeline called runs until the end?
Thanks once again.
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https://api.github.com/repos/kubeflow/pipelines/issues/9958 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9958/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9958/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9958/events | https://github.com/kubeflow/pipelines/issues/9958 | 1,882,400,753 | I_kwDOB-71UM5wMyfx | 9,958 | chore(frontend): Refactor the class component to functional component | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9956 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9956/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9956/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9956/events | https://github.com/kubeflow/pipelines/issues/9956 | 1,879,683,257 | I_kwDOB-71UM5wCbC5 | 9,956 | [bug] Tensorboard gives "No dashboards are active for the current data set." when passing minio path. | {
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<!-- 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. -->
1.8.1
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
1.8.19
### 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.
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Create a function to check logging of scalars:
def foo(runid) ->NamedTuple("EvaluationOutput", [('mlpipeline_ui_metadata', 'UI_metadata')]):
import subprocess
subprocess.run(["pip","install","tensorboardX==2.5"])
from tensorboardX import SummaryWriter
from minio import Minio
log_dir = " /tensorboard_logs/"
writer = SummaryWriter(log_dir)
for i in range(20):
# Log loss to TensorBoard
acc = (i+2)**2/(i+1)**2 + i-1
writer.add_scalar("Accuracy",acc, i+1)
writer.flush()
from pipelines.utils.minio_utils import upload_local_directory_to_minio
upload_local_directory_to_minio(client, bucket_name, " /tensorboard_logs/","foo_logs")
metadata = {
'outputs': [{
'type': 'tensorboard',
'source': f'http://<Minio_IP>:<Minio_Port>/minio/datasets/foo_logs',
}]
}
out_tuple = namedtuple("EvaluationOutput", ["mlpipeline_ui_metadata"])
return out_tuple(json.dumps(metadata))
### Expected result
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Tensorboard should show a graph for the scalars written to events file. The issue is not with the events file as it works for a locally deployed tensorboard server.
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"**In Kfp 2.0.1 retry on policy is not present.**\r\n\r\n**Old Code**\r\n```\r\n def set_retry(self,\r\n num_retries: int,\r\n policy: Optional[str] = None,\r\n backoff_duration: Optional[str] = None,\r\n backoff_factor: Optional[float] = None,\r\n backoff_max_duration: Optional[str] = None):\r\n \"\"\"Sets the number of times the task is retried until it's declared\r\n failed.\r\n\r\n Args:\r\n num_retries: Number of times to retry on failures.\r\n policy: Retry policy name.\r\n backoff_duration: The time interval between retries. Defaults to an\r\n immediate retry. In case you specify a simple number, the unit\r\n defaults to seconds. You can also specify a different unit, for\r\n instance, 2m (2 minutes), 1h (1 hour).\r\n backoff_factor: The exponential backoff factor applied to\r\n backoff_duration. For example, if backoff_duration=\"60\"\r\n (60 seconds) and backoff_factor=2, the first retry will happen\r\n after 60 seconds, then after 120, 240, and so on.\r\n backoff_max_duration: The maximum interval that can be reached with\r\n the backoff strategy.\r\n \"\"\"\r\n if policy is not None and policy not in ALLOWED_RETRY_POLICIES:\r\n raise ValueError('policy must be one of: %r' %\r\n (ALLOWED_RETRY_POLICIES,))\r\n\r\n self.num_retries = num_retries\r\n self.retry_policy = policy\r\n self.backoff_factor = backoff_factor\r\n self.backoff_duration = backoff_duration\r\n self.backoff_max_duration = backoff_max_duration\r\n return self\r\n```\r\n\r\n**New Code**\r\n```\r\ndef set_retry(self,\r\n num_retries: int,\r\n backoff_duration: Optional[str] = None,\r\n backoff_factor: Optional[float] = None,\r\n backoff_max_duration: Optional[str] = None) -> 'PipelineTask':\r\n\r\n Args:\r\n num_retries : Number of times to retry on failure.\r\n backoff_duration: Number of seconds to wait before triggering a retry. Defaults to ``'0s'`` (immediate retry).\r\n backoff_factor: Exponential backoff factor applied to ``backoff_duration``. For example, if ``backoff_duration=\"60\"`` (60 seconds) and ``backoff_factor=2``, the first retry will happen after 60 seconds, then again after 120, 240, and so on. Defaults to ``2.0``.\r\n backoff_max_duration: Maximum duration during which the task will be retried. Maximum duration is 1 hour (3600s). Defaults to ``'3600s'``.\r\n\r\n Returns:\r\n Self return to allow chained setting calls.\r\n \"\"\"\r\n self._task_spec.retry_policy = structures.RetryPolicy(\r\n max_retry_count=num_retries,\r\n backoff_duration=backoff_duration,\r\n backoff_factor=backoff_factor,\r\n backoff_max_duration=backoff_max_duration,\r\n )\r\n return self\r\n```\r\n\r\nFYI @chensun ",
"retry-random-failures-xgqdv-1470953391 0/2 Completed 0 69s\r\nretry-random-failures-xgqdv-2197860257 0/2 Error 0 59s\r\nretry-random-failures-xgqdv-2342167526 0/2 Completed 0 69s\r\nretry-random-failures-xgqdv-3239894960 0/2 Error 0 58s\r\nretry-random-failures-xgqdv-611022813 0/2 Completed 0 79s"
] | "2023-08-31T12:31:13" | "2023-09-02T13:17:07" | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
* KFP version: 2.0.0
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 2.0.1
### Steps to reproduce
You can run the following code to reproduce the issue.
```
from kfp import dsl
@dsl.component
def random_failure_op(exit_codes: str):
"""A component that fails randomly."""
import random
import sys
exit_code = int(random.choice(exit_codes.split(",")))
print(exit_code)
sys.exit(exit_code)
@dsl.pipeline(
name="retry-random-failures",
description="The pipeline includes two steps which fail randomly. It shows how to use ContainerOp(...).set_retry(...).",
)
def retry_random_failures():
op1 = random_failure_op(exit_codes="0,1,2,3").set_retry(10)
op2 = random_failure_op(exit_codes="0,1").set_retry(5)
```
### Expected result
The component should retry on failure but it never does not even once. In the pipeline spec I also see the following policy
```
retryPolicy:
backoffDuration: 0s
backoffFactor: 2
backoffMaxDuration: 3600s
maxRetryCount: 10
```
### Materials and reference
This is the documentation I referred - https://kubeflow-pipelines.readthedocs.io/en/latest/source/dsl.html#kfp.dsl.PipelineTask.set_retry
### 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/9949 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9949/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9949/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9949/events | https://github.com/kubeflow/pipelines/issues/9949 | 1,875,251,131 | I_kwDOB-71UM5vxg-7 | 9,949 | Deprecated setup.cfg used in pyyaml@5.4.1 version not compatible with Python versions 3.10 and above | {
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"This is fixed. See [requirements](https://github.com/kubeflow/pipelines/blob/3b8cea060fc3088520666fea26e6452bda2fdb15/sdk/python/requirements.in#L22)."
] | "2023-08-31T10:34:16" | "2023-09-07T22:36:55" | "2023-09-07T22:36:54" | NONE | null | ### Description
`pip install -r requirements.txt` fails on Python 3.10 and 3.11 due to deprecated setup.cfg in pyyaml 5.4.1 using `license_file` instead of `license_files` . According to PyYaml this problem was fixed and recommended version is 6.0.1.
Fixing this issue may require to update requirements to accept `pyyaml>=5.4.1`.
### Materials and reference
https://github.com/yaml/pyyaml/issues/724
### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? Private fork
* KFP version: 1.8.19
* KFP SDK version: 1.8.22
### Steps to reproduce
```
$ pip install -r requirements.txt
Collecting PyYAML==5.4.1 (from -r requirements.txt (line 43))
Using cached .../PyYAML-5.4.1.tar.gz (175 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [62 lines of output]
.../python3.9/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg`
!!
********************************************************************************
The license_file parameter is deprecated, use license_files instead.
By 2023-Oct-30, you need to update your project and remove deprecated calls
or your builds will no longer be supported.
See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
********************************************************************************
!!
...
AttributeError: cython_sources
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
```
### Expected result
`pip install -r requirements.txt` installs all dependencies successfully.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9942 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9942/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9942/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9942/events | https://github.com/kubeflow/pipelines/issues/9942 | 1,871,704,455 | I_kwDOB-71UM5vj_GH | 9,942 | [feature] Add support for Habana device cards | {
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] | open | false | null | [] | null | [] | "2023-08-29T13:40:47" | "2023-08-29T13:40:50" | null | NONE | null | ### Feature Area
/area sdk
/area samples
### What feature would you like to see?
I would like to have the option to choose if I would like to use Nvidia gpu cards or Habana pci cards.
The only difference is with the requests to change it from `nvidia.com/gpu` to `habana.ai/gaudi`
At the backend we are running our device plugin and other stuff to manage the driver.
### What is the use case or pain point?
Currently I cannot work on Habana device cards. With this feature I would be able to train models with their solution.
### Is there a workaround currently?
We have custom YAMLs to run on the training-operator (TF jobs, Pytorch, etc), with specific mounts. but we would like to add it also in the UI, notebooks, etc.
---
If you can help me with how to do it, that would be great :)
I am happy to submit that feature request of course.
<!-- Don't delete message below to encourage users to support your feature request! -->
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"You need first to create an experiment and than use it's id in run, passing it as a parameter.",
"> \r\n\r\nPlease provide advice on how to create an [experiment](https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v2beta1-experiments-post) using [Kubeflow API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/) as the error is **Job has no experiment** .I'm literally stuck and need assistance to move on. Also how to create [Job](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-jobs-post).",
"Just check the API specification for the case:\r\nhttps://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-experiments-post",
"> Just check the API specification for the case: https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-experiments-post\r\n\r\ncan you please give me one example on creation of [Experiment ](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-experiments-post)using kubeflow API"
] | "2023-08-29T07:41:43" | "2023-09-07T22:47:11" | null | NONE | null | I am following tutorial of Kubeflow Pipelines API: [[Kubeflow](https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/api-pipelines/)] & [Kubeflow Pipelines API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/)
I've installed Kubeflow on a MicroK8s and started experimenting with the Kubeflow Pipelines API, I was able to upload a pipeline to the central dashboard by using [kubeflow upload API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/), but when I tried utilizing the [Kubeflow RUN API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs-post), I was unable to establish a RUN. As RUN_ID is showing as null but in the [document](https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/api-pipelines/), by running this command curl ${SVC}/apis/v1beta1/runs/${RUN_ID} | jq it is showing the value of RUN_ID.Can anyone offer assistance.
I tried several various approaches, but they were unsuccessful, I was literally struck at my work regarding this issue can anyone please help on this issue..
RUN_ID=$((
curl -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs
\-d @- \<\< EOF
{
"name":"${PIPELINE_NAME}\_run",
"pipeline_spec":{
"pipeline_id":"${PIPELINE_ID}"
}
}
EOF
) | jq -r .run.id)
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 388 100 277 100 111 23083 9250 --:--:-- --:--:-- --:--:-- 32333
$ echo $RUN_ID
null
$ curl -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs
\-d @- \<\< EOF
{
"name":"${PIPELINE_NAME}\_run",
"pipeline_spec":{
"pipeline_id":"${PIPELINE_ID}"
}
}
EOF
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https://api.github.com/repos/kubeflow/pipelines/issues/9940 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9940/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9940/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9940/events | https://github.com/kubeflow/pipelines/issues/9940 | 1,870,687,285 | I_kwDOB-71UM5vgGw1 | 9,940 | [bug] Always 404 on kfp sdk | {
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"Which version of the KFP BE do you have deployed?\r\n\r\ncc @chensun"
] | "2023-08-29T00:25:14" | "2023-09-07T22:44:39" | null | NONE | null | I was using kfp==2.0.0b12 and everything was working great. But as soon as i got into 2.0.1 I am always getting 404 when trying to use methods from client, like this:
`kfp_server_api.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'x-powered-by': 'Express', 'content-security-policy': "default-src 'none'", 'x-content-type-options': 'nosniff', 'content-type': 'text/html; charset=utf-8', 'content-length': '170', 'date': 'Mon, 28 Aug 2023 23:52:33 GMT', 'x-envoy-upstream-service-time': '2', 'server': 'istio-envoy'})
HTTP response body: <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Error</title>
</head>
<body>
<pre>Cannot GET /pipeline/apis/v2beta1/pipelines</pre>
</body>
</html>`
I am now using kfp==2.0.1 kfp-pipeline-spec==0.2.2 kfp-server-api==2.0.1 and I am creating the client this way:
`session = requests.Session()
response = session.get(HOST)
headers = {
"Content-Type": "application/x-www-form-urlencoded",
}
data = {"login": USERNAME, "password": PASSWORD}
session.post(response.url, headers=headers, data=data)
return session.cookies.get_dict()["authservice_session"]
client = Client(host=f"{HOST}/pipeline",
cookies=f"authservice_session={session_cookie}",
namespace=NAMESPACE)
return client
`
It used to work great, but now using kfp sdk 2.0.1 if I try something like list_pipelines() I am always getting the error.
What am I doing wrong here?
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9937 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9937/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9937/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9937/events | https://github.com/kubeflow/pipelines/issues/9937 | 1,869,601,448 | I_kwDOB-71UM5vb9qo | 9,937 | [feature] Make Persistence Agent use SA Token when calling KF APIServer endpoints | {
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"/assign @difince "
] | "2023-08-28T11:48:07" | "2023-09-07T23:49:34" | "2023-09-07T23:49:34" | 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?
Currently, when the Persistence Agent (PA) calls KFPipeline APIs - **readArtifacts** and **ReportMetrics** - It authenticates as a user.
See:
https://github.com/kubeflow/pipelines/blob/110e0824812883b74c73b26603a78d8cc00548d5/backend/src/agent/persistence/client/pipeline_client.go#L196-L204
The proper way for service-to-service authentication/authorization is the use of a Service Account Token. PA SA [token](https://github.com/kubeflow/pipelines/blob/110e0824812883b74c73b26603a78d8cc00548d5/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml#L39-L41C19) has been already introduced by this [PR](https://github.com/kubeflow/pipelines/pull/9699) .
My suggestion is to use the PA SA token for **readArtifacts** and **ReportMetrics** as well.
Thus the communication between PA and KFPipeline Api service will be done in the correct way. The code will become clearer/shorter and the unnecessary [requests](https://github.com/kubeflow/pipelines/blob/110e0824812883b74c73b26603a78d8cc00548d5/backend/src/agent/persistence/client/kubernetes_core.go#L33-L46) to the kubeapi will be removed.
### What is the use case or pain point?
### Is there a workaround currently?
---
<!-- 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/9933 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9933/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9933/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9933/events | https://github.com/kubeflow/pipelines/issues/9933 | 1,867,215,948 | I_kwDOB-71UM5vS3RM | 9,933 | module 'kfp.components' has no attribute 'create_component_from_func' | {
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"For KFP SDK v2, please use the `@dsl.component` decorator. See [documentation on Lightweight Python Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/lightweight-python-components/) for more information."
] | "2023-08-25T15:02:38" | "2023-09-07T22:42:16" | "2023-09-07T22:42:15" | NONE | null | ### Environment
I am trying to build the Kubeflow pipeline. i am using version 2.0.1.
I am getting this error:
module 'kfp.components' has no attribute 'create_component_from_func'
And along i i followed the document of KFPv2 there are some code already written in docs for the testing it has already created some function and pipelines I copy paste all things from docs for test then I compiled then it's created yaml file I simply upload in kubeflow ui which install in a cluster
But I am getting this error :
Cannot get MLMD object from meta store
again and again I getting same error also it has shown a pop-up and says:
Unkown content type recived
Is there any other way to import this or this is a version error?
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https://api.github.com/repos/kubeflow/pipelines/issues/9924 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9924/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9924/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9924/events | https://github.com/kubeflow/pipelines/issues/9924 | 1,863,477,869 | I_kwDOB-71UM5vEmpt | 9,924 | [backend] Cannot inject environment variables and volumes into components with the KFP CLI | {
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* How did you deploy Kubeflow Pipelines (KFP)?
Full Kubeflow Deployment
* KFP version: 1.7.0 (we are using Kubeflow 1.7.0)
* KFP SDK version: 2.0.1
### Steps to reproduce
The injection of environment variables and secrets into components is not working correctly with kfpv2. Please look below for the python code and the generated yaml.
We could use volumes and environment variables in KFPv1 but when updating to kfpv2 and Kubeflow 1.7 the (updated) code does not work anymore. The Pipeline yaml looks fine from our perspective but in Kubernetes the pipeline component pod does not get the required configurations. (volume mount, env)
### Expected result
The Kubernetes Pod has access to the volumes and environment variables and we can use them inside a Kubeflow pipeline component.
### Materials and Reference
We are using this to add the secret in the kfp cli.
```
def add_gsc_access(task):
task.set_env_variable(
name="GOOGLE_APPLICATION_CREDENTIALS", value="/var/secrets/google/key.json"
)
kubernetes.use_secret_as_volume(
task,
secret_name="pubsub-key",
mount_path="/var/secrets/google",
)
```
Generated Pipeline YAML:
```
components:
comp-download-from-gcs:
executorLabel: exec-download-from-gcs
inputDefinitions:
parameters:
gcs_path:
parameterType: STRING
outputDefinitions:
artifacts:
data:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
...
deploymentSpec:
executors:
exec-download-from-gcs:
container:
command:
- ...
```
---
<!-- 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/9906 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9906/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9906/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9906/events | https://github.com/kubeflow/pipelines/issues/9906 | 1,857,198,876 | I_kwDOB-71UM5uspsc | 9,906 | [frontend] No Logs tab in UI | {
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"Hi @Saurav-D,\r\n\r\nLogs are available in KFP 2.0.0. It is expected to be included in Kubeflow 1.8 (releasing in 2023 Q4)"
] | "2023-08-18T19:33:17" | "2023-08-24T22:48:53" | "2023-08-24T22:48:52" | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
AWS, Terraform, Vanilla
* KFP version:
KFP 1.7
pipeline: 2.0.0-alpha.7
### Steps to reproduce
Run any pipeline
### Expected result
Logs tab with logs. Right now, I just see Input/Output and Task Details. Also, I don't see pod ID.
### 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/9904 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9904/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9904/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9904/events | https://github.com/kubeflow/pipelines/issues/9904 | 1,856,240,751 | I_kwDOB-71UM5uo_xv | 9,904 | [backend] Persistence agent failing `kubeflow-pipeline-mkp-test` | {
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"### Exploration\r\n\r\nI granted `roles/iam.serviceAccountTokenCreator` to the GCE default service account: https://cloud.google.com/iam/docs/service-account-permissions#token-creator-role, and retried the mkt-test. If this permission unblocks the test, then we will need to do one of the followings:\r\n\r\n1. Make SA token requirement configurable (which means it can become optional), it allows us to exclude volume token projection for KFP standalone mode. We still need to clearly document the permission needed in the full kubeflow mode.\r\n2. Develop a solution for full Kubeflow to make workload identity association for the persistence-agent-sa, or the default-editor which is configured by default in full Kubeflow: https://googlecloudplatform.github.io/kubeflow-gke-docs/docs/pipelines/authentication-pipelines/#cluster-setup-to-use-workload-identity-for-full-kubeflow. Update all existing clusters to grant this additional permission. Put up a warning to all users about this breaking change and how should they unblock the KFP deployment. Update documentations for KFP standalone and full Kubeflow.",
"Hi @zijianjoy Thanks for reporting this issue. \r\nWhat is the result - does your change unblock the test? If not the root cause could be some kustomize file I missed ?! \r\nI would like to clearly state that in the current (the new) implementation, PA( Persistence Agent) authentication is not related to the multiuser mode at all. No matter the multiuser mode, PA is the only one that has the permissions and the right identity to call these two endpoints (no matter the namespace the reported workflow belongs to). \r\nFrom the log message, it looks like in GCE the SA token hasn't been created. As I do not have any knowledge of GCE .. I will totally count on your decision and if really needed I can work to make PA auth configurable .. \r\n",
"Yes, in a normal kubeflow there is a serviceaccount\r\n\r\n```\r\n volumeMounts:\r\n - mountPath: /var/run/secrets/kubeflow/tokens\r\n name: persistenceagent-sa-token\r\n serviceAccountName: ml-pipeline-persistenceagent\r\n volumes:\r\n - name: persistenceagent-sa-token\r\n projected:\r\n sources:\r\n - serviceAccountToken:\r\n path: persistenceagent-sa-token\r\n expirationSeconds: 3600\r\n audience: pipelines.kubeflow.org\r\n```\r\n\r\nno such file looks like a GKE specific issue.\r\n\r\nCan you check manually whether `/var/run/secrets/kubeflow/tokens` exists in the pod? `ls -lah` recursively for that directory tree might help\r\n\r\n\"volume token projection\" is also used in other parts of Kubeflow and should be there by default on a kubernetes cluster.",
"Thank you both @difince and @juliusvonkohout for the help. \r\n\r\nThe IAM role grant doesn't make a difference in terms of the error message. Persistence agent is in crashloop as well.\r\n\r\nI added the volume mount sections mentioned by @juliusvonkohout to a released KFP persistence agent's yaml file. After manually adding the volume mount, the SA token exists.\r\n\r\nNext step is to try using a persistence agent image that is built by `kubeflow-pipeline-mkp-test` in a self-deployed cluster to see if it works.",
"Current suspect:\r\n\r\nWe haven't upgrade the mkp manifest for persistence-agent in https://github.com/kubeflow/pipelines/blob/master/manifests/gcp_marketplace/chart/kubeflow-pipelines/templates/pipeline.yaml#L532C1-L563. It might be the reason causing this failure.",
"Update: PR https://github.com/kubeflow/pipelines/pull/9908 is passing `mkp-test`."
] | "2023-08-18T07:49:37" | "2023-08-21T20:08:43" | "2023-08-21T20:08:43" | COLLABORATOR | null | ### Environment
Currently `/test kubeflow-pipeline-mkp-test` is failing on HEAD.
### Steps to reproduce
In https://github.com/kubeflow/pipelines/commit/cb18d00bbbaed9cd77fc50dce739ed62c72b2356#diff-7df17678529866306ef6665ffbeeda377a1e869aca24856f7cf8a99891877fe8, service account token projection is introduced to enable Auth. However, it is failing the `kubeflow-pipeline-mkp-test` test because persistent agent is unable to find the service account token. Error message as below:
cc @difince @chensun
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9893 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9893/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9893/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9893/events | https://github.com/kubeflow/pipelines/issues/9893 | 1,855,183,517 | I_kwDOB-71UM5uk9qd | 9,893 | [bug] Can't increase/attach shared memory to pipeline task in kfp sdk v2, e.g. PyTorch training fails. | {
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"Hi @hsteude!\r\n\r\nYou can specify memory requests using SDK: `.set_memory_request()` and `.set_memory_limit()`: https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/source/dsl.html?h=set_memory#kfp.dsl.PipelineTask.set_memory_request",
"Hi @gkcalat and thanks for your answer!\r\n\r\nUnfortunately, using `.set_memory_request()` and `.set_memory_limit()` doesn't address the actual issue I am facing. These methods set the general memory allocation for the container, not the *shared memory*, which is the limiting factor here. PyTorch's DataLoader uses shared memory when `num_workers > 0`, and it's this shared memory that gets exhausted.\r\n\r\nThe example above still fails when using the methods to increase memory requests and limits.\r\n\r\nCould this issue be reopened since the problem still persists even when applying the suggested memory settings?\r\n\r\nThanks a lot in advance!\r\n\r\nCheers,\r\nHenrik\r\n"
] | "2023-08-17T15:01:45" | "2023-09-06T11:48:12" | "2023-08-24T22:54:22" | CONTRIBUTOR | null | ### Environment
* How do you deploy Kubeflow Pipelines (KFP)?
Manifests
* KFP version:
2.0.0
* KFP SDK version:
2.0.1
### Steps to reproduce
Create and run a simple PyTorch (lightning) training pipeline:
```python
from kfp import dsl
from kfp.client import Client
# define pytorch (lightning) training component
@dsl.component(
packages_to_install=["torch==2.0.0", "pytorch_lightning"],
base_image="python:3.9",
)
def pytorch_training(num_workers: int, input_size: int, batch_size: int):
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
import pytorch_lightning as pl
class ExampleDataset(Dataset):
def __init__(self, size: int = 1000, number_samples: int = 100):
self.x = torch.randn(number_samples, size, size)
self.size = 1000
def __len__(self):
return self.x.shape[0]
def __getitem__(self, idx):
return self.x[idx, :, :]
class ExampleModel(pl.LightningModule):
def __init__(self, inputsize: int):
super(ExampleModel, self).__init__()
self.linear = nn.Linear(inputsize, inputsize)
def forward(self, x):
return self.linear(x)
def training_step(self, batch, batch_idx):
x = batch
loss = F.mse_loss(self(x), x)
return loss
def configure_optimizers(self):
return torch.optim.SGD(self.parameters(), lr=0.1)
dataset = ExampleDataset(size=input_size, number_samples=100)
train_loader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size)
model = ExampleModel(inputsize=input_size)
trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, train_loader)
# define pipeline
@dsl.pipeline
def shared_memory_pipeline(
num_workers: int = 0, input_size: int = 1000, batch_size: int = 100
):
pytorch_training_task = pytorch_training(
num_workers=num_workers, input_size=input_size, batch_size=batch_size
)
# compile and run pipeline
client = Client()
client.create_run_from_pipeline_func(
shared_memory_pipeline,
arguments=dict(num_workers=5, input_size=1_000, batch_size=100),
experiment_name="shared-memory",
enable_caching=True,
)
```
### Expected result
Pipelne gets compiled and executed without errors.
### Materials and reference
* This is the error I get:
```
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/usr/local/lib/python3.9/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/usr/local/lib/python3.9/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/usr/local/lib/python3.9/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/usr/local/lib/python3.9/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/usr/local/lib/python3.9/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/usr/local/lib/python3.9/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 503) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
```
* There is a workaround in skd v1: https://github.com/kubeflow/pipelines/issues/6880
* Default values for shared memory for containerd and Docker is 64MD. Thus, the pipeline above works fine, if a batch is small enough (e.g. for batch_size=1). It will also run just fine if the number of workers for the Data Loader is set to 0 and no shared memory is required.
### Labels
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"/assign @difince ",
"Fixed by https://github.com/kubeflow/pipelines/pull/9892"
] | "2023-08-17T14:04:00" | "2023-08-17T17:09:24" | "2023-08-17T17:09:24" | MEMBER | null | ### Environment
The issue is introduced by [PR](https://github.com/kubeflow/pipelines/pull/9699)
1. `DefaultTokenRefresherInterval` of 1 hour is initialized in nanoseconds (3600000000000) see here:
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/backend/src/agent/persistence/main.go#L68
2. But the following line of code transforms it to just seconds (3600) ( done by `DefaultTokenRefresherInterval.Seconds()`)
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/backend/src/agent/persistence/main.go#L154
3. The problem comes when we create the TokenRefresher which uses `time.Duration()`.
`time.Duration` expects nanoseconds - > because of **2.** the passed interval is a smaller number and this makes the Ticker executes much more often than it should.
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/backend/src/agent/persistence/main.go#L103
* How do you deploy Kubeflow Pipelines (KFP)?
locally
* KFP version: latest https://github.com/kubeflow/pipelines/commit/cb18d00bbbaed9cd77fc50dce739ed62c72b2356
### 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
The ticker to execute every hour ( set as a default) .
### Materials and reference
**Side note:** I would like the input parameter to be in seconds, keeping it consistent with the persistence agent deployment yaml file where this value is provided in seconds.
https://github.com/kubeflow/pipelines/blob/cb18d00bbbaed9cd77fc50dce739ed62c72b2356/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml#L49
Future enhancement could be to keep these two values in sync by an env variable.
### 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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The listruns query is also very slow (seconds for large run_details tables).
It must also be migrated to joins or something other more performant
Already this small database leads to 15 seconds per listruns api call
It regressed from 5 to 15 seconds with Kubeflow 1.5 -> 1.7.
```
mysql> SELECT
-> namespace,
-> COUNT(*) AS entry_count,
-> SUM(LENGTH(CAST(namespace AS CHAR))) AS column_size
-> FROM
-> run_details
-> GROUP BY
-> namespace
-> ORDER BY
-> column_size DESC;
+------------------------------------------------+-------------+-------------+
| namespace | entry_count | column_size |
+------------------------------------------------+-------------+-------------+
| namespace-xxx | 93931 | 1033241 |
| namespace-yyy | 22645 | 362320 |
| namespace-zzz | 16700 | 317300 |
| namespace-www | 9894 | 187986
````
### Steps to reproduce
Just let users run a lot of pipelines per year, e.g. 200,000 and see how slow the listruns query becomes.
### Expected result
Listruns in less than one second for 200,000 runs, which is a very small database task
### Materials and Reference
Impacted by this bug? Give it a 👍. | {
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* How did you deploy Kubeflow Pipelines (KFP)? Kubeflow 1.7/1.8
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0alpha7, but its also in the master branch
<!-- 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: irrelevant, since it is a mlpipeline-ui issue
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
This image here shows Bobs S3 artifacts produced by a pipeline run (e.g. tensorflow model)
Sadly the KFP UI allows Alice to read Bobs artifacts content
But how?
If Alice spies the S3 artifact location from Bob, as seen at the bottom of the image,
She can just can remove the yellow namespace parameter and the UI server will skip permission checks
So you still need to know the S3/GCS path which you can for example get from an insecure (that is the default in KFP) not-disabled ML-metadata
### Expected result
The namespace parameter of the readartifact api call should not be user-configurable.
That should have been achieved for the APIserver in another PR from me and @difince https://github.com/difince/pipelines/blob/d0424bf86de5217d41dd03b50f91ac5ec3489df3/backend/src/apiserver/server/run_server.go#L313
But with mlpipeline-ui you can circumvent the permission check in the apiserver as seen on the screenshot.
You can just remove the namespace parameter and the artifact-proxy mode is disabled.
So mlpipeline-ui must set the namespace parameter based on the namespace dropdown selection for an authenticated user and not expose the namespace parameter to the user in the URL.
https://github.com/kubeflow/pipelines/issues/8074 is also important to secure the apiserver in the future and
in the long term i would really like to get rid of the artifactproxy altogether as stated in https://github.com/kubeflow/pipelines/issues/8406#issuecomment-1643579605 and https://github.com/kubeflow/pipelines/issues/4790#issuecomment-1643574994 but that is for another day.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
This has been discussed in the last KFP meeting (16th August 2023) and acknowledged by @zijianjoy on a technical level.
From the Kubecon presentation https://static.sched.com/hosted_files/kccnceu2023/f8/Hardening%20Kubeflow%20Security%20for%20Enterprise%20Environments%20-%20Julius%20von%20Kohout%2C%20DPDHL%20%26%20Diana%20Dimitrova%20Atanasova%2C%20VMware.pdf
---
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"The frontend test failure seems to be flaky. \r\n\r\nIn another run, it failed on basic sample tests:\r\n```\r\nintegration-test-q5mqw-2112930252: Traceback (most recent call last):\r\nintegration-test-q5mqw-2112930252: File \"exit_handler.py\", line 26, in <module>\r\nintegration-test-q5mqw-2112930252: @component(kfp_package_path=_KFP_PACKAGE_PATH)\r\nintegration-test-q5mqw-2112930252: TypeError: component() got an unexpected keyword argument 'kfp_package_path'\r\nintegration-test-q5mqw-2392976712: /usr/local/lib/python3.7/dist-packages/kfp/dsl/_container_op.py:1268: FutureWarning: Please create reusable components instead of constructing ContainerOp instances directly. Reusable components are shareable, portable and have compatibility and support guarantees. Please see the documentation: https://www.kubeflow.org/docs/pipelines/sdk/component-development/#writing-your-component-definition-file The components can be created manually (or, in case of python, using kfp.components.create_component_from_func or func_to_container_op) and then loaded using kfp.components.load_component_from_file, load_component_from_uri or load_component_from_text: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_file\r\nintegration-test-q5mqw-2392976712: category=FutureWarning,\r\n```\r\nhttps://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9699/kubeflow-pipeline-e2e-test/1691812763929677824"
] | "2023-08-16T16:54:10" | "2023-08-17T09:33:39" | "2023-08-17T09:33:39" | COLLABORATOR | null |
https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9873/kubeflow-pipeline-e2e-test/1691525546791407616
```
integration-test-g8t6t-369894966: [0-0] AssertionError in "deploy helloworld sample run.shows a 4-node static graph"
integration-test-g8t6t-369894966: AssertionError [ERR_ASSERTION]: should have a 4-node graph, instead has: 0
integration-test-g8t6t-369894966: at Context.<anonymous> (/src/helloworld.spec.js:70:5)
integration-test-g8t6t-369894966: at processTicksAndRejections (node:internal/process/task_queues:96:5)
integration-test-g8t6t-369894966: 19:49:32.603 WARN [SeleniumSpanExporter$1.lambda$export$3] - {"traceId": "2ae031a384cadb8eb7f70e9a187d622b","eventTime": 1692128972602956682,"eventName": "HTTP request execution complete","attributes": {"http.flavor": 1,"http.handler_class": "org.openqa.selenium.remote.http.Route$PredicatedRoute","http.host": "127.0.0.1:4444","http.method": "POST","http.request_content_length": "8346","http.scheme": "HTTP","http.status_code": 404,"http.target": "\u002fsession\u002fd0c75f008d8950640953b1914f2f233a\u002fexecute\u002fsync","http.user_agent": "webdriver\u002f8.3.2"}}
integration-test-g8t6t-369894966:
integration-test-g8t6t-369894966: 19:49:33.711 WARN [SeleniumSpanExporter$1.lambda$export$3] - {"traceId": "72fe6381777527734f56dd38817f7a8e","eventTime": 1692128973710578070,"eventName": "HTTP request execution complete","attributes": {"http.flavor": 1,"http.handler_class": "org.openqa.selenium.remote.http.Route$PredicatedRoute","http.host": "127.0.0.1:4444","http.method": "POST","http.request_content_length": "8346","http.scheme": "HTTP","http.status_code": 404,"http.target": "\u002fsession\u002fd0c75f008d8950640953b1914f2f233a\u002fexecute\u002fsync","http.user_agent": "webdriver\u002f8.3.2"}}
integration-test-g8t6t-369894966:
integration-test-g8t6t-369894966: Handling connection for 3000
integration-test-g8t6t-369894966: Handling connection for 3000
integration-test-g8t6t-369894966: 19:50:20.670 INFO [LocalSessionMap.lambda$new$0] - Deleted session from local Session Map, Id: d0c75f008d8950640953b1914f2f233a
integration-test-g8t6t-369894966: 19:50:20.672 INFO [GridModel.release] - Releasing slot for session id d0c75f008d8950640953b1914f2f233a
integration-test-g8t6t-369894966: 19:50:20.675 INFO [SessionSlot.stop] - Stopping session d0c75f008d8950640953b1914f2f233a
integration-test-g8t6t-369894966: [0-0] FAILED in chrome - file:///helloworld.spec.js
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<!-- 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?
Allow dataflow to be launched with a fixed python and beam sdk version.
Apache Beam sdk was locked within `google_cloud_pipeline_components.container.v1.dataflow.dataflow_launcher`
to apache_beam[gcp]<2.34.0 via [this commit](https://github.com/kubeflow/pipelines/commit/63724207bceb189a7e1a78a9b8b374fefc14135e#diff-0feaf0e5009fa36aa80e31cd4e896cced929633a334e425add6e75663ef27983R28) .
Beam is apparently launched with whatever current python and beam_sdk is in this container.
Can we launch dataflow jobs with a fixed python and beam sdk?
At least upgrade to a more recent sdk.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
The locked sdk within DataflowPythonJobOp is quite outdated and is missing features. It starts dataflow
with a fixed old apache beam runtime
### Is there a workaround currently?
- Create a completly custom pipeline component with own docker image which pins exact python version and apache_beam
version.
or
- use FlexTemplate component which allows to specify custom dockerfiles
---
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/area sdk
### What feature would you like to see?
When using the GPU Operator a lot of labels are generated[^1] to the nodes such as:
- `nvidia.com/gpu.memory` (Integer)
- `nvidia.com/cuda.runtime.major` (Integer)
etc..
It is possible in Kubernetes to add nodeAffinity with comparaison (greater, lower). The following syntax can be used for a pod
```yaml
apiVersion: v1
kind: Pod
metadata:
name: with-affinity-anti-affinity
spec:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: nvidia.com/gpu.memory
operator: Gt
values:
- 50
...
```
This would allows to specify the minimum amount of memory required, or the minimum cuda version, or the interval allowed.
The kfp[kubernetes] package have the `add_node_selector` function, that could (probably?) be extended to support this ?
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍.
[^1]: [Generated labels](https://github.com/NVIDIA/gpu-feature-discovery#generated-labels) | {
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"@samoturk did you check your cluster's resource usage? Is that possible it run out of memory, etc.?",
"No the cluster didn't run out of memory. I reproduced this error on two cluster, a smaller one with 64GB and a one with >1TB RAM. The attached example uses alpine Linux image which is quite frugal and that pipeline runs pods sequentially. The error nonetheless appears at exactly the same step on both clusters. Same happens with a more complex pipeline using Debian derived images - always 22nd step."
] | "2023-08-16T08:07:02" | "2023-08-21T06:49:12" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Manifests
* KFP version:
2.0.0
* KFP SDK version:
2.0.1
### Steps to reproduce
Running a pipeline that has more than 22 tasks. Can be completely sequential or branched. Minimal example to reproduce:
```python
from utils.auth_session import get_istio_auth_session
from kfp.client import Client
from kfp import dsl, compiler
from kfp.dsl import Input, Output, Dataset, Model
import os
import datetime
COMPONENTS_IMAGE = 'alpine:3.18.2'
auth_session = get_istio_auth_session(
url=os.environ['KUBEFLOW_ENDPOINT'],
username=os.environ['KUBEFLOW_USERNAME'],
password=os.environ['KUBEFLOW_PASSWORD']
)
namespace = os.environ.get('KUBEFLOW_NAMESPACE', None) or \
os.environ['KUBEFLOW_USERNAME'].split("@")[0].replace(".", "-")
@dsl.container_component
def task00(output: Output[Dataset]):
return dsl.ContainerSpec(
image=COMPONENTS_IMAGE,
command=['sh', '-c', 'echo hello >> $0'],
args=[output.path])
@dsl.container_component
def task01(dataset: Input[Dataset], output: Output[Dataset]):
return dsl.ContainerSpec(
image=COMPONENTS_IMAGE,
command=['sh', '-c', 'cp -a $0 $1'],
args=[dataset.path, output.path])
@dsl.pipeline
def debug_containerized_pipeline():
result00 = task00()
result01 = task01(dataset=result00.output)
result02 = task01(dataset=result01.output)
result03 = task01(dataset=result02.output)
result04 = task01(dataset=result03.output)
result05 = task01(dataset=result04.output)
result06 = task01(dataset=result05.output)
result07 = task01(dataset=result06.output)
result08 = task01(dataset=result07.output)
result09 = task01(dataset=result08.output)
result10 = task01(dataset=result09.output)
result11 = task01(dataset=result10.output)
result12 = task01(dataset=result11.output)
result13 = task01(dataset=result12.output)
result14 = task01(dataset=result13.output)
result15 = task01(dataset=result14.output)
result16 = task01(dataset=result15.output)
result17 = task01(dataset=result16.output)
result18 = task01(dataset=result17.output)
result19 = task01(dataset=result18.output)
result20 = task01(dataset=result19.output)
result21 = task01(dataset=result20.output)
result22 = task01(dataset=result21.output)
if __name__ == "__main__":
client = Client(host=f"{os.environ['KUBEFLOW_ENDPOINT']}/pipeline", namespace=namespace,
cookies=auth_session["session_cookie"], verify_ssl=False)
compiler.Compiler().compile(debug_containerized_pipeline, 'pipeline-debug.yaml')
run = client.create_run_from_pipeline_package(
'pipeline-debug.yaml',
enable_caching=False,
arguments={
},
run_name=f'Debug simple {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}'
)
```
### Expected result
Pipelines to run beyond 22nd task.
### Materials and Reference
In the frontend the pipeline simply fails, but there are no logs of the pod that actually failed. Looking into loki reveals that the pod raises some error before our code actually runs: `F0815 13:04:52.601362 34 main.go:76] KFP driver: driver.Container(pipelineName=debug-containerized-pipeline, runID=fd3db286-a450-4b57-b66e-8dd11fbcc1e8, task="task01-21", component="comp-task01-21", dagExecutionID=1359, componentSpec) failed: failed to resolve inputs: failed to resolve input artifact dataset with spec task_output_artifact:{producer_task:"task01-20" output_artifact_key:"output"}: cannot find producer task "task01-20"`
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9872 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9872/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9872/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9872/events | https://github.com/kubeflow/pipelines/issues/9872 | 1,851,485,953 | I_kwDOB-71UM5uW28B | 9,872 | [feature] Update default base image for lightweight components | {
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"@ianonavy, this would unfortunately be a breaking change and cannot be done within KFP SDK major version 2. While the Python language features in 3.11 should be backward compatible with those in 3.7, there almost certainly exists some user code which has [dependencies](https://kubeflow-pipelines.readthedocs.io/en/master/source/dsl.html#kfp.dsl.component.packages_to_install) that require a Python version <3.11."
] | "2023-08-15T13:56:22" | "2023-08-24T23:05:49" | "2023-08-24T23:05:48" | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
I'd like to see the default base image for Python function-based components be updated to Python 3.11. Example [here](https://github.com/kubeflow/pipelines/commit/7e9ea23612da0be10e784aea1f1a6812964b58fa).
### What is the use case or pain point?
When authoring new lightweight Python components, it is tedious to have to set `base_image="python3.11"` for each component. I can't find a setting to up the default `base_image`, and for compliance reasons we need to use a version of Python that still receives security patches.
I can see that the SDK overall supports as far back as Python 3.7, and I am guessing that is why the base image is so old. Perhaps for compatibility, the default base image could be derived from `sys.version`? So for example if I'm running the KFP SDK in a Python 3.10 virtual environment, maybe it would default to `python:3.10`
### Is there a workaround currently?
Currently we are wrapping the `component` decorator with `functools.wraps` to set the default base image.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"Hi @GfxKai!\r\n\r\nHave you considered keeping track of the Model artfiact uris and passing it to the model eval pipeline?",
"Given that artifact URIs are generated by Kubeflow internally and the whole point of MLMD is to track them, I feel like the best practice should be to query KFP + MLMD to retrieve those URIs, especially if pipelines are running frequently and evaluation happens at a different cadence to training.\r\n\r\nI've since managed to work around this issue—turns out `PodDefault` can in fact be used to configure pipeline pods using label matching. Using this method, the required service account token can be directly projected into pipeline component pods:\r\n```\r\nmatchLabels:\r\n pipelines.kubeflow.org/v2_component: 'true'\r\n```\r\n\r\nI'm sure there are many other use-cases that would benefit from more granular configuration of pipeline pods via `PodDefault`. Some documentation on this would be great! 👍"
] | "2023-08-15T13:07:44" | "2023-09-13T15:15:51" | null | NONE | null | ### Feature Area
/area sdk
/area components
### What feature would you like to see?
In KFP v2, there doesn't seem to be any way of using the KFP API from inside a pipeline component.
It would be great if the `PodDefault` for a Kubeflow Profile could be applied to pipeline task pods, enabling the KF service account token to be mounted into pipeline task pods as well as Notebook pods.
### What is the use case or pain point?
I'm trying to create a model evaluation pipeline which:
1. Imports the latest available test data
2. Imports the latest successfully trained model **from a different pipeline**
3. Calculates performance metrics for the existing model
With this workflow, I can create recurring runs to measure model degradation over time.
### Is there a workaround currently?
Currently, the best I can do is to just compute the model performance at train time within the model pipeline. However, this doesn't allow for re-evaluating the performance when new data is added.
---
Love this idea? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9864 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9864/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9864/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9864/events | https://github.com/kubeflow/pipelines/issues/9864 | 1,850,520,185 | I_kwDOB-71UM5uTLJ5 | 9,864 | [bug] Pipeline being compiled with cyclical reference | {
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] | open | false | null | [] | null | [] | "2023-08-14T20:50:52" | "2023-08-14T20:50:55" | null | NONE | null | ### Environment
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.8.22
<!-- 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
Just compiling the pipeline.
<!--
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
Some error in the compilation, not when the yaml is being uploaded in the UI.
<!-- What should the correct behavior be? -->
### Materials and reference
I was building a pipeline and I just found out that I mistakenly have done a circular reference in the tasks using the `.after()` method and when I compiled the pipeline I did not took any error.
I do not know if is this a designed behaviour, but if is would you mind to share the rationale behind the decision to let the pipeline compile even in to a state that is not "uploadable"?
Thanks for the great effort.
Snippet:
```python
task_1.after(start_task)
task_2.after(task_1)
start_task.after(task_2)
```
### 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/9863 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9863/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9863/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9863/events | https://github.com/kubeflow/pipelines/issues/9863 | 1,850,505,726 | I_kwDOB-71UM5uTHn- | 9,863 | Pipeline component that trigger another pipeline | {
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"@fclesio, the closest thing KFP has to this is the ability to use pipelines within other pipelines (i.e., pipeline as a component). Here is some [documentation on this](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines/pipeline-basics/#pipelines-as-components)."
] | "2023-08-14T20:39:26" | "2023-09-07T22:50:51" | "2023-09-07T22:50:51" | NONE | null | First at all thanks for the collaboration and put so much effort in this tool.
I would like to know if there's some component that emulates the behavior of the [TriggerDagRunOperator](https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/operators/trigger_dagrun/index.html#airflow.operators.trigger_dagrun.TriggerDagRunOperator) in Apache Airflow?
The reason of my question is that in some situations in Airflow it's quite common to break large DAGs in small ones and call them via triggers and/or sensors. Without that, folks that are migrating from Airflow to Kubeflow will need to migrate all those large DAGs in a single pipeline file.
I've checked the [docs](https://kubeflow-pipelines.readthedocs.io/en/latest/source/dsl.html) and the issues but I did not managed to find something similar.
Thanks for the work once again. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9861 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9861/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9861/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9861/events | https://github.com/kubeflow/pipelines/issues/9861 | 1,850,284,070 | I_kwDOB-71UM5uSRgm | 9,861 | Full Kubeflow manifests for postgres | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9860 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9860/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9860/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9860/events | https://github.com/kubeflow/pipelines/issues/9860 | 1,850,283,220 | I_kwDOB-71UM5uSRTU | 9,860 | Standalone KFP manifests for postgres | {
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"closed by #9855 "
] | "2023-08-14T18:04:30" | "2023-08-21T22:12:39" | "2023-08-21T22:12:38" | COLLABORATOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9859 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9859/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9859/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9859/events | https://github.com/kubeflow/pipelines/issues/9859 | 1,850,282,637 | I_kwDOB-71UM5uSRKN | 9,859 | Upgrade GORM to v2 | {
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"After some investigation, it feels like this is a colossal change that's impossible to break down. It also involves lots of trivial changes, such as change of function signatures, variable fields, and some functionalities no longer supported. There's a really good summary by another googler on the work items: https://github.com/google/exposure-notifications-verification-server/issues/857\r\nSince this upgrade does not block PostgreSQL support, after some discussion we decided to stay with GORM v1."
] | "2023-08-14T18:04:04" | "2023-08-23T17:46:44" | "2023-08-23T17:46:43" | COLLABORATOR | null | null | {
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"/assign @chensun \r\n\r\nHere is a reference to the Pipeline Artifact: https://www.kubeflow.org/docs/components/pipelines/v2/data-types/artifacts/. We currently suspect that this field in the API is under development and hasn't been populated yet."
] | "2023-08-14T16:16:56" | "2023-08-31T22:49:22" | null | NONE | null | ### Issue
With kfp v2 and v2 runtime:
1. The call to `client.get_run(id)` returns a `V2beta1Run` object
2. `V2beta1Run` has a `run_details` property of type `V2beta1RunDetails`
3. `V2beta1RunDetails` has a `task_details` property of type `V2beta1PipelineTaskDetail`
4. `V2beta1PipelineTaskDetail` has properties `inputs` and `outputs` of type `V2beta1ArtifactList`
This implies that a single call to `client.get_run` will return the artifact metadata for each pipeline task.
However, the `inputs` and `outputs` field for each task is always `None`.
### Example
```py
import kfp
client = kfp.Client()
run = client.get_run('1234')
for task in run.run_details.task_details:
print('Inputs:', task.inputs)
print('Outputs:', task.outputs)
```
### Expected
```sh
Inputs: { 'artifact_ids': ['1234', '2345', '3456'] }
Outputs: { 'artifact_ids': ['4567', '5678', '6789'] }
```
### Actual
```sh
Inputs: None
Outputs: None
```
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] | open | false | null | [] | null | [] | "2023-08-11T20:53:56" | "2023-08-11T20:59:39" | 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. -->
Add CLI commands for KFP registry functions
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Uploading a compiled pipeline (for example) as part of CI/CD requires the user to write their own Python script to run
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Write standalone Python scripts using the `kfp.registry` functions
---
<!-- 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/9837 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9837/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9837/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9837/events | https://github.com/kubeflow/pipelines/issues/9837 | 1,843,706,754 | I_kwDOB-71UM5t5LuC | 9,837 | [bug] Failed to create a new run: etcdserver request is too large | {
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"Hello @vkatrychenko , can you share your pipeline template? Note that the etcdserver has a limitation on the size of request on 1.5MB. If the pipeline template is too large, it cannot be transmitted. ",
"Yeah, right. Dev team splitted the workflow and now everything is working fine. Thanks.",
"The ticket can be closed."
] | "2023-08-09T17:21:15" | "2023-09-06T11:19:29" | "2023-09-06T11:19:29" | NONE | null | ### Environment
Running kubeflow on AKS cluster v1.26.3
<!-- 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/. -->
Used this method: Kubeflow Pipelines as [part of a full Kubeflow deployment](https://www.kubeflow.org/docs/components/pipelines/v1/installation/overview/#full-kubeflow-deployment)
* KFP version: 2.0.0-alpha.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. -->
* 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.
-->
Using kubeflow UI to operate pipelines. After creating a pipeline, create a run
<img width="847" alt="kubeflow-ui" src="https://github.com/kubeflow/pipelines/assets/25283055/81b6e798-81fb-4952-a052-e30a46c430a3">
Then try to run it, but receive this error:
<img width="992" alt="kubeflow-error" src="https://github.com/kubeflow/pipelines/assets/25283055/44a6dc84-4a69-4cd3-b8e1-7dcb41f5c3cf">
I though that I had an issue with the argo workflow controller and followed this guide to enable offload large workflows: https://argoproj.github.io/argo-workflows/offloading-large-workflows/ but it did not help.
Here is the log from ml-pipeline pod:
```
0809 17:09:47.070371 7 error.go:259] etcdserver: request is too large
InternalServerError: Failed to create a workflow for ()
github.com/kubeflow/pipelines/backend/src/common/util.NewInternalServerError
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:142
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreateRun
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:387
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:135
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
Failed to create a new run.
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrap
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:252
github.com/kubeflow/pipelines/backend/src/common/util.Wrap
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:285
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:137
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2162
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
/api.RunService/CreateRun call failed
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:247
github.com/kubeflow/pipelines/backend/src/common/util.Wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:272
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:32
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:2164
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
```
### Expected result
<!-- What should the correct behavior be? -->
Once run is created, it should be executed
### 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 backend
<!-- /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/9836 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9836/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9836/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9836/events | https://github.com/kubeflow/pipelines/issues/9836 | 1,843,066,297 | I_kwDOB-71UM5t2vW5 | 9,836 | [feature] Default Credentials for Custom Training Jobs in Vertex AI Pipelines. | {
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"@jirapast You need to provide your custom credential/SA when trying to bring up the custom training job within the pipeline. Reference: https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.3.0/api/v1/custom_job.html#v1.custom_job.create_custom_training_job_from_component.service_account."
] | "2023-08-09T11:46:16" | "2023-08-31T22:57:10" | null | NONE | null | My colleague (**https://github.com/WanchanaMoonmuang**) and I implemented a Vertex AI Pipeline that included a custom training job. However, during our implementation, we encountered a bug related to credentials. After conducting a thorough investigation, we discovered that the credentials being used to run the Vertex AI Pipeline were not being passed to the custom training job unless explicitly specified. Upon examining the file _"components/google-cloud/google_cloud_pipeline_components/v1/custom_job/utils.py,"_ we noticed that the argument for inputting the service account was set to an empty string by default.
To address this issue, we proposed the idea of raising an exception if the service account argument is left empty. Alternatively, we considered the possibility of utilizing the default credentials used in the Vertex AI Pipeline to ensure consistency and proper functioning.
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https://api.github.com/repos/kubeflow/pipelines/issues/9835 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9835/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9835/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9835/events | https://github.com/kubeflow/pipelines/issues/9835 | 1,842,438,896 | I_kwDOB-71UM5t0WLw | 9,835 | [frontend] [sdk] kfp.dsl.PipelineParams not present in KFP Pipeline v2 | {
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"@annovi As of v1, we don't expect users to directly interact with `PipelineParams`. Assigning to @connor-mccarthy to answer this question. ",
"@annovi, as @zijianjoy mentioned, the `PipelineParam` class is never imported and instances should not be created directly when authoring a pipeline. You use them indirectly when you pass pipeline inputs to components (it is an implementation detail). This same intended usage is supported in KFP SDK v2. Closing as \"works as intended\", but please feel free to ask more questions or demonstrate how you are using these objects if it would be helpful."
] | "2023-08-09T04:26:42" | "2023-09-05T15:32:41" | "2023-09-05T15:32:41" | NONE | null | V2 doesn't have the feature that was present in V1 "kfp.dsl.PipelineParams" the feature Representing a future value that is passed between pipeline components. It was a really convenient feature to pass parameters between UI and component.
From the SDK KFP 1.8 documentation:
A PipelineParam object can be used as a pipeline function argument so that it will be a pipeline parameter that shows up in ML Pipelines system UI. It can also represent an intermediate value passed between components. | {
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"@Aditya-171 It's not a feature in 1.8.19. We suggest to use the 2.0.0.",
"Closing this issue since we have provided an answer, feel free to reopen if this is not the case."
] | "2023-08-07T11:39:18" | "2023-08-31T22:50:28" | "2023-08-31T22:50:27" | CONTRIBUTOR | null |
* KFP SDK version: 1.8.19
* All dependencies version:
kfp 1.8.19
kfp-pipeline-spec 0.1.16
kfp-server-api 1.6.0
Hi i was trying to use dsl.Collected , but was facing the error as
AttributeError: module 'kfp.dsl' has no attribute as 'Collected'
Also the following sdk docs for the 1.8.19 kfp version dos not exist
https://kubeflow-pipelines.readthedocs.io/en/1.8.19/source/dsl.html
---
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"@gkcalat Is this issue still relevant after fixing [this](https://github.com/kubeflow/pipelines/issues/9770) by [PR](https://github.com/kubeflow/pipelines/pull/9748)? "
] | "2023-08-05T00:36:30" | "2023-09-05T11:52:18" | null | MEMBER | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
[kfp-standalone-1](https://github.com/gkcalat/kubeflow-testing/tree/master/test-infra/kfp/kfp-standalone-1) cluster
* KFP version:
2.0.0
* KFP SDK version:
2.0.1
### Steps to reproduce
1. Run the functional test with enabled experiment archiving call.
```
host is https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com
Creating experiment
Experiment details: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/experiments/details/40b49e68-5808-47e4-a64a-12884b697890
Experiment with id 40b49e68-5808-47e4-a64a-12884b697890 created
Creating Run from Pipeline Func
Experiment details: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/experiments/details/40b49e68-5808-47e4-a64a-12884b697890
Run details: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/7f4a0f90-b9e8-44d9-ab30-aa0a3d2bf9d3
Run 7f4a0f90-b9e8-44d9-ab30-aa0a3d2bf9d3 created
Run succeeded in 63 seconds
Archiving experiment
Traceback (most recent call last):
File "/home/ablai_kubeflow_org/src/pipelines/./test/kfp-functional-test/run_kfp_functional_test.py", line 92, in <module>
main()
File "/home/ablai_kubeflow_org/src/pipelines/./test/kfp-functional-test/run_kfp_functional_test.py", line 87, in main
client.archive_experiment(experiment_id)
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp/client/client.py", line 623, in archive_experiment
return self._experiment_api.archive_experiment(
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/api/experiment_service_api.py", line 65, in archive_experiment
return self.archive_experiment_with_http_info(experiment_id, **kwargs) # noqa: E501
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/api/experiment_service_api.py", line 145, in archive_experiment_with_http_info
return self.api_client.call_api(
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/api_client.py", line 407, in request
return self.rest_client.POST(url,
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/rest.py", line 265, in POST
return self.request("POST", url,
File "/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (524)
Reason: status code 524
HTTP response headers: HTTPHeaderDict({'Content-Length': '1440', 'Content-Type': 'text/html; charset=utf-8', 'Date': 'Sat, 05 Aug 2023 00:28:11 GMT', 'Vary': 'Origin', 'X-Content-Type-Options': 'nosniff', 'X-Frame-Options': 'SAMEORIGIN', 'X-Xss-Protection': '0', 'Set-Cookie': 'S=cloud_datalab_tunnel=I7lCyXLQueelexnHs44ORZeTaTV7NT4yU3g92AiQU6Q; Path=/; Max-Age=3600'})
HTTP response body:
<!DOCTYPE html>
<html lang=en>
<meta charset=utf-8>
<meta name=viewport content="initial-scale=1, minimum-scale=1, width=device-width">
<title>Error 524 ()!!1</title>
<style>
*{margin:0;padding:0}html,code{font:15px/22px arial,sans-serif}html{background:#fff;color:#222;padding:15px}body{margin:7% auto 0;max-width:390px;min-height:180px;padding:30px 0 15px}* > body{background:url(//www.google.com/images/errors/robot.png) 100% 5px no-repeat;padding-right:205px}p{margin:11px 0 22px;overflow:hidden}ins{color:#777;text-decoration:none}a img{border:0}@media screen and (max-width:772px){body{background:none;margin-top:0;max-width:none;padding-right:0}}#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54.png) no-repeat;margin-left:-5px}@media only screen and (min-resolution:192dpi){#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) no-repeat 0% 0%/100% 100%;-moz-border-image:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) 0}}@media only screen and (-webkit-min-device-pixel-ratio:2){#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) no-repeat;-webkit-background-size:100% 100%}}#logo{display:inline-block;height:54px;width:150px}
</style>
<a href=//www.google.com/><span id=logo aria-label=Google></span></a>
<p><b>524.</b> <ins>That’s an error.</ins>
<p> <ins>That’s all we know.</ins>
```
2. Try archiving the experiment via UI:
```
Failed to archive experiment: 40b49e68-5808-47e4-a64a-12884b697890 with error: "{"error":"Failed to archive experiment: InternalServerError: Failed to archive experiment 40b49e68-5808-47e4-a64a-12884b697890. error: 'Error 1205: Lock wait timeout exceeded; try restarting transaction': Error 1205: Lock wait timeout exceeded; try restarting transaction","code":13,"message":"Failed to archive experiment: InternalServerError: Failed to archive experiment 40b49e68-5808-47e4-a64a-12884b697890. error: 'Error 1205: Lock wait timeout exceeded; try restarting transaction': Error 1205: Lock wait timeout exceeded; try restarting transaction","details":[{"@type":"type.googleapis.com/google.rpc.Status","code":13,"message":"Internal Server Error"}]}"
```
This is related to #9770 and #6845.
# Possible solution:
We may need to release a patch version with #9680 and #9730 and upgrade the [kfp-standalone-1](https://github.com/gkcalat/kubeflow-testing/tree/master/test-infra/kfp/kfp-standalone-1) cluster.
Confirmed this after building `api-server` image from `master` branch and updating the manifests of the deployment:
```
githubusercontent.com/kubeflow/testing/master/test-infra/kfp/endpoint)"
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 62 100 62 0 0 529 0 --:--:-- --:--:-- --:--:-- 529
/home/ablai_kubeflow_org/src/pipelines/.venv/lib/python3.9/site-packages/kfp/client/client.py:158: FutureWarning: This client only works with Kubeflow Pipeline v2.0.0-beta.2 and later versions.
warnings.warn(
host is https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com
Creating experiment
Experiment details: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/experiments/details/553570b1-6dbf-4b14-a166-660c3cbb00ba
Experiment with id 553570b1-6dbf-4b14-a166-660c3cbb00ba created
Creating Run from Pipeline Func
Experiment details: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/experiments/details/553570b1-6dbf-4b14-a166-660c3cbb00ba
Run details: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/59119a83-8119-40b8-b67a-00725bfe9e03
Run 59119a83-8119-40b8-b67a-00725bfe9e03 created
Run succeeded in 31 seconds
Archiving experiment
Archived experiment with id 553570b1-6dbf-4b14-a166-660c3cbb00ba
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"We already yanked KFP SDK 2.1.2, so that users' won't upgrade to this version unless they pin the version specifically in the command.\r\n\r\nIn case you run into this issue, and stuck with a bad environment. The remedy steps are:\r\n1. Uninstall both `kfp` and `kfp-dsl` package: `pip3 uninstall -y kfp kfp-dsl`\r\n2. Reinstall an previous version, e.g.: `pip3 install kfp==2.0.1`."
] | "2023-08-04T23:35:28" | "2023-08-31T22:53:32" | "2023-08-31T22:53:32" | COLLABORATOR | null | ### Environment
* KFP version:
N/A
* KFP SDK version:
2.1.2
* All dependencies version:
N/A
### Steps to reproduce
- Step 1: `pip3 install kfp==2.0.1`
- Step 2: `pip3 install kfp==2.1.2`
- Step 3: `import kfp`
### Expected result
import kfp should work.
### Actual result
```
>>> import kfp
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/kfp/__init__.py", line 23, in <module>
from kfp import components
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/kfp/components/__init__.py", line 27, in <module>
from kfp.components.load_yaml_utilities import load_component_from_file
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/kfp/components/load_yaml_utilities.py", line 20, in <module>
from kfp.dsl import structures
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/kfp/dsl/structures.py", line 25, in <module>
from kfp.dsl import placeholders
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/kfp/dsl/placeholders.py", line 22, in <module>
from kfp.dsl.types import type_utils
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/kfp/dsl/types/type_utils.py", line 239, in <module>
expected_spec: Union[structures.InputSpec, structures.OutputSpec],
AttributeError: partially initialized module 'kfp.dsl.structures' has no attribute 'InputSpec' (most likely due to a circular import)
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9813 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9813/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9813/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9813/events | https://github.com/kubeflow/pipelines/issues/9813 | 1,835,714,884 | I_kwDOB-71UM5taslE | 9,813 | Tracking: PostgreSQL support in KFP | {
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"cc @chensun "
] | "2023-08-03T20:47:02" | "2023-08-28T20:53:40" | null | COLLABORATOR | null | PostgreSQL request has become the top upvoted issue on KFP repo: https://github.com/kubeflow/pipelines/issues/7512. This issue is for tracking the work of this integration.
- [ ] KFP Backend integration
- [x] Define the DB config for PostgreSQL DB info
- [x] Basic connection config
- [ ] Secure connection config: passfile, hostaddr, ssl mode, certificate, etc.
- [x] Flag to switch between Postgresql driver and MySQL driver
- [x] ~~#9859~~
- [ ] Syntax support of PostgreSQL DB in backend
- [ ] Adopt a different syntax during initialization. (schema difference)
- [ ] Adopt a different syntax during execution. (Modification of data)
- [ ] Explore the dialect difference and develop a control mechanism to enable easy testing on the storage layer.
- [ ] Testing
- [ ] Unit testing for Postgresql behavior.
- [ ] Functional testing for Postgresql behavior.
- [ ] E2E testing for Postgresql behavior.
- [ ] Cache server integration
- [ ] KFP API server integration
- [ ] Manifest support for Postgresql
- [x] #9860
- [ ] #9861
- [ ] Marketplace KFP (managed CloudSQL instance)
- [ ] MLMD integration
- [ ] Develop Postgresql integration on MLMD
- [ ] Release new version of MLMD
- [x] #9848
- [ ] Manifest support for Postgresql in MLMD
- [ ] Standalone KFP
- [ ] Full Kubeflow Kubeflow
- [ ] Marketplace KFP (managed CloudSQL instance) | {
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"Hey!\r\nWe have the same issue with the same versions. \r\n\r\n@rmoesbergen where you able to improvise a fix? ",
"@svyatoslavmo Unfortunately I have not been able to find a workaround to stop the barrage of errors."
] | "2023-08-02T14:07:55" | "2023-08-24T22:44:04" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
In GKE with kustomize, following the installation guide : https://www.kubeflow.org/docs/components/pipelines/v1/installation/localcluster-deployment/#deploying-kubeflow-pipelines
* KFP version:
2.0.0 (upgraded from 1.8.5)
* KFP SDK version:
1.8.22
### Steps to reproduce
1. Install (or upgrade in our case) kubeflow pipelines 2.0.0
2. Create a Scheduled Pipeline using kfp 1.8.22
3. The ml-pipeline component floods the logs with this error:
```
I0802 13:55:02.690971 7 interceptor.go:29] /api.ReportService/ReportScheduledWorkflowV1 handler starting
I0802 13:55:02.695027 7 error.go:278] Invalid input error: Found a ScheduledWorkflow with empty type metadata. ScheduledWorkflow must have valid APIVersion and Kind. ObjectMeta: {my-pipeline-dfx2js my-pipeline-d kubeflow f5ef2aa3-21fb-46ec-9c88-3dc4de490567 1029101411 64 2023-08-02 13:44:14 +0000 UTC <nil> <nil> map[scheduledworkflows.kubeflow.org/enabled:false scheduledworkflows.kubeflow.org/status:Disabled] map[] [] [] [{apiserver Update kubeflow.org/v1beta1 2023-08-02 13:44:14 +0000 UTC FieldsV1 {"f:metadata":{"f:generateName":{}},"f:spec":{}} } {controller Update kubeflow.org/v1beta1 2023-08-02 13:55:01 +0000 UTC FieldsV1 {"f:metadata":{"f:labels":{".":{},"f:scheduledworkflows.kubeflow.org/enabled":{},"f:scheduledworkflows.kubeflow.org/status":{}}},"f:status":{}} }]}
github.com/kubeflow/pipelines/backend/src/common/util.NewInvalidInputError
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:185
github.com/kubeflow/pipelines/backend/src/common/util.(*ScheduledWorkflow).ParametersAsString
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/scheduled_workflow.go:140
github.com/kubeflow/pipelines/backend/src/apiserver/storage.(*JobStore).UpdateJob
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/storage/job_store.go:433
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).ReportScheduledWorkflowResource
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1246
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ReportServer).reportScheduledWorkflow
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/report_server.go:83
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ReportServer).ReportScheduledWorkflowV1
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/report_server.go:93
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportScheduledWorkflowV1_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:356
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportScheduledWorkflowV1_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:358
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1598
/api.ReportService/ReportScheduledWorkflowV1 call failed
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:266
github.com/kubeflow/pipelines/backend/src/common/util.Wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:337
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:32
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportScheduledWorkflowV1_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:358
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1598
```
The metadata of the pipeline looks like this:
```
apiVersion: kubeflow.org/v1beta1
kind: ScheduledWorkflow
metadata:
creationTimestamp: "2023-08-02T13:44:14Z"
generateName: my-pipeline-d
generation: 118
labels:
scheduledworkflows.kubeflow.org/enabled: "false"
scheduledworkflows.kubeflow.org/status: Disabled
name: my-pipeline-dfx2js
namespace: kubeflow
resourceVersion: "1029112770"
uid: f5ef2aa3-21fb-46ec-9c88-3dc4de490567
```
I've removed and re-created all (scheduled) pipelines, but that didn't help. Any idea what could be wrong?
### Expected result
A working scheduled pipeline, no errors
### 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/9808 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9808/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9808/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9808/events | https://github.com/kubeflow/pipelines/issues/9808 | 1,832,773,068 | I_kwDOB-71UM5tPeXM | 9,808 | [sdk] dsl.Collected doesn't work within dsl.Condition when dsl.ParallelFor is outside that dsl.Condition | {
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"repos_url": "https://api.github.com/users/connor-mccarthy/repos",
"events_url": "https://api.github.com/users/connor-mccarthy/events{/privacy}",
"received_events_url": "https://api.github.com/users/connor-mccarthy/received_events",
"type": "User",
"site_admin": false
} | [
{
"login": "connor-mccarthy",
"id": 55268212,
"node_id": "MDQ6VXNlcjU1MjY4MjEy",
"avatar_url": "https://avatars.githubusercontent.com/u/55268212?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/connor-mccarthy",
"html_url": "https://github.com/connor-mccarthy",
"followers_url": "https://api.github.com/users/connor-mccarthy/followers",
"following_url": "https://api.github.com/users/connor-mccarthy/following{/other_user}",
"gists_url": "https://api.github.com/users/connor-mccarthy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/connor-mccarthy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/connor-mccarthy/subscriptions",
"organizations_url": "https://api.github.com/users/connor-mccarthy/orgs",
"repos_url": "https://api.github.com/users/connor-mccarthy/repos",
"events_url": "https://api.github.com/users/connor-mccarthy/events{/privacy}",
"received_events_url": "https://api.github.com/users/connor-mccarthy/received_events",
"type": "User",
"site_admin": false
}
] | null | [
"Thanks, @bogdanCsn. This is indeed a bug. We've put this in our backlog."
] | "2023-08-02T09:12:53" | "2023-08-11T19:54:37" | null | NONE | null | ### Environment
* KFP version: 2.0.1
### Steps to reproduce
```
from typing import List
from kfp import compiler, dsl
from kfp.dsl import Input, Model, Output
@dsl.component
def some_string(k: int, model: Output[Model]):
model = "x"
@dsl.component
def always_one() -> int:
return 1
@dsl.component
def component_with_collection(models: Input[List[Model]]) -> str:
return ".".join([model for model in models])
@dsl.pipeline
def main_pipeline():
always_one_task = always_one()
with dsl.ParallelFor(items=[1, 5, 10, 25], parallelism=2) as i:
some_string_task = some_string(k=i)
with dsl.Condition(always_one_task.output == 1):
component_task = component_with_collection(
models=dsl.Collected(some_string_task.outputs["model"])
)
compiler.Compiler().compile(main_pipeline, "main_compiled.yaml")
```
### Expected result
Right now the error received is: `TypeError: None has type NoneType, but expected one of: int`
Expected result: `component_task.output` should be 'x.x.x.x'
### Materials and Reference
SO topic: https://stackoverflow.com/questions/76815129/use-dsl-collected-inside-a-dsl-condition-context
<!-- 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/9805 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9805/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9805/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9805/events | https://github.com/kubeflow/pipelines/issues/9805 | 1,830,582,884 | I_kwDOB-71UM5tHHpk | 9,805 | [bug] KFP SDK API Reference doc links need to be updated throughout pipelines docs | {
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"html_url": "https://github.com/ryanjung-jm",
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"following_url": "https://api.github.com/users/ryanjung-jm/following{/other_user}",
"gists_url": "https://api.github.com/users/ryanjung-jm/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ryanjung-jm/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ryanjung-jm/subscriptions",
"organizations_url": "https://api.github.com/users/ryanjung-jm/orgs",
"repos_url": "https://api.github.com/users/ryanjung-jm/repos",
"events_url": "https://api.github.com/users/ryanjung-jm/events{/privacy}",
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"type": "User",
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} | [
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4",
"url": "https://api.github.com/repos/kubeflow/pipelines/labels/kind/bug",
"name": "kind/bug",
"color": "fc2515",
"default": false,
"description": ""
}
] | closed | false | null | [] | null | [
"reopened the issue in the kubeflow/website. https://github.com/kubeflow/website/issues/3569\r\nSorry for the confusion."
] | "2023-08-01T06:50:02" | "2023-08-01T08:04:12" | "2023-08-01T07:57:38" | CONTRIBUTOR | null | The KFP SDK API Reference doc link `https://kubeflow-pipelines.readthedocs.io/.../source/kfp. ...` needs to be updated to `https://kubeflow-pipelines.readthedocs.io/.../source/...`.
There are 114 broken links.
```
❯ grep -r source/kfp. ./*
./content/en/docs/components/pipelines/v1/sdk/parameters.md:class](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.PipelineParam)
./content/en/docs/components/pipelines/v1/sdk/component-development.md:[kfp-load-comp-file]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_file
./content/en/docs/components/pipelines/v1/sdk/component-development.md:[kfp-load-comp-url]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_url
./content/en/docs/components/pipelines/v1/sdk/component-development.md:[kfp-load-comp-text]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_text
./content/en/docs/components/pipelines/v1/sdk/component-development.md:[kfp-containerop]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md:SDK](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.html)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md:* [`kfp.compiler`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.compiler.html)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md:* [`kfp.components`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: ([`ContainerOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp))
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: ([`ContainerOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp))
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: ([`ContainerOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp))
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md:* [`kfp.dsl`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: ([`ContainerOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp)).
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.types`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.types.html)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.ResourceOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ResourceOp)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.VolumeOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.VolumeOp)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.VolumeSnapshotOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.VolumeSnapshotOp)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.PipelineVolume`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.PipelineVolume)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.ParallelFor`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ParallelFor)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.ExitHandler`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ExitHandler)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md: * [`kfp.dsl.Condition`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.Condition)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md:* [`kfp.Client`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html)
./content/en/docs/components/pipelines/v1/sdk/sdk-overview.md:* [Kubeflow Pipelines extension modules](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.extensions.html)
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.client.html#kfp.Client\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp"
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[create-component-from-func]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.create_component_from_func\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputPath\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputPath"
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[input-binary]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputBinaryFile\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputPath\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[input-text]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputTextFile\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[output-binary]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputBinaryFile\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputPath\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[output-text]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputTextFile\n",
./content/en/docs/components/pipelines/v1/sdk/python-function-components.ipynb: "[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp"
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[create-component-from-func]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.create_component_from_func
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[input-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputPath
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[output-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputPath
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[input-binary]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputBinaryFile
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[input-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputPath
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[input-text]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputTextFile
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[output-binary]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputBinaryFile
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[output-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputPath
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[output-text]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputTextFile
./content/en/docs/components/pipelines/v1/sdk/python-function-components.md:[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[container-op]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ContainerOp\n",
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html?highlight=inputpath#kfp.components.InputPath\n",
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html?highlight=outputpath#kfp.components.OutputPath"
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[create_component_from_func]: (https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.create_component_from_func\n",
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp"
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[load_component_from_url]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html?highlight=load_component_from_url#kfp.components.load_component_from_url"
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.ipynb: "[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.client.html#kfp.Client\n",
./content/en/docs/components/pipelines/v1/sdk/manipulate-resources.md:[Link](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ResourceOp)
./content/en/docs/components/pipelines/v1/sdk/manipulate-resources.md: [`kfp.dsl.ContainerOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp).
./content/en/docs/components/pipelines/v1/sdk/manipulate-resources.md:[Link](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.VolumeOp)
./content/en/docs/components/pipelines/v1/sdk/manipulate-resources.md:[Link](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.PipelineVolume)
./content/en/docs/components/pipelines/v1/sdk/manipulate-resources.md:[Link](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.VolumeSnapshotOp)
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[input-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html?highlight=inputpath#kfp.components.InputPath
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[output-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html?highlight=outputpath#kfp.components.OutputPath
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[create_component_from_func]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.create_component_from_func
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[load_component_from_url]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html?highlight=load_component_from_url#kfp.components.load_component_from_url
./content/en/docs/components/pipelines/v1/sdk/build-pipeline.md:[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client
./content/en/docs/components/pipelines/v1/sdk/enviroment_variables.md:[kfp.components.func_to_container_op(func)](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.func_to_container_op) to convert the
./content/en/docs/components/pipelines/v1/sdk/enviroment_variables.md:[kfp.components.func_to_container_op(func)](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.func_to_container_op).
./content/en/docs/components/pipelines/v1/sdk/enviroment_variables.md:Add this component to a pipeline. Use [add_env_variable](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp.container) to pass an
./content/en/docs/components/pipelines/v1/sdk/enviroment_variables.md:using python lightweight components or a [container operation](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp).
./content/en/docs/components/pipelines/v1/sdk/enviroment_variables.md:[add_env_variable()](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp.container). Use the following command to run this pipeline using the
./content/en/docs/components/pipelines/v1/overview/pipeline-root.md:* [add-op-transformer](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.PipelineConf.add_op_transformer)
./content/en/docs/components/pipelines/v1/overview/pipeline-root.md:* [use-aws-secret](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.extensions.html#kfp.aws.use_aws_secret)
./content/en/docs/components/pipelines/v1/overview/pipeline-root.md:* [create_run_from_pipeline_func](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.create_run_from_pipeline_func)
./content/en/docs/components/pipelines/v1/overview/pipeline-root.md:* [create_run_from_pipeline_package](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.create_run_from_pipeline_package)
./content/en/docs/components/pipelines/v1/overview/pipeline-root.md:* [run_pipeline](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.run_pipeline).
./content/en/docs/components/pipelines/v1/overview/caching-v2.md:You can turn off execution caching for pipeline runs that are created using Python. When you run a pipeline using [create_run_from_pipeline_func](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.create_run_from_pipeline_func) or [create_run_from_pipeline_package](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.create_run_from_pipeline_package) or [run_pipeline](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.run_pipeline,) you can use the `enable_caching` argument to specify that this pipeline run does not use caching.
./content/en/docs/components/pipelines/v1/overview/multi-user.md:* To set a default namespace for Pipelines SDK commands, use the [`kfp.Client().set_user_namespace()`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client.set_user_namespace) method,
./content/en/docs/components/pipelines/v1/overview/multi-user.md:* Detailed documentation for `kfp.Client()` can be found in the [Kubeflow Pipelines SDK Reference](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html).
./content/en/docs/components/pipelines/v1/overview/multi-user.md:The following code uses the [generated python API client](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.server_api.html) to create an experiment and pipeline run.
./content/en/docs/components/pipelines/v1/sdk-v2/component-development.md:[kfp-load-comp-file]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_file
./content/en/docs/components/pipelines/v1/sdk-v2/component-development.md:[kfp-load-comp-url]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_url
./content/en/docs/components/pipelines/v1/sdk-v2/component-development.md:[kfp-load-comp-text]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_text
./content/en/docs/components/pipelines/v1/sdk-v2/component-development.md:[kfp-containerop]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.client.html#kfp.Client\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp"
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[create-component-from-func]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.create_component_from_func\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputPath\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputPath\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[input-binary]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputBinaryFile\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputPath\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[input-text]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputTextFile\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[output-binary]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputBinaryFile\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputPath\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.ipynb: "[output-text]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputTextFile\n",
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[create-component-from-func]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.create_component_from_func
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[input-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputPath
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[output-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputPath
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[input-binary]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputBinaryFile
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[input-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputPath
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[input-text]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.InputTextFile
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[output-binary]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputBinaryFile
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[output-path]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputPath
./content/en/docs/components/pipelines/v1/sdk-v2/python-function-components.md:[output-text]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.OutputTextFile
./content/en/docs/components/pipelines/v1/sdk-v2/build-pipeline.ipynb: "[container-op]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ContainerOp\n",
./content/en/docs/components/pipelines/v1/sdk-v2/build-pipeline.ipynb: "[load_component_from_url]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html?highlight=load_component_from_url#kfp.components.load_component_from_url"
./content/en/docs/components/pipelines/v1/sdk-v2/build-pipeline.ipynb: "[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.client.html#kfp.Client\n",
./content/en/docs/components/pipelines/v1/sdk-v2/build-pipeline.md:[container-op]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp
./content/en/docs/components/pipelines/v1/sdk-v2/build-pipeline.md:[load_component_from_url]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html?highlight=load_component_from_url#kfp.components.load_component_from_url
./content/en/docs/components/pipelines/v1/sdk-v2/build-pipeline.md:[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client
./content/en/docs/components/pipelines/v1/sdk-v2/v2-compatibility.md:[kfp-client]: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html#kfp.Client
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9804 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9804/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9804/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9804/events | https://github.com/kubeflow/pipelines/issues/9804 | 1,829,983,184 | I_kwDOB-71UM5tE1PQ | 9,804 | [frontend] Frontend Dockerfile don't build | {
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"Hello @SamirPS , would you like to try the following using the latest KFP repo?\r\n\r\n```\r\ncd pipelines/\r\ndocker build -f frontend/Dockerfile .\r\n```",
"With the ```2.0.0``` it's work but not with ```2.0.0-alpha.7```",
"We recommend using a newer version, since we have announce GA for KFP 2.0."
] | "2023-07-31T20:30:59" | "2023-08-01T19:29:37" | "2023-08-01T19:29:37" | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
I didn't deploy KFP, I only tried to build the frontend container image
### Steps to reproduce
git clone this repo with this tag ``2.0.0-alpha.7``
```
cd pipelines/frontend
docker build
```
and you will get this error:
```
Error workbox-webpack-plugin@6.6.1: The engine "node" is incompatible with this module. Expected version ">=16.0.0
```
### Expected result
<!-- What should the correct behavior be? -->
The dockerfile should build
### 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/9803 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9803/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9803/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9803/events | https://github.com/kubeflow/pipelines/issues/9803 | 1,827,780,212 | I_kwDOB-71UM5s8bZ0 | 9,803 | [sdk] kfp.deprecated fails to import | {
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"> Expected result\r\n>\r\n> kfp.deprecated is still usable on current kfp..\r\n\r\n@hmc-cs-mdrissi `kfp.deprecated` is meant to be cleanup but we haven't done so. It's not expected for it to continue working inside kfp 2.0+. But the features are still available via kfp sdk 1.8.*.\r\n\r\nIs there any reason you upgrade to KFP 2.0+ and still want to use `kfp.deprecated`?",
"I was exploring it to try upgrading. For codebase that uses kfp in several spots it would be helpful to be able to migrate portion of it to true kfp v2 and keep other parts as kfp.deprecated instead of updating all at once.\r\n\r\nI did end up swapping to latest 1.8 though and my need to go through kfp.deprecated is low."
] | "2023-07-30T03:34:07" | "2023-09-07T22:35:32" | "2023-09-07T22:35:32" | CONTRIBUTOR | null | ### Environment
* KFP version: kfp 2.1.2
### Steps to reproduce
```
>>> import kfp.deprecated
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/__init__.py", line 19, in <module>
from . import dsl
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/dsl/__init__.py", line 16, in <module>
from ._pipeline import Pipeline, PipelineExecutionMode, pipeline, get_pipeline_conf, PipelineConf
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/dsl/_pipeline.py", line 21, in <module>
from kfp.deprecated.dsl import _component_bridge
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/dsl/_component_bridge.py", line 29, in <module>
from kfp.deprecated.dsl import component_spec as dsl_component_spec
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/dsl/component_spec.py", line 21, in <module>
from kfp.deprecated.dsl import type_utils
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/dsl/type_utils.py", line 19, in <module>
from kfp.components.types import type_utils
ModuleNotFoundError: No module named 'kfp.components.types'
```
2.0.1/2.0.0 leads to a different incompatibility,
```
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/_client.py", line 97, in <module>
class Client(object):
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/deprecated/_client.py", line 396, in Client
def get_kfp_healthz(self) -> kfp_server_api.ApiGetHealthzResponse:
AttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'
```
ApiGetHealthzResponse is present in kfp-server-api <2.0, but gone in 2.0. Except kfp depends on >=2.0. So it's unclear to me kfp.deprecated is usable. I'd also expect some unit test that imports/uses kfp.deprecated. Are all those tests disabled?
Using kfp-server-api v1.8 + kfp2 importing kfp.deprecated also fails because kfp v2 apis have type hints that refer to kfp-server-api v2 like,
```
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/client/__init__.py", line 20, in <module>
from kfp.client.client import Client
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/client/client.py", line 78, in <module>
class _JobConfig:
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/kfp/client/client.py", line 80, in _JobConfig
pipeline_version_reference: kfp_server_api.V2beta1PipelineVersionReference
AttributeError: module 'kfp_server_api' has no attribute 'V2beta1PipelineVersionReference'
```
### Expected result
kfp.deprecated is still usable on current kfp..
### 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/9783 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9783/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9783/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9783/events | https://github.com/kubeflow/pipelines/issues/9783 | 1,822,682,585 | I_kwDOB-71UM5so-3Z | 9,783 | [feature] Configuring securityContext at pod level | {
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"What are the versions of KFP and SDK you're using? Also what container image being used for the component?"
] | "2023-07-26T15:33:20" | "2023-08-11T00:05:25" | 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?
Being able to configure securityContext in v2.
### What is the use case or pain point?
When using an image from dockerhub for example, which has not a default user 0 in the `@dsl.component(base_image="<non-root-image>")`, when trying to write to OutputPath, got the following error:
> failed to execute component: unable to create directory "/minio/mlpipeline/v2/artifacts/pipeline-name/uuid/custom-function" for output artifact "model": mkdir /minio: permission denied
## Details
Worflow support the security at spec level [Workflow Pod Security Context](https://argoproj.github.io/argo-workflows/workflow-pod-security-context/).
---
<!-- 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/9780 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9780/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9780/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9780/events | https://github.com/kubeflow/pipelines/issues/9780 | 1,821,147,065 | I_kwDOB-71UM5sjH-5 | 9,780 | [backend] performance issues with list runs API | {
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"Hi @deepk2u, thank you for bringing this issue up and offer a solution! If you would like to create a PR to fix this problem, it would be great.",
"We also impacted by this issue, most of our namespace contains > 200k runs. Anh now we cannot view any run on UI. Hope there will be a fix soon!",
"@Linchin @tam0201 I have tried to fix it in my PR https://github.com/kubeflow/pipelines/pull/9806 . Please take a look when you have time."
] | "2023-07-25T21:02:49" | "2023-08-04T20:34:24" | "2023-08-04T20:34:24" | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
full kubeflow deployment using manifests repo
* KFP version:
sdk-2.0.0b7
* KFP SDK version:
sdk-2.0.0b7
### Steps to reproduce
1. create more than 200k runs in at least one namespace
2. other namespaces can have any number of runs
3. click on the runs tab on Kubeflow UI, it starts to timeout
I did some digging and found that in case if the number of runs is really high, then the select query starts to take more than a minute.
`select * from run_details where Namespace = 'namespace-with-200k-runs' limit 1;` this query took 1 minute 46 sec
I tried to query using the experiment tab, where we pass the experiment id, and that query is still performing as expected.
`select * from run_details where ExperimentUUID = 'a0dd8afa-d481-4c83-b2c2-31ef4b3d12ec' limit 1;` this query is taking milliseconds
On a side note, 200k runs are really high, and I checked. Someone created a few Recurring Runs for that particular run using a cron schedule, which was running every second. That was a mistake. I feel we should add some warning below the cron schedule box if it is per minute or per second, or there should be a mechanism to not allow these kinds of cron schedules for administrators.
### Expected result
Listing runs should not time out, irrespective of how much data we have in the database.
### Materials and Reference
Below is a snapshot of the data we have in the run_details table.
```
mysql> select count(*) from run_details;
+----------+
| count(*) |
+----------+
| 227025 |
+----------+
1 row in set (1.91 sec)
mysql> SELECT Namespace,COUNT(*) as count FROM run_details GROUP BY Namespace ORDER BY count DESC;
+-----------------------------------------------------+--------+
| Namespace | count |
+-----------------------------------------------------+--------+
| n1 | 219437 |
| n2 | 2032 |
| n3 | 1478 |
| n4 | 1090 |
| n5 | 384 |
| n6 | 367 |
| n7 | 285 |
| n8 | 283 |
.....
56 rows in set (1 min 45.73 sec)
```
To fix this query, I manually created an Index on the Namespace column and everything seems to be running fine for now.
```
mysql> show indexes from run_details;
+-------------+------------+-------------------------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------------+------------+-------------------------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| run_details | 0 | PRIMARY | 1 | UUID | A | 108032 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_createatinsec | 1 | ExperimentUUID | A | 148 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_createatinsec | 2 | CreatedAtInSec | A | 112366 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_conditions_finishedatinsec | 1 | ExperimentUUID | A | 150 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_conditions_finishedatinsec | 2 | Conditions | A | 254 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_conditions_finishedatinsec | 3 | FinishedAtInSec | A | 112366 | NULL | NULL | YES | BTREE | | |
+-------------+------------+-------------------------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
6 rows in set (0.00 sec)
mysql> CREATE INDEX namespace ON run_details (Namespace);
Query OK, 0 rows affected (8 min 52.07 sec)
Records: 0 Duplicates: 0 Warnings: 0
mysql>
mysql>
mysql> show indexes from run_details;
+-------------+------------+-------------------------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------------+------------+-------------------------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| run_details | 0 | PRIMARY | 1 | UUID | A | 108032 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_createatinsec | 1 | ExperimentUUID | A | 148 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_createatinsec | 2 | CreatedAtInSec | A | 112367 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_conditions_finishedatinsec | 1 | ExperimentUUID | A | 150 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_conditions_finishedatinsec | 2 | Conditions | A | 254 | NULL | NULL | | BTREE | | |
| run_details | 1 | experimentuuid_conditions_finishedatinsec | 3 | FinishedAtInSec | A | 112367 | NULL | NULL | YES | BTREE | | |
| run_details | 1 | namespace | 1 | Namespace | A | 55 | NULL | NULL | | BTREE | | |
+-------------+------------+-------------------------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
7 rows in set (0.00 sec)
```
---
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"Same error that was commented here:\r\nhttps://github.com/kubeflow/pipelines/issues/9366"
] | "2023-07-24T20:53:11" | "2023-09-08T18:49:37" | null | NONE | null | ### Environment
* KFP version: **2**
* KFP SDK version: **2.0.1**
* All dependencies version:
```bash
➜ pip list | grep kfp
kfp 2.0.1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0
```
### Steps to reproduce
Previously, in KFP SDK v1.8.*, the following worked when the `my_component` component accepted 2 arguments, the first being `int` and the second being a `str`.
```python
@dsl.component
def my_component(first_param: int, second_param: str):
# do some things
return
with dsl.ParallelFor(
[
{"first_param": 1, "second_param": "hello"},
{"first_param": 0, "second_param": "world"},
{"first_param": 1, "second_param": "hello"},
],
parallelism=3,
) as params:
my_component(first_param=params.first_param, second_param=params.second_param)
```
When upgrading to KFP SDK v2.0.1, this is now thrown as an error when compiling the pipeline:
```
ERROR:__main__:Incompatible argument passed to the input 'first_param' of component 'my_component': Argument type 'STRING' is incompatible with the input type 'NUMBER_INTEGER'
```
### Expected result
No error and a correct passage of `first_param` as an integer parameter to `my_component`.
### Materials and Reference
It could be that I'm misusing the arguments to `dsl.ParallelFor` as I couldn't find very much more similar examples other than [the main docs](https://kubeflow-pipelines.readthedocs.io/en/master/source/dsl.html?h=parallel#kfp.dsl.ParallelFor).
A possible angle to the issue could be regarding this: https://github.com/kubeflow/pipelines/blob/6dfcd533f495bf19019bb09baf5ec0d3775e0f1c/sdk/python/kfp/components/for_loop.py#L46
The type that comes in here is `dict`, no regex match is found and None is returned. Because of that, an automatic fallback to `String` is done here: https://github.com/kubeflow/pipelines/blob/6dfcd533f495bf19019bb09baf5ec0d3775e0f1c/sdk/python/kfp/components/for_loop.py#L243-L244
---
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"Thank you @axel7083 , we are welcoming contributions for making pod labels configurable in KFP."
] | "2023-07-24T12:22:00" | "2023-08-10T22:42:05" | 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?
When running a pipeline, after some time the logs are removed, since the terminated pods are cleanup. When using [Loki](https://github.com/grafana/loki), we can fetch the logs using the label `pipeline/runid`.
However we cannot filter by pipeline name/version/step in Grafana.
Adding the following elements would allow to easily explore the logs later:
- `pipeline/id`
- `pipeline/versionid`
- `pipeline/name`
- `pipeline/stepid`
- `pipeline/stepname`
## Going further
The log tab should not fetch the logs from the kubernetes api but rather a Loki instance. Maybe both, during the run, using the kubernetes api for watching them, then after completion using Loki.
---
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"Hi @khituras, thank you for your question. I see from the instruction the command [also included the credential information](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/#full-kubeflow-subfrom-inside-clustersub:~:text=credentials%3Dcredentials)), maybe try adding it and see if it works? Feel free to reopen if you have any further problem.",
"Hello @Linchin and thank you very much for the answer. You're right, I did omit the credential information. This was because I was using kfp v2 where `kfp.auth` does not exist and I also didn't find a replacement.\r\n\r\nI have now created a new notebook server where I use `kfp==1.8.22`. Unfortunately, the passing of credentials did not solve the issue. I think I must have some misconfiguration but I can't see it.\r\n\r\n1. My Python code is now an exact copy&and paste from the instructions at https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/#full-kubeflow-subfrom-inside-clustersub\r\n2. I created the PodDefaults in my user profile namespace, just as shown in the instructions. I checked in OpenLens that they are present and in the right namespace.\r\n3. I created a new notebook server that uses those PodDefaults as its configuration.\r\n4. The notebook container has the `default-editor` service account: \r\n ```yaml\r\n serviceAccountName: default-editor\r\n serviceAccount: default-editor\r\n ```\r\n5. The ServiceAccount token is accessible in the notebook servier:\r\n ```bash\r\n $ ls /var/run/secrets/kubeflow/pipelines/\r\n token\r\n ```\r\n6. I do use the token credentials in my Python code:\r\n ```python\r\n import kfp\r\n \r\n # the namespace in which you deployed Kubeflow Pipelines\r\n namespace = \"kubeflow\"\r\n \r\n # the KF_PIPELINES_SA_TOKEN_PATH environment variable is used when no `path` is set\r\n # the default KF_PIPELINES_SA_TOKEN_PATH is /var/run/secrets/kubeflow/pipelines/token\r\n credentials = kfp.auth.ServiceAccountTokenVolumeCredentials(path=None)\r\n \r\n client = kfp.Client(host=f\"http://ml-pipeline-ui.{namespace}\", credentials=credentials)\r\n \r\n print(client.list_experiments())\r\n ```\r\n7. The RBAC authorization looks like it did before, I repeat it from above (of course, USERNS is set to my actual user profile namespace):\r\n ```yaml\r\n apiVersion: rbac.authorization.k8s.io/v1\r\n kind: RoleBinding\r\n metadata:\r\n name: allow-USERNS-kubeflow-edit\r\n ## this RoleBinding is in `namespace-1`, because it grants access to `namespace-1`\r\n namespace: kubeflow\r\n roleRef:\r\n apiGroup: rbac.authorization.k8s.io\r\n kind: ClusterRole\r\n name: kubeflow-edit\r\n subjects:\r\n - kind: ServiceAccount\r\n name: default-editor\r\n ## the ServiceAccount lives in `namespace-2`\r\n namespace: USERNS\r\n ```\r\n8. There is a `default-editor` service account in my user namespace, it wasn't changed since profile creation\r\n9. The `kubeflow-edit` cluster role also exists and was not changed since cluster creation. Here are the RBAC verbs currently set for `experiments` which (I think) need for my code example (perhaps there is another verb like 'read' or 'list' that I don't know about?):\r\n ```yaml\r\n - verbs:\r\n - archive\r\n - create\r\n - delete\r\n - unarchive\r\n apiGroups:\r\n - pipelines.kubeflow.org\r\n resources:\r\n - experiments\r\n ```\r\n\r\nSo I basically think it ought to work. Still, I get the error\r\n\r\n```\r\nERROR:root:Failed to get healthz info attempt 1 of 5.\r\nTraceback (most recent call last):\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp/_client.py\", line 410, in get_kfp_healthz\r\n response = self._healthz_api.get_healthz()\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/api/healthz_service_api.py\", line 63, in get_healthz\r\n return self.get_healthz_with_http_info(**kwargs) # noqa: E501\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/api/healthz_service_api.py\", line 134, in get_healthz_with_http_info\r\n return self.api_client.call_api(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py\", line 364, in call_api\r\n return self.__call_api(resource_path, method,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py\", line 188, in __call_api\r\n raise e\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py\", line 181, in __call_api\r\n response_data = self.request(\r\n ^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py\", line 389, in request\r\n return self.rest_client.GET(url,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/rest.py\", line 230, in GET\r\n return self.request(\"GET\", url,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/conda/lib/python3.11/site-packages/kfp_server_api/rest.py\", line 224, in request\r\n raise ApiException(http_resp=r)\r\nkfp_server_api.exceptions.ApiException: (403)\r\nReason: Forbidden\r\nHTTP response headers: HTTPHeaderDict({'content-length': '19', 'content-type': 'text/plain', 'date': 'Fri, 28 Jul 2023 07:41:12 GMT', 'server': 'envoy', 'x-envoy-upstream-service-time': '0'})\r\nHTTP response body: RBAC: access denied\r\n```\r\n\r\nI notice that the HTTP response mentions `envoy`. Am I perhaps required to add configuration for Istio?",
"It seems I am not able to re-open the issue. Could you perhaps do it, @Linchin ?",
"I finally managed to solve this. The issue seems to be that in the full Kubeflow installation, Istio is also installed which takes precedence in authorization issues. Thus, the vanilla-Kubernetes RBAC `RoleBinding` seems not be used at all. In my current working setup it is not present in the cluster.\r\nInstead, I edited the `AuthorizationPolicy` CRD with name `ml-pipeline-ui` in the `kubeflow` namespace. It originally reads\r\n\r\n```yaml\r\nspec:\r\n rules:\r\n - from:\r\n - source:\r\n namespaces:\r\n - istio-system\r\n selector:\r\n matchLabels:\r\n app: ml-pipeline-ui\r\n```\r\nPer the Istio authorization policy rules (check out the very informative [policy precedence graph](https://istio.io/latest/docs/concepts/security/#implicit-enablement)) this means that the `ml-pipeline-ui` service only allows requests from the `istio-system` namespace. All that must be done is to add the namespace where your notebook server resides in - i.e. your kubeflow profile name - to the list right under `istio-system`:\r\n```yaml\r\nspec:\r\n rules:\r\n - from:\r\n - source:\r\n namespaces:\r\n - istio-system\r\n - MY_NAMESPACE\r\n selector:\r\n matchLabels:\r\n app: ml-pipeline-ui\r\n```\r\nThen, the access to the `ml-pipeline-ui` service is allowed and works. I successfully listed experiments and also submitted a pipeline."
] | "2023-07-21T13:49:12" | "2023-08-02T09:46:21" | "2023-07-27T22:48:43" | NONE | null | ### Environment
* KFP version:
Full Kubeflow v1.7 on prem installation via https://github.com/kubeflow/manifests on K3s, Multi-User-Setup
* KFP SDK version:
2.0.0-alpha.7
* All dependencies version:
kfp 2.0.1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0
### Steps to reproduce
1. Follow instructions at https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/#full-kubeflow-subfrom-inside-clustersub for Full Kubeflow including
* Creation of PodDefaults in the user namespace
* Creation of RBAC Authorization via
```yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: allow-USERNS-kubeflow-edit
## this RoleBinding is in `namespace-1`, because it grants access to `namespace-1`
namespace: kubeflow
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: kubeflow-edit
subjects:
- kind: ServiceAccount
name: default-editor
## the ServiceAccount lives in `namespace-2`
namespace: USERNS
```
2. Create a new Notebook server where specifying the PodDefaults above as the configuration
3. Make sure that the authorization token is actually present in the notebook server:
```
/var/run/secrets/kubeflow/pipelines$ ls
token
```
4. Create a new notebook with the following contents
```python
import kfp
# the namespace in which you deployed Kubeflow Pipelines
namespace = "kubeflow"
client = kfp.Client(host=f"http://ml-pipeline-ui.{namespace}")
print(client.list_experiments())
```
Executing this code should connect to the pipeline server and return the experiments. Instead, there is an error message:
```
ERROR:root:Failed to get healthz info attempt 1 of 5.
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/site-packages/kfp/client/client.py", line 435, in get_kfp_healthz
return self._healthz_api.get_healthz()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/api/healthz_service_api.py", line 63, in get_healthz
return self.get_healthz_with_http_info(**kwargs) # noqa: E501
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/api/healthz_service_api.py", line 134, in get_healthz_with_http_info
return self.api_client.call_api(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (403)
Reason: Forbidden
HTTP response headers: HTTPHeaderDict({'content-length': '19', 'content-type': 'text/plain', 'date': 'Fri, 21 Jul 2023 13:10:37 GMT', 'server': 'envoy', 'x-envoy-upstream-service-time': '1'})
HTTP response body: RBAC: access denied
```
### Expected result
This should have listed the experiments in my namespace.
### Materials and Reference
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---
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] | null | [] | "2023-07-21T13:21:57" | "2023-08-17T22:48:58" | null | MEMBER | null | Hi KFP team is this the correct way to use VertexNotificationEmailOp? I want to give email as a parameter of the pipeline to notify a user
### Environment
* KFP version:
Vertex AI Pipelines
* KFP SDK version:
-
* All dependencies version:
kfp==2.0.1
### Steps to reproduce
The following pipeline:
```
from typing import List
from google_cloud_pipeline_components.v1.vertex_notification_email import (
VertexNotificationEmailOp,
)
from kfp import dsl
@dsl.component
def how_many_emails() -> int:
return 1
@dsl.component
def get_email() -> List[str]:
return ["anemail@kfp.ca"]
# %%
@dsl.pipeline()
def test_pipeline():
emails = get_email()
exit_task_job = VertexNotificationEmailOp(
recipients=emails.output,
)
with dsl.ExitHandler(exit_task=exit_task_job):
_ = how_many_emails()
if __name__ == "__main__":
import kfp
kfp.compiler.Compiler().compile(test_pipeline, __file__ + ".yaml")
```
Fails with the following traceback:
```
Traceback (most recent call last):
File "/home/dgromov/Documents/personal/poc-email/poc_email/pipeline.py", line 21, in <module>
@dsl.pipeline()
^^^^^^^^^^^^^^
File "/home/dgromov/Documents/personal/poc-email/.venv/lib/python3.11/site-packages/kfp/components/pipeline_context.py", line 65, in pipeline
return component_factory.create_graph_component_from_func(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/dgromov/Documents/personal/poc-email/.venv/lib/python3.11/site-packages/kfp/components/component_factory.py", line 616, in create_graph_component_from_func
return graph_component.GraphComponent(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/dgromov/Documents/personal/poc-email/.venv/lib/python3.11/site-packages/kfp/components/graph_component.py", line 68, in __init__
pipeline_spec, platform_spec = builder.create_pipeline_spec(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/dgromov/Documents/personal/poc-email/.venv/lib/python3.11/site-packages/kfp/compiler/pipeline_spec_builder.py", line 1681, in create_pipeline_spec
inputs = compiler_utils.get_inputs_for_all_groups(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/dgromov/Documents/personal/poc-email/.venv/lib/python3.11/site-packages/kfp/compiler/compiler_utils.py", line 258, in get_inputs_for_all_groups
_get_uncommon_ancestors(
File "/home/dgromov/Documents/personal/poc-email/.venv/lib/python3.11/site-packages/kfp/compiler/compiler_utils.py", line 584, in _get_uncommon_ancestors
raise ValueError(task2.name + ' does not exist.')
ValueError: vertex-pipelines-notification-email does not exist.
```
If we change the parameter of recipients to string (like "email@domain.co") the pipeline compiles successfully
### Expected result
Use email as parameter
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"@grapefruitL Would you mind telling us if you run the pipeline on KFP or Vertex Pipeline? Thanks."
] | "2023-07-21T08:52:36" | "2023-08-17T22:51:12" | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
I wish to retry run with kfp sdk.
### What is the use case or pain point?
I start run with python, it will fail due to network problem sometime, usually retry will solve it. So, I hope it can be executed automatically.
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https://api.github.com/repos/kubeflow/pipelines/issues/9770 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9770/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9770/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9770/events | https://github.com/kubeflow/pipelines/issues/9770 | 1,814,461,431 | I_kwDOB-71UM5sJnv3 | 9,770 | (test): Fix periodic functional tests | {
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"@gkcalat Here is my update..\r\nI tried to run kfp-functional-test in local and found out that `../../backend/update_requirements.sh` does not exists\r\nhttps://github.com/kubeflow/pipelines/blob/1619f2c0b605f202c1cbb8488c64805ccff759c8/test/kfp-functional-test/README.md?plain=1#L10\r\nIt has been deleted (`git log --full-history -1 -- [file path] backend/update_requirements.sh`) by commit\r\n``` \r\nee2102a9b59862c1b6b19f17484d6bc05c7ffd80\r\nAuthor: Chen Sun <chensun@users.noreply.github.com>\r\nDate: Wed Jun 7 15:06:57 2023 -0700\r\n```\r\n\r\nI noticed that this file is referenced in a few other places. Guess that these files need to be updated so they do not reference to update_requirement.sh file anymore. Opinion? \r\n\r\n- https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/visualization/update_requirements.sh\r\n- https://github.com/kubeflow/pipelines/blob/1619f2c0b605f202c1cbb8488c64805ccff759c8/backend/src/apiserver/visualization/README.md?plain=1#L175-L178\r\n- https://github.com/kubeflow/pipelines/blob/1619f2c0b605f202c1cbb8488c64805ccff759c8/hack/update-all-requirements.sh#L21\r\n- https://github.com/kubeflow/pipelines/blob/1619f2c0b605f202c1cbb8488c64805ccff759c8/backend/metadata_writer/README.md?plain=1#L5-L8\r\n- https://github.com/kubeflow/pipelines/blob/1619f2c0b605f202c1cbb8488c64805ccff759c8/backend/README.md?plain=1#L60-L65\r\n\r\n\r\nStill, I installed pip-tools and tried to run kfp-functional-test in local (via python ) and I ended up with the error below I need to investigate :\r\n```\r\npython3 ./test/kfp-functional-test/run_kfp_functional_test.py --host \"https://$(curl https://raw.githubusercontent.com/kubeflow/testing/master/test-infra/kfp/endpoint)\"\r\n % Total % Received % Xferd Average Speed Time Time Time Current\r\n Dload Upload Total Spent Left Speed\r\n100 62 100 62 0 0 221 0 --:--:-- --:--:-- --:--:-- 220\r\nTraceback (most recent call last):\r\n File \"/home/dianaa/git/pipelines/./test/kfp-functional-test/run_kfp_functional_test.py\", line 21, in <module>\r\n from kfp.v2 import dsl\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/__init__.py\", line 21, in <module>\r\n from . import components\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/components/__init__.py\", line 15, in <module>\r\n from ._airflow_op import *\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/components/_airflow_op.py\", line 23, in <module>\r\n from ._python_op import _func_to_component_spec, _create_task_factory_from_component_spec\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/components/_python_op.py\", line 48, in <module>\r\n from kfp.dsl import io_types\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/dsl/__init__.py\", line 16, in <module>\r\n from ._pipeline import Pipeline, PipelineExecutionMode, pipeline, get_pipeline_conf, PipelineConf\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/dsl/_pipeline.py\", line 19, in <module>\r\n from kfp.dsl import _container_op\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/dsl/_container_op.py\", line 29, in <module>\r\n from kfp.dsl import dsl_utils\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/dsl/dsl_utils.py\", line 20, in <module>\r\n from kfp.pipeline_spec import pipeline_spec_pb2\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/kfp/pipeline_spec/pipeline_spec_pb2.py\", line 16, in <module>\r\n from google.rpc import status_pb2 as google_dot_rpc_dot_status__pb2\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/google/rpc/status_pb2.py\", line 52, in <module>\r\n _descriptor.FieldDescriptor(\r\n File \"/home/dianaa/git/pipelines/venv/lib/python3.10/site-packages/google/protobuf/descriptor.py\", line 561, in __new__\r\n _message.Message._CheckCalledFromGeneratedFile()\r\nTypeError: Descriptors cannot not be created directly.\r\nIf this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.\r\nIf you cannot immediately regenerate your protos, some other possible workarounds are:\r\n 1. Downgrade the protobuf package to 3.20.x or lower.\r\n 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).\r\n ```\r\nAny suggestions or comments? \r\n \r\n\r\n",
"@difince, thank you for looking into this.\r\n\r\nLet's treat the process for updating requirements as a separate issue. For now, you can set `kfp==2.0.1` and generate a new requirements.txt.\r\n\r\nFor the dependency error, could you try 1) updating `kfp` to `2.0.1` and 2) running it via a docker? You can also try running the test under Python 3.7-3.9.\r\n\r\nIt's been 3 years since the instructions were last updated, so it may be worth trying to upgrade to a newer Python version (3.7 is EOL already).\r\n\r\n/cc @chensun can you comment on the `update_requirements.sh` and the `pip-tools`?",
"hi @gkcalat my update for today \r\nI tried both Python and Docker ways to run `kfp-functional-test` in local (using kfp==2.0.1) - both failed because of 401 exceptions.\r\n```\r\nERROR:root:Failed to get healthz info attempt 5 of 5.\r\nTraceback (most recent call last):\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp/client/client.py\", line 435, in get_kfp_healthz\r\n return self._healthz_api.get_healthz()\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/api/healthz_service_api.py\", line 63, in get_healthz\r\n return self.get_healthz_with_http_info(**kwargs) # noqa: E501\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/api/healthz_service_api.py\", line 134, in get_healthz_with_http_info\r\n return self.api_client.call_api(\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/api_client.py\", line 364, in call_api\r\n return self.__call_api(resource_path, method,\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/api_client.py\", line 188, in __call_api\r\n raise e\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/api_client.py\", line 181, in __call_api\r\n response_data = self.request(\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/api_client.py\", line 389, in request\r\n return self.rest_client.GET(url,\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/rest.py\", line 230, in GET\r\n return self.request(\"GET\", url,\r\n File \"/home/dianaa/git/pipelines/test/kfp-functional-test/diana_venv/lib/python3.10/site-packages/kfp_server_api/rest.py\", line 224, in request\r\n raise ApiException(http_resp=r)\r\nkfp_server_api.exceptions.ApiException: (401)\r\nReason: Unauthorized\r\nHTTP response headers: HTTPHeaderDict({'Content-Length': '1452', 'Content-Type': 'text/html; charset=utf-8', 'Date': 'Tue, 25 Jul 2023 14:28:55 GMT', 'Vary': 'Origin', 'X-Content-Type-Options': 'nosniff', 'X-Frame-Options': 'SAMEORIGIN', 'X-Xss-Protection': '0', 'Set-Cookie': 'S=cloud_datalab_tunnel=o5n06VLCFJWPiw-qYKW8qaofTtZo4x3eok1ivlC98Pk; Path=/; Max-Age=3600', 'Alt-Svc': 'h3=\":443\"; ma=2592000,h3-29=\":443\"; ma=2592000'})\r\nHTTP response body: \r\n<!DOCTYPE html>\r\n<html lang=en>\r\n <meta charset=utf-8>\r\n <meta name=viewport content=\"initial-scale=1, minimum-scale=1, width=device-width\">\r\n <title>Error 401 (Unauthorized)!!1</title>\r\n <style>\r\n *{margin:0;padding:0}html,code{font:15px/22px arial,sans-serif}html{background:#fff;color:#222;padding:15px}body{margin:7% auto 0;max-width:390px;min-height:180px;padding:30px 0 15px}* > body{background:url(//www.google.com/images/errors/robot.png) 100% 5px no-repeat;padding-right:205px}p{margin:11px 0 22px;overflow:hidden}ins{color:#777;text-decoration:none}a img{border:0}@media screen and (max-width:772px){body{background:none;margin-top:0;max-width:none;padding-right:0}}#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54.png) no-repeat;margin-left:-5px}@media only screen and (min-resolution:192dpi){#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) no-repeat 0% 0%/100% 100%;-moz-border-image:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) 0}}@media only screen and (-webkit-min-device-pixel-ratio:2){#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) no-repeat;-webkit-background-size:100% 100%}}#logo{display:inline-block;height:54px;width:150px}\r\n </style>\r\n <a href=//www.google.com/><span id=logo aria-label=Google></span></a>\r\n <p><b>401.</b> <ins>That’s an error.</ins>\r\n <p> <ins>That’s all we know.</ins>\r\n```\r\nWhen I do `gcloud auth application-default login` I reference to my Gmail - Is this wrong? ",
"The test has been fixed: https://oss.gprow.dev/?job=kubeflow-pipelines-periodic-functional-test"
] | "2023-07-20T17:16:14" | "2023-08-18T20:34:03" | "2023-08-15T17:07:18" | MEMBER | null | It seems to me that the functional testing is [broken](https://oss.gprow.dev/?job=kubeflow-pipelines-periodic-functional-test) due to [outdated `kfp` version](https://oss.gprow.dev/view/gs/oss-prow/logs/kubeflow-pipelines-periodic-functional-test/1681338338910408704).
We may need to fix the test and verify it (at least [locally](https://github.com/kubeflow/pipelines/blob/master/test/kfp-functional-test/README.md#run-kfp-functional-test-in-local)) before reviewing #9748.
_Originally posted by @gkcalat in https://github.com/kubeflow/pipelines/pull/9748#discussion_r1267026079_
/cc @chensun @Linchin @difince
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"Is this already being worked or is it up for grabs? What is contribution guideline and workflow in case I volunteer to implement the `tolerations` model? We really need it for our project."
] | "2023-07-20T15:43:17" | "2023-08-05T09:59:01" | 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?
`kfp-kubernetes` is a library that aims to extend kfp v2 with kubernetes functionality. Currently the `kfp-kubernetes` package is still missing some key features imo:
- affinities/tolerations https://github.com/kubeflow/pipelines/issues/9682
- extended resource requests/limits
- init_containers + sidecars https://github.com/kubeflow/pipelines/issues/9679 and https://github.com/kubeflow/pipelines/issues/9700
- pod annotation/labels https://github.com/kubeflow/pipelines/issues/9714
- Something like ResourceOp from v1: https://github.com/kubeflow/pipelines/issues/9703
Some of the implemented features of `kfp-kubernetes` are currently limited. For example, only secrets can be loaded as env vars while this could also be from a Config Map, a resource's field or an object's field: (via [V1EnvVarSource](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1EnvVarSource.md) ) . Integration with the `kubernetes` package (as was the case with KFP v1) could facillitate this. The protos that `kfp-kubernetes` requires could be generated based on the OpenAPI spec. The methods in go/python for manipulating the objects would still need to be implemented. I believe integrating the models from the `kubernetes` python client package is a natural way for the `kfp-kubernetes` to grow in functionality and ability.
### What is the use case or pain point?
- It would enable different kind of Env var sources, tolerations and affinities would be easier to set/reused from the KFP v1.
- Init_containers and sidecars can be again set (via [V1Container](https://github.com/kubernetes-client/python/blob/master/kubernetes/client/models/v1_container.py)).
### Is there a workaround currently?
N/A
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"Hi, @JacusSH. Thanks for the question. For KFP SDK v2, the \"workarounds\" you describe are indeed the supported paths to the outcome you describe. There are currently no plans to support `modules_to_capture` though we're exploring some other improvements on the containerization experience that may make it to a future KFP SDK release.",
"Hi @connor-mccarthy. The current suggested solutions result in quite a lot of friction in the development process. Consider the situation where multiple components use a common module. If you create a new component that also depends on this common module, but requires some extra functionality, you are forced to update the common module *first* and upload it to a repository or container before you can experiment with using that module in a component.\r\n\r\nPreviously users of kubeflow would be able to create a small change in the common module and test it out in their component before committing to creating images or uploading to a remote repository. This was enabled via the `modules_to_capture` feature and was extremely useful.\r\n\r\nIt's very rare for components not to share modules with other components so I suspect this is a very common use-case. What was the reasoning behind removing support for the `modules_to_capture` feature?",
"> What was the reasoning behind removing support for the `modules_to_capture` feature?\r\n\r\nShort answer is we weren't aware of active usage to this feature. \r\nAs part of the `ContainerOp` deprecation announced in 2020 (https://github.com/kubeflow/pipelines/issues/4713), we've been recommending using `create_component_from_from` instead of `func_to_container_op` to avoid feature \"duplication\" between the two and the leaking of the word `container_op` in public interface. \r\n\r\n> Previously users of kubeflow would be able to create a small change in the common module and test it out in their component before committing to creating images or uploading to a remote repository. This was enabled via the modules_to_capture feature and was extremely useful.\r\n\r\n@WalterSmuts, can you share a bit more details on how you test it out in a component before making it available from a remote image or module? There's a chance we might be able to close the feature gap via our upcoming local testing support.\r\n\r\n",
"Consider the following setup:\r\n```\r\nwalter@rocky:~/development/kubeflow-issue-9765$ tree\r\n.\r\n├── library.py\r\n└── my_pipeline.py\r\n\r\n1 directory, 2 files\r\n```\r\n```python\r\nwalter@rocky:~/development/kubeflow-issue-9765$ cat library.py\r\nMY_KUBEFLOW_HOST = \"HOST_DOMAIN_NAME\"\r\n\r\n\r\ndef super_useful_common_function():\r\n print(\"I am super useful\")\r\n```\r\n```python\r\nwalter@rocky:~/development/kubeflow-issue-9765$ cat my_pipeline.py\r\nimport kfp\r\nimport library\r\n\r\n\r\ndef my_component_op():\r\n def my_component():\r\n library.super_useful_common_function()\r\n\r\n kfp.components.func_to_container_op(\r\n my_component, use_code_pickling=True, modules_to_capture=[\"library\"]\r\n )\r\n\r\n\r\ndef my_pipeline_func():\r\n my_component_op()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n client = kfp.Client(host=library.MY_KUBEFLOW_HOST)\r\n\r\n run_result = client.create_run_from_pipeline_func(\r\n pipeline_func=my_pipeline_func,\r\n arguments={},\r\n )\r\n```\r\n\r\nNow I want to add a new pipeline using the basic template of `my_pipeline.py`:\r\n```\r\nwalter@rocky:~/development/kubeflow-issue-9765$ cp my_pipeline.py my_other_pipeline.py\r\n```\r\nI want to add a new feature to the `super_useful_common_function` but I don't want to change existing behavior(to keep backwards compatibility with other piplines), so I may do the following change:\r\n```diff\r\nwalter@rocky:~/development/kubeflow-issue-9765$ git diff\r\ndiff --git a/library.py b/library.py\r\nindex 5b8be79..7e86686 100644\r\n--- a/library.py\r\n+++ b/library.py\r\n@@ -1,5 +1,7 @@\r\n MY_KUBEFLOW_HOST = \"HOST_DOMAIN_NAME\"\r\n\r\n\r\n-def super_useful_common_function():\r\n+def super_useful_common_function(enable_new_feature: bool = False):\r\n print(\"I am super useful\")\r\n+ if enable_new_feature:\r\n+ print(\"I am a super useful new feature\")\r\n```\r\n\r\nThe `modules_to_capture` feature means my *local* workspace is all I need to change to actually test out this new pipeline before pushing any changes and going through some code-review process:\r\n```diff\r\nwalter@rocky:~/development/kubeflow-issue-9765$ git diff my_other_pipeline.py\r\ndiff --git a/my_other_pipeline.py b/my_other_pipeline.py\r\nindex 7981846..f21b467 100644\r\n--- a/my_other_pipeline.py\r\n+++ b/my_other_pipeline.py\r\n@@ -4,7 +4,7 @@ import library\r\n\r\n def my_component_op():\r\n def my_component():\r\n- library.super_useful_common_function()\r\n+ library.super_useful_common_function(enable_new_feature=True)\r\n\r\n kfp.components.func_to_container_op(\r\n my_component, use_code_pickling=True, modules_to_capture=[\"library\"]\r\n```",
"We are spiking out KFP V2 and this is one feature our usecases are using extensively as well. Our usecases like to structure their code and test their code as well. \r\n\r\nWithout `modules_to_capture`, all the dependent functions needs to be inside of the component code which is ugly and violates the DRY principle because any small helper function would either need to be copied to individual component functions or the approaches mentioned in the original feature request needs to be done which is an overkill for such small helper functions. \r\n\r\nWe probably wouldn't be able to move to KFP v2/SDK v2 unless the usecases can somehow efficiently include dependent functions and modules at runtime. ",
"Thank you for the feedback, @revolutionisme. We will keep this open and continue to explore the source code packaging options available, incorporating your feedback.\r\n\r\nI do want to highlight for future viewers that this is not strictly true:\r\n\r\n> Without modules_to_capture, all the dependent functions needs to be inside of the component code\r\n\r\nAs you mention, [Containerized Python Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/), provide an alternative approach to this, though it is not as lightweight as the `modules_to_capture` approach, which comes with its own set of challenges."
] | "2023-07-20T13:35:50" | "2023-08-14T19:43:04" | 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 -->
### Question
In the KFP SDK v1 using the `func_to_container_op` one could use the `modules_to_capture` parameter to add additional python modules that should be captured. This allowed one to reuse helper/utility functions across multiple components or provide modular design for complex components.
According to the documentation for lightweight python components in [v1](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/python-function-components/) and [v2](https://www.kubeflow.org/docs/components/pipelines/v2/components/lightweight-python-components/), the python function should be self contained, but v1 had a way around this using the `modules_to_capture` functionality.
For v2 the only workarounds I see:
- Publish the python code (not contained in the function) to a repository and use the `pip_index_urls` currently supported by the component decorator in v2
- Build and push a container image containing both the helper utility and component function (as demonstrated by the v2 docs for [containerized python components](https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/) to an image registry
For both these options one would need to push an image/package before being able to use the component. This causes friction during the component development process.
Could/will this functionality be added to KFP SDK v2?
Is there another way to achieve this that I am missing?
What is best practise for using helper functions across multiple components?
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"My work has literally struck at this point, could someone kindly assist me?"
] | "2023-07-18T10:56:38" | "2023-09-11T06:58:29" | null | NONE | null | I am following tutorial of Kubeflow Pipelines API: [Kubeflow](https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/api-pipelines/) & [Kubeflow Pipelines API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/)
I've installed Kubeflow on a MicroK8s and started experimenting with the Kubeflow Pipelines API, I was able to upload a pipeline to the central dashboard by using[ kubeflow upload API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/), but when I tried utilizing the [Kubeflow RUN API](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs-post), I was unable to establish a RUN. It initially asks me to enter my experiment ID, which I did after creating the experiment in the central dashboard, and then I entered my experiment ID. After passing Experiment ID facing with the below issue, can anyone offer assistance?
RUN_ID=$((
curl -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs \
-d @- << EOF
{
"name":"${PIPELINE_NAME}_run",
"pipeline_spec":{
"pipeline_id":"${PIPELINE_ID}"
}
}
EOF
) | jq -r .run.id)
{"error":"**Invalid input error: Job has no experiment.","code":3,"message**":"Invalid input error: Job has no experiment.","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Job has no experiment.","error_details":"Invalid input error: Job has no experiment."}]}
**After passing the Experiment Id Getting with this below error**
curl -v -H "Content-Type: application/json" -X POST ${SVC}/apis/v1beta1/runs -d @- << EOF
{
"name":"${PIPELINE_NAME}_run",
"pipeline_spec":{
"pipeline_id":"${PIPELINE_ID}"
},
"resource_references": [
{
"key": {
"type": "EXPERIMENT",
"id": "376a6097-67e3-48b4-bda1-26538abae21d"
},
"relationship": "OWNER"
}
]
}
EOF
Note: Unnecessary use of -X or --request, POST is already inferred.
Trying 127.0.0.1:8888...
TCP_NODELAY set
Connected to localhost (127.0.0.1) port 8888 (#0)
> POST /apis/v1beta1/runs HTTP/1.1
> Host: localhost:8888
> User-Agent: curl/7.68.0
> Accept: */*
> Content-Type: application/json
> Content-Length: 278
>
upload completely sent off: 278 out of 278 bytes
Mark bundle as not supporting multiuse
< HTTP/1.1 500 Internal Server Error
< Content-Type: application/json
< Date: Mon, 17 Jul 2023 10:08:03 GMT
< Transfer-Encoding: chunked
<
{"error":"Internal error: Unauthenticated: **Request header error: there is no user identity header**.: Request header error: there is no user identity header.\nFailed to authorize with API resource references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go: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).
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"I saw your PR for that. Did you find any workaround in the meantime? ",
"> I saw your PR for that. Did you find any workaround in the meantime?\r\n\r\nSadly, no, looking for the pipeline name was kinda hard as explained in the PR, maybe a metadata was not added properly or a CustomProperties. But from the current element, I inspected the networks logs in the current version, and there is not the pipeline name attached.\r\n\r\n## Edit\r\n\r\nI am not a big fan of my implementation in the PR, since it had a lot of requests, just like the pipeline run page where I opened an issue https://github.com/kubeflow/pipelines/issues/9701 explaining that we get a very long time for displaying all the informations, I simply used the same logic in the artifacts page to get all the pipeline information."
] | "2023-07-18T07:16:47" | "2023-08-22T08:50:29" | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? manifests
* KFP version: 2.0.0
### Description
No matter how I start my run, (from UI or SDK) I always get the `[unknown]` in the Artifact panel for pipeline/workspace.
### Expected result
The name of the pipeline.
---
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"Seeing this issue on GCP Vertex that uses Kubeflow internally. Forcing `pyyaml==5.3.1` with non-working `kfp==1.8.21` fixes the issue (following already mentioned [comment](https://github.com/yaml/pyyaml/issues/724#issuecomment-1638587228))"
] | "2023-07-18T00:19:25" | "2023-07-24T12:51:17" | null | MEMBER | null | > TLDR: Cython 3.0.0 just GA'd and introduced a regression which affects building `pyyaml` on _some_ operating systems with _some_ versions of Python. Upgrade to the latest version of the KFP SDK (v1 or v2) and recompile your components to get the fix.
**Short term fix options**
* Upgrade to the latest version of the KFP SDK within the major version you are using (`1.8.22` and `2.0.1` at the time of writing this)
* Switch to a Python image/version that does not exhibit this error (`python:3.7` appears to work in some cases)
With either option, recompile your components to get the fix.
**Useful information**
* This is an issue originating from outside the KFP.
* https://github.com/yaml/pyyaml/issues/601 is good public documentation of this issue.
* This is thematically similar to https://github.com/kubeflow/pipelines/issues/9326#issuecomment-1535491761, but appears to have smaller impact due to fewer affected combinations of KFP SDK versions and environments.
**Reproducing**
* `docker run python:3.7 pip install 'kfp==1.8.20'` PASS
* `docker run python:3.10 pip install 'kfp==1.8.20'` FAIL
* `docker run python:3.10 pip install 'kfp==1.8.22'` PASS
* `docker run python:3.10 pip install 'kfp>=2.0.0,<3.0.0'` PASS
**Long term fix**
We are nearing completion a change that extracts a KFP runtime package, preventing similar failures for existing components that use prior versions of the KFP SDK (versions we cannot patch once released). | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9731 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9731/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9731/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9731/events | https://github.com/kubeflow/pipelines/issues/9731 | 1,803,437,713 | I_kwDOB-71UM5rfkaR | 9,731 | bug(frontend): Rendering pipeline details page is slow | {
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"@jlyaoyuli should this issue be close after your fix?"
] | "2023-07-13T17:10:29" | "2023-07-25T17:37:53" | "2023-07-25T17:37:53" | COLLABORATOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9727 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9727/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9727/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9727/events | https://github.com/kubeflow/pipelines/issues/9727 | 1,801,829,741 | I_kwDOB-71UM5rZb1t | 9,727 | [feature] add pipeline prefix in display name tasks for subpipelines | {
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"@iuiu34 can you describe what's the use case for this feature?",
"it's to better readability of the tasks.\r\n\r\nBut after playing around for some weeks now with the pipelines inside pipelines.\r\nI'll admit, that having 2 components with the same name, but in different sub-pipelines, is already quite easy to read just by the display.\r\n\r\nSo not sure anymore, about adding the suffix pipeline is so critic for readability. \r\nBut still, would be nice as an optional feature"
] | "2023-07-12T22:11:00" | "2023-08-11T07:59:42" | null | NONE | null | hi,
in kfp 2.0 you can call pipelines inside pipeline. But task name (Set_display_name) is at build time, not run time, so you can't add a suffix to the task names. Is it plan to add dynamically names, in case not, to at least put some prefix coming from the pipeline?
```py
from kfp import dsl
@dsl.component
def square(x: float) -> float:
return x ** 2
@dsl.component
def add(x: float, y: float) -> float:
return x + y
@dsl.component
def square_root(x: float) -> float:
return x ** .5
@dsl.pipeline
def square_and_sum(a: float, b: float) -> float:
a_sq_task = square(x=a)
b_sq_task = square(x=b)
return add(x=a_sq_task.output, y=b_sq_task.output).output
@dsl.pipeline
def pythagorean( b: float = 1.2) -> float:
for a in [1,2,3]:
sq_and_sum_task = square_and_sum(a=a, b=b)
out = square_root(x=sq_and_sum_task.output)
out.set_display_name(f"pipeline-{a}")
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9723 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9723/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9723/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9723/events | https://github.com/kubeflow/pipelines/issues/9723 | 1,799,750,087 | I_kwDOB-71UM5rRgHH | 9,723 | test(frontend): Add end-to-end test for KFP v2 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9722 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9722/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9722/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9722/events | https://github.com/kubeflow/pipelines/issues/9722 | 1,797,064,464 | I_kwDOB-71UM5rHQcQ | 9,722 | [sdk] dsl.Condition doesn't execute the pipeline after passing the condition. | {
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"/assign @Linchin "
] | "2023-07-10T15:55:53" | "2023-08-28T20:53:45" | null | NONE | null | ### Environment
* KFP version: 2
* KFP SDK version: 2.0.1
* All dependencies version:
kfp 2.0.1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0
### Description
Artifacts passing across dsl.Condition doesn't execute tasks that follow the condition. This IMHO should perhaps be the most basic use-case of what dsl.Condition is supposedly designed for. While most of the examples around only addresses simple function-based components, the params with artifacts don't work. Following is a sample codebase to reproduce this behaviour.
### Steps to reproduce
```
import os
import kfp
from kfp.dsl import component, Output, Artifact, Input, OutputPath, pipeline, Condition
@component(base_image="python:3.10-slim")
def comp1(a: int, b: int, c: OutputPath(str), d: Output[Artifact]):
import json
with open(d.path, "w") as fp:
json.dump({"a": a, "b": b}, fp)
pass
with open(c, "w") as fp:
fp.write("render" if a + b > 0 else "skip")
pass
pass
@component(base_image="python:3.10-slim")
def comp2(d: Input[Artifact]):
import json
with open(d.path, "r") as fp:
obj = json.load(fp)
pass
print(obj)
pass
@pipeline(name="test-artifact-passing-pipeline")
def pipeline1(a: int, b: int):
task1 = comp1(a=a, b=b)
with Condition(task1.outputs["c"] == "render"):
comp2(d=task1.outputs["d"])
pass
pass
def compile_pipeline(pipeline, yaml_filename, dirname="."):
from kfp.compiler import Compiler
path = os.path.join(dirname, yaml_filename)
cmplr = Compiler()
cmplr.compile(pipeline_func=pipeline, package_path=path)
pass
if __name__ == "__main__":
compile_pipeline(
pipeline1,
yaml_filename=os.path.join(os.path.dirname(__file__), "test_artifact_passing_pipeline.yaml"),
)
pass
```
### Expected result
Object `d` holding the parameters `a` and `b` should be printed when `comp2` is executed for any valid inputs of `a` and `b`.
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9721 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9721/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9721/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9721/events | https://github.com/kubeflow/pipelines/issues/9721 | 1,797,050,456 | I_kwDOB-71UM5rHNBY | 9,721 | [sdk] Setting the Display name of the component doesn't show updates to the pipeline | {
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"/assign @jlyaoyuli ",
"Hello @hugzee3 , looks like you are hitting a cache in `Expected result`, so it is using the same artifact path as the first run. So maybe you can try creating a new pipeline without hitting the cache?",
"Hey @zijianjoy , I did try running a new pipeline with `run_pipeline(enable_caching=False)` it doesn't seem to take any effect. Still the same. ",
"@hugzee3 Is the problem regarding `Output Artifact` still using `Add Component` in its path name? What is the new artifact path name when you disable caching?"
] | "2023-07-10T15:47:05" | "2023-07-27T20:25:05" | null | NONE | null | ### Environment
* KFP: 2
* KFP SDK version: 2.0.1
* All dependencies version:
kfp 2.0.1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0
### Description
Renaming a component only reflects the name change, but doesn't update the KFP UI with the logs/ IO params, etc.
### Steps to reproduce
```
import os
import kfp
from kfp.dsl import component, Output, Artifact, pipeline
@component(base_image="python:3.10-slim")
def comp(a: int, b: int, c: Output[Artifact]):
import json
with open(c.path, "w") as fp:
json.dump({"result": a + b}, fp)
pass
pass
@pipeline(name="test-component-renaming-pipeline")
def test_pipeline(a: int, b: int):
task = comp(a=a, b=b)
task.set_display_name("Add Component"). # uncommenting this would render the screenshot 2 below
pass
def compile_pipeline(pipeline, yaml_filename, dirname="."):
path = os.path.join(dirname, yaml_filename)
cmplr = kfp.compiler.Compiler()
cmplr.compile(pipeline_func=pipeline, package_path=path)
pass
if __name__ == "__main__":
compile_pipeline(
test_pipeline,
yaml_filename=os.path.join(os.path.dirname(__file__), "test-component-renaming-pipeline.yaml"),
)
pass
```
### Current result
### Expected result
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9719 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9719/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9719/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9719/events | https://github.com/kubeflow/pipelines/issues/9719 | 1,795,873,252 | I_kwDOB-71UM5rCtnk | 9,719 | [bug] proxy-agent and controller-manager pods are in CrashLoopBackOff | {
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"I found a same issue at https://github.com/kubeflow/pipelines/issues/9546.\r\nI will close mine."
] | "2023-07-10T03:41:33" | "2023-07-10T05:55:35" | "2023-07-10T05:47:48" | 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: 2.0.0
* KFP SDK version: 2.0.1
### Steps to reproduce
I have a local Kubernetes created by [Rancher Desktop](https://rancherdesktop.io/).
```shell
export PIPELINE_VERSION=2.0.0
kubectl apply --kustomize="github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=${PIPELINE_VERSION}"
kubectl wait crd/applications.app.k8s.io --for=condition=established --timeout=60s
kubectl apply --kustomize="github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=${PIPELINE_VERSION}"
```
The proxy-agent pod is in CrashLoopBackOff with log:
```shell
+++ dirname /opt/proxy/attempt-register-vm-on-proxy.sh
++ cd /opt/proxy
++ pwd
+ DIR=/opt/proxy
++ kubectl get configmap inverse-proxy-config -o json
++ jq -r '.data.Hostname // empty'
E0710 03:35:41.871871 18 memcache.go:287] couldn't get resource list for packages.operators.coreos.com/v1: the server is currently unable to handle the request
E0710 03:35:41.896626 18 memcache.go:121] couldn't get resource list for packages.operators.coreos.com/v1: the server is currently unable to handle the request
E0710 03:35:41.920864 18 memcache.go:121] couldn't get resource list for packages.operators.coreos.com/v1: the server is currently unable to handle the request
E0710 03:35:41.951394 18 memcache.go:121] couldn't get resource list for packages.operators.coreos.com/v1: the server is currently unable to handle the request
+ HOSTNAME=
+ [[ -n '' ]]
+ [[ ! -z '' ]]
++ curl http://metadata.google.internal/computeMetadata/v1/instance/zone -H 'Metadata-Flavor: Google'
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- 0:00:01 --:--:-- 0curl: (6) Could not resolve host: metadata.google.internal
+ INSTANCE_ZONE=/
```
The controller-manager pod is in CrashLoopBackOff with log:
```shell
logs are hidden because volume is too excessive
```
For proxy-agent, it seems it is related with Google Cloud. As I installed it in local Kubernetes. So I feel it is not neccessary.
Based on https://github.com/kubeflow/pipelines/issues/5411#issuecomment-812235251, seems controller-manager is okay to remove. (What does it really do?)
So I tried to install by
```shell
kubectl create namespace kubeflow
export PIPELINE_VERSION=2.0.0
kubectl apply --kustomize="github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic?ref=${PIPELINE_VERSION}"
kubectl wait crd/applications.app.k8s.io --for=condition=established --timeout=60s
kubectl apply --kustomize="github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=${PIPELINE_VERSION}"
```
But they are still got installed. Any ideas? Thanks!
---
<!-- 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/9718 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9718/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9718/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9718/events | https://github.com/kubeflow/pipelines/issues/9718 | 1,795,556,905 | I_kwDOB-71UM5rBgYp | 9,718 | [sdk] google_cloud_pipeline_component ModelImportEvaluationOp fails in v2.0.0 | {
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"Hi Paul, can you import the `ModelImportEvaluationOp` from `google_cloud_pipeline_components._implementation.model_evaluation` instead of from `google_cloud_pipeline_components.experimental.evaluation`?\r\n\r\nThat will solve this issue, apologies for the regression here!",
"Thank you for the suggestion @KevinBNaughton , but I now have these imports:\r\n\r\n```\r\nfrom google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp\r\nfrom google_cloud_pipeline_components.v1.endpoint import EndpointCreateOp, ModelDeployOp\r\nfrom google_cloud_pipeline_components.experimental.evaluation import (\r\n EvaluationDataSamplerOp, ModelImportEvaluationOp, TargetFieldDataRemoverOp)\r\nfrom google_cloud_pipeline_components._implementation.model_evaluation import ModelEvaluationClassificationOp\r\n```\r\n\r\nAnd trying to compile my pipeline fails with:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"pipeline.py\", line 11, in <module>\r\n from google_cloud_pipeline_components._implementation.model_evaluation import ModelEvaluationClassificationOp\r\n File \"/Users/pbalm/customers/bpi/kfp_reference/venv/lib/python3.8/site-packages/google_cloud_pipeline_components/_implementation/model_evaluation/__init__.py\", line 18, in <module>\r\n from google_cloud_pipeline_components._implementation.model_evaluation.data_sampler.component import evaluation_data_sampler as EvaluationDataSamplerOp\r\n File \"/Users/pbalm/customers/bpi/kfp_reference/venv/lib/python3.8/site-packages/google_cloud_pipeline_components/_implementation/model_evaluation/data_sampler/component.py\", line 15, in <module>\r\n from google_cloud_pipeline_components.v1.model_evaluation.version import EVAL_IMAGE_TAG\r\n File \"/Users/pbalm/customers/bpi/kfp_reference/venv/lib/python3.8/site-packages/google_cloud_pipeline_components/v1/model_evaluation/__init__.py\", line 18, in <module>\r\n from google_cloud_pipeline_components.v1.model_evaluation.error_analysis_pipeline import vision_model_error_analysis_pipeline\r\n File \"/Users/pbalm/customers/bpi/kfp_reference/venv/lib/python3.8/site-packages/google_cloud_pipeline_components/v1/model_evaluation/error_analysis_pipeline.py\", line 16, in <module>\r\n from google_cloud_pipeline_components._implementation.model_evaluation import ErrorAnalysisAnnotationOp\r\nImportError: cannot import name 'ErrorAnalysisAnnotationOp' from partially initialized module 'google_cloud_pipeline_components._implementation.model_evaluation' (most likely due to a circular import) (/Users/pbalm/customers/bpi/kfp_reference/venv/lib/python3.8/site-packages/google_cloud_pipeline_components/_implementation/model_evaluation/__init__.py)\r\n```\r\n"
] | "2023-07-09T20:48:04" | "2023-08-08T14:53:57" | null | NONE | null | ### Environment
* KFP version: Vertex AI
* KFP SDK version: 2.0.1
* All dependencies version:
```
kfp 2.0.1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0
google_cloud_pipeline_components 2.0.0
```
### Steps to reproduce
The component `ModelImportEvaluationOp` from `google_cloud_pipeline_components.experimental.evaluation` in version 2.0.0 fails with:
```
/usr/bin/python3: Error while finding module specification for
'google_cloud_pipeline_components.container.experimental.
evaluation.import_model_evaluation'
(ModuleNotFoundError: No module named
'google_cloud_pipeline_components.container.experimental')
```
(some line breaks mine for readability)
### Analysis
This happens because the `experimental` packages were removed from the image that this component runs on.
The image is: `gcr.io/ml-pipeline/google-cloud-pipeline-components:2.0.0`
On the other hand, for example `ModelEvaluationClassificationOp` still works, because it uses a different image.
This was still working in `2.0.0b5` so downgrading to that version avoids the issue.
### Like and subscribe
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"@jlyaoyuli can you please help verify if there's a bug? Thanks!"
] | "2023-07-07T09:32:44" | "2023-08-10T22:50:56" | null | CONTRIBUTOR | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? manifests
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0
<!-- 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: 2.0.0
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
## Description
When opening an url with a run-id like `https://{hostname}/_/pipeline/#/runs/details/{run-id}` we are redirected to `https://{hostname}/_/pipeline/?ns={namespace}#/runs/details/{run-id}`
When clicking on the `back` button.
The url changed from `https://{hostname}/_/pipeline/?ns={namespace}#/runs/details/{run-id}` to the original link we clicked on without the namespace without the page changing. (Probably using back or previous url)
### Expected result
Going back to the list of run ? or experiment ?
<!-- What should the correct behavior be? -->
### 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/9714 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9714/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9714/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9714/events | https://github.com/kubeflow/pipelines/issues/9714 | 1,792,917,028 | I_kwDOB-71UM5q3b4k | 9,714 | [feature] Editing Pod's spec. | {
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] | open | false | null | [] | null | [] | "2023-07-07T06:50:09" | "2023-07-07T06:56:02" | 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?
I would like to understand what is the plan or the best practices for customizing the the Pod or container spec using `kfp`.
[Is this feature going to be deprecated together with the rest of the v1 sdk](https://github.com/search?q=repo%3Akubeflow%2Fpipelines+%22add_pod_annotation%22&type=code)?
### What is the use case or pain point?
In my case, I'm trying to add some _annotations_ (e.g. like it's defined [here](https://github.com/kubeflow/pipelines/blob/3b6201779c7db58c6cbc5fae1e02cac5420280af/samples/core/pipeline_transformers/pipeline_transformers.py#L27)). The pain point is that I don't feel like using a deprecated feature.
<!-- It helps us understand the benefit of this feature for your use case. -->
Having some control over the final manifest is critical, especially in cases where you need sidecars, annotations, etc. The lack of this flexibility makes it difficult to deal with critical requirements such as secrets handling or cluster-specific customizations.
[This](https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/source/dsl.html#kfp.dsl.PipelineTask) in particular:
> (`PipelineTask`) Replaces ContainerOp from kfp v1. Holds operations available on a task object, such as .after(), .set_memory_limit(), .enable_caching(), etc.
That `etc` is critical, we'll users be able to manipulate the final manifest? e.g. `set_annotations`?
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Not quite? Other than using deprecated APIs?
Related issues:
* #4020
---
<!-- 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/9709 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9709/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9709/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9709/events | https://github.com/kubeflow/pipelines/issues/9709 | 1,791,812,941 | I_kwDOB-71UM5qzOVN | 9,709 | [bug] runtimeValue constant not correct when concatenating parameters with file components | {
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"@axel7083 quick question, did you get `{{$.inputs.parameters['pipelinechannel--variable_a']}} {{$.inputs.parameters['pipelinechannel--variable_b']}} ` at runtime or after compiling the Python code into YAML? If it's the latter case, then this is expected SDK behavior, if it's the former, then this is a backend bug. \r\n\r\nThat being said, it's recommended containerizing all operations into components so the logic are executed at runtime. The string concatenation case is special case we supported for users' convenience but that's a bit hacky in my opinion."
] | "2023-07-06T15:47:18" | "2023-07-27T23:04:05" | null | CONTRIBUTOR | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? manifest
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0-rc.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 2.0.0
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
#### `component.yaml`
```yaml
name: echo variable
description: pass
inputs:
- {name: variable, type: String}
implementation:
container:
image: python:3.10
command:
- echo
args:
- {inputValue: variable}
```
#### `pipeline.py`
```
from kfp.components import load_component_from_file
import kfp.dsl as dsl
echo = load_component_from_file("component.yaml")
@dsl.pipeline
def pipeline(
variable_a: str = "hello",
variable_b: str = "world",
):
echo(variable=f'{variable_a} {variable_b}')
```
### Expected result
`hello world` would expected but got `{{$.inputs.parameters['pipelinechannel--variable_a']}} {{$.inputs.parameters['pipelinechannel--variable_b']}}
`
<!-- What should the correct behavior be? -->
### Materials and reference
This is working file when using function components.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
/area sdk
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9708 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9708/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9708/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9708/events | https://github.com/kubeflow/pipelines/issues/9708 | 1,790,479,815 | I_kwDOB-71UM5quI3H | 9,708 | [backend] Optional parameters to custom component appear oddly if not provided | {
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] | open | false | null | [] | null | [] | "2023-07-05T23:01:08" | "2023-08-28T20:53:50" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Using the recommended `kubectl apply -k`, i.e.
```shell
ENV=dev
CRDS="github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$KFP_VERSION"
RSRC="github.com/kubeflow/pipelines/manifests/kustomize/env/${ENV}?ref=$PIPELINE_VERSION"
$KUBECTL apply -k $CRDS
$KUBECTL wait --for condition=established --timeout=60s crd/applications.app.k8s.io
$KUBECTL apply -k $RSRC
```
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0
<!-- 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: 2.latest
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Create a component.yaml with optional parameters such as:
```yaml
inputs:
- {name: name, type: String, description: 'Name of Application.'}
- {name: namespace, type: String, description: 'Namespace of Application.', optional: True}
- {name: script, type: String, description: 'Name of your Python entry point file [defaults to {name}.py].', optional: True}
...
args: [
-a, {inputValue: name},
-n, {inputValue: namespace},
-s, {inputValue: script}
]
```
And then use it in a pipeline:
```python
app = components.load_component_from_url('http://components.kubeflow/application/component.yaml')
...
app(name="test4-step1")
```
the component impl (just a shell script, for now) echos the three argument values that were passed in. looking at the test4-step1 pipeline-runner pod's logs, i see
```
name=test4-step1 namespace={{$.inputs.parameters['namespace']}} script={{$.inputs.parameters['script']}}
```
i.e. the not-provided optional parameters appear with funky handlebar leftovers.
<!--
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
Not-provided optional parameters should have null or empty-string values?
<!-- 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/9706 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9706/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9706/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9706/events | https://github.com/kubeflow/pipelines/issues/9706 | 1,790,226,219 | I_kwDOB-71UM5qtK8r | 9,706 | Seeking alternatives to building test images via docker-in-docker. | {
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"Suspect it's caused by GKE upgrade. \r\n\r\nThe last succeeded test run was on June 29, [with GKE version 1.25.8-gke.1000](https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9693/kubeflow-pipeline-e2e-test/1674513454611828736#)\r\nThen it starts to fail since June 30, [with GKE version 1.26.5-gke.1200](https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9694/kubeflow-pipeline-e2e-test/1674873953513902080#)\r\n\r\nLooking through the K8s 1.26 release note, I couldn't identify a specific deprecation that could affect us. However, [this Argo doc](https://argoproj.github.io/argo-workflows/walk-through/docker-in-docker-using-sidecars/) warned us that docker-in-docker (dind) using sidecar--that [we're currently using for building test images](https://github.com/kubeflow/pipelines/blob/4a57ab69ddc0abf08daa73473646492bad7ab38d/test/e2e_test_gke_v2.yaml#L271-L278)--are likely to break with future upgrades. It's advised to seek alternatives to building image via dind."
] | "2023-07-05T18:02:49" | "2023-07-05T22:28:53" | "2023-07-05T22:28:52" | COLLABORATOR | null | Error log:
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