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https://api.github.com/repos/kubeflow/pipelines/issues/5902 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5902/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5902/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5902/events | https://github.com/kubeflow/pipelines/issues/5902 | 926,906,366 | MDU6SXNzdWU5MjY5MDYzNjY= | 5,902 | Gcp Ml training and deploying prebuild component from on premise | {
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"Hi @motilalmeher, you can find how to mount GCP service account credentials in doc: https://www.kubeflow.org/docs/distributions/gke/pipelines/authentication-pipelines/#authoring-pipelines-to-use-gsa-keys",
"This issue has been automatically marked as stale because it has not had recent activity. It will be close... | 2021-06-22T07:09:23 | 2022-03-03T03:05:34 | 2022-03-03T03:05:34 | NONE | null | In kubeflow prebuild component(Gcp Ml training and deploying) there is no option for giving credential to use AI platform of a GCP for training and deployment purpose from on premise. How to handle that? Do we need to write our customize code for traing job and deplying job? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5901 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5901/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5901/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5901/events | https://github.com/kubeflow/pipelines/issues/5901 | 926,862,447 | MDU6SXNzdWU5MjY4NjI0NDc= | 5,901 | How to use customized name of the component(pre build component) used in kubeflow. | {
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"Hi @motilalmeher, you can refer to https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/samples/core/continue_training_from_prod/continue_training_from_prod.py#L63 which sets a display name for different tasks.",
"This issue has been automatically marked as stale because it has not... | 2021-06-22T06:03:31 | 2022-03-03T03:05:35 | 2022-03-03T03:05:35 | NONE | null | I am using GCP ml traning and deploying prebuild component for training and deployment. In my kubeflow pipliline, I am using multiple models for hyperparameter tuning and final model traing on best set of parameters. But I can not able to add a name to the pre build component. So in pipeline dashboard, every training component have same name. I want to add name by my own like for logistic regression model, I want different name and for other model like random forest, i want to give another name. So that from UI , I can able to know which component is for what. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5900 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5900/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5900/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5900/events | https://github.com/kubeflow/pipelines/issues/5900 | 926,856,969 | MDU6SXNzdWU5MjY4NTY5Njk= | 5,900 | kfp.components.structures.ComponentsSpec to create run in kubeflow | {
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"Did kfp.components.structures.ComponentsSpec.save() and loaded the yaml file.. But unable to get how to pass file outputs from one component to other component.Right now we are using\r\n **kfp.components.structures.InputSpec(name=<file name which was output of the previous component> )**\r\nThis shows below error... | 2021-06-22T05:53:30 | 2022-03-03T03:05:36 | 2022-03-03T03:05:36 | NONE | null | ### Environment
* KFP version:1.2
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.6.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
I am currently using containerop to create pipeline functions. But I see warnings saying that it will be deprecated. Is there any equivalent kfp package which uses docker image.? I tried kfp.components.structures.ComponentsSpec but this does not get executed. Created componentsspec, saved the yaml file and tried to load using load_component_from_file inside pipeline. Though the run gets triggered, it does not execute the module
### Expected result
Expected the run to happen executing the module
### Materials and Reference
I am currently using containerop to create pipeline functions. But I see warnings saying that it will be deprecated. Is there any equivalent kfp package which uses docker image.? I tried kfp.components.structures.ComponentsSpec but this does not create container pods and am unable to see the logs as well
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https://api.github.com/repos/kubeflow/pipelines/issues/5897 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5897/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5897/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5897/events | https://github.com/kubeflow/pipelines/issues/5897 | 926,709,276 | MDU6SXNzdWU5MjY3MDkyNzY= | 5,897 | [bug] outputUri prevents caching | {
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"Hi @boarder7395, we are redesigning caching, see https://github.com/kubeflow/pipelines/issues/5667.\r\nThe problem will be solved with the brand new caching mechanism that understands KFP semantics.",
"The issue you reported is expected, and we'll not fix because of v1 caching design limitations."
] | 2021-06-22T00:12:55 | 2021-06-25T01:07:42 | 2021-06-25T01:07:42 | CONTRIBUTOR | null | ### What steps did you take
Ran a pipeline with an outputUri, and ran a pipeline with an outputValue.
### What happened:
The pipeline using outputValue cached the component correctly, while the pipeline using outputUri did not cache the component.
### What did you expect to happen:
I expected both pipelines to cache.
### Environment:
KFP 1.4.1
Launched with minikube locally. Also so similar behavior with KFP on EKS.
* How do you deploy Kubeflow Pipelines (KFP)?
* KFP version:
1.4.1
* KFP SDK version:
1.6.3
### Anything else you would like to add:
I'm not sure if caching support has been added to outputUri's yet so it could be that functionality has not yet been added for outputUri's.
### Labels
/area backend
---
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"Agree the feature would be a nice to have!\r\nWelcome contribution on this",
"Created a small fix and opened a pull request in https://github.com/kubeflow/pipelines/pull/5927"
] | 2021-06-21T14:44:53 | 2021-07-01T10:23:26 | 2021-07-01T10:23:26 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
This feature concerns having list/dict inputs to pipelines when running them with the SDK. Right now, when starting runs with the python SDK, one must manually serialize lists to json, otherwise it will get falsely serialized and it can't be passed to components properly.
It would be nice if the `arguments` input to `client.create_run_from_pipeline_func` or the `params` input to `run_pipeline` could automatically json serialize values whos type is list/dict
### What is the use case or pain point?
The pain point is that this is a hard issue to track down, and it's unintuitive to have list/dict inputs to pipelines and running them from a python script
### Is there a workaround currently?
The current workaround is to manually use `json.dumps()` on any lists in the input argument to the run
---
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https://api.github.com/repos/kubeflow/pipelines/issues/5895 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5895/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5895/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5895/events | https://github.com/kubeflow/pipelines/issues/5895 | 926,250,970 | MDU6SXNzdWU5MjYyNTA5NzA= | 5,895 | [bug] Error when naming components dynamically in a recursion | {
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"I have the same problem. Every comment is really welcomed!! ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recen... | 2021-06-21T14:02:15 | 2022-03-03T03:05:30 | 2022-03-03T03:05:30 | NONE | null | /kind bug
### What steps did you take
I inserted a cycle in my pipeline, in order to define a new component at each iteration. My pipeline looks like this (where op in my real pipeline is the training component):
```python
import kfp
from kfp import dsl, components
import kfp.components as comp
def iterator(i: int) -> int:
print('i: {}'.format(i))
yyy=1
return i+yyy
iter_op = comp.create_component_from_func(iterator,base_image='python:3.7')
def init() -> int:
return 0
init_op = comp.create_component_from_func(init,base_image='python:3.7')
@dsl.graph_component
def recursion(kk: int,xx: int):
iter = iter_op(i=xx)
Op = dsl.ContainerOp(
name="train",
image="library/bash:4.4.23",
command=["sh", "-c"],
arguments=['echo "$0"',iter.output]).after(iter).set_display_name('iteration-'+str(iter.output))
with kfp.dsl.Condition(iter.output < kk):
recursion(kk,xx=iter.output).after(Op)
@dsl.pipeline(
name="loop",
description="loop test",
)
def pp(k:int):
i0 = init_op().output
recursion(kk=k, xx=i0)
kfp.compiler.Compiler().compile(pp, __file__ + ".tar.gz")
````
### What happened:
The following image is an example of execution of the pipeline for k=3. Notice that the name of the op component is iteration-{{inputs.parameters.iterator-Output}}
### What did you expect to happen
I expected each op component to be named with the actual value of the iterator at each cycle (e.g. "iteration-1", "iteration-2", ...) instead of {{inputs.parameters.iterator-Output}}
**Environment:**
* KFP version: 1.3
* KFP SDK version: 1.3
### Anything else you would like to add:
In my real pipeline the op component will be a training component. I need to do it in this "sequential" way instead of using dsl.ParallelFor for infrastructural constraints.
### Labels
/area components
---
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https://api.github.com/repos/kubeflow/pipelines/issues/5893 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5893/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5893/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5893/events | https://github.com/kubeflow/pipelines/issues/5893 | 925,586,863 | MDU6SXNzdWU5MjU1ODY4NjM= | 5,893 | [backend] column name same with reserved keyword of mysql 8.0 | {
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... | open | false | null | [] | null | [
"https://dev.mysql.com/doc/refman/8.0/en/keywords.html\r\nIt says description is a nonreserved keyword, am I missing anything?\r\n\r\nWelcome contribution if anyone figures out how to deploy KFP with mysql 8.0",
"> https://dev.mysql.com/doc/refman/8.0/en/keywords.html\r\n> It says description is a nonreserved key... | 2021-06-20T12:18:44 | 2022-03-03T02:05:38 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
* create table in mysql and deploy the KFP services
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
* 1.5
<!-- 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
When creating table in mysql 8.0, it would report fail because there are columns name `Description` in tables `experiments`, `jobs`, `pipelines`, `run_details`, which is the reserved keyword of mysql
<!--
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
Creating tables without error.
Shall we use `Descriptions` as the columns' name?
<!-- 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 👍. We prioritise the issues with the most 👍.
cc @Jeffwan | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5885 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5885/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5885/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5885/events | https://github.com/kubeflow/pipelines/issues/5885 | 924,276,957 | MDU6SXNzdWU5MjQyNzY5NTc= | 5,885 | [components]: TrainingPipeline Custom Task accepting multiple datasets defaulting to incorrect dataset type | {
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"Hi team, any update on this one? "
] | 2021-06-17T19:52:02 | 2021-07-02T19:41:49 | 2021-07-02T19:41:49 | COLLABORATOR | null | Originally raised in [stackoverflow - #68025813](https://stackoverflow.com/questions/67999255/error-tabular-dataset-treated-as-image-dataset-vertex-ai-pipelines-custom-tra/68025813#68025813)
## Environment
python 3.7.3
kfp==1.6.2
kfp-pipeline-spec==0.1.7
kfp-server-api==1.6.0
google-cloud-pipeline-components==0.1.1
## Error message
```
ValueError: ImageDataset class can not be used to retrieve dataset resource projects/nnnnnnnnnnnn/locations/us-central1/datasets/3781554739456507904, check the dataset type
f"{self.__class__.__name__} class can not be used to retrieve "
File "/opt/python3.7/lib/python3.7/site-packages/google/cloud/aiplatform/datasets/dataset.py", line 100, in _validate_metadata_schema_uri
self._validate_metadata_schema_uri()
File "/opt/python3.7/lib/python3.7/site-packages/google/cloud/aiplatform/datasets/dataset.py", line 82, in __init__
return annotation_type(value)
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 176, in cast
value = cast(value, param_type)
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 205, in prepare_parameters
prepare_parameters(serialized_args[METHOD_KEY], method, is_init=False)
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 236, in runner
print(runner(args.cls_name, args.method_name, executor_input, kwargs))
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 280, in main
main()
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 284, in <module>
exec(code, run_globals)
File "/opt/python3.7/lib/python3.7/runpy.py", line 85, in _run_code
"__main__", mod_spec)
File "/opt/python3.7/lib/python3.7/runpy.py", line 193, in _run_module_as_main
Traceback (most recent call last):
```
## Sample pipeline:
```python
import datetime
from kfp.v2 import dsl, compiler
from kfp.v2.google.client import AIPlatformClient
import google_cloud_pipeline_components.aiplatform as gcc_ai
PROJECT = "my-project"
PIPELINE_NAME = "test-pipeline"
PIPELINE_ROOT_PATH = f"gs://test-pipeline-20210525/{PIPELINE_NAME}"
@dsl.pipeline(
name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT_PATH
)
def test_pipeline(
display_name: str=f"{PIPELINE_NAME}-2021MMDD-nn"
):
dataset_create_op = gcc_ai.TabularDatasetCreateOp(
project=PROJECT, display_name=display_name,
gcs_source="gs://used_apartment/datasource/train.csv"
)
training_job_run_op = gcc_ai.CustomContainerTrainingJobRunOp(
project=PROJECT, display_name=display_name,
container_uri="us-central1-docker.pkg.dev/my-project/dataops-rc2021/custom-train:latest",
staging_bucket="vertex_ai_staging_rc2021",
base_output_dir="gs://used_apartment/cstm_img_scrf/artifact",
model_serving_container_image_uri="us-central1-docker.pkg.dev/my-project/dataops-rc2021/custom-pred:latest",
model_serving_container_predict_route="/",
model_serving_container_health_route="/health",
model_serving_container_ports=[8080],
training_fraction_split=0.8,
validation_fraction_split=0.1,
test_fraction_split=0.1,
dataset=dataset_create_op.outputs["dataset"]
)
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5882 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5882/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5882/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5882/events | https://github.com/kubeflow/pipelines/issues/5882 | 923,809,927 | MDU6SXNzdWU5MjM4MDk5Mjc= | 5,882 | [launcher] make debugging launcher easier | {
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"I'm now leaning towards turn on warning level logging by default.\r\nDuring developing, developers can build a launcher image with default changed to INFO level. This sounds like a good enough stopgap solution."
] | 2021-06-17T11:23:50 | 2021-06-21T01:55:16 | 2021-06-21T01:55:16 | CONTRIBUTOR | null | KFP v2 compatible launcher doesn't log anything unless an error occurs, therefore when there's an launcher logic error. It's very hard to debug what actually happened.
Propose inventing some mechanism to support this use-case.
## Option 1 - add basic logging which is turned on by default
Pros:
* we can always find some information in logs
Cons:
* confusing to users, because launcher log is now mixing with user component logs
## Option 2 - add a config that can turn on verbose launcher logging
Pros: minimal confusion usually
Cons: the complexity to design the config switch & efforts to turn it on & rerun a pipeline is huge
What do you think? Are there better options? | {
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"option 1: upload version api can accept pipeline parameter\r\noption 2: combine upload pipeline and upload version in one api, which means user can specify pipeline and version at the same time.",
"> option 1: upload version api can accept pipeline parameter\r\n\r\nIt already accepts the pipeline name or id as a... | 2021-06-17T09:54:05 | 2022-03-04T08:07:00 | 2022-03-03T03:05:29 | NONE | null | ### Feature Area
/area backend
/area sdk
### What feature would you like to see?
Currently, when uploading a new pipeline, no version can be specified and the name of the pipeline is used to create an initial version. Uploading a pipeline version without first creating a pipeline by uploading that initial version will fail. This is not intuitive and breaks automation or requires workarounds like first uploading the initial version and then creating the proper version.
### What is the use case or pain point?
We are currently building automation and continuous deployment around our ML infrastructure. As part of this, we want all of our pipelines to be versioned so that code changes can be tracked consistently. This should also be true when creating a new pipeline with the first version.
### Is there a workaround currently?
upload pipeline ( => version == name)
upload version (=> version == 'abcdef')
delete version 'name'
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
| {
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"/assign @chensun "
] | 2021-06-17T09:40:43 | 2021-06-23T20:25:20 | 2021-06-23T20:25:20 | CONTRIBUTOR | null | Reproduction: see PR https://github.com/kubeflow/pipelines/pull/5879 | {
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"fixed by #5738 . You need to wait for the next KFP release. ",
"Ok, thanks! ",
"Any idea when this fix will land in a 1.6 release?"
] | 2021-06-17T09:27:46 | 2021-06-25T14:42:46 | 2021-06-18T00:47:04 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? KF 1.3 Manifests release, on premises
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
$ pip list | grep kfp
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
```
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. Run a pipeline with a failing component
2. Retry the run
```
Failed to retry run: 10ff9a33-bc37-4224-914a-8278036a163c with error: "{"error":"Retry run failed.: InternalServerError: Workflow cannot be retried with node pipeline-vlkt4-400844542 in Omitted phase: workflow cannot be retried","code":13,"message":"Retry run failed.: InternalServerError: Workflow cannot be retried with node pipeline-vlkt4-400844542 in Omitted phase: workflow cannot be retried","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal Server Error","error_details":"Retry run failed.: InternalServerError: Workflow cannot be retried with node pipeline-vlkt4-400844542 in Omitted phase: workflow cannot be retried"}]}"
```
### Expected result
A successful retry of the run, starting off from the failed component.
<!-- 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. -->
I've found this Argo bug.
https://github.com/argoproj/argo-workflows/issues/3524
But unclear if that's the root cause. Retries worked previously when I ran KF 1.1 on premises, but since upgrading to 1.3 they fail with this error.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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* KFP version:
1.0.4
* KFP SDK version:
1.3.0
### Steps to reproduce
```
client.pipeline_uploads.upload_pipeline_version(
pipeline_file, name="my_pipeline", pipelineid="0.0.1"
)
```
will create a new pipeline with a single version of "my_pipeline"
### Expected result
A new Pipeline with an initial version of "0.0.1"
### Materials and Reference
I believe that this, at the moment, is the documented behaviour. I would argue, however, that this is unintuitive and breaks automation (or requires workarounds)
---
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"followers_url": "https://api.githu... | null | [] | 2021-06-17T04:39:36 | 2021-07-20T00:14:18 | 2021-07-20T00:14:18 | CONTRIBUTOR | null | It'll be ideal to refer to documentation https://www.kubeflow.org/docs/components/pipelines/sdk/v2/v2-compatibility/#building-pipelines-using-the-kubeflow-pipelines-sdk-v2, when we detect a type mismatch error.
Because the extra context in the doc helps users understand how to fix the issue. | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-06-16T16:12:22 | 2022-03-03T03:05:27 | 2022-03-03T03:05:27 | NONE | null | There is an error in metadata writer component that seems to be not propagated to the application. When I run describe on application, it is coming back with "NoError".
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1 .5.0
### Steps to reproduce
**kubectl apply -k ./**. (this is creating issues)
**Error from the logs:**
2021-06-16T14:07:48.349192561Z2021-06-16 14:07:48.348369: F ml_metadata/metadata_store/metadata_store_server_main.cc:226] Non-OK-status: status status: Internal: mysql_real_connect failed: errno: 0, error: MetadataStore cannot be created with the given connection config.
(this is failing because of metadata component issues from above command)
**kubectl wait applications/pipeline -n kubeflow --for condition=Ready --timeout=1800s**
error: timed out waiting for the condition on applications/pipeline
**kubectl get applications -n kubeflow**
NAME AGE
pipeline 8d
**kubectl describe applications -n kubeflow**
Conditions:
Last Transition Time: 2021-06-16T04:18:53Z
Last Update Time: 2021-06-16T04:18:53Z
Message: some components not ready
Reason: ComponentsNotReady
Status: False
Type: Ready
Last Transition Time: 2021-06-07T19:19:14Z
Last Update Time: 2021-06-07T19:19:14Z
Message: No error seen
Reason: NoError
Status: False
Type: Error
<!--
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
this should run without errors
**kubectl wait applications/pipeline -n kubeflow --for condition=Ready --timeout=1800s**
<!-- 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 👍. We prioritise the issues with the most 👍.
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"Can you help share your pipeline? In addition, KFP supports cache https://www.kubeflow.org/docs/components/pipelines/caching/",
"Hi, sorry for late reply.\r\n\r\nSo here is an illustrative example of my pipeline's behavior:\r\n\r\n```\r\nimport kfp\r\nfrom kfp import dsl\r\nfrom kfp.dsl.types import GCSPath\r\n\... | 2021-06-16T13:59:20 | 2022-03-03T03:05:37 | 2022-03-03T03:05:37 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
/area sdk
/area samples
<!-- /area components -->
### What feature would you like to see?
Is it possible to cache steps in a pipeline if the output already exists?
### What is the use case or pain point?
My scenario here is that I'm running a bunch of jobs with https://github.com/kubeflow/pipelines/tree/master/components/gcp/container/component_sdk/python/kfp_component/google/ml_engine (with Docker images) and if working on a specific pipeline need to constantly recalculate all the steps. This could be somewhat mitigated by creating a very small subset of the target data, but I'm wondering if there's a better approach here.
I've found some indications that caching could solve this issue, but I don't see the connection here. Especially since AI platform jobs currently do not emit any artefacts.
### Is there a workaround currently?
The workaround I have for this right now is creating dummy jobs, that are fast to complete, but this is quite complex approach.
Other one would be to have a relatively small subset of my data and iterate on that.
Both of theses approaches still need to rerun the whole pipeline over and over again.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"https://github.com/kubeflow/pipelines/issues/1327\r\n",
"I think this is the same issue as https://github.com/kubeflow/pipelines/issues/5844",
"@skogsbrus Yes it is. Does anyone know if this issue has been solved in the latest kubeflow \r\nversion which is 1.6.",
"Don't know, but I suggest you close this and... | 2021-06-16T12:10:02 | 2021-06-17T10:39:16 | 2021-06-17T10:39:16 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
I deployed kubeflow in GCP using AI Platform
* KFP version:
1.4.1
* KFP SDK version:
1.4.1
### Steps to reproduce
1. Create a pipeline that first creates a pvc that uses a dynamicaly generated disk of size 10Gi which is then used by subsequent containerops.
2. After successful execution , I manually delete the job pods and pvc that automatically deletes the dynamically generated disk
3. Next time when I schedule a pipeline run, I changed my pvcname but not size. I notice that the pod uses the deleted old pvc and the subsequent container ops shows the below error
**_This step is in Pending state with this message: Unschedulable: persistentvolumeclaim "{{tasks.new-disk.outputs.parameters.new-disk-name}}" not found_**
This is so frustrating since I have to change the disk size each time to create a new pipeline.
How to avoid the pvc to be used from cache.
### Expected result
A new pvc needs to get created of same size 10Gi instead of the old pvc
### Materials and Reference
https://github.com/kubeflow/pipelines/blob/1.4.1/samples/contrib/volume_ops/volumeop_sequential.py
A similar pipeline used.
Kindly help regarding this issue as soon as possible.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Did you build under root directory or under backend directory? You need to build under root directory",
"Thanks @capri-xiyue. This was solved with your suggestions and a few other things we were doing wrong. 😄 "
] | 2021-06-16T08:36:35 | 2021-06-21T10:06:42 | 2021-06-21T10:06:42 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Not yet deployed
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: Release v1.6.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: None
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
* Docker build `backend/dockerfile` fails with the following trail logs
```bash
Setting up llvm-3.8 (1:3.8.1-24) ...
Setting up cmake (3.7.2-1) ...
Setting up llvm-3.8-dev (1:3.8.1-24) ...
Setting up clang-3.8 (1:3.8.1-24) ...
Setting up clang (1:3.8-36) ...
Processing triggers for sgml-base (1.29) ...
Processing triggers for libc-bin (2.24-11+deb9u4) ...
Removing intermediate container b0ebf531b1ac
---> a76cd53b1770
Step 3/36 : WORKDIR /go/src/github.com/kubeflow/pipelines
---> Running in 1e93112fe0a1
Removing intermediate container 1e93112fe0a1
---> d50ce7ad7ae0
Step 4/36 : COPY . .
---> 730bd4c847e8
Step 5/36 : RUN go mod vendor
---> Running in d94a1eeedc7f
268
go: cannot find main module; see 'go help modules'
```
<!--
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
* `backend` successfully built as a docker image
<!-- What should the correct behavior be? -->
### Materials and Reference
Trying to build the backend service as a docker image, but it fails to build.
<!-- 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 👍. We prioritise the issues with the most 👍.
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"Hi team, any progress on this issue?\r\n\r\nMy team is use KFP in production, and we want to add monitoring/alerting to our production pipelines so we know when things fail. The KFP ExitHandler seems like the perfect tool for this, but we cannot use it due to this bug.\r\n\r\nAny hints about workarounds would also... | 2021-06-14T22:37:13 | 2022-03-02T21:05:26 | null | CONTRIBUTOR | null | I ran into this issue while trying to use ExitHandler for workflow completion/failure notifications, see https://github.com/kubeflow/pipelines/issues/3516 for context.
### Environment
* KFP version: 1.4.1
* KFP SDK version: 1.4.0
* All dependencies version:
* kfp 1.4.0
* kfp-pipeline-spec 0.1.7
* kfp-server-api 1.5.0
### Steps to reproduce
- Try to use `dsl.ExitHandler` with a function that uses workflow variable `{{workflow.failures}}`. (See sample pipeline code at the bottom of this issue.)
- Launch pipeline run on KFP.
- Get error `SyntaxError: invalid syntax`.
My exit handler component uses a bunch of other workflow variables, and all the other ones seem to work. When I remove `{{workflow.failures}}`, my exit handler runs correctly.
When I change my example task to fail so that the `workflow.failures` variable is populated, I get a different SyntaxError (see below for full details).
### Materials and Reference
Here's the full pipeline that results in the syntax error:
```python
from kfp import dsl
import kfp.components as comp
@dsl.pipeline(name="Pipeline for testing exit handler")
def exit_handler_test_pipeline():
@comp.create_component_from_func
def exit_handler_component(
name: str = "{{workflow.name}}",
namespace: str = "{{workflow.namespace}}",
uid: str = "{{workflow.uid}}",
creationTimestamp: str = "{{workflow.creationTimestamp}}",
failures: str = "{{workflow.failures}}",
status: str = "{{workflow.status}}",
):
print(f"my test exit handler!")
print(f"name: {name}")
print(f"namespace: {namespace}")
print(f"uid: {uid}")
print(f"creationTimestamp: {creationTimestamp}")
print(f"failures: {failures}")
print(f"status: {status}")
@comp.create_component_from_func
def some_example_component():
print("do some task")
exit_handler_op = exit_handler_component()
with dsl.ExitHandler(exit_handler_op):
_example_task = some_example_component()
```
I get the following error in the exit handler task logs on KFP:
```python
File "/tmp/tmp.jPJXqdBDsc", line 6
failures = ""null"",
^
SyntaxError: invalid syntax
```
Here's a screenshot of the error from the run on KFP:
---
**Similar SyntaxError when the pipeline has a task failure**
I also tested the exit handler when there are task failures by changing `some_example_component` to this:
```python
@comp.create_component_from_func
def some_example_component():
print("do some task")
raise ValueError("cause task failure")
```
This results in `workflow.failures` being populated with some JSON, but this still causes a SyntaxError:
```python
File "/tmp/tmp.W7x1yGRiSo", line 6
failures = ""[{\"displayName\":\"some-example-component\",\"message\":\"failed with exit code 1\",\"templateName\":\"some-example-component\",\"phase\":\"Failed\",\"podName\":\"pipeline-for-testing-exit-handler-pcm5g-1464849803\",\"finishedAt\":\"2021-06-14T22:27:44Z\"},{\"displayName\":\"exit-handler-1\",\"message\":\"\",\"templateName\":\"exit-handler-1\",\"phase\":\"Failed\",\"podName\":\"pipeline-for-testing-exit-handler-pcm5g-655720480\",\"finishedAt\":\"2021-06-14T22:27:45Z\"},{\"displayName\":\"pipeline-for-testing-exit-handler-pcm5g\",\"message\":\"\",\"templateName\":\"pipeline-for-testing-exit-handler\",\"phase\":\"Failed\",\"podName\":\"pipeline-for-testing-exit-handler-pcm5g\",\"finishedAt\":\"2021-06-14T22:27:45Z\"}]"",
^
SyntaxError: unexpected character after line continuation character
```
It looks like the underlying problem is the same: KFP (or maybe it's Argo?) is inserting extra double quotes when replacing `"{{workflow.failures}}"`, which is causing a syntax error in Python.
I also tried annotating the `faillures` argument with `Optional[str]` instead of `str`, but the result seems to be the same.
Is there any way to work around this? Is there a way to access these workflow variables via Python code rather than having KFP/Argo replacing these placeholder strings?
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"You can use https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html?highlight=node#kfp.dsl.PipelineConf.set_default_pod_node_selector to add a constraint for nodeSelector for a pipeline.\r\nYou can use https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html?highlight=node#kfp.dsl.Si... | 2021-06-14T08:00:05 | 2021-06-24T11:30:50 | 2021-06-18T01:01:47 | NONE | null | ### What steps did you take
I have installed kubeflow 1.12 inAWS environment. Its running on EKS cluster with one gpu node and one cpu node
### What happened:
When am creating the kfp pipeline and creating run am unable to select the node(gpu/cpu) on which it can run
### What did you expect to happen:
I can select the node on which it can run
### Environment:
AWS
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
https://www.kubeflow.org/docs/distributions/aws/deploy/install-kubeflow/
* KFP version: 1.12
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.6.0
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"I noticed that I can compile without using the exit handler.\r\nI just did a print debugging.\r\nThe target_date is recognized as a input_parameter of the subgroup component, but there is no such parameter in the parent, which may be causing the above error.\r\n\r\n```\r\n**********************group.name**********... | 2021-06-14T04:37:16 | 2021-06-22T09:21:56 | 2021-06-22T09:21:56 | CONTRIBUTOR | null | ### Environment
* KFP SDK version:
1.6.3
### Steps to reproduce
Define and execute a pipeline with the following code
```python
slack_notification_op = comp.load_component_from_text(
read_file("components/slack-notification/component.yaml")
)
def get_before_days(date: str, fmt: str, days: int) -> str:
"""
Subtract the number of days from the date from the date and the number of days specified in the argument.
Parameters
----------
date: str
Original Date
fmt: str
Date formate with a (strftime, strptime) format
days: int
The number of days to subtract.
"""
from datetime import datetime, timedelta
dt = datetime.strptime(date, fmt)
new_dt = dt - timedelta(days=days)
return new_dt.strftime(fmt)
@dsl.pipeline(
name="test",
description="",
)
def feature_extract_pipeline(
vertex_pipelines_project_id: str,
dataset_id: str,
pipelines_url: str,
slack_webhook: str,
target_date: str,
):
with dsl.ExitHandler(
exit_op=slack_notification_op(
job_name="{{workflow.name}}",
webhook_url=slack_webhook,
status="{{workflow.status}}",
pipelines_url=f"{pipelines_url}/pipeline/#/runs/details/{{{{workflow.uid}}}}",
)
):
get_before_a_days_op = comp.create_component_from_func(
func=get_before_days, base_image="python:alpine"
)
before_a_day = get_before_a_days_op(
target_date, "%Y%m%d", 1
)
from kfp.v2 import compiler
import tempfile
with tempfile.TemporaryDirectory() as tempdir:
package_path = os.path.join(tempdir, "pipeline.json")
# NOTE: The type_check argument is probably not working.
compiler.Compiler().compile(pipeline_func=feature_extract_pipeline, package_path=package_path)
```
following is error messsage
```
ata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py", line 687, in _group_to_dag_spec
input_artifacts_in_current_dag,
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/kfp/dsl/component_spec.py", line 338, in update_task_inputs_spec
component_input_parameter, parent_component_inputs)
AssertionError: component_input_parameter: pipelineparam--pipelineparam--target_date not found. All inputs:
```
I got same error occurred when using `from kfp.v2.dsl import component`.
~If you use a python function with component, you will get an error like the one above~
### Expected result
No errors occur during compilation.
---
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"The error is from k8s itself. It is not related to KFP. You will need to check the logs of your cluster."
] | 2021-06-14T01:21:23 | 2021-06-18T01:05:34 | 2021-06-18T01:05:33 | NONE | null | ### What steps did you take
Run the built-in pipeline experiment: **[Tutorial] Data passing in python components**
<!-- A clear and concise description of what the bug is.-->
### What happened:
After the Kubeflow v1.20.0 installation, I try to run the built-in pipeline **[Tutorial] Data passing in python components**.
Those pipeline components stuck for a long time.
The following is the Pods info:
```bash
$ k get po -n admin
NAME READY STATUS RESTARTS AGE
file-passing-pipelines-8r7df-1316978937 0/2 Init:0/1 0 126m
file-passing-pipelines-8r7df-3202420753 0/2 ContainerCreating 0 126m
file-passing-pipelines-8r7df-4267582061 0/2 ContainerCreating 0 126m
ml-pipeline-ui-artifact-c8fbcb8f9-p9t8l 0/2 Init:0/1 0 140m
ml-pipeline-visualizationserver-6b78c9646-b7jgk 0/2 Init:0/1 0 11h
```
```bash
$ k describe po file-passing-pipelines-8r7df-1316978937 -n admin
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedSync 14s (x67 over 113m) kubelet error determining status: rpc error: code = DeadlineExceeded desc = context deadline exceeded
```
```bash
$ k describe po file-passing-pipelines-8r7df-3202420753 -n admin
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedSync 2m36s (x64 over 105m) kubelet error determining status: rpc error: code = DeadlineExceeded desc = context deadline exceeded
```
```bash
$ k describe po file-passing-pipelines-8r7df-4267582061 -n admin
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedSync 2m33s (x66 over 107m) kubelet error determining status: rpc error: code = DeadlineExceeded desc = context deadline exceeded
```
```bash
$ k describe po ml-pipeline-ui-artifact-c8fbcb8f9-p9t8l -n admin
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedSync 2m50s (x79 over 124m) kubelet error determining status: rpc error: code = DeadlineExceeded desc = context deadline exceeded
```
```bash
$ k describe po ml-pipeline-visualizationserver-6b78c9646-b7jgk -n admin
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedSync 3m32s (x735 over 11h) kubelet error determining status: rpc error: code = DeadlineExceeded desc = context deadline exceeded
```
### Environment:
<!-- Please fill in those that seem relevant. -->
|Application|Version|
|---|---|
|OS|Ubuntu Desktop 18.04.5 LTS|
|Kubernetes on-premise|v1.20.7|
|Container runtime|Docker 19.03.15|
|Network plugin|flannel 0.13.0|
|Kubeflow|v1.2.0 (**kfctl_istio_dex.v1.2.0.yaml**)|
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5851 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5851/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5851/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5851/events | https://github.com/kubeflow/pipelines/issues/5851 | 919,791,952 | MDU6SXNzdWU5MTk3OTE5NTI= | 5,851 | [bug] Kubeflow Pipeline experiment run is always running | {
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"u... | closed | false | null | [] | null | [
"Have you set TTL via https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html?highlight=node#kfp.dsl.PipelineConf.set_ttl_seconds_after_finished?\r\nIt is not the correct way to set TTL.\r\nThe correct way to set TTL in KFP is recorded in #3938 ",
"@capri-xiyue \r\nCan you give some more details ... | 2021-06-13T12:48:42 | 2022-03-03T03:05:13 | 2022-03-03T03:05:13 | NONE | null | ### What steps did you take
Ran an experiment , created in a workflow.py file and run from jupyterlab.
<!-- A clear and concise description of what the bug is.-->
### What happened:
I have multiple experiment runs(workflow) which runs sets of kuberenets pods(children inside the workflow) but from some reason some of the runs are still running for days even though all the child pods are completed (Succeeded/Failed).
I have been looking at the logs of the workflow controller but couldn't find the issue. I have went through the workflow.py file to see if something is wrong there but could find the reason why some of the runs are completed and some are still running. Just to remind - I am talking on different runs of the same experiment.
When reviewing the logs of succeeded workflow vs a keep running workflow I some differences:
From the successful workflow:
time="2021-06-11T17:38:37Z" level=info msg="Found Workflow default-tenant/txyz set expire at 2021-06-11 17:50:37 +0000 UTC (11m59.461359494s from now)"
time="2021-06-11T17:38:37Z" level=info msg="Queueing workflow default-tenant/xyz for delete in 11m59.461359494s"
time="2021-06-11T17:50:37Z" level=info msg="Deleting TTL expired workflow default-tenant/xyz"
time="2021-06-11T17:50:37Z" level=info msg="Successfully deleted 'default-tenant/xyz'"
. .
From the still running workflow:
time="2021-06-11T09:13:10Z" level=info msg="Node not set to be retried after status: Error" namespace=default-tenant workflow=xyz
and getting the following message until now:
time="2021-06-11T09:38:42Z" level=info msg="Processing workflow" namespace=default-tenant workflow=xyz
### What did you expect to happen:
I was expecting to see all runs in a completed state (Succeeded/Failed)
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
1.0.1
* KFP SDK version:
1.0.1
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5848 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5848/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5848/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5848/events | https://github.com/kubeflow/pipelines/issues/5848 | 919,074,713 | MDU6SXNzdWU5MTkwNzQ3MTM= | 5,848 | [frontend] [UI] [aws] [need help] Not able to retrieve the image to KFP UI from S3 | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | closed | false | null | [] | null | [
"Thank you for your understanding ",
"No good PC and Tools to work on further. Need more times in learning more on how to smoothen enhanced works.",
"@JODIN1727 Do you mean we do not have a solution to it now?\r\n",
"It is moderately good to go as way out but need more times for tools to support ",
"I don't... | 2021-06-11T18:30:30 | 2022-03-03T03:05:28 | 2022-03-03T03:05:28 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow pipeline runs normally, but the frontend is not working as expected.
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.1
<!-- 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. -->
### 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.
-->
By using the similar code in [`this example`](https://github.com/kubeflow/pipelines/tree/master/samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/visualize_html), I do not know how to do the configuration to let the UI have the access to S3 without making it public.
<img width="1024" alt="Screen Shot 2021-06-11 at 2 27 51 PM" src="https://user-images.githubusercontent.com/38673967/121733706-d078ab00-cac1-11eb-8938-7d1e2a966c63.png">
### Expected result
<!-- What should the correct behavior be? -->
It will show the image
### 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 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5846 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5846/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5846/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5846/events | https://github.com/kubeflow/pipelines/issues/5846 | 919,049,771 | MDU6SXNzdWU5MTkwNDk3NzE= | 5,846 | [feature] Azure - Authenticating Kfp client using client id, secret id and tenant id | {
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"cc @aronchick ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reop... | 2021-06-11T18:05:44 | 2022-03-05T01:10:21 | null | NONE | null | Is that possible? Something like the following (point 3) but for Azure: https://www.kubeflow.org/docs/distributions/gke/pipelines/authentication-sdk/#connecting-to-kubeflow-pipelines-in-a-full-kubeflow-deployment | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5844 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5844/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5844/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5844/events | https://github.com/kubeflow/pipelines/issues/5844 | 918,787,641 | MDU6SXNzdWU5MTg3ODc2NDE= | 5,844 | [backend] Cached volume creations leads to unschedulable runs, if the pvc is deleted in the pipeline | {
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"Hi there,\r\n\r\nthis is a duplicate of https://github.com/kubeflow/pipelines/issues/5257. Should we close this in favor of the other one.\r\n\r\nCopying [my comment](https://github.com/kubeflow/pipelines/issues/5257#issuecomment-852326538) here as well, for the sake of completeness of this issue:\r\n>I think the ... | 2021-06-11T13:58:01 | 2021-06-17T10:21:30 | 2021-06-17T10:21:30 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? KF 1.3 Manifests release, on premises
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
$ pip list | grep kfp
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
```
### Steps to reproduce
1. Define a pipeline that creates a volume, mounts it, and then destroys it:
```
import kfp
from kfp import dsl
def glob_volume(volume):
@kfp.components.create_component_from_func
def glob_files(directory: str) -> list:
import pathlib
paths = pathlib.Path(directory).glob('**/*')
filepaths = [str(p) for p in paths if p.is_file()]
return filepaths
component_function = glob_files
return component_function("/volume") \
.add_pvolumes({
"/volume": volume
})
@dsl.pipeline(name="vol-cache-bug", description="Demonstrates a caching bug in KF 1.3")
def volume_caching_bug():
vop = dsl.VolumeOp(name='create-a-volume', resource_name='a-volume', size="1Gi", modes=dsl.VOLUME_MODE_RWO)
glob_vol_op = glob_volume(vop.volume)
vop.delete().after(glob_vol_op)
if __name__ == "__main__":
kfp.compiler.Compiler().compile(volume_caching_bug, "volume_caching_bug.yaml")
```
2. Create a pipeline and an experiment in the UI from the above sample
3. Launch a run, let it complete
4. (delete mounting pods if the PVC is stuck at terminating)
5. Clone the run
6. The first step is cached and never run
7. The second step is never scheduled, because the volume got deleted in the previous run and was never created in this run.
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
I expected that volume ops should not be cached at all. If the PVC already exists it should be reused and if it doesn't it should be created. Alternatively, that VolumeOp supported an equivalent to `.execution_options.caching_strategy.max_cache_staleness = "P0D"`.
FWIW: Recently upgraded from KF 1.1 to KF 1.3. Did not experience this on 1.1.
### Workarounds
1. Rename the volume creation step to prevent it from fetching from cache. Needs to be done after each run (i.e. upload a new pipeline version before issuing a new run)
2. Disable caching on the whole KF instance.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"`func_to_container_op` function creates components. This function does not access images, so it does not need secrets.\r\n\r\nComponents are expected to be portable and self-contained. We do not support adding cluster-specific information like secret names or volumes to the component definitions.\r\n\r\nHowever, a... | 2021-06-11T13:52:38 | 2021-06-12T05:57:19 | 2021-06-12T05:54:53 | NONE | null | Is it possible to use an ImagePullSecret on a func_to_container_op? | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-06-11T07:08:42 | 2022-03-03T03:05:18 | 2022-03-03T03:05:18 | CONTRIBUTOR | null | Rationale: a linter can catch non-obvious but common problems, for example, I guess https://github.com/kubeflow/pipelines/pull/5836/files#r649683859 should be an issue that can be caught by a golang linter.
Candidates:
* https://github.com/golangci/golangci-lint | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5840 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5840/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5840/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5840/events | https://github.com/kubeflow/pipelines/issues/5840 | 918,308,738 | MDU6SXNzdWU5MTgzMDg3Mzg= | 5,840 | [feature] dsl-compile supports v2 compatible mode | {
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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?
Add a flag to enable v2 compatible mode when using dsl-compile.
Ideally, v2 compatible mode can be enabled using an env var, so that we can switch to v2 compatible mode and do not need an explicit flag each time.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5839 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5839/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5839/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5839/events | https://github.com/kubeflow/pipelines/issues/5839 | 918,302,758 | MDU6SXNzdWU5MTgzMDI3NTg= | 5,839 | [samples] consistent main block for pipeline samples | {
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"## Idea for long term\r\n\r\nI think the best experience is doing https://github.com/kubeflow/pipelines/issues/5381 first.\r\n\r\nThen samples do not even need a main block.\r\n\r\nTo run a sample: `kfp run xxxxx.py`.\r\nTo compile a sample: `kfp compile xxxx.py`.\r\n\r\nIf env-specific args are needed (e.g. KFP h... | 2021-06-11T06:49:31 | 2022-03-03T03:05:20 | 2022-03-03T03:05:20 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
/area samples
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
I'd like to see a consistent __main__ block for pipeline samples.
### What is the use case or pain point?
Make samples easy to use with just `python xxxx.py`.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
1. Use `dsl-compile` to compile a sample and run it
2. Edie the sample or copy the sample elsewhere to inject a `kfp.Client()` call
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Thank you Yuan for the fix!\r\n\r\nValidated artifact is created for minio path:\r\n\r\n\r\n"
] | 2021-06-11T06:32:52 | 2021-06-24T15:53:38 | 2021-06-24T07:47:19 | CONTRIBUTOR | null | ### Environment
* KFP SDK version: head
### Steps to reproduce
1. run https://github.com/kubeflow/pipelines/blob/master/samples/test/lightweight_python_functions_v2_pipeline.py with minio pipeline root
2. it fails with
```
Traceback (most recent call last):
File "/tmp/tmp.k9PitIcFpw", line 841, in <module>
executor_main()
File "/tmp/tmp.k9PitIcFpw", line 835, in executor_main
function_to_execute=function_to_execute)
File "/tmp/tmp.k9PitIcFpw", line 549, in __init__
artifacts_list[0])
File "/tmp/tmp.k9PitIcFpw", line 562, in _make_output_artifact
os.makedirs(os.path.dirname(artifact.path), exist_ok=True)
File "/usr/local/lib/python3.7/posixpath.py", line 156, in dirname
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
F0611 06:22:43.823995 1 main.go:56] Failed to execute component: exit status 1
```
I further outputted v2 python component wrapper code and found that it only supports gs:// pipeline root:
### Expected result
<!-- What should the correct behavior be? -->
This pipeline should work regardless of pipeline root chosen.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
The current launcher contract for other types of storage is: https://github.com/kubeflow/pipelines/blob/a37ba15706f0f5c3e07afe42602a457c217e928c/v2/component/launcher.go#L472-L483
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/assign @chensun ",
"Feel free to update the name as well @chensun. ",
"Hi @NikeNano, we continue to welcome contribution to this.\r\n\r\nI heard that you were blocked by permission to access v2 sample test.\r\n\r\nTwo paths forward:\r\n* get https://github.com/kubeflow/pipelines/tree/master/v2/test to run on ... | 2021-06-10T20:22:45 | 2022-03-03T02:05:32 | null | MEMBER | null | ### Environment
For more info: https://github.com/kubeflow/pipelines/pull/5447#issuecomment-858482668
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
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### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
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---
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https://api.github.com/repos/kubeflow/pipelines/issues/5834 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5834/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5834/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5834/events | https://github.com/kubeflow/pipelines/issues/5834 | 917,225,205 | MDU6SXNzdWU5MTcyMjUyMDU= | 5,834 | [v2compat] cannot override container image as parameters | {
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"Generated workflow. It's clear that the error message means that the \"train\" template uses `{{inputs.parameters.tf_image}}` in image field, but the template does not have `tf_image` added as an input parameter.\r\n\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n generateName: p... | 2021-06-10T11:16:40 | 2021-07-26T03:17:47 | 2021-07-20T03:48:33 | CONTRIBUTOR | null | ### Environment
* KFP SDK version: master
### Steps to reproduce
1. Write any pipeline that tries to override task container image at pipeline level using a parameter, e.g.
https://github.com/kubeflow/pipelines/blob/b3c2351a02231f02ecb3f144d3bade06d854ad18/samples/core/visualization/tensorboard_minio.py#L182-L183
1. compile in v2 compatible mode
1. It fails immediately when running the pipeline
I got 500 api error when running the pipeline, error log on kfp api server is:
```
I0630 07:49:58.838375 6 error.go:247] templates.pipeline-tensorboard-minio.tasks.train templates.train: failed to resolve {{inputs.parameters.tf_image}}
```
so it seems we generated an invalid argo workflow
### Expected result
<!-- What should the correct behavior be? -->
The pipeline should work
### 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 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5831 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5831/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5831/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5831/events | https://github.com/kubeflow/pipelines/issues/5831 | 917,099,567 | MDU6SXNzdWU5MTcwOTk1Njc= | 5,831 | [v2compat] pipeline with output name containing space cannot compile | {
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"/assign @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to r... | 2021-06-10T09:03:16 | 2022-03-03T03:05:14 | 2022-03-03T03:05:14 | CONTRIBUTOR | null | ### Environment
* KFP SDK version: master
### Steps to reproduce
1. Start with https://github.com/kubeflow/pipelines/blob/master/samples/test/reused_component.py,
1. Rename output name from `sum` to `Total Value`.
1. Compile and run in v2 compatible mode.
1. It fails with a KeyError looking for `Total Value`
### Expected result
<!-- What should the correct behavior be? -->
The pipeline should compile and run.
### 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 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5830 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5830/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5830/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5830/events | https://github.com/kubeflow/pipelines/issues/5830 | 917,091,304 | MDU6SXNzdWU5MTcwOTEzMDQ= | 5,830 | [v2compat] wrong param passed when param contains JSON | {
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"/assign @chensun ",
"this is obviously a corner case we didn't think of for the \"BEGIN-KFP-PARAM[...]END-KFP-PARAM\" hack",
"There is a corner case where we pass empty string to a component function: https://github.com/kubeflow/pipelines/pull/5938#discussion_r660498207.\r\n\r\nIn the example above, I passed `... | 2021-06-10T08:55:22 | 2021-07-09T17:37:00 | 2021-07-09T05:02:23 | CONTRIBUTOR | null | ### Environment
* KFP SDK version: master branch
### Steps to reproduce
```python
import kfp
from kfp import dsl
from kfp.components import create_component_from_func
@create_component_from_func
def test(json_param: str):
print(json_param)
@dsl.pipeline(name='bug-json-param')
def my_pipeline(
json_param: str = '{"a":"b"}',
value: str = 'c',
):
test(json_param).set_display_name('test1')
test('{"a":"' + f'{value}' + '"}').set_display_name('test2')
if __name__ == '__main__':
kfp.Client().create_run_from_pipeline_func(
my_pipeline,
arguments={},
mode=dsl.PipelineExecutionMode.V2_COMPATIBLE,
)
```
I got the following log for `test2` task (`task1` is expected).
```
{\"a\":\"c\"}
```
### Expected result
<!-- What should the correct behavior be? -->
```
{"a":"c"}
```
### 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 👍. We prioritise the issues with the most 👍.
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"/assign @capri-xiyue "
] | 2021-06-08T21:39:29 | 2021-07-15T07:25:48 | 2021-07-15T07:25:48 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
### What feature would you like to see?
cache integration test
Set up integration test. One for enabling caching scenario. One for disabling caching scenario.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
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Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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### What feature would you like to see?
Fetch cache result from MLMD and put cache result into argo output artifacts/parameters
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"fyi: @neuromage @chensun ",
"fixed by https://github.com/kubeflow/pipelines/pull/6032"
] | 2021-06-08T21:20:54 | 2021-07-15T07:38:25 | 2021-07-15T07:38:25 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
Similar to <KFP V2>{https://github.com/kubeflow/pipelines/blob/28431fc3fdf6b117eaad0a2986e7fa270a50596d/sdk/python/kfp/v2/google/client/client.py#L264}, add enable cache option in v2 compatible mode. Compiler should be able to pass enable cache option to launcher
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5817 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5817/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5817/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5817/events | https://github.com/kubeflow/pipelines/issues/5817 | 915,522,351 | MDU6SXNzdWU5MTU1MjIzNTE= | 5,817 | [v2 compat] DB and API for creating/querying cache entries | {
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/area backend
### What feature would you like to see?
Set up DB and create API for for creating/querying cache entries
--
</div></b>
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
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### What feature would you like to see?
generate cache key and calculate hash of the cache key.
key.
Specifically the cache key include:
- the mapping between the input artifact names and MLMD artifact ids
- the mapping between input parameter name and input parameter value
- the mapping between output artifact name and output artifact spec
- the mapping between output parameter name and output parameter spec
- the full execution template/spec.
We will follow [KFP V1](https://github.com/kubeflow/pipelines/blob/43994abb7e07c71084d7b0fbebab8c9e6b7aa33f/backend/src/cache/server/mutation.go#L252) which uses the [sha256](https://pkg.go.dev/crypto/sha256) to generate the hash of the cache key.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"I'm working together with @pablofiumara-azumo to get this solved, so the issue can be closed.\r\n\r\n/close",
"@DavidSpek: Closing this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5815#issuecomment-857621354):\n\n>I'm working together with @pablofiumara-azumo to get ... | 2021-06-08T18:10:26 | 2021-06-09T11:38:03 | 2021-06-09T11:38:02 | NONE | null | How can I setup SSL for a Kubeflow installation (using Istio and Cert Manager) on Azure? URL is https://mydns.westus.cloudapp.azure.com/
Thanks in advance | {
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"/cc @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Bump",
"This issue has been automatically marked as stale because it has not had recent activity. It will be close... | 2021-06-07T11:31:06 | 2023-07-27T08:36:09 | null | NONE | null | ### Environment
* KFP SDK version:
kfp 1.6.3
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
### Steps to reproduce
Using a boolean parameter in an "if" condition in container arguments used to work in kfp==1.4.0, but kfp==1.6.x gives the following "invalid truth value" error (apparently trying to pass the parameter placeholder to `strtobool()` even if the value is available):
```bash
Traceback (most recent call last):
File "test_case.py", line 25, in <module>
compiler.Compiler().compile(pipeline, 'pipeline.yaml')
File ".../lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1053, in compile
self._create_and_write_workflow(
File ".../lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1106, in _create_and_write_workflow
workflow = self._create_workflow(
File ".../lib/python3.9/site-packages/kfp/compiler/compiler.py", line 896, in _create_workflow
pipeline_func(*args_list, **kwargs_dict)
File "test_case.py", line 22, in pipeline
op()
File ".../lib/python3.9/site-packages/kfp/components/_dynamic.py", line 34, in Component
return dict_func(locals()) # noqa: F821 TODO
File ".../lib/python3.9/site-packages/kfp/components/_components.py", line 343, in create_task_object_from_component_and_pythonic_arguments
return _create_task_object_from_component_and_arguments(
File ".../lib/python3.9/site-packages/kfp/components/_components.py", line 290, in _create_task_object_from_component_and_arguments
task = _container_task_constructor(
File ".../lib/python3.9/site-packages/kfp/dsl/_component_bridge.py", line 302, in _create_container_op_from_component_and_arguments
_attach_v2_specs(task, component_spec, original_arguments)
File ".../lib/python3.9/site-packages/kfp/dsl/_component_bridge.py", line 596, in _attach_v2_specs
resolved_cmd = _resolve_commands_and_args_v2(
File ".../lib/python3.9/site-packages/kfp/dsl/_component_bridge.py", line 442, in _resolve_commands_and_args_v2
resolved_cmd = _components._resolve_command_line_and_paths(
File ".../lib/python3.9/site-packages/kfp/components/_components.py", line 511, in _resolve_command_line_and_paths
expanded_args = expand_argument_list(container_spec.args)
File ".../lib/python3.9/site-packages/kfp/components/_components.py", line 502, in expand_argument_list
expanded_part = expand_command_part(part)
File ".../lib/python3.9/site-packages/kfp/components/_components.py", line 482, in expand_command_part
condition_result_bool = condition_result and strtobool(condition_result) #Python gotcha: bool('False') == True; Need to use strtobool; Also need to handle None and []
File ".../lib/python3.9/distutils/util.py", line 320, in strtobool
raise ValueError("invalid truth value %r" % (val,))
ValueError: invalid truth value "{{$.inputs.parameters['enable']}}"
```
Code to reproduce the problem:
```python
# test_case.py
@dsl.pipeline(name="Pipeline", description="Pipeline")
def pipeline() -> None:
op = components.load_component_from_text(
"""
name: Component
inputs:
- {name: enable, type: Bool, default: False}
implementation:
container:
image: some/container
args:
- if:
cond:
inputValue: enable
then:
- '--enable'
"""
)
op()
if __name__ == '__main__':
compiler.Compiler().compile(pipeline, 'pipeline.yaml')
```
### Expected result
The pipeline is expected to compile. In the code sample, the component would not have any arguments. Calling `op(True)` would add the `--enable` argument to the component. (This is how kfp==1.4.0 works.)
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Welcome contribution! This seems useful",
"/assign @anencore94 "
] | 2021-06-07T09:55:25 | 2021-06-23T21:13:19 | 2021-06-23T21:13:19 | 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?
- When we use `kfp run submit -w`, we have to wait forever when run stay at pending.
- However, I'd like to specify the timeout option with it.
### What is the use case or pain point?
- `kfp run submit -t 60` will wait 60 seconds max, until run be completed
- it would use [kfp client's wait_for_run_completion function](https://github.com/kubeflow/pipelines/blob/6a1b841db923e39016f5c6e66eac8698af27831d/sdk/python/kfp/_client.py#L922)
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
If you guys agree, I'd like to implement this feature.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Okay I found a workaround by leveraging `experimental.run_as_aiplatform_custom_job` where I could specify the machine type."
] | 2021-06-06T15:05:07 | 2021-06-07T15:42:23 | 2021-06-07T15:42:23 | NONE | null | I am currently using Vertex AI pipelines and using `component` decorator to preprocess data. When I submit the pipeline I am getting OOM error, I checked the documentation and couldn't find a way to specify `machineType` in `create_component_from_func`.
Is the option to resolve the issue is by submitting a CustomTrainingJob ?
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"Confirmed this is intentional for us, because KFP doesn't have non-declared inputs and outputs. So this is simpler for us.\n\nWe might need a doc to introduce KFP MLMD spec differences from the original spec.",
"/cc @zijianjoy \nThis answers one of your questions"
] | 2021-06-06T10:13:14 | 2021-07-07T02:16:13 | 2021-07-07T02:16:13 | CONTRIBUTOR | null | > For example, the DECLARED_INPUT and DECLARED_OUTPUT events are part of the signature of an execution.
https://github.com/google/ml-metadata/blob/47150524ee5ceee9766a034c4fbe5427440dd79e/ml_metadata/proto/metadata_store.proto#L100-L138
> However, until now, because KFP does not have the non-declared inputs/outputs concept, we are logging all inputs & outputs as pure inputs and outputs. We need to confirm whether this is sth we need to change.
Figure out what should be KFP's canonical way to log input/output events | {
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"/assign",
"Lets hold this for now given the new focus on KFP parity and compatibility with existing customer components and pipelines.",
">An example could be the type GCPProjectID. It is meant to be a string parameter instead of an artifact.\r\n\r\nGCPProjectID type is not the same as a string. Just like an i... | 2021-06-04T07:55:33 | 2022-03-03T04:05:14 | 2022-03-03T04:05:14 | COLLABORATOR | null | KFP SDK v2 has the distinction between Artifacts vs. Parameters. And they are decided by the input/output type annotation.
See the doc for more details: https://www.kubeflow.org/docs/components/pipelines/sdk/v2/component-development/#designing-a-pipeline-component
Some of our existing components or samples may use types that are intended to be parameters but would result as artifacts instead when compiling to v2. An example could be the type [`GCPProjectID`](https://github.com/kubeflow/pipelines/blob/1c66f93f5149a8d5ed7f33895d3ebc01e662d837/components/gcp/bigquery/query/to_CSV/component.yaml#L30). It is meant to be a string parameter instead of an artifact. We should update our components and samples to change such types to `String` instead.
/area samples
/area components
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"Thank you @zijianjoy! for starting the issue\r\n\r\nslightly adjusting some wordings",
"Several options for open discussion:\r\n* Add new system artifact types for HTML, Markdown, etc in https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py.\r\n* Use duck typing to determine type, e.g.... | 2021-06-04T04:01:59 | 2021-10-07T17:11:00 | 2021-10-07T17:11:00 | COLLABORATOR | null | Reference: https://github.com/kubeflow/pipelines/issues/5668
Blocks: https://github.com/kubeflow/pipelines/issues/3970
In KFP v1, we log output artifact types to MLMD as annotated in DSL. KFP UI can use it to visualize HTML, Markdown, etc as planned in https://github.com/kubeflow/pipelines/issues/3970. However, in KFP v2 compatible mode, such artifact type is `system.Artifact`. We are not able to retrieve type name from component definition in UI.
In order to support visualization compatibility in V2 compatible mode, we need to support custom artifact types from SDK. Once UI can retrieve artifact type from MLMD, we can visualize HTML/Markdown according to the artifact type.
cc @Bobgy @Ark-kun
---
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### Steps to reproduce
We are adding ad-hoc fix if we cannot upgrade to latest version of react-scripts: https://github.com/kubeflow/pipelines/blob/0b32f9f9d8a572d48257f06c3b0683c047c027f2/frontend/src/components/tabs/MetricsTab.test.tsx#L222-L224.
### Expected result
We should upgrade react-scripts and remove ad-hoc fix.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
https://github.com/testing-library/dom-testing-library/issues/477
cc @Bobgy
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/assign @zijianjoy \r\nlogging an improvement I just came up with, I think we can add more links like this, to make KFP UI inter-connected. For any resource shown on the UI, we can link to a page where we can observe its details.",
"Thank you Yuan!\r\n\r\nI confirm that we can get the related `kfp.PipelineRun` c... | 2021-06-03T14:45:50 | 2021-09-16T00:13:56 | 2021-09-16T00:13:56 | CONTRIBUTOR | null | A KFP task execution has one context of KFP run. So it's possible to get the related KFP run context (type `kfp.PipelineRun`), and extract run id from the context's custom properties.
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"They should be kept"
] | 2021-06-03T14:42:47 | 2021-06-21T08:31:29 | 2021-06-21T08:31:29 | CONTRIBUTOR | null | “pipeline_name”, “pipeline_run_id”
These fields are already present in pipeline/pipeline run contexts, so they are unnecessary | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5783 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5783/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5783/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5783/events | https://github.com/kubeflow/pipelines/issues/5783 | 910,073,385 | MDU6SXNzdWU5MTAwNzMzODU= | 5,783 | [feature] How can I delete a run completely, including the records in the cachedb and metadb mysql database? | {
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"I guess the problem may arise from the mechanism of **Argo**. Because kfp will add suffix for runs with the same name by default. By the way, is the suffix generated uniquely by taking the previously generated suffixes into consideration?\r\n\r\nI submit 4 runs with the same **RUN_NAME** as `add_two_numbers`. The... | 2021-06-03T04:06:51 | 2022-08-03T23:14:44 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
Currently, I can use the `kfp` Python SDK to delete runs by following codes.
```py
from kfp_server_api.api.run_service_api import RunServiceApi
run_api = RunServiceApi()
def list_all_runs(sort_by='name'):
list_runs_rsp = run_api.list_runs(sort_by=sort_by)
runs = list_runs_rsp.runs
if runs is None:
print('no runs')
else:
print('list runs')
for run in runs:
print(run.id, run.name)
return runs
def delete_all_runs(runs):
if runs is not None:
print('delete runs')
for run in runs:
print('delete', run.name)
run_api.delete_run(id=run.id)
if __name__ == '__main__':
runs = list_all_runs()
delete_all_runs(runs)
list_all_runs()
```
**However, I find there are still many artifacts not cleared even if I have deleted all the runs, which may have an impact on the runs submitted later if the run names are the same. Supposing we have two submitted runs both named `add_two_numbers`. The component states of the latter run may use the states of the earlier one. I mean the component state of the latter run may be `Succeed` when the run just starts as it uses the state of the earlier one.**
A great number of cache and meta records are not cleared in mysql database.
```mysql
# exec into the kubeflow mysql pod, and run the following queries.
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| cachedb | # new
| metadb | # new
| mlpipeline | # new
| mysql |
| performance_schema |
| sys |
+--------------------+
7 rows in set (0.00 sec)
# cache db
mysql> show tables;
+-------------------+
| Tables_in_cachedb |
+-------------------+
| execution_caches |
+-------------------+
1 row in set (0.00 sec)
mysql> select count(*) from execution_caches;
+----------+
| count(*) |
+----------+
| 204 | # many cache records
+----------+
1 row in set (0.00 sec)
# meta db
mysql> show tables;
+-------------------+
| Tables_in_metadb |
+-------------------+
| Artifact |
| ArtifactProperty |
| Association |
| Attribution |
| Context |
| ContextProperty |
| Event |
| EventPath |
| Execution |
| ExecutionProperty |
| MLMDEnv |
| ParentContext |
| ParentType |
| Type |
| TypeProperty |
+-------------------+
15 rows in set (0.01 sec)
mysql> select count(*) from Artifact;
+----------+
| count(*) |
+----------+
| 395 | # many meta records
+----------+
1 row in set (0.01 sec)
```
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
If I delete a run, the records in the cachedb and metadb mysql database should be deleted, too.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
**Pain Point:** Supposing we have two submitted runs both named `add_two_numbers`. The component states of the latter run may use the states of the earlier one, which may make the latter run not execute normally.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Maybe directly delete the records in mysql by myself.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Updated to 1.7.0 release, because we have big changes like upgrading MLMD store to 1.0.0",
"https://github.com/kubeflow/testing/commit/03c625828ac9cc13283bd226371280b0f5704c09\r\nupdated kfp-ci test cluster to 1.7.0-alpha.2 for further testing (with argo v3.1.1)",
"Issue: #6294\r\n\r\nkfp gcp marketplace -- su... | 2021-06-02T11:44:12 | 2022-01-20T12:23:52 | 2021-09-09T05:53:56 | CONTRIBUTOR | null | UPDATE: 1.7.0 release is out! https://github.com/kubeflow/pipelines/releases/tag/1.7.0
Code release blockers:
* [x] TFX issues
* [x] https://github.com/kubeflow/pipelines/issues/6311
* [x] (blockers resolved) https://github.com/kubeflow/pipelines/issues/6138
* [x] https://github.com/kubeflow/pipelines/pull/6331
* [x] https://github.com/kubeflow/pipelines/pull/6342
* [x] https://github.com/kubeflow/pipelines/issues/6252
After code release:
* [x] https://github.com/kubeflow/pipelines/issues/6350
* [ ] if argo releases 3.1.7, we should upgrade
Docs release blockers:
* [x] https://github.com/kubeflow/pipelines/issues/6306
* [x] https://github.com/kubeflow/pipelines/issues/6310
* [x] https://github.com/kubeflow/pipelines/issues/6294#issuecomment-897340923
DONE:
* [x] https://github.com/kubeflow/pipelines/issues/6293
* [x] https://github.com/kubeflow/pipelines/issues/5689
* [x] [Mitigated] https://github.com/kubeflow/pipelines/issues/6189
* [x] https://github.com/kubeflow/pipelines/issues/6130
* [x] https://github.com/kubeflow/pipelines/issues/6053
* [x] https://github.com/kubeflow/pipelines/issues/5303#issuecomment-879565407
* [x] https://github.com/kubeflow/pipelines/issues/5954 (waiting for revert)
* [x] https://github.com/kubeflow/pipelines/issues/5943
* [x] https://github.com/tensorflow/tfx/issues/3933
After release:
* [x] manual verification of fix https://github.com/kubeflow/pipelines/issues/5717#issuecomment-852955515
* [x] we can update reference and merge https://github.com/kubeflow/gcp-blueprints/pull/284
Prepare:
* [x] https://github.com/kubeflow/pipelines/issues/4800
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"ai_platform sample is failing\r\nhttps://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1396731834997411840#1:build-log.txt%3A8245"
] | 2021-06-02T09:28:37 | 2021-06-02T11:27:02 | 2021-06-02T11:27:02 | CONTRIBUTOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1396731834997411840
https://github.com/kubeflow/pipelines/commit/fd72b3c9d59803726c41f287cd1ee98b8f95cc5a | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5774 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5774/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5774/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5774/events | https://github.com/kubeflow/pipelines/issues/5774 | 909,262,597 | MDU6SXNzdWU5MDkyNjI1OTc= | 5,774 | [sdk] kfp cli giving errors | {
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"u... | closed | false | null | [] | null | [
"Hi @Ruminateer, did you configure access to your KFP API endpoint?\r\nhttps://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/\r\n\r\ne.g. for the CLI, configure your endpoint using env vars\r\n```\r\nexport KF_PIPELINES_ENDPOINT=http://localhost:3000\r\n```",
"> Hi @Ruminateer, did you configure acce... | 2021-06-02T08:59:25 | 2022-03-03T04:05:29 | 2022-03-03T04:05:29 | MEMBER | null | ### Environment
* KFP version: 1.6.0
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.4.0
<!-- 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. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```console
$ pip list | grep kfp
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
```
### Steps to reproduce
Installed Kubeflow pipeline on an existing cluster. Installed SDK
```console
export PIPELINE_VERSION=1.6.0
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
pip3 install kfp --upgrade
```
Got these errors. What I am doing wrong? Have no idea how to solve it.
```console
$ kfp run list
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.5) or chardet (4.0.0) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
Failed to get healthz info attempt 1 of 5.
Traceback (most recent call last):
File "/home/zzy/.local/lib/python3.8/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (503)
Reason: Service Unavailable
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 02 Jun 2021 07:23:00 GMT', 'Content-Length': '178'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"no endpoints available for service \"ml-pipeline:http\"","reason":"ServiceUnavailable","code":503}
Failed to get healthz info attempt 2 of 5.
Traceback (most recent call last):
File "/home/zzy/.local/lib/python3.8/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (503)
Reason: Service Unavailable
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 02 Jun 2021 07:23:05 GMT', 'Content-Length': '178'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"no endpoints available for service \"ml-pipeline:http\"","reason":"ServiceUnavailable","code":503}
^C
Aborted!
$ kfp -n default run list
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.5) or chardet (4.0.0) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
Failed to get healthz info attempt 1 of 5.
Traceback (most recent call last):
File "/home/zzy/.local/lib/python3.8/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 02 Jun 2021 07:23:25 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
Failed to get healthz info attempt 2 of 5.
Traceback (most recent call last):
File "/home/zzy/.local/lib/python3.8/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 02 Jun 2021 07:23:30 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
^C
Aborted!
$ kfp -n default run submit -e Default -w -f mnist.py.yml
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.5) or chardet (4.0.0) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
Failed to get healthz info attempt 1 of 5.
Traceback (most recent call last):
File "/home/zzy/.local/lib/python3.8/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 02 Jun 2021 09:01:22 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
Failed to get healthz info attempt 2 of 5.
Traceback (most recent call last):
File "/home/zzy/.local/lib/python3.8/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/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 "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/home/zzy/.local/lib/python3.8/site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 02 Jun 2021 09:01:27 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
^C
Aborted!
```
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
kfp doesn't give the errors and tells that there is 0 run history.
### 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 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5773 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5773/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5773/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5773/events | https://github.com/kubeflow/pipelines/issues/5773 | 909,222,345 | MDU6SXNzdWU5MDkyMjIzNDU= | 5,773 | deploying the trained model using deploy component is throwing an Internal Error: | {
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"Hi @priya170807 \r\n> Internal error occurred. Please retry in a few minutes. If you still experience errors, contact Cloud ML.\r\n\r\nDid you retry? This seems like a problem with the service, rather than KFP/component.",
"Hi Bobgy,\r\n\r\nThank you responding...I was continuosly retrying. It worked today...\r\... | 2021-06-02T07:49:48 | 2021-06-03T16:41:42 | 2021-06-03T16:41:42 | NONE | null | I have created an XgBoost model and performed hyperparameter tuning. I am trying to deploy using deploy component,
mlengine_deploy_op = component_store.load_component('ml_engine/deploy').
I am getting the following error.
Traceback (most recent call last):
File "/usr/local/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/local/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/ml/kfp_component/launcher/__main__.py", line 45, in <module>
main()
File "/ml/kfp_component/launcher/__main__.py", line 42, in main
launch(args.file_or_module, args.args)
File "kfp_component/launcher/launcher.py", line 45, in launch
return fire.Fire(module, command=args, name=module.__name__)
File "/usr/local/lib/python2.7/site-packages/fire/core.py", line 127, in Fire
component_trace = _Fire(component, args, context, name)
File "/usr/local/lib/python2.7/site-packages/fire/core.py", line 366, in _Fire
component, remaining_args)
File "/usr/local/lib/python2.7/site-packages/fire/core.py", line 542, in _CallCallable
result = fn(*varargs, **kwargs)
File "kfp_component/google/ml_engine/_deploy.py", line 68, in deploy
wait_interval)
File "kfp_component/google/ml_engine/_create_version.py", line 64, in create_version
replace_existing, wait_interval).execute_and_wait()
File "kfp_component/google/ml_engine/_create_version.py", line 101, in execute_and_wait
created_version = self._create_version_and_wait()
File "kfp_component/google/ml_engine/_create_version.py", line 135, in _create_version_and_wait
self._wait_interval)
File "kfp_component/google/ml_engine/_common_ops.py", line 67, in wait_for_operation_done
error.get('message', 'Unknown message'),
**RuntimeError**: Failed to complete create version operation projects/test-project-one-307022/operations/create_cashdemand_regressor_v01-1622618056480: 13 Internal error occurred. Please retry in a few minutes. If you still experience errors, contact Cloud ML.
Please help.
priya | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5768 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5768/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5768/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5768/events | https://github.com/kubeflow/pipelines/issues/5768 | 908,395,312 | MDU6SXNzdWU5MDgzOTUzMTI= | 5,768 | [feature] Add PyTorch Components for KFP | {
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"Added support for [PyTorch KFP Components](https://github.com/kubeflow/pipelines/tree/master/components/PyTorch/pytorch-kfp-components) and [Samples](https://github.com/kubeflow/pipelines/tree/master/components/PyTorch/pytorch-kfp-components) for:\r\n- Model training for PyTorch \r\n- Profiling and visualizations ... | 2021-06-01T15:07:53 | 2021-08-16T04:58:13 | 2021-08-16T04:58:12 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
/area components
/area samples
### What feature would you like to see?
This feature will add support for PyTorch components in Kubeflow pipelines, providing ability to train models and deploy them using KFServing with TorchServe for an End to end PyTorch model training to prediction loop.
The components will be packaged as a pip install and getting started examples will be provided for Computer Vision and NLP.
The components and examples are expected to run on any deployment scenarios, for both cloud and on-prem scenarios.
### What is the use case or pain point?
Currently there is no easy solution for this and KFP community is expected to figure out on their own.
### Is there a workaround currently?
Write own components
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"/assign",
"Hi @cloudbow, this seems like a feature request to Kale, because using a new volume snapshot as pipeline input makes it impossible for KFP to predict whether there were any changes in the pipeline.\r\n\r\nTo utilize caching, you should better have smaller granularity for your input data, so that the p... | 2021-06-01T06:49:29 | 2022-03-03T04:05:23 | 2022-03-03T04:05:23 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
/area sdk
/area samples
<!-- /area components -->
### What feature would you like to see?
Use cache feature to rerun kale generated pipelines which will reduce the total amount of time to re-run the pipelines with code changes
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Currently when I installed minkf-1.3 for datascientists the compiled pipelines are taking a long time to execute. The reason is that the steps are not getting cached. There is no way to create a cache from converting jupyter to pipelines because everytime it creates a new rok snashot. Is there anyway to provide a "Compile and Run with Cache" option so that only changed code is converted to kubeflow pipelines.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
Not to my knowledge
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5764 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5764/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5764/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5764/events | https://github.com/kubeflow/pipelines/issues/5764 | 907,952,144 | MDU6SXNzdWU5MDc5NTIxNDQ= | 5,764 | [backend] Steps not getting cached when input parameters are same | {
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"Hi @cloudbow, I tried running your example on KFP standalone 1.6.0 on GCP, it is cached as expected after second run.\r\n\r\n\r\n\r\nTherefore, this issue might be specific to either your env or MiniKF. Ca... | 2021-06-01T06:41:49 | 2022-03-03T02:05:26 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
minikf-1.3 from aws market place
* KFP version:
Same as minikf-1.3 provided
<!-- 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:
kfp 1.5.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
I have attached the notebook I used. Please try it with that . The input is already provided. Its a simple add_op pipeline which adds two numbers. But why is the step being executed again and again even if the run is cloned or a new run created using the same 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
The steps should have been cached as the input the docker image, the output everything is same.
<!-- What should the correct behavior be? -->
### Materials and Reference
Attached sample [code]([simple_function_based_component_pipeline (1).ipynb.zip](https://github.com/kubeflow/pipelines/files/6573897/simple_function_based_component_pipeline.1.ipynb.zip))
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5763 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5763/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5763/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5763/events | https://github.com/kubeflow/pipelines/issues/5763 | 907,787,587 | MDU6SXNzdWU5MDc3ODc1ODc= | 5,763 | [bug] pipeline's child pods are not deleted after workflow's status became success | {
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"This is expected behavior, default TTL is 1 day.\r\nSee https://github.com/kubeflow/pipelines/blob/5d0f3a3d32f35d3dbca9cd27f4ee476e87f6d41e/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml#L24-L25\r\n\r\nAfter the workflow is GCed, Pods will be removed.",
"Thanks @Bobgy !\r\nI'm a b... | 2021-06-01T01:18:36 | 2021-06-03T00:09:47 | 2021-06-03T00:09:47 | MEMBER | null | ### What steps did you take
- create kf pipeline
- all components were spawned well as pods.
- all components' status became COMPLETED
- pipeline's status became SUCCESS
### What happened:
- HOWEVER, child pods didn't killed automatically.
- I should kill them manually.
### What did you expect to happen:
- They should be killed after pipeline succeeded.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
* with kubeflow manifests repo, kustomize + kubectl apply
* KFP version:
- tag : 1.5.0 (https://github.com/kubeflow/pipelines/tree/1.5.0/manifests/kustomize)
- on UI : build version v1beta1
* KFP SDK version:
* Kubernetes env:
* on-premise k8s v1.19.3
### Anything else you would like to add:
I searched about this and I guess `spec.podGC.strategy` has some releation on this issue.
But, I didn't specify that parameter on my pipeline.yaml (kind : workflow).
In my case, is this situation is normal ? (all completed pods are not deleted until their parent pipeline will be deleted)
Also, if i want my pod would be deleted when my pipeline finished successfully, Do I have to specify spec.podGC.strategy everytime?
### 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 👍. We prioritise the issues with the most 👍.
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"@Ark-kun is this sample still working properly?\r\nIt fails in our test infra too:\r\nhttps://testgrid.k8s.io/googleoss-kubeflow-pipelines#kubeflow-pipeline-postsubmit-integration-test",
"I can give you more information about my case as I have now found a way to install KFP 1.6 with caching on GCP. \r\nMy proble... | 2021-05-31T17:31:57 | 2022-03-03T04:05:29 | 2022-03-03T04:05:29 | NONE | null | ### What steps did you take:
- [Deploy Kubeflow Pipelines on a new GKE cluster](https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deploying-kubeflow-pipelines)
- Run the [Kubeflow caching sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/caching/caching_sample.py)
### What happened:
The sample failed with error :
`RuntimeError: The cached execution was not re-used or pipeline run took to long to complete.`
### What did you expect to happen:
The sample should have run successfully.
### Environment:
Kubernetes : 1.18.18-gke.1100
KFP version : 1.6.0
KFP SDK version: 1.6.2
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https://api.github.com/repos/kubeflow/pipelines/issues/5758 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5758/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5758/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5758/events | https://github.com/kubeflow/pipelines/issues/5758 | 906,508,856 | MDU6SXNzdWU5MDY1MDg4NTY= | 5,758 | [feature] Pipeline parameter description | {
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"Implementing this in the SDK, one suggestion would be to follow the same principle as #5757 and add a description decorator that would assign a private field to the pipeline function (e.g. `_param_descriptions`). The compiler would read this field, and write it to the pipeline yaml.\r\n\r\n`{\"name\": \"num_batche... | 2021-05-29T16:26:49 | 2022-03-03T04:05:26 | 2022-03-03T04:05:26 | 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. -->
Pipeline parameter descriptions / comments, as offered by e.g. Jenkins.
In the UI, pipeline parameters would have an optional field that describes that parameter. For example any considerations to make when deciding on the parameter value, how it would interact with other pipeline parameters, etc.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
As of today, documentation is only visible in the source code of the pipeline definition (if it exists). Adding this to PipelineParam would make it visible in the UI, but still have it documented in the code.
Suggestions:
- parse docstrings
- add a 'description' field to PipelineParam
### Is there a workaround currently?
As of today, documentation is forced to be separate from the pipeline. Giving it a native place would increase developer productivity and onboarding.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5757 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5757/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5757/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5757/events | https://github.com/kubeflow/pipelines/issues/5757 | 906,507,364 | MDU6SXNzdWU5MDY1MDczNjQ= | 5,757 | [feature] Multiple choice pipeline parameter | {
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"Haven't dived deep enough into kubeflow's implementation to know how compilation works and how this would propagate to the UI / API, but I'm thinking something along the lines of this (akin to the `@pipeline` decorator):\r\n\r\n```\r\nfrom functools import wraps\r\n\r\ndef pipeline_multiplechoice(**valid_kwargs):\... | 2021-05-29T16:19:07 | 2022-03-03T04:05:18 | 2022-03-03T04:05:18 | 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. -->
Multiple choice pipeline parameters, as offered by e.g. Jenkins.
User experience:
- When triggering a run via UI, all valid multiple choice values are displayed as radio buttons. The user selects one of them.
- When triggering a run via API with a bad parameter value, the user gets an error response (400) with a message explaining the valid values for the relevant parameter.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
As of today, multiple choice parameters are implicit. Users have to know the implementation details of the pipeline in order to know which values are permitted.
This discourages unfamiliar users to use the pipeline, as well as being an obstacle in the workflow of the ordinary developer. Having it available as a native form of PipelineParam would:
1. Enable people that have not read the pipeline definition to trigger a run
2. Increase documentation: having multiple choice parameters is a form of 'documentation as code'
### Is there a workaround currently?
Validate parameter value inside the relevant component.
This is hidden from the user. The user has to trigger the pipeline, watch it fail, and then check the logs in order to find out what went wrong. This also assumes that the pipeline developer has put in the effort of making a well formatted error message.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Hi @priya170807, it seems you should move `--endpoint $ENDPOINT` argument to after `run submit`,\r\n\r\ne.g.\r\n```\r\nkfp run submit --endpoint $ENDPOINT ...\r\n```",
"> \r\n> \r\n> Hi @priya170807, it seems you should move `--endpoint $ENDPOINT` argument to after `run submit`,\r\n> \r\n> e.g.\r\n> \r\n> ```\r\... | 2021-05-29T02:27:13 | 2021-06-03T19:51:32 | 2021-06-03T19:51:32 | NONE | null | Hi,
I have created a pipeline and compile it. I am getting an error submitting a run to the end point.
Command:
`!kfp --endpoint $ENDPOINT run submit \ -e $EXPERIMENT_NAME \ -r $RUN_ID \ -p $PIPELINE_ID \ project_id=$PROJECT_ID \ gcs_root=$GCS_STAGING_PATH \ region=$REGION \ source_bucket_name=$SOURCE_BUCKET_NAME \ source_prefix=$SOURCE_PREFIX \ dest_bucket_name=$DEST_BUCKET_NAME \ dest_prefix=$DEST_PREFIX \ dest_file_name=$DEST_FILE_NAME \ evaluation_metric_name=$EVALUATION_METRIC \ evaluation_metric_threshold=$EVALUATION_METRIC_THRESHOLD \ model_id=$MODEL_ID \ version_id=$VERSION_ID \ replace_existing_version=$REPLACE_EXISTING_VERSION`
Error:
Usage: kfp [OPTIONS] COMMAND [ARGS]...
Try 'kfp --help' for help.
**Error: No such command 'submit'.**
Any help is appreciated.
Priya | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-29T01:04:12 | 2022-03-03T06:05:20 | 2022-03-03T06:05:20 | CONTRIBUTOR | null | {
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"workaround\r\n1. path region\r\n2. fix Cloud Scheduler Job [request body `_url`](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/v2/google/client/schedule.py#L108)\r\n\r\n```python\r\nfrom kfp.v2.google.client.schedule import create_from_pipeline_file\r\nresponse = create_from_pipeline_file(\r\n ... | 2021-05-29T00:49:09 | 2022-03-03T06:05:19 | 2022-03-03T06:05:19 | NONE | null | ### Environment
* KFP version:
Vertex Pipelines
* KFP SDK version:
```
!pip list | grep kfp
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.6.0
```
### Steps to reproduce
Create App Engine app in asia-northeast1 and follow [this notebook](https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/ai-platform-unified/notebooks/unofficial/pipelines/pipelines_intro_kfp.ipynb).
```console
!gcloud app describe --project $PROJECT_ID
authDomain: gmail.com
codeBucket: staging.PROJECT_ID.appspot.com
databaseType: CLOUD_FIRESTORE
defaultBucket: PROJECT_ID.appspot.com
defaultHostname: PROJECT_ID.an.r.appspot.com
featureSettings:
splitHealthChecks: true
useContainerOptimizedOs: true
gcrDomain: asia.gcr.io
id: PROJECT_ID
locationId: asia-northeast1
name: apps/PROJECT_ID
servingStatus: SERVING
```
```python
REGION = "us-central1"
```
```python
from kfp.v2.google.client import AIPlatformClient # noqa: F811
api_client = AIPlatformClient(
project_id=PROJECT_ID,
region=REGION,
)
response = api_client.create_schedule_from_job_spec(
job_spec_path="hw_pipeline_job.json",
schedule="2 * * * *",
time_zone="America/Los_Angeles", # change this as necessary
parameter_values={"text": "Hello world!"},
# pipeline_root=PIPELINE_ROOT # this argument is necessary if you did not specify PIPELINE_ROOT as part of the pipeline definition.
)
```
```console
HttpError: <HttpError 400 when requesting https://cloudscheduler.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/jobs?alt=json returned "Location must equal asia-northeast1 because the App Engine app that is associated with this project is located in asia-northeast1". Details: "Location must equal asia-northeast1 because the App Engine app that is associated with this project is located in asia-northeast1">
```
### Expected result
Cloud Scheduler Job and Cloud Function will be created in specified location.
Should be able to pass **scheduler job** location.
### Materials and Reference
- https://cloud.google.com/scheduler/docs#supported_regions
- https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/v2/google/client/schedule.py#L40
---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/lifecycle frozen",
"@bobgy TFX pipelines already supports this by appending pipeline wide env vars and by not assuming a default S3 ... | 2021-05-28T10:53:24 | 2023-01-18T08:21:56 | 2023-01-18T08:21:56 | CONTRIBUTOR | null | Part of https://github.com/kubeflow/pipelines/issues/5704
Support s3 pipeline_root with generic options: endpoint, region, insecure, etc, so that it can be used with user maintained MinIO or other compatible storages.
Credentials can be set similar to aws/gcp credentials, but not using the same env vars as AWS. (Needs a detailed design) | {
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"@jmendesky Could you please move this issue to https://github.com/kubeflow/pipelines so that the feature request can be properly tracked? The Pipeline developers do not regularly go through the issues in this repository. ",
"cc @Bobgy I think you have permissions to move the issue.",
"Would be happy to move it... | 2021-05-28T08:15:53 | 2022-03-03T03:05:19 | 2022-03-03T03:05:19 | NONE | null | /kind feature
Good morning,
when deploying ML pipelines, we would like to build on existing tooling and follow similar patterns as when deploying other resources to our infrastructure. This would usually be done declaratively rather than programmatically. From our research, it seems that the only way to deploy Kubeflow pipelines at the moment is to use either kfctl or the Kubeflow Client libraries.
**Why you need this feature:**
Instead of having to execute Kubeflow clients, we'd like to have a declarative way of deploying Kubeflow Pipelines.
**Describe the solution you'd like:**
Provide a Kubeflow Pipeline Custom Resource Definition which gets picked up by an operator which then applies the changes to Kubeflow.
**Anything else you would like to add:**
We are considering implementing the above approach but would like to validate whether this would be useful for other people as well or whether something similar already exists. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5748 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5748/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5748/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5748/events | https://github.com/kubeflow/pipelines/issues/5748 | 904,401,031 | MDU6SXNzdWU5MDQ0MDEwMzE= | 5,748 | [frontend] CRON schedule default in UI once per minute - too frequent | {
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"Hi @hsezhiyan, this sounds reasonable.\r\nWelcome contribution on this",
"/cc @zijianjoy \r\nwhat do you think?",
"I agree with making `run every hour` as the default setting. To me it usually is too frequent as once per minute, and it actually takes longer than 1 minute to start the recurring run in my case. ... | 2021-05-27T23:52:48 | 2021-06-10T21:34:02 | 2021-06-10T21:34:02 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
We deployed by using the Kubeflow manifests, which comes with the default UI provided in this repo.
* KFP version: `v.1.2.0`
### Issue
When you do onto the Kubeflow UI and try to create a recurring run, the default periodic scheduled/cron schedule is `once per minute`, or `0 * * * *`.
Our deployment of Kubeflow is now being used by a large number of customers, so there is a real possibility that a customer will accidentally schedule a large pipeline run every minute. There is also the possibility that someone will experiment with scheduled runs and forget to the off the run after done testing. In both these scenarios, there will be a lot of wasteful use of the K8s clusters (added cost) and possible overloading of the cluster (affecting SLAs, reducing reliability).
Is it possible to remove the "run every minute" dropdown menu bar, or at least make the "run every hour" the default setting?
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/5746 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5746/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5746/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5746/events | https://github.com/kubeflow/pipelines/issues/5746 | 904,166,231 | MDU6SXNzdWU5MDQxNjYyMzE= | 5,746 | [feature] Add retry in dataproc component | {
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"This issue is breaking customers who are using https://github.com/kubeflow/pipelines/tree/master/components/gcp/dataproc/submit_pyspark_job with large data sets."
] | 2021-05-27T20:37:11 | 2021-07-30T23:18:36 | 2021-07-30T23:18:36 | CONTRIBUTOR | null | ### Feature Area
/area components
### What feature would you like to see?
Add retry in dataproc component. Similar to https://github.com/kubeflow/pipelines/pull/5250
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
`BrokenPipeError` when using https://github.com/kubeflow/pipelines/tree/master/components/gcp/dataproc/submit_pyspark_job with large datasets
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5745 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5745/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5745/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5745/events | https://github.com/kubeflow/pipelines/issues/5745 | 904,162,924 | MDU6SXNzdWU5MDQxNjI5MjQ= | 5,745 | Error occured while trying to proxy to: pipelines/upload | {
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"Note, this is corrected by specifying a namespace (rather than accepting \"Default\"). ",
"Strange, I don't expect it being a problem when namespace is default.\r\nThere might be sth else going wrong.\r\n\r\nI guess we'll have to wait for more feedback",
"This issue has been automatically marked as stale becau... | 2021-05-27T20:32:53 | 2022-03-03T04:05:26 | 2022-03-03T04:05:26 | NONE | null | When entering the Kubeflow Pipeline, received the error:
```
Error occured while trying to proxy to:
#############-dot-us-central1.pipelines.googleusercontent.com/apis/v1beta1/pipelines?page_token=&page_size=10&sort_by=created_at%20desc&filter=
```
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Via the [GCP AI Platform Pipelines UI](https://console.cloud.google.com/ai-platform/pipelines/clusters). This instance uses managed storage.
* KFP version: **1.4.1**
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
```
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.6.0
```
### Steps to reproduce
1. Initially, I tried to load the pipeline from GCP hosted Jupyter Notebook via the Python API:
```
self.load_response = self.client.upload_pipeline(
pipeline_package_path=self.pipeline_path,
pipeline_name=self.pipeline_name,
description=self.pipeline_description
)
```
I also tried to enter `client.list_pipelines()` in a hosted Notebook. In both scenarios, I receive the error: "Error 504 (Gateway Timeout)!"
2. Secondly, I tried to upload the .zip from the Kubeflow dashboards interface. The screenshot below shows the error message I received.
[Error in Kubeflow Dashboard](https://storage.googleapis.com/algom-tmp-files/Screen%20Shot%202021-05-27%20at%2010.20.50%20PM.png)
### Expected result
Expect for pipeline to be added to Kubeflow interface.
### Materials and Reference
Based on the error logs, the source of the issue is the initialization of MySQL. The container logs show multiple errors after initialization, MySQL shuts down, reinitializes ... and this cycle repeats itself. Also, **this error did not occur when not using managed storage**.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"What is the version of KFP when you use Kubeflow on MicroK8s?",
"Sorry, I already deleted MicroK8s. I did not install KFP on my own, and I am not sure KFP is installed by MicroK8s or not. In case i am not understanding this correctly. Do you have to install Kubeflow pipeline after install Kubeflow in order to ru... | 2021-05-27T15:19:07 | 2021-06-04T21:18:19 | 2021-06-04T21:18:19 | NONE | null | ### What steps did you take
Running Kubeflow on Microk8s cluster that is running on my local computer.
Got access to Kubeflow dashboard and trying to run pipeline.
<!-- A clear and concise description of what the bug is.-->
### What happened:
When I try to run any of the pipeline examples, The job never show up on `Runs` tab. And when I try to create experiment, it has this error:
```
{"error":"Validate experiment request failed.: Invalid input error: In single-user mode, CreateExperimentRequest shouldn't contain resource references.","code":3,"message":"Validate experiment request failed.: Invalid input error: In single-user mode, CreateExperimentRequest shouldn't contain resource references.","details":[{"@type":"type.googleapis.com/api.Error","error_message":"In single-user mode, CreateExperimentRequest shouldn't contain resource references.","error_details":"Validate experiment request failed.: Invalid input error: In single-user mode, CreateExperimentRequest shouldn't contain resource references."}]}
```
### What did you expect to happen:
To be able to run the example pipeline.
### Environment:
Microk8s v1.21
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
I am not sure if KFP is need if Kubeflow is deployed by Microk8s
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
/area samples
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/cc @elikatsis ",
"/cc @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment... | 2021-05-27T10:39:56 | 2022-03-03T04:05:27 | 2022-03-03T04:05:27 | NONE | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
A FutureWarning re: direct use of `ContainerOp` is raised when using `VolumeOp.delete()`
```
# example.py
import kfp
def pipeline_with_delete():
data_vop = kfp.dsl.VolumeOp(name='create-data-volume', resource_name='data-volume', size="2Gi")
data_vop.delete()
def pipeline_without_delete():
data_vop = kfp.dsl.VolumeOp(name='create-data-volume', resource_name='data-volume', size="2Gi")
if __name__ == "__main__":
print("Pipeline with delete")
kfp.compiler.Compiler().compile(pipeline_with_delete, "pipeline.yaml")
print("Pipeline without delete")
kfp.compiler.Compiler().compile(pipeline_without_delete, "pipeline.yaml")
print("-"*40)
print(f"KFP version: {kfp.__version__}")
print("-"*40)
```
### What happened:
```
$ python example.py
Pipeline with delete
.../venv/lib/python3.8/site-packages/kfp/dsl/_container_op.py:1149: 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
warnings.warn(
Pipeline without delete
----------------------------------------
KFP version: 1.6.2
----------------------------------------
```
### What did you expect to happen:
There is no direct use of ContainerOp in this example. The warning should not have been raised.
AFAIK using volumes is not detrimental to reusability of components, so the warning doesn't seem relevant either.
### Environment:
<!-- Please fill in those that seem relevant. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
```
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5737 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5737/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5737/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5737/events | https://github.com/kubeflow/pipelines/issues/5737 | 901,725,933 | MDU6SXNzdWU5MDE3MjU5MzM= | 5,737 | [frontend] Add `Create Experiment` button when selecting experiement | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/lifecycle frozen"
] | 2021-05-26T04:08:57 | 2021-09-14T05:57:18 | null | COLLABORATOR | null |
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
In Full Kubeflow mode, select a pipeline and click `Create Run`. During selecting experiment, open a dialog to select existing experiment.
By default, there is no available experiment, but there is no way to create new experiment on the current dialog. User needs to go back to experiment entry to create one.
### Expected result
<!-- What should the correct behavior be? -->
Have a button on experiment selection dialog to create experiment. Or automatically create one experiment for user.
### 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 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5730 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5730/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5730/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5730/events | https://github.com/kubeflow/pipelines/issues/5730 | 899,947,661 | MDU6SXNzdWU4OTk5NDc2NjE= | 5,730 | [backend] Wait container stuck. | {
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"u... | closed | false | null | [] | null | [
"Hi @boarder7395, this issue seems only related to argo workflow.\r\nCan open up an issue in argo workflows github repo instead?",
"@Bobgy Sure! How familiar is the argo workflow team with kfp? If I submit the same code to replicate the issue will that be enough or should I update my code to replicate the issue t... | 2021-05-24T19:36:24 | 2021-06-15T11:48:11 | 2021-06-15T11:48:11 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
EKS
* KFP version:
1.3.0
* KFP SDK version:
1.1.1
### Steps to reproduce
Using multiple processes in python to log messages causes the docker logs command in the argo wait container to get stuck indefinitely, resulting in the component running indefinitely even though the main container completed successfully. Exec'ing into the pod's wait container and killing the process results in the pipeline completing successfully.
Code snippet to replicate the issue.
```
import argparse
# You'll need these imports in your own code
import logging
import logging.handlers
import multiprocessing
# Next two import lines for this demo only
from random import choice, random
import time
#
# Because you'll want to define the logging configurations for listener and workers, the
# listener and worker process functions take a configurer parameter which is a callable
# for configuring logging for that process. These functions are also passed the queue,
# which they use for communication.
#
# In practice, you can configure the listener however you want, but note that in this
# simple example, the listener does not apply level or filter logic to received records.
# In practice, you would probably want to do this logic in the worker processes, to avoid
# sending events which would be filtered out between processes.
#
# The size of the rotated files is made small so you can see the results easily.
def listener_configurer():
root = logging.getLogger()
# h = logging.handlers.RotatingFileHandler('mptest.log', 'a', 300, 10)
h = logging.StreamHandler()
f = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s')
h.setFormatter(f)
root.addHandler(h)
def handle_command_line():
parser = argparse.ArgumentParser()
parser.add_argument('--n-logs', type=int, required=True, help='Number of logs to print.')
return parser.parse_args()
# This is the listener process top-level loop: wait for logging events
# (LogRecords)on the queue and handle them, quit when you get a None for a
# LogRecord.
def listener_process(queue, configurer):
configurer()
while True:
try:
record = queue.get()
if record is None: # We send this as a sentinel to tell the listener to quit.
break
logger = logging.getLogger(record.name)
logger.handle(record) # No level or filter logic applied - just do it!
except Exception:
import sys, traceback
print('Whoops! Problem:', file=sys.stderr)
traceback.print_exc(file=sys.stderr)
# Arrays used for random selections in this demo
LEVELS = [logging.DEBUG, logging.INFO, logging.WARNING,
logging.ERROR, logging.CRITICAL]
LOGGERS = ['a.b.c', 'd.e.f', 'd.e.f', 'g.h.i', 'j.k.l']
MESSAGES = [
'Random message #1',
'Random message #2',
'Random message #3',
'Random message #4',
'Random message #5',
]
# The worker configuration is done at the start of the worker process run.
# Note that on Windows you can't rely on fork semantics, so each process
# will run the logging configuration code when it starts.
def worker_configurer(queue):
h = logging.handlers.QueueHandler(queue) # Just the one handler needed
root = logging.getLogger()
root.addHandler(h)
# send all messages, for demo; no other level or filter logic applied.
root.setLevel(logging.DEBUG)
# This is the worker process top-level loop, which just logs ten events with
# random intervening delays before terminating.
# The print messages are just so you know it's doing something!
def worker_process(queue, configurer):
configurer(queue)
name = multiprocessing.current_process().name
print('Worker started: %s' % name)
for i in range(100):
time.sleep(random())
logger = logging.getLogger(choice(LOGGERS))
level = choice(LEVELS)
message = choice(MESSAGES)
logger.log(level, message)
print('Worker finished: %s' % name)
# Here's where the demo gets orchestrated. Create the queue, create and start
# the listener, create ten workers and start them, wait for them to finish,
# then send a None to the queue to tell the listener to finish.
def main():
queue = multiprocessing.Queue(-1)
listener = multiprocessing.Process(target=listener_process,
args=(queue, listener_configurer))
listener.start()
workers = []
for i in range(96):
worker = multiprocessing.Process(target=worker_process,
args=(queue, worker_configurer))
workers.append(worker)
worker.start()
for w in workers:
w.join()
queue.put_nowait(None)
listener.join()
if __name__ == '__main__':
main()
```
### Expected result
The pipeline should finish when the main container finishes.
### Materials and Reference
Minimal example. Comment out set_project (test.py line 76) call to remove taints specific to my cluster.
[debug.zip](https://github.com/kubeflow/pipelines/files/6534609/debug.zip)
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5728 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5728/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5728/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5728/events | https://github.com/kubeflow/pipelines/issues/5728 | 899,575,293 | MDU6SXNzdWU4OTk1NzUyOTM= | 5,728 | [feature] Merge Pipelines Profile Controller functionality into the general profile controller | {
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"Thank you for raising the concern @DavidSpek!\r\n\r\nLet me share some past context, at the time when KFP introduced KFP profile controller, we had many discussions with Kubeflow engineering team at Google (who was maintaining the main profile controller). The conclusion we reached was that it's better for everyon... | 2021-05-24T11:29:37 | 2022-03-03T06:05:15 | 2022-03-03T06:05:15 | CONTRIBUTOR | null | ### Feature Area
/area backend
The Kubeflow Pipelines profile controller now takes care of launching 2 deployments (UI Artifact and Visualization Server) in each user namespace when a profile is created. While busy implementing AWS RDS and S3 support for KFP in [ArgoFlow](https://github.com/argoflow/argoflow-aws/tree/master/distribution/kubeflow/pipelines) @karlschriek ended up using the KFP profile controller to create other resources (in this case an ExternalSecret) when a profile is created.
### What feature would you like to see?
The flexibility and extendable nature of the KFP profile controller should be part of the main [profile controller](https://github.com/kubeflow/kubeflow/tree/master/components/profile-controller).
### What is the use case or pain point?
Many Kubeflow admins would most likely want to automate creating arbitrary resources when a new Kubeflow profile is created. For example, registry secrets or secrets with credentials to object storage buckets.
### Is there a workaround currently?
The KFP profile controller can be extended and used to create arbitrary resources on profile creation. However, similar to https://github.com/kubeflow/pipelines/issues/5681, it would be best to consolidate this functionality into a single profile controller. Especially given that the use cases are not strictly tied to KFP.
/cc @kubeflow/wg-pipeline-leads @kubeflow/wg-notebooks-leads
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Hello.\r\nThank you for trying Vertex AI.\r\nDirectly creating instances of `ContainerOp` has been deprecated for ~11 months. See https://github.com/kubeflow/pipelines/pull/4166\r\nThere should be a deprecation warning every time you call `ContainerOp` manually.\r\n\r\nCould you please use components instead? The... | 2021-05-24T09:46:10 | 2021-09-09T10:49:36 | 2021-05-31T20:39:05 | CONTRIBUTOR | null | ### What steps did you take
If you run the following code, you will get an error
```python
import kfp.v2.dsl as dsl
from kfp.v2 import compiler
from kfp.v2.google.client import AIPlatformClient
pipeline_root_path = ....
@dsl.pipeline(
name="hello-world",
description="test",
pipeline_root=pipeline_root_path
)
def pipeline():
dsl.ContainerOp(
name="whalesay",
image="docker/whalesay:latest",
command=["cowsay"],
arguments=["hello world"])
client = AIPlatformClient(project_id=MyProject, region="asia-east1")
with tempfile.TemporaryDirectory() as tmpdir:
pipeline_package_path = os.path.join(tmpdir, 'pipeline.json')
compiler.Compiler().compile(pipeline_func=pipeline, package_path=pipeline_package_path)
client.create_run_from_job_spec(pipeline_package_path,)
```
### What happened:
The error message is as follows.
```
Traceback (most recent call last):
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "pipelines/test/__main__.py", line 43, in <module>
app()
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/typer/main.py", line 214, in __call__
return get_command(self)(*args, **kwargs)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/typer/main.py", line 497, in wrapper
return callback(**use_params) # type: ignore
File "pipelines/test/__main__.py", line 34, in create_run
parameter_values={},
File "pipelines/test/../client_wrapper.py", line 38, in create_run_from_pipeline_func
labels
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/kfp/v2/google/client/client.py", line 344, in create_run_from_job_spec
job_id=job_id,
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/kfp/v2/google/client/client.py", line 228, in _submit_job
response = request.execute()
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/googleapiclient/_helpers.py", line 130, in positional_wrapper
return wrapped(*args, **kwargs)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/googleapiclient/http.py", line 856, in execute
raise HttpError(resp, content, uri=self.uri)
googleapiclient.errors.HttpError: <HttpError 400 when requesting https://asia-east1-aiplatform.googleapis.com/v1beta1/projects/MyProject/locations/asia-east1/pipelineJobs?pipelineJobId=hello-world-20210524165548&alt=json returned "Exactly one of deployment_config and deployment_spec is expected.">
```
### What did you expect to happen:
Successful deploy of the pipeline
### Environment:
<!-- Please fill in those that seem relevant. -->
* KFP SDK version:
* 1.6.2
If I have a problem with vertex ai pipelines, is it okay to create an issue in this repository?
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"@bobgy please keep in mind that cpu and memory ressource requests must be set for compatibility with kubeflow profile quotas. I tried something but could not get it working yet https://github.com/kubeflow/pipelines/pull/5743",
"Hi @juliusvonkohout , the problem you mentioned is with KFP v1 caching.\r\n\r\nIn [v2... | 2021-05-24T07:51:00 | 2021-06-16T23:28:09 | 2021-06-16T23:28:09 | CONTRIBUTOR | null | Part of #5667 | {
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"Duplicate of https://github.com/kubeflow/pipelines/issues/5705"
] | 2021-05-24T07:00:23 | 2021-05-24T07:01:01 | 2021-05-24T07:01:01 | CONTRIBUTOR | null | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/area components/aws/sagemaker",
"This issue has been automatically marked as stale because it has not had recent activity. It will b... | 2021-05-24T05:00:44 | 2022-04-28T04:59:40 | 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?
<!-- Provide a description of this feature and the user experience. -->
We have get_pipeline_conf().xxxx which can apply some config to the entire pipeline. See example: https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/components/aws/sagemaker/tests/integration_tests/resources/definition/update_endpoint_pipeline.py#L65
https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html?highlight=pipelineconf#kfp.dsl.PipelineConf.add_op_transformer
Use the pipelineconf.add_op_transformer method to apply methods like use_gcp_secret etc
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Thank you @algs for flagging this bug report!\r\nThis is a real problem from the persistence agent design.\r\n\r\nMy rough idea to improve would be that, persistence agent should also periodically list all runs from KFP API that are still in running state, if any of the runs do not have a corresponding argo workfl... | 2021-05-23T03:33:21 | 2021-09-12T13:26:17 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. launch a pipeline run, and observe the status of nodes in the run as expected from UI.
2. use command `kubectl delete workflow xxx` to delete the workflow corresponding to the pipeline run, then the unfinished nodes in the pipeline run stay **running**, and never been updated.
3. the `delete workflow` may happen when we delete the scheduledworkflow, and then the workflow without the parent scheduledworkflow got GC
### Expected result
The running nodes in the pipeline run been marked as `terminated`
<!-- 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 👍. We prioritise the issues with the most 👍.
cc @Jeffwan | {
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"@juliusvonkohout can you clarify how visualization is broken?\r\n> Sadly visualizations are broken(26.04.2021) for k8sapi and emissary",
"> @juliusvonkohout can you clarify how visualization is broken?\n> > Sadly visualizations are broken(26.04.2021) for k8sapi and emissary\n\nThe pipeline finishes, but e.g. no ... | 2021-05-21T07:33:15 | 2022-09-05T12:18:16 | 2022-03-02T23:43:53 | MEMBER | null | #### Update - 2021.9.13
edited by @Bobgy
KFP 1.7.0 is released.
documentation is live: https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/
choosing an executor will be an option starting from KFP 1.7.
#### Update - 2021.8.6
edited by @Bobgy
Discussed with @kramachandran and decided to delay the default change for at least one KFP minor version, because argo emissary executor is still in Alpha state. There can be other corner cases that we haven't found, so we should give people some time to test it out.
Therefore, I'd like to propose the following:
* still default to docker executor in 1.7.0 release
* provide emissary executor as an option and document how to migrate
* collect feedback from people who starts to try emissary executor
TODOs:
* [x] https://github.com/kubeflow/website/pull/2857
* [x] Make emissary available in installations
* [x] #6294
* [x] Make env/platform-agnostic-emissary
* [x] #6252
* [x] Upgrade kfp-ci test infra to argo v3.1.1 https://github.com/kubeflow/testing/commit/03c625828ac9cc13283bd226371280b0f5704c09
Issues discovered when testing argo v3.1.0 with KFP:
* [x] https://github.com/kubeflow/pipelines/issues/5930
* [x] https://github.com/kubeflow/pipelines/issues/5937 (fixed in v3.1.1)
* [x] https://github.com/kubeflow/pipelines/issues/5944 (fixed in v3.1.1)
* [x] https://github.com/argoproj/argo-workflows/issues/6238
=== the following are the original proposal ===
### What feature would you like to see?
Update argo to 3.11 such that we can use the emissary executor as decided by @bobgy in https://github.com/kubeflow/pipelines/pull/4645#issuecomment-845691359
### What is the use case or pain point?
The Docker, Kubelet, PNS and K8sapi executors have severe limitations that will be solved by Argo 3.1 and the emissary executor.
Here is an overview https://argoproj.github.io/argo-workflows/workflow-executors/
In a nutshell docker breaks security completely and is incompatible with kubernetes 1.19
https://github.com/kubeflow/pipelines/issues/5714 Kubernetes is also moving to containerd anyway and a lot of users want proper non-docker support for a long time https://github.com/kubeflow/pipelines/issues/1654
Kubelet needs some configuration and has the same limitation as k8sapi and rootless PNS: "Output artifacts must be saved on volumes (e.g. emptyDir) and not the base image layer (e.g. /tmp)" so it breaks the leightweight python components if there is no volume mounted or the output directory is inside the base layer https://github.com/kubeflow/pipelines/pull/4645
One can use a PNS with root rights (PTRACE and CHROOT) to circumvent this, but this obviously breaks the security again.
So only the emissary executor https://argoproj.github.io/argo-workflows/workflow-executors/#emissary-emissary is feasible which does more ore less what i proposed in https://github.com/kubeflow/pipelines/pull/4645 and what was started by Argos @alexec in https://github.com/argoproj/argo-workflows/pull/4766
The Argo update has been done before in https://github.com/kubeflow/pipelines/pull/5266 and https://github.com/kubeflow/pipelines/pull/4693 by @xinbinhuang @Bobgy
### Is there a workaround currently?
```
# Emmissary executor, needs argo 3.1+
# Sadly visualizations are broken(26.04.2021) for k8sapi and emissary
kubectl -n kubeflow patch configmap/workflow-controller-configmap --type='json' -p='[{"op": "replace", "path": "/data/containerRuntimeExecutor", "value": "emissary"}]'
kubectl edit deployment/workflow-controller -n kubeflow
...
spec:
containers:
- env:
- name: "LEADER_ELECTION_DISABLE"
value: true
- args:
- --configmap
- workflow-controller-configmap
- --executor-image
- docker.io/argoproj/argoexec:latest
command:
- workflow-controller
image: docker.io/argoproj/workflow-controller:latest
env:
- name: "LEADER_ELECTION_DISABLE"
value: "true"
...
kubectl -n kubeflow patch configmap/workflow-controller-configmap --type='json' -p='[{"op": "replace", "path": "/data/containerRuntimeExecutor", "value": "emissary"}]'
kubectl rollout restart deployment workflow-controller -n kubeflow
or manually patching the sdk and using k8sapi as done in https://github.com/kubeflow/pipelines/pull/4645
Or use kfp-tekton
```
---
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"@Bobgy: The label(s) `area/deployment` cannot be applied, because the repository doesn't have them.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5717):\n\n>### What steps did you take\r\n>\r\n>\r\n>1. Install KFP from AI Platform Pipelines\r\n>2. check managed storage\r\n>3. f... | 2021-05-21T07:13:41 | 2021-08-11T11:55:06 | 2021-08-11T11:55:06 | CONTRIBUTOR | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
1. Install KFP from [AI Platform Pipelines](https://console.cloud.google.com/marketplace/product/google-cloud-ai-platform/kubeflow-pipelines)
2. check managed storage
3. fill in db connection name and gcs bucket name
4. leave the rest as empty
### What happened:
All the default values are filled by empty strings.
### What did you expect to happen:
I was expecting default values in values.yaml are set, e.g. https://github.com/kubeflow/pipelines/blob/a1ecb3dc0af905b6fd5710b10d635b7666745a4c/manifests/gcp_marketplace/chart/kubeflow-pipelines/values.yaml#L27.
However, I verified that cache configmap in fact has db name as `_cache` without the prefix.
### 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 -->
/area deployment
---
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"/assign",
"I have created an extended description of everything at https://github.com/kubeflow/pipelines/issues/5718 . What you describe here is just one single out of many reasons to upgrade argo and use the emissary executor.",
"This issue has been automatically marked as stale because it has not had recent ... | 2021-05-21T01:15:50 | 2022-03-02T21:47:20 | 2022-03-02T21:47:19 | COLLABORATOR | null | ## UPDATE 2021-8-16
you can use emissary executor instead starting from Kubeflow Pipelines 1.7, this is the only executor that works, if you don't have Docker runtime in your cluster (Docker runtime is not the default installation option since GKE 1.18).
Documentation: https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/
## issue reference
https://github.com/kubeflow/pipelines/issues/5663
### Error message:
1. If you deploy KFP on Google Cloud Marketplace, you will see the following message if you select `create a cluster`
```
Basic authentication was removed for GKE cluster versions >= 1.19.
The cluster cannot be created with basic authentication enabled.
Instructions for choosing an alternative authentication method can be found at: https://cloud.google.com/kubernetes-engine/docs/how-to/api-server-authentication.
```
2. If you deploy KFP on an existing K8s cluster with version v1.19 or above, you will see following error if you run any pipeline:
```
This step is in Error state with this message: failed to save outputs: Error response from daemon: No such container: XXXXXX
```
### Workaround
Kubeflow Pipelines docker executor doesn't support containerd nodes which come by default with Kubernetes 1.19 or above.
To deploy KFP, you need to create a cluster satisfying one of the following conditions:
* a K8s cluster with version 1.18 or earlier
* Explicitly using docker nodepools (Container-Optimized OS with Docker (cos)) https://cloud.google.com/kubernetes-engine/docs/concepts/node-images
Example command to create kubernetes cluster:
```bash
# The following parameters can be customized based on your needs.
CLUSTER_NAME="kubeflow-pipelines-standalone"
ZONE="us-central1-a"
MACHINE_TYPE="e2-standard-2"
SCOPES="cloud-platform" # This scope is needed for running some pipeline samples. Read the warning below for its security implication
gcloud container clusters create $CLUSTER_NAME \
--zone $ZONE \
--machine-type $MACHINE_TYPE \
--scopes $SCOPES \
--cluster-version 1.18
```
Warning: Using SCOPES="cloud-platform" grants all GCP permissions to the cluster. For a more secure cluster setup, refer to [Authenticating Pipelines to GCP](https://www.kubeflow.org/docs/gke/authentication/#authentication-from-kubeflow-pipelines).
Install KFP on Google Cloud:
If you are on Google Cloud, install KFP by following this documentation: https://cloud.google.com/ai-platform/pipelines/docs/setting-up#existing-cluster
Install KFP standalone:
If you are on other platform, follow the documentation here to install standalone KFP: https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deploying-kubeflow-pipelines
### Root cause
Kubeflow Pipelines docker executor doesn't support GKE 1.19 or above, because new GKE 1.19 clusters have containerd nodes by default.
Kubeflow Pipelines need to migrate to new argo version and use emissary executor. tracking issue: https://github.com/kubeflow/pipelines/issues/1654
---
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">The fix appears straightforward, to include the String type on the Data\r\n\r\nThis won't work. The v2 compiler imposes severe limitations on the `String` type. The pipeline will still not compile and also you'll lose the ability to upload any binary data like models.\r\n\r\nA fix for this v2 compiler issue was p... | 2021-05-20T18:02:34 | 2021-08-20T06:14:10 | 2021-07-26T22:34:03 | CONTRIBUTOR | null | ### Environment
kfp 1.6.2
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
### Steps to reproduce
Use the `upload_to_explicit_url` component in a pipeline (picking a version that includes the latest v2 updates):
```python
upload_op = load_component_from_url("https://raw.githubusercontent.com/kubeflow/pipelines/961b17fa6844e1d79e5d3686bb557d830d7b5a95/components/google-cloud/storage/upload_to_explicit_uri/component.yaml")
upload_task = upload_op(eval_task.output, results) # pylint: disable=not-callable
```
As the `Data` input is untyped, it is treated as an artifact, and compiling produces this error:
```
TypeError: Passing PipelineParam "run-predictions-Results" with type "String" (as "Parameter") to component input "Data" with type "None" (as "Artifact") is incompatible. Please fix the type of the component input.
```
The fix appears straightforward, to include the `String` type on the `Data` input as well as the `GCS path` input and output.
### Expected result
Pipeline compiles successfully.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
https://raw.githubusercontent.com/kubeflow/pipelines/961b17fa6844e1d79e5d3686bb557d830d7b5a95/components/google-cloud/storage/upload_to_explicit_uri/component.yaml
---
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https://api.github.com/repos/kubeflow/pipelines/issues/5710 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5710/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5710/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5710/events | https://github.com/kubeflow/pipelines/issues/5710 | 897,092,224 | MDU6SXNzdWU4OTcwOTIyMjQ= | 5,710 | [sdk] Regression in 1.6.2, it generates wrong indentation when compiling to YAML | {
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"It seems I can't reproduce it anymore, closing..."
] | 2021-05-20T16:35:08 | 2021-05-26T09:03:10 | 2021-05-26T09:03:10 | NONE | null | ### Environment
* KFP version: 1.6.2
* KFP SDK version: 1.6.2
* All dependencies version:
### Steps to reproduce
Upgrade to latest kfp (1.6.2)
Compile a pipeline (using dsl.component annotations in my case)
When executing pipeline, an "Indent" error will show up.
It works in 1.6.0. Fails in 1.6.2
### Expected result
It executes pipeline properly.
### Materials and Reference
---
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5709 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5709/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5709/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5709/events | https://github.com/kubeflow/pipelines/issues/5709 | 897,070,930 | MDU6SXNzdWU4OTcwNzA5MzA= | 5,709 | [sdk] Regression - func_to_container_op no longer handles backslash characters correctly | {
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"@Ark-kun ",
"This was expected to be fixed in https://github.com/kubeflow/pipelines/pull/4993 v1.4.0\r\nLooking...",
"This does not reproduce for me in v1.4.0+\r\n\r\n\r\nHere is the generated `compone... | 2021-05-20T16:21:28 | 2021-05-26T21:21:21 | 2021-05-26T21:21:21 | NONE | null | ### Environment
* KFP version: Works <1.2.0, broken in 1.2.0+
### Steps to reproduce
This is a super simplified version of what I've found:
```python
def break_ftco() -> str:
print("Printing an escaped quote \\\" will break")
return "something"
@pipeline(name="test-ftco", description="")
def pipeline():
irrelevant = func_to_container_op(
func=break_ftco,
base_image="gcr.io/my-image/name:tag
)()
```
### Expected result
Component should print:
```
Printing an escaped quote \" will break
```
This works in kfp<1.2.0, but not >=1.2.0, in >=1.2.0, the component fails and the following is logged:
```
File "/tmp/tmp.87n1N4N8zw", line 2
print("Printing an escaped quote \\" will break")
^
SyntaxError: invalid syntax
```
### Materials and Reference
Context:
When updating kfp SDK recently, one of our customers reported that one of their func_to_container_op functions stopped working properly. It ran, but was producing invalid results. In their case, they were doing a regex substitution on a string, where the regex substitution contained a backslash (`\`).
I compared pod definitions between the two versions and discovered that KFP changed the way they were executing python code for func_to_container_op, found the location in the code, and the relevant PR that introduced this regression: https://github.com/kubeflow/pipelines/pull/4861
More info on the original use case can be found there, but I narrowed it down to an example where `func_to_container_op` will fail to run for what is otherwise valid python code.
It may also be worth noting that since the code changed from being directly run with python to being passed through `sh` into a file, there may be other similar special shell characters to be escaped, but that's beyond my knowledge
---
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5708 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5708/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5708/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5708/events | https://github.com/kubeflow/pipelines/issues/5708 | 896,680,591 | MDU6SXNzdWU4OTY2ODA1OTE= | 5,708 | [feature] Provide a `kfp.components.load_component_from_dict` function | {
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"Would a `load_component_spec_from_component_spec(ComponentSpec)` function be desirable as a replacement?",
"> Would a `load_component_spec_from_component_spec(ComponentSpec)` function be desirable as a replacement?\r\n\r\nyes absolutely, as long as this way of building containers is guaranteed to be maintained i... | 2021-05-20T11:27:13 | 2021-10-28T17:27:58 | 2021-10-28T17:27:58 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
Provide a `load_component_from_dict` function that calls `ComponentSpec.from_dict` under the hood, which would allw the user to create operators in a fashion similar to the old `dsl.ContainerOp` without having to generate an intermediate yaml representation. The spec of this dictionary can be 1-to-1 with the yaml and this would be easy to maintain.
Today the main loader (`_load_component_spec_from_component_text`) looks like this:
```
component_dict = load_yaml(text)
component_spec = ComponentSpec.from_dict(component_dict)
```
Let me skip the yaml...
### What is the use case or pain point?
Forcing the use of yaml files on the SDK users is an arbitrary decision/choice of configuration vessel, and is fairly unnatural in an environment where users are already using a fully featured programming language like python
### Is there a workaround currently?
https://github.com/kubeflow/pipelines/issues/3748#issuecomment-627698554 however it seems to be using the internals of the SDK which means its not future proof or safe to use.
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5706 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5706/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5706/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5706/events | https://github.com/kubeflow/pipelines/issues/5706 | 896,454,302 | MDU6SXNzdWU4OTY0NTQzMDI= | 5,706 | [sdk] compile() got an unexpected keyword argument 'mode' | {
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"Also, if I change the package_path to 'prophet-test.yaml' the upper error disappears, but the compiler error remains.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"> This issue... | 2021-05-20T08:07:09 | 2022-03-03T04:05:33 | 2022-03-03T04:05:33 | NONE | null | ### Environment
I have a minikube cluster running on a linux server (Ubuntu 18.04) where kubeflow pipelines is running. I am connected via an ssh tunnel from my laptop running Windows 10. I am writing code in Python 3.8.5. I can connect with kf pipelines and list current experiments/runs and all that from my environment. I also previously used function-based components and was able to submit those to KFP for execution.
* KFP version: 1.5.0
* KFP SDK version: 1.6.2
### Steps to reproduce
I am new to kfp, so I tried to model my own pipeline after the example found here: https://www.kubeflow.org/docs/components/pipelines/sdk/v2/build-pipeline/
When I run:
```
compiler.Compiler().compile(
pipeline_func=prophet_pipeline,
package_path='prophet-test.yaml',
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE
)
client.create_run_from_pipeline_package('prophet-test.yaml', arguments=arguments)
```
I get the error:
```
Traceback (most recent call last):
File "kfp_prophet.py", line 138, in <module>
run_pipeline(args.path)
File "kfp_prophet.py", line 126, in run_pipeline
client.create_run_from_pipeline_package('prophet-test.json', arguments=arguments)
File "\lib\site-packages\kfp\_client.py", line 843, in create_run_from_pipeline_package
run_info = self.run_pipeline(
File "\Anaconda3\lib\site-packages\kfp\_client.py", line 603, in run_pipeline
job_config = self._create_job_config(
File "\Anaconda3\lib\site-packages\kfp\_client.py", line 706, in _create_job_config
pipeline_obj = self._extract_pipeline_yaml(pipeline_package_path)
File "\Anaconda3\lib\site-packages\kfp\_client.py", line 532, in _extract_pipeline_yaml
raise ValueError('The package_file '+ package_file + ' should end with one of the following formats: [.tar.gz, .tgz, .zip, .yaml, .yml]')
ValueError: The package_file prophet-test.json should end with one of the following formats: [.tar.gz, .tgz, .zip, .yaml, .yml]
Prophet> python kfp_prophet.py --f data.pkl
Traceback (most recent call last):
File "kfp_prophet.py", line 131, in <module>
compiler.Compiler().compile(
TypeError: compile() got an unexpected keyword argument 'mode'
```
### Expected result
The pipeline should be compiled and submitted to KFP for execution.
### Materials and Reference
I followed the example provided here https://www.kubeflow.org/docs/components/pipelines/sdk/v2/build-pipeline/ and modified it to execute my own code.
Thanks for your help! I can provide more code snippets if necessary to debug the issue. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5705 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5705/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5705/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5705/events | https://github.com/kubeflow/pipelines/issues/5705 | 896,301,948 | MDU6SXNzdWU4OTYzMDE5NDg= | 5,705 | [pH] move kubeflow/frontend into KFP repo | {
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"/assign @zijianjoy "
] | 2021-05-20T05:35:04 | 2021-07-03T11:16:49 | 2021-07-03T11:16:49 | CONTRIBUTOR | null | https://github.com/kubeflow/frontend
imports: https://github.com/kubeflow/pipelines/search?q=kubeflow%2Ffrontend | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5704 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5704/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5704/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5704/events | https://github.com/kubeflow/pipelines/issues/5704 | 896,249,280 | MDU6SXNzdWU4OTYyNDkyODA= | 5,704 | [v2compat] configurable default pipeline root | {
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"/assign @capri-xiyue ",
"Discussed with @capri-xiyue, we agree on:\r\n\r\n* P0 configurable default pipeline root #5750 \r\n* P1 support s3 pipeline_root with generic options: endpoint, region, insecure, etc, so that it can be used with user maintained MinIO or other compatible storages. Credentials can be set s... | 2021-05-20T04:10:05 | 2021-06-02T15:52:02 | 2021-06-02T15:52:02 | CONTRIBUTOR | null | Quoted from https://github.com/kubeflow/pipelines/issues/4649#issuecomment-844035914
Blocks https://github.com/kubeflow/pipelines/issues/5680
> FYI, KFP v2 compatible mode has been released, see documentation: https://www.kubeflow.org/docs/components/pipelines/sdk/v2/.
>
> It doesn't support artifact repository configuration, this is one of the things we want to support too. So I'm posting early thoughts on this related issue.
>
> Let me first try to summarize requirements for configuring artifact repositories for both KFP v2 compatible and v2.
>
> ## Object store specific credentials requirements
> For GCS, AWS S3, we suggest setting up credentials, so that they represent identity of the pipeline step, so that not only artifact upload/download, calls to other Cloud services should also use the same credentials. For this reason, we don't recommend setting credentials in artifact repository config. The suggestion is to configure the identity transparently if possible using GCP workload identity or AWS IRSA. If credentials are really necessary, they can be configured using pipeline DSL via [kfp.gcp.use_gcp_secret](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.extensions.html#kfp.gcp.use_gcp_secret) or [kfp.aws.use_aws_secret](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.extensions.html#kfp.aws.use_aws_secret) etc. These principles should apply to other Cloud Providers that has credentials that can be used with all its services.
>
> For on-prem object store like MinIO, the credentials do not represent an identity, they are only used to access a specified object store instance. Therefore, it's reasonable to include them in artifact repository config.
>
> ## In summary
> * For GCS, only pipeline root needs to be configurable.
> * For AWS S3, besides pipeline root, we also need region, endpoint etc to be configurable.
> * For MinIO or similar on-prem object stores, besides pipeline root, we also need endpoint, credentials to be configurable.
>
> We cannot implement a spec for every possible object stores, so likely we should use the same spec as what [Go CDK](https://gocloud.dev/) supports or depend on cloud provider contributions.
>
> Go CDK supports provider specific query params to configure some things other than object key, so we might consider adopting these query params, so that pipeline root can be more expressive, so we might not need other configurations.
> e.g. for S3, it's possible to configure region via a query param: https://gocloud.dev/howto/blob/#s3
>
> ```
> s3://my-bucket?region=us-west-1
> ```
>
> ## How we configure pipeline root, other configs and credentials?
> Ideally, pipeline root can include all other configs, so that we can uniquely identify an artifact.
> Ideally, credentials should be configurable transparently.
>
> When both ideal requirements are met, we only need to support namespace level default pipeline root. All other configurations can be done by specifying different pipeline roots.
>
> However, now MinIO violates the requirement that credentials can be configured transparently. Therefore, we need a mechanism to either
>
> * configure which credentials should be used with which pipeline root (probably, write rules like which pipeline_root prefix/query param should use which credentials)
> * or configure credentials with pipeline root together as artifact repository (but then we should specify artifact repos, not pipeline roots)
> * or ask users to configure credentials separately from pipeline_root
>
> We probably need more thoughts on the exact config format, this seems like a complex problem.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/5703 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5703/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5703/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5703/events | https://github.com/kubeflow/pipelines/issues/5703 | 896,050,194 | MDU6SXNzdWU4OTYwNTAxOTQ= | 5,703 | [feature] Consistent timezones | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-19T23:41:30 | 2022-03-03T06:05:16 | 2022-03-03T06:05:16 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Consistent timezone experience in KFP.
Config: default to local time zone, configurable to any timezone.
UX: all the code that deals with time should act in this timezone:
- API server - time related pipeline placeholders
- Cron expression timezone
- are there more?
Challenge:
How to handle upgrade? This is a breaking change.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
It's easier for people to work with the local timezone.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Use a different cron scheduler than KFP recurring runs.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/5700 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5700/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5700/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5700/events | https://github.com/kubeflow/pipelines/issues/5700 | 895,856,799 | MDU6SXNzdWU4OTU4NTY3OTk= | 5,700 | [bug] kserving-component still uses minio however boto3 is officially added in kf-serving to use IRSA | {
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"@ahsan-n, I think the problem is solved in next release `0.6` of kfserving.\r\n\r\nBut the docker image `quay.io/aipipeline/kfserving-component:v0.6.0` is not yet available. Think this still needs some attention to confirm it's been resolved.",
"Hi @rawc0der just wondering what is the release timeline for `quay... | 2021-05-19T20:29:58 | 2022-03-03T04:05:23 | 2022-03-03T04:05:23 | NONE | null | ### What steps did you take
Hi, so i am creating pipeline to host / serve model that resides in s3. For that I used [example for sample pipeline](https://github.com/kubeflow/kfserving/blob/master/docs/samples/pipelines/sample-tf-pipeline.py) which has [kfserving-component ](https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml). I replaced gc path with my s3 bucket.
### What happened:
I get the following error in storage
```
[I 210519 20:13:05 initializer-entrypoint:13] Initializing, args: src_uri [s3://my-bucket/saved_model] dest_path[ [/mnt/models]
[I 210519 20:13:05 storage:45] Copying contents of s3://my-bucket/saved_model to local
Traceback (most recent call last):
File "/storage-initializer/scripts/initializer-entrypoint", line 14, in <module>
kfserving.Storage.download(src_uri, dest_path)
File "/usr/local/lib/python3.7/site-packages/kfserving/storage.py", line 62, in download
Storage._download_s3(uri, out_dir)
File "/usr/local/lib/python3.7/site-packages/kfserving/storage.py", line 89, in _download_s3
for obj in objects:
File "/usr/local/lib/python3.7/site-packages/minio/api.py", line 2297, in _list_objects
query=query,
File "/usr/local/lib/python3.7/site-packages/minio/api.py", line 2229, in _url_open
object_name).get_exception()
minio.error.AccessDenied: AccessDenied: message: Access Denied
```
### What did you expect to happen:
When looking over github i found out that because minio does not support iam role for service accounts, [boto3 has replaced minio](https://github.com/kubeflow/kfserving/pull/1472). Now I am wondering how can i use that kfserving-component but with boto3 :)
### Environment:
kubeflow v1.3
* How do you deploy Kubeflow Pipelines (KFP)?
use kfp.Compile to extract out yaml and uploaded in pipelines UI
* KFP SDK version:
v1.6.2
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
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---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5694 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5694/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5694/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5694/events | https://github.com/kubeflow/pipelines/issues/5694 | 895,067,746 | MDU6SXNzdWU4OTUwNjc3NDY= | 5,694 | [bug] Broken pipe when using AI platform training operator | {
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"I see this was solved by a previous CR: https://github.com/kubeflow/pipelines/issues/5085"
] | 2021-05-19T07:02:39 | 2021-05-19T08:58:26 | 2021-05-19T08:58:26 | NONE | null | ### What steps did you take
Start a long-running job on AI platform and have it succeed, to be able to progress to subsequent steps.
I have a job defined using this operator:
```
mlengine_train_op = kfp.components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/gcp/ml_engine/train/component.yaml')
```
and I start it with the following parameters set (note that I'm running the job from docker image):
```
mlengine_train_op(
name="",
project_id=project_id,
region=region,
job_dir=job_dir,
master_image_uri=master_image_uri,
training_input=training_input,
job_id=job_id,
wait_interval=wait_interval
```
### What happened:
After some time the job fails with the following error - this is roughly an hour.
```
INFO:root:job status is RUNNING, wait for 30s
INFO:googleapiclient.discovery:URL being requested: GET
https://ml.googleapis.com/v1/projects/xxx/jobs/xxxyyy_20210518_17000?alt=json
Traceback (most recent call last):
File "/ml/kfp_component/google/ml_engine/_client.py", line 40, in wrapper
return func(self, *args, **kwargs)
File "/ml/kfp_component/google/ml_engine/_client.py", line 113, in get_job
name=job_name).execute()
File "/usr/local/lib/python3.7/site-packages/googleapiclient/_helpers.py", line 130, in positional_wrapper
return wrapped(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 846, in execute
method=str(self.method), body=self.body, headers=self.headers)
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 183, in _retry_request
raise exception
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 164, in _retry_request
resp, content = http.request(uri, method, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/google_auth_httplib2.py", line 209, in request
self.credentials.before_request(self._request, method, uri, request_headers)
File "/usr/local/lib/python3.7/site-packages/google/auth/credentials.py", line 133, in before_request
self.refresh(request)
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/credentials.py", line 111, in refresh
self._retrieve_info(request)
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/credentials.py", line 88, in _retrieve_info
request, service_account=self._service_account_email
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/_metadata.py", line 234, in get_service_account_info
return get(request, path, params={"recursive": "true"})
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/_metadata.py", line 150, in get
response = request(url=url, method="GET", headers=_METADATA_HEADERS)
File "/usr/local/lib/python3.7/site-packages/google_auth_httplib2.py", line 120, in __call__
url, method=method, body=body, headers=headers, **kwargs
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 1724, in new_request
redirections, connection_type)
File "/usr/local/lib/python3.7/site-packages/httplib2/__init__.py", line 1991, in request
cachekey,
File "/usr/local/lib/python3.7/site-packages/httplib2/__init__.py", line 1651, in _request
conn, request_uri, method, body, headers
File "/usr/local/lib/python3.7/site-packages/httplib2/__init__.py", line 1558, in _conn_request
conn.request(method, request_uri, body, headers)
File "/usr/local/lib/python3.7/http/client.py", line 1277, in request
self._send_request(method, url, body, headers, encode_chunked)
File "/usr/local/lib/python3.7/http/client.py", line 1323, in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "/usr/local/lib/python3.7/http/client.py", line 1272, in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "/usr/local/lib/python3.7/http/client.py", line 1032, in _send_output
self.send(msg)
File "/usr/local/lib/python3.7/http/client.py", line 993, in send
self.sock.sendall(data)
BrokenPipeError: [Errno 32] Broken pipe
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/ml/kfp_component/launcher/__main__.py", line 45, in <module>
main()
File "/ml/kfp_component/launcher/__main__.py", line 42, in main
launch(args.file_or_module, args.args)
File "/ml/kfp_component/launcher/launcher.py", line 45, in launch
return fire.Fire(module, command=args, name=module.__name__)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 127, in Fire
component_trace = _Fire(component, args, context, name)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 366, in _Fire
component, remaining_args)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 542, in _CallCallable
result = fn(*varargs, **kwargs)
File "/ml/kfp_component/google/ml_engine/_train.py", line 105, in train
job_dir_output_path=job_dir_output_path)
File "/ml/kfp_component/google/ml_engine/_create_job.py", line 62, in create_job
job_dir_output_path=job_dir_output_path,
File "/ml/kfp_component/google/ml_engine/_create_job.py", line 89, in execute_and_wait
job_dir_output_path=self._job_dir_output_path,
File "/ml/kfp_component/google/ml_engine/_common_ops.py", line 93, in wait_for_job_done
job = ml_client.get_job(project_id, job_id)
File "/ml/kfp_component/google/ml_engine/_client.py", line 48, in wrapper
e._class__.__name__, _delay
AttributeError: 'BrokenPipeError' object has no attribute '_class__
```
### What did you expect to happen:
Correct job tracking behaviour. The job itself is still running in AI platform, and finishes successfully after some more time - this particular job takes around 2h30min to complete.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
Deployed to GCP
* KFP version:
1.4.1
* KFP SDK version:
1.4.0
### Anything else you would like to add:
### Labels
/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 👍. We prioritise the issues with the most 👍.
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"The pending changes can be released in 1.6.1 either, so let's release 1.6.0 first.",
"DONE: https://github.com/kubeflow/pipelines/releases/tag/1.6.0"
] | 2021-05-19T06:22:16 | 2021-05-24T07:26:15 | 2021-05-24T06:44:26 | CONTRIBUTOR | null | Changes we need:
* add permission for token request (prepare for auth token change in SDK)
* running v2 compatible mode in Kubeflow 1.3.1 https://github.com/kubeflow/pipelines/issues/5680
* https://github.com/kubeflow/pipelines/issues/5717
Only the first change seems reasonable in a short time period, so I plan to implement it. The other changes can probably wait for 1.7.0 or 1.6.x | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-19T06:20:23 | 2022-03-03T06:05:12 | 2022-03-03T06:05:12 | CONTRIBUTOR | null | This helps:
* validation of v2 compatible mode
* inspire new ideas to improve the UX | {
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we also need to link to this section in kubeflow.org documentation https://www.kubeflow.org/docs/components/pipelines/sdk/v2/v2-compatibility/ | {
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