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https://api.github.com/repos/kubeflow/pipelines/issues/6750 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6750/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6750/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6750/events | https://github.com/kubeflow/pipelines/issues/6750 | 1,028,353,599 | I_kwDOB-71UM49S24_ | 6,750 | [bug] Pipeline UI couldn't load mlpipline-ui-metadata (v1) with Minio gateway in Multi-user install on GCP | {
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"If you are using V2 compatible mode, can you trying using the new approach to visualize your metrics instead of using `mlpipeline-ui-metadata`? https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#v2-sdk-use-sdk-visualization-apis",
"1. No , I don't use __V2 compatible mode__ ; it still misses s... | 2021-10-17T15:12:42 | 2022-03-11T06:21:22 | 2022-03-11T06:21:22 | NONE | null | ### What steps did you take
I ran a simple 1 step pipeline, which aimed to display a webpage in visualization UI (the Output).
### What happened:
In a KF cluster with MinIO gateway (GCS is the actual storage), Multi-users install (on GCP), the Visualization UI couldn't display the webpage.
[Screenshot](https://imgur.com/5rC0YDg)
### What did you expect to happen:
In a KF cluster with in-cluster MinIO setup (a PD is the actual storage), Multi-users install (on GCP), the Visualization UI could display the webpage
[Screenshot](https://imgur.com/8IZn43o)
### Environment:
Platform: GCP, IAP-ingress enabled, CloudSQL, MinIO gateway (with a GCS bucket)
* How do you deploy Kubeflow Pipelines (KFP)?
A custom from kubeflow/gcp-blueprints, because of my company policies, CloudSQL/MySQL is connected via a private connection (VPC) instead of using cloudsql proxy. I also can't use Cluster management.
* KFP version: 1.7.0
* KFP SDK version:
kfp 1.8.4
kfp-pipeline-spec 0.1.12
kfp-server-api 1.7.0
### Anything else you would like to add:
The Visualization UI looks like loading an artifact with namespace
`https://{host}/pipeline/artifacts/minio/om-ml-pipeline-np/artifacts/viz-sample-rtdtl/2021/10/17/viz-sample-rtdtl-2393626054/mlpipeline-ui-metadata.tgz?namespace=om`
Clicking to the link I should get:
`Failed to get object in bucket om-ml-pipeline-np at path artifacts/viz-sample-rtdtl/2021/10/17/viz-sample-rtdtl-2393626054/mlpipeline-ui-metadata.tgz: S3Error: The request signature we calculated does not match the signature you provided. Check your key and signing method.`
Remove the `?namespace=om` in the link then I can download the artifact.
I've already follow https://github.com/kubeflow/pipelines/issues/4649 , but v2 is still in Beta and lacks some features I need in v1, so I have to stick with v1.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area frontend
/area backend
/area sdk
---
<!-- 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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"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"
] | 2021-10-15T11:39:26 | 2022-03-02T15:05:26 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
I would like to see the feature described in https://github.com/kubeflow/pipelines/issues/2372
let me copy/paste my comments from there.
### What is the use case or pain point?
I would be happy to have the same functionality. I've even tested that already by manual change of workflow.yaml:
```
dag:
tasks:
- name: catch-fail
template: catch-fail
continueOn:
failed: true
```
so I might try to propose the implementation looking into how dependencies are handled in the kfp code.
Scenario: we have a pipeline containing a few steps, but some are "optional" which means if they fail it's acceptable for us.
Sure that the best would be then to have pipeline finished with the status "Warning" instead of "Success" but still such an functionality is fine.
### Is there a workaround currently?
We've implemented an workaround by using dsl.Condition but it's ugly and makes the stuff less readable.
---
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/6745 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6745/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6745/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6745/events | https://github.com/kubeflow/pipelines/issues/6745 | 1,026,662,957 | I_kwDOB-71UM49MaIt | 6,745 | [bug] issues importing google_cloud_pipeline_components.experimental.hyperparameter_tuning_job component | {
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"I am facing the same issue. It seems that is missing _init_.py in experimental/hyperparameter_tuning_job with something like that:\r\n```\r\nfrom .component import serialize_parameters\r\nfrom .component import HyperparameterTuningJobRunOp\r\n```\r\n\r\nin the file \"hyperparameter_tuning_job/component.py\"\r\n\"... | 2021-10-14T17:48:53 | 2021-12-09T08:25:34 | 2021-12-09T08:25:34 | CONTRIBUTOR | null | with this component (using the most recent pypi release of `google_cloud_pipeline_components`):
https://github.com/kubeflow/pipelines/tree/master/components/google-cloud/google_cloud_pipeline_components/experimental/hyperparameter_tuning_job
while this imports without probs:
`from google_cloud_pipeline_components.experimental import hyperparameter_tuning_job` ,
I see this error:
```
hyperparameter_tuning_job.serialize_parameters({})
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-736c1bfffbf7> in <module>
----> 1 hyperparameter_tuning_job.serialize_parameters({})
AttributeError: module 'google_cloud_pipeline_components.experimental.hyperparameter_tuning_job' has no attribute 'serialize_parameters'
```
...
Similarly, I get this error trying to use the component method:
```
/tmp/ipykernel_9718/3749818753.py in hpt_pipeline()
1 @dsl.pipeline(pipeline_root='', name='htp-pipeline')
2 def hpt_pipeline():
----> 3 hp_tuning_task = hyperparameter_tuning_job.HyparameterTuningJobRunOp(
4 display_name='hp-job',
5 project='aju-vtests2',
AttributeError: module 'google_cloud_pipeline_components.experimental.hyperparameter_tuning_job' has no attribute 'HyparameterTuningJobRunOp'
```
(Note: the 'misspelling' of 'hyperparameter' in the method name is what is used in the code, so the above is correct... though trying with the 'correct' spelling unsurprisingly didn't work any better. :)
/cc @SinaChavoshi | {
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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"
] | 2021-10-14T09:42:57 | 2022-03-02T15:05:36 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area components -->
### What feature would you like to see?
using function based python components I would like to be able to define the artefact registry that is used to install packages. Another consideration (unsure how) Is to have an authentication mechanism for the given artefact repos.
As an example:
```
@component(
artefact_index=["us-central1-python.pkg.dev/..."] // allow for multiple indexes as pip can support multiples
packages_to_install=['argparse','pandas','xgboost','numpy','sklearn','fsspec','gcsfs'])
def train( ):
...
...
...
```
### What is the use case or pain point?
I cannot define the arefact repo I want to use to install my packages
### Is there a workaround currently?
Use pre-baked container images that point to the artefact registry of choice
---
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/6738 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6738/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6738/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6738/events | https://github.com/kubeflow/pipelines/issues/6738 | 1,025,907,149 | I_kwDOB-71UM49JhnN | 6,738 | [feature] Provides option to install python packages in component image | {
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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"
] | 2021-10-14T03:32:36 | 2022-03-02T15:05:34 | null | MEMBER | null | ### Feature Area
/area sdk
### What feature would you like to see?
Provides option to install python packages in the target image of component.
For example:
```
@dsl.component(target_image="a_component:0.0.1",
packages_to_install=["kfp"],
install_packages_in_image=True)
def a_component():
pass
```
### What is the use case or pain point?
In some use cases, kubeflow pipeline is deployed in intranet cluster without access to internet, especially in financial companies, so it is not possible to install python package during runtime.
### Is there a workaround currently?
Manually build the base image with required python packages before defining the component.
---
<!-- 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/6736 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6736/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6736/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6736/events | https://github.com/kubeflow/pipelines/issues/6736 | 1,025,809,771 | I_kwDOB-71UM49JJ1r | 6,736 | [deployment] [FAIL] "gcr.io/kpt-fn/apply-setters:v0.1" | {
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"Close this issue.\r\n\r\nIt turns out I did not put env into system properly"
] | 2021-10-14T00:28:15 | 2021-10-14T00:37:11 | 2021-10-14T00:37:11 | NONE | null | Hi Team,
Thanks for checking this thread despite there are already so many opening issues.
I have search internet and I haven't found duplicated issues.
I was following the instruction [here](https://www.kubeflow.org/docs/distributions/gke/deploy/management-setup/) to deploy a management cluster.
I followed the instruction and have got the dependecies installed:
```
$ sudo apt-get install kubectl google-cloud-sdk-kpt google-cloud-sdk google-cloud-sdk
[sudo] password for ruodingt:
Reading package lists... Done
Building dependency tree
Reading state information... Done
google-cloud-sdk is already the newest version (360.0.0-0).
The following NEW packages will be installed:
google-cloud-sdk-kpt kubectl
0 to upgrade, 2 to newly install, 0 to remove and 51 not to upgrade.
Need to get 17.5 MB of archives.
After this operation, 91.1 MB of additional disk space will be used.
Do you want to continue? [Y/n] Y
Get:1 https://packages.cloud.google.com/apt cloud-sdk/main amd64 google-cloud-sdk-kpt amd64 360.0.0-0 [8,442 kB]
Get:2 https://packages.cloud.google.com/apt cloud-sdk/main amd64 kubectl amd64 1.22.2-00 [9,038 kB]
Fetched 17.5 MB in 8s (2,131 kB/s)
debconf: unable to initialise frontend: Dialog
debconf: (Dialogue frontend requires a screen at least 13 lines tall and 31 columns wide.)
debconf: falling back to frontend: Readline
Selecting previously unselected package google-cloud-sdk-kpt.
(Reading database ... 265766 files and directories currently installed.)
Preparing to unpack .../google-cloud-sdk-kpt_360.0.0-0_amd64.deb ...
Unpacking google-cloud-sdk-kpt (360.0.0-0) ...
Selecting previously unselected package kubectl.
Preparing to unpack .../kubectl_1.22.2-00_amd64.deb ...
Unpacking kubectl (1.22.2-00) ...
Setting up google-cloud-sdk-kpt (360.0.0-0) ...
Setting up kubectl (1.22.2-00) ...
```
I have configured the`env.sh` as instructed:
```
export MGMT_PROJECT=example-249918
export MGMT_DIR=~/gcp-blueprints/management/
export MGMT_NAME=kfp-management
export LOCATION=australia-southeast1-b
```
Then When I run `bash kpt-set.sh`, I got following
```
$ bash kpt-set.sh
[RUNNING] "gcr.io/kpt-fn/apply-setters:v0.1"
[PASS] "gcr.io/kpt-fn/apply-setters:v0.1"
Results:
[INFO] set field value to "" in file "kpt-setter-config.yaml" in field "data.name"
[INFO] set field value to "" in file "kpt-setter-config.yaml" in field "data.gcloud.core.project"
[INFO] set field value to "" in file "kpt-setter-config.yaml" in field "data.location"
[RUNNING] "gcr.io/kpt-fn/apply-setters:v0.1"
[FAIL] "gcr.io/kpt-fn/apply-setters:v0.1"
error: output resource list must contain only KRM resources: cluster/cluster.yaml: resource must have `metadata.name`
```
Could you please give me some hint where should I move next?
Kind Regards,
Ruoding | {
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"Would you like to check `proxy-agent` Deployment and its log? From the error in the description, there is still connecting from UI to proxy agent.\r\n\r\nCan you also check all your Deployments in the GKE cluster - workload and make sure all Pods are available?",
"`proxy-agent` is okay. However, there are a few ... | 2021-10-13T21:52:41 | 2023-04-07T18:17:27 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
- Created a custom Kubernetes cluster
- Created the pipeline through [AI Platform Pipelines](https://console.cloud.google.com/ai-platform/pipelines/clusters)
* KFP version: 1.7.0
### Steps to reproduce
- This error is received when opening the UI and/or loading a pipeline from the CLI
```
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=
```
### Expected result
The result should be a list of the available pipelines.
<img width="1217" alt="kubeflow-issue-gui" src="https://user-images.githubusercontent.com/13377661/137217476-a2d17cbd-ea45-46c5-842a-15ab75a40272.png">
<img width="1023" alt="kubeflow-issue-cli" src="https://user-images.githubusercontent.com/13377661/137217671-71d4e4e6-4ecf-480e-b187-5829549f32da.png">
### 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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"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"
] | 2021-10-13T18:27:27 | 2022-03-11T06:26:01 | null | COLLABORATOR | null | # Argo
In KFPv1, we are able to get `StartedAt` and `FinishedAt` from each `node` in argoworkflow.
# MLMD
In KFPv2, we don't have such properties filled with this information in MLMD execution object. | {
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"node_id": "MDU6TGFiZWwyMTU3NjM0MjA0"... | open | false | null | [] | null | [
"Hello @feizerl , I am assuming you are using V2 caching mechansim: https://www.kubeflow.org/docs/components/pipelines/caching-v2/.\r\n\r\nNote that V2 caching is different from V1 caching. V1 Caching has a Cache server, but V2 doesn't expose such endpoint. \r\n\r\nAre you using V1 pipeline, or V2-compatible pipeli... | 2021-10-13T14:38:30 | 2022-03-02T15:05:33 | null | CONTRIBUTOR | null | Hello,
In the current setup, whenever a cache server is not able to find a corresponding key, it generate the following lines in the logs:
```
2021/10/13 14:36:49 the server could not find the requested resource
2021/10/13 14:36:49 ...
2021/10/13 14:36:49 Input cache: ac8e44042f652b336247b7355e388d68246491b6e9afe60e2102e5637c255728
2021/10/13 14:36:49 New cache key: ac8e44042f652b336247b7355e388d68246491b6e9afe60e2102e5637c255728
```
Is it possible that give a cache key (in this case `ac8e44042f652b336247b7355e388d68246491b6e9afe60e2102e5637c255728`) a user can query the cache server whether it is already present or not?
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https://api.github.com/repos/kubeflow/pipelines/issues/6727 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6727/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6727/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6727/events | https://github.com/kubeflow/pipelines/issues/6727 | 1,025,313,185 | I_kwDOB-71UM49HQmh | 6,727 | Ability to generate cache key locally by end user | {
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"Also, it would be great if I can generate this in python directly. ",
"Hello @feizerl ,\r\n\r\nWhat is the usecase for cache key before starting a run? How do you use this key if you get it?\r\n\r\n@capri-xiyue ",
"Hi @zijianjoy \r\n\r\nWe have a complicated pipeline involving more than 20 steps, and I am curr... | 2021-10-13T14:19:08 | 2022-03-02T15:05:30 | null | CONTRIBUTOR | null | Hello,
One of the features of pipelines is Step Caching (https://www.kubeflow.org/docs/components/pipelines/caching/) to avoid running the costly computations again and again.
The key for caching is:
```
message CacheKey {
map<string, ArtifactNameList> inputArtifactNames = 1;
map<string, Value> inputParameters = 2;
map<string, RuntimeArtifact> outputArtifactsSpec = 3;
map<string, string> outputParametersSpec=4;
ContainerSpec containerSpec=5;
}
```
When looking at logs of cache server, we see the following log lines:
```
2021/10/13 13:50:58 the server could not find the requested resource
2021/10/13 13:50:58 Input cache: c96896be84ad7d36b126ff29e4f0a5e370424f5f7c6ebf682bcb56df4b12d8f7
2021/10/13 13:50:58 New cache key: c96896be84ad7d36b126ff29e4f0a5e370424f5f7c6ebf682bcb56df4b12d8f7
2021/10/13 13:50:58 Cache entry created with cache key: c96896be84ad7d36b126ff29e4f0a5e370424f5f7c6ebf682bcb56df4b12d8f7
```
Is there a way, a user can generate the cache key locally on their end (in this case `c96896be84ad7d36b126ff29e4f0a5e370424f5f7c6ebf682bcb56df4b12d8f7`) before starting the pipeline run?
I would like to get an idea of what steps are going to be cached and what are going to be run again BEFORE starting the pipeline.
Note that I can print the entire compiler output with:
```
with open("graph.yaml", "w") as f:
Compiler().compile(
pipeline_function, f.name, pipeline_conf=pipeline_conf
)
```
Thanks! | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6726 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6726/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6726/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6726/events | https://github.com/kubeflow/pipelines/issues/6726 | 1,025,166,048 | I_kwDOB-71UM49Gsrg | 6,726 | client.list_experiments() not working as well as creating runs, authentication failing | {
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"Hello @rexad , what is the platform you use to deploy Kubeflow? The Kubeflow Pipelines endpoint require an identity to be provided in request header when being called.",
"@zijianjoy thanks for the reply, \r\nmakes sense but im calling from the notebook the same cluster, how can i pass this identity? ",
"should... | 2021-10-13T12:08:35 | 2023-07-28T11:01:18 | 2021-10-27T17:35:14 | NONE | null | ### What steps did you take
We deployed kubeflow on premise using kustomize file https://github.com/kubeflow/manifests/tree/v1.3.1, v 1.3.1
Created a notebook, tried to create a run from it using kfp
<!-- A clear and concise description of what the bug is.-->
I can't connect from the notebook to pipelines (to create runs and see experiments )
`import kfp`
`kfp_endpoint=None`
`client.list_experiments()`
but i can enumerate pipelines
`import kfp`
`kfp_endpoint=None`
`client = kfp.Client(host=kfp_endpoint) `
`print(client.list_pipelines())`
### What happened:
i'am getting
`Reason: Internal Server Error
HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'trailer': 'Grpc-Trailer-Content-Type', 'date': 'Wed, 13 Oct 2021 11:48:57 GMT', 'x-envoy-upstream-service-time': '2', 'server': 'envoy', 'transfer-encoding': 'chunked'})
HTTP response body: {"error":"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\nFailed to authorize with API resource references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines .....`
I tried multiple things, some poeple faced the same issue, adding the namespace, creting a manifest as described in
https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#connect-to-kubeflow-pipelines-from-the-same-cluster
Nothing worked for me .
I would really appreciate any help
### What did you expect to happen:
See the expierments, create runs from my pipelines
### 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/. -->
Deployed using Kustomize https://github.com/kubeflow/manifests/tree/v1.3.1
* 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:
if i pass the YRL of the pipeline UI in kfp client, there is no error but nothing is returned
`{'experiments': None, 'next_page_token': None, 'total_size': None}`
### 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/6718 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6718/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6718/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6718/events | https://github.com/kubeflow/pipelines/issues/6718 | 1,023,533,504 | I_kwDOB-71UM49AeHA | 6,718 | [bug] ParallelFor overwrite output artifacts at each iteration | {
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"cc @chensun ",
"@pretidav Could you share the code to reproduce?\r\n",
"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",
"We have the same problem.\r\n\r\nDo you have any ides how t... | 2021-10-12T08:39:50 | 2022-05-13T16:44:28 | null | NONE | null | ### What steps did you take
from kfp.v2 import dsl
from kfp import dsl as v1dsl
Using v2 compatible pipeline, using a combination of
with v1dsl.SubGraph(parallelism=1):
with dsl.ParallelFor(generate_task.output) as item:
where generate_task is a component with a dictionary as output (whose length is decided dynamically)
### What happened:
Output artifact produced inside the loop are written using the same path generated at compilation time.
### What did you expect to happen:
Artifacts from a given iteration should be saved and not overwritten by the subsequent iteration.
### Environment:
Kubeflow Pipeline 1.6
### Labels
/area frontend
/area backend
/area sdk
/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://github.com/google/ml-metadata/issues/20",
"Tried to pass `metadata_store_server_config_file` command line flag to `/bin/metadata_store_server` in text protobuf format, but observed a similar error: \r\n```\r\n - args:\r\n - --grpc_port=8080\r\n - --enable_database_upgrade=true\r\n ... | 2021-10-10T21:59:42 | 2023-07-27T06:17:15 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
1.7.0-alpha.1
* KFP SDK version:
1.6.2
### Steps to reproduce
1. Enable SSL on external MySQL configuration (Azure MySQL Server)
2. Restart metadata-grpc-deployment pod
3. Observe error:
```
2021-10-08 19:32:42.461171: 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.
```
### Expected result
metadata-grpc-deployment should be initialized (not sure if SSL parameter is expected here in deployment config).
### Materials and Reference
Running gcr.io/tfx-oss-public/ml_metadata_store_server:1.0.0.
---
<!-- 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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"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",
"I believe all these features are done. Let me know if my memory is incorrect. @jlyaoyuli ",
"Question: Is `Terminate Run` feature alr... | 2021-10-10T21:42:44 | 2023-03-17T23:05:38 | 2023-03-17T23:05:38 | COLLABORATOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6705 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6705/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6705/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6705/events | https://github.com/kubeflow/pipelines/issues/6705 | 1,021,425,748 | I_kwDOB-71UM484bhU | 6,705 | kfp local runner not working with dsl conditionals | {
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"@etsyamitra, maybe you should reference the output of op, e.g:\r\n```\r\nwith dsl.Condition(\r\n execution_context.output == \"evaluation\", name=\"eval-task\"\r\n)\r\n```\r\nAlso, you can check the unit test about condition in https://github.com/kubeflow/pipelines/blob/master/sdk/python/tests/local_runner_test... | 2021-10-08T20:05:40 | 2022-03-02T15:05:37 | null | NONE | null | ### What steps did you take
### What happened:
Running the kfp with local client is ignoring the dsl.conditionOps
```
execution_context = execution_context_op(execution_context)
print('current context is ', execution_context)
with dsl.Condition(
execution_context == "evaluation", name="eval-task"
):
"""Only the evaluation component should run when it's the `evaluation`
execution context.
"""
print('starting with evaluations...')
```
```
current context is development
starting with evaluations...
current context is development
starting with evaluations...
```
### What did you expect to happen:
Expect the block to be skipped
### 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 SDK version: 1.8.0
---
<!-- 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/6704 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6704/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6704/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6704/events | https://github.com/kubeflow/pipelines/issues/6704 | 1,020,770,760 | I_kwDOB-71UM4817nI | 6,704 | [frontend] Pipeline logs persistence | {
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"Possible related issue: https://github.com/kubeflow/pipelines/issues/6428\r\n\r\nOne thing we can do is to capture the `main.log` from output Artifact, and visualize it in Logs tab. Welcome contribution for making this change for KFPv1.",
"This issue has been automatically marked as stale because it has not had ... | 2021-10-08T07:09:25 | 2022-03-02T15:05:35 | null | NONE | null | I'm trying to persist the pipeline pod logs so that if pod gets deleted then logs should be available for the pipeline runs.
I added `archiveLogs: true` to the argo/base config-map and restarted pipelines deployment but still after deleting the pod, im not seeing logs.
Is there any other step which needs to be done ?
kubeflow version - 1.0.2 and argo version - 2.3.0 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6699 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6699/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6699/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6699/events | https://github.com/kubeflow/pipelines/issues/6699 | 1,020,227,091 | I_kwDOB-71UM48z24T | 6,699 | Upgrade cloudpickle to > 2.0.0 | {
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"cc @chensun ",
"@feizerl, it's fine to merge the PR, but I think in general pickling isn't a good idea, the result of pickling is tied to a specific Python version. And we're not offering the pickling option in sdk v2.",
"@chensun I agree with you that pickling in general is not a good idea. \r\n\r\nThe advant... | 2021-10-07T16:16:51 | 2022-05-17T22:30:07 | 2021-10-11T18:43:45 | CONTRIBUTOR | null | Hello,
One of the features of pipelines is Step Caching (https://www.kubeflow.org/docs/components/pipelines/caching/) to avoid running the costly computations again and again.
The key for caching is:
```
message CacheKey {
map<string, ArtifactNameList> inputArtifactNames = 1;
map<string, Value> inputParameters = 2;
map<string, RuntimeArtifact> outputArtifactsSpec = 3;
map<string, string> outputParametersSpec=4;
ContainerSpec containerSpec=5;
}
```
When using the option `use_code_pickling` from https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/sdk/python/kfp/components/_python_op.py#L516
the pickle of the function gets embedded in the `ContainerSpec` (and hence becomes part of the key).
So far, all good.
However, the pickle is generated with `cloudpickle` which leads to non deterministic pickles every time you run the pipeline. As you can imagine, this makes caching feature useless because it will invalidate the cache every time it is run.
This non determinism was removed from `cloudpickle` with the following commit:
https://github.com/cloudpipe/cloudpickle/pull/428 and released as part of `2.0.0` release:
https://github.com/cloudpipe/cloudpickle/releases/tag/v2.0.0
Currently, `kfp` has bounded cloudpickle to less than v2.0.0 here:
https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/sdk/python/setup.py#L37
Would it be possible to make a new `kfp` release with upgraded cloudpickle? Without this cloudpickle version, step caching is currently impossible to use (or at the mercy of dictionary insertion order of cloudpickle).
Thanks! | {
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"Potential cause of this issue: Kubeflow pipelines service account should have permission to communicate with BigQuery. Documentation: http://cloud/bigquery/docs/access-control. Feel free to comment here if this doesn't solve your problem."
] | 2021-10-06T22:19:33 | 2023-04-06T22:49:16 | null | NONE | null | I am trying to store the output of my final process to a big query table using kubeflow component, pls can you suggest best practise for the same. Accessing "google.cloud" in python from lightweight component is throwing errors, hence I need alternative suggestions for the same | {
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"cc @Bobgy @zijianjoy ",
"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"
] | 2021-10-06T20:56:01 | 2022-03-02T18:05:20 | null | CONTRIBUTOR | null | ### Feature Area
Starting with GKE node version 1.19, the default node image for Linux nodes is the Container-Optimized OS with Containerd (cos_containerd) variant instead of the Container-Optimized OS with Docker (cos) variant. And GKE will not be able to support Docker container runtime beyond GKE version 1.24.
Test infra has dependencies on Docker Runtime. Need to migrate from cos to cos_containerd as soon as possible
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"The issue is mitigated by manually deleting the model resources in the test project. \r\nFor the record, here's the script I used to batch deleting the models:\r\n```\r\ngcloud config set project <test-project-id>\r\ngcloud config set ai_platform/region global\r\n\r\ngcloud ai-platform models list > models.txt\r\n... | 2021-10-06T20:54:08 | 2021-10-09T06:51:16 | 2021-10-09T06:51:16 | COLLABORATOR | null | The symptom is
```
Exceeded the max allowed number of models per project: 100. To read more about Cloud ML Engine quota, see https://cloud.google.com/ml-engine/quotas. To read more about Cloud ML Engine quota, see https://cloud.google.com/ml-engine/quotas."
``` | {
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"followers_url": "https://api.git... | null | [] | 2021-10-05T21:36:37 | 2022-02-15T18:54:47 | 2022-02-15T18:54:47 | CONTRIBUTOR | null | ### Proposed Changes
Drop support for Python 3.6 in January 2022.
### Justification
Python 3.6 will become unsupported by PSF in December 2021, and dropping support for it will allow us to begin using [Python 3.7 features](https://docs.python.org/3/whatsnew/3.7.html#summary-release-highlights).
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https://api.github.com/repos/kubeflow/pipelines/issues/6685 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6685/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6685/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6685/events | https://github.com/kubeflow/pipelines/issues/6685 | 1,016,135,303 | I_kwDOB-71UM48kP6H | 6,685 | [sdk] failure launching Dataproc jobs | {
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"Hello @ckchow , would you like to share the source code for DataprocClient()? looks like it is some usage error.",
"cc @IronPan ",
"DataprocClient is in google contributed code, https://github.com/kubeflow/pipelines/blob/master/components/gcp/container/component_sdk/python/kfp_component/google/dataproc/_client... | 2021-10-05T10:09:56 | 2022-03-01T22:51:53 | 2022-03-01T22:51:53 | NONE | null | ### Environment
* KFP version: Vertex AI
* KFP SDK version: 1.7
* All dependencies version:
kfp 1.8.2
kfp-pipeline-spec 0.1.11
kfp-server-api 1.7.0
### Steps to reproduce
```
@dsl.pipeline(
name='pipeline',
pipeline_root='gs://bucket'
)
def pipeline():
dataproc_submit_pyspark_op(
project_id=project,
region=region,
cluster_name=cluster_name,
main_python_file_uri='gs://bucket/valid_job.py',
)
```
### Expected result
submission of a dataproc job.
### Materials and Reference
error from vertex runner:
```
File "/ml/kfp_component/google/dataproc/_submit_job.py", line 49, in submit_job "
Error
2021-10-05 02:41:50.279 PDT
workerpool0-0
" client = DataprocClient() "
Error
2021-10-05 02:41:50.279 PDT
workerpool0-0
" File "/ml/kfp_component/google/common/_utils.py", line 170, in __init__ "
Error
2021-10-05 02:41:50.279 PDT
workerpool0-0
" self._build_client() "
Error
2021-10-05 02:41:50.279 PDT
workerpool0-0
"TypeError: _build_client() takes 0 positional arguments but 1 was given"
```
---
<!-- 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 @chensun ",
"Morning\r\nany updates?",
"Hi @SaschaHeyer , this is indeed a known limitation and we plan to discuss the best solution for this in Q1/Q2 2022.\r\n\r\nCan you help us understand what's your use case to set a dynamic value for cpu limit, and how critical is this feature to you? Thanks!",
"Hi @... | 2021-10-05T06:57:45 | 2022-12-01T14:56:27 | null | NONE | null | Hello Kubeflow Team,
Hello Google Team,
The container OP `.set_cpu_limit`only works when the value is set explicit and not via parameter_values or at runtime
https://github.com/kubeflow/pipelines/blob/4906ab2f1142043517249a62b9f22bc122971fdf/sdk/python/kfp/dsl/_container_op.py#L378
**Reproduce**
1. parameter_values: see steps to reproduce
2. runtime: see https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_spec/runtime_resource_request.py
### Environment
* How did you deploy Kubeflow Pipelines (KFP)? Vertex AI Pipelines
* kfp 1.8.4
* kfp-pipeline-spec 0.1.11
* kfp-server-api 1.7.0
### Steps to reproduce
**Not working**
````
from kfp.v2.dsl import pipeline
@pipeline(name="reproduction",
pipeline_root="ADD PIPELINE ROOT")
def pipeline(cpu_limit: str):
train_op = train().set_cpu_limit(cpu_limit)
compiler.Compiler().compile(pipeline_func=pipeline,
package_path='pipeline.json')
api_client = AIPlatformClient(
project_id="ADD PROJECT",
region="us-central1"
)
response = api_client.create_run_from_job_spec(
'pipeline.json',
parameter_values={
'cpu_limit': "16"
}
)
````
**Working**
````
from kfp.v2.dsl import pipeline
@pipeline(name="reproduction",
pipeline_root="ADD PIPELINE ROOT")
def pipeline():
train_op = train().set_cpu_limit("16")
compiler.Compiler().compile(pipeline_func=pipeline,
package_path='pipeline.json')
api_client = AIPlatformClient(
project_id="ADD PROJECT",
region="us-central1"
)
response = api_client.create_run_from_job_spec(
'pipeline.json'
)
````
### Expected result
The CPU limits can be set via parameter_values
Looking forward to your feedback | {
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"Hi @zijianjoy,\r\n\r\nI am trying to create a pipeline that will read from a table during pipeline execution, and launch a few pod operations for each row that is in the table at that time. Will the feature that is mentioned here allow that capability? Or, do you know if that capability already exists? Thanks.",
... | 2021-10-05T00:47:53 | 2023-01-13T07:00:11 | 2023-01-13T07:00:11 | COLLABORATOR | null | Prerequisite: https://github.com/kubeflow/pipelines/issues/6779 | {
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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"
] | 2021-10-05T00:47:23 | 2022-09-01T19:25:27 | 2022-09-01T19:25:27 | COLLABORATOR | null |
- [x] Take a public URL for json file
- [x] Take a local json file
- [x] Use the pipeline spec from an existing run to create a new run.
- [x] #7571 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6669 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6669/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6669/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6669/events | https://github.com/kubeflow/pipelines/issues/6669 | 1,015,628,838 | I_kwDOB-71UM48iUQm | 6,669 | 10/04/2021 Presubmit kubeflow-pipelines-tfx-python36 failing | {
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+ cd /home/prow/go/src/github.com/kubeflow/pipelines/tfx
+ python3 -m pip install '.[test]' --upgrade --extra-index-url https://pypi-nightly.tensorflow.org/simple
Looking in indexes: https://pypi.org/simple, https://pypi-nightly.tensorflow.org/simple
Processing /home/prow/go/src/github.com/kubeflow/pipelines/tfx
ERROR: Package 'tfx-dev' requires a different Python: 3.6.15 not in '>=3.7,<3.9'
WARNING: You are using pip version 20.2.3; however, version 21.2.4 is available.
You should consider upgrading via the '/usr/local/bin/python3 -m pip install --upgrade pip' command.
```
https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6666/kubeflow-pipelines-tfx-python36/1445118411406315520 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6651 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6651/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6651/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6651/events | https://github.com/kubeflow/pipelines/issues/6651 | 1,012,454,041 | I_kwDOB-71UM48WNKZ | 6,651 | [sdk] dependency conflict with tensorflow 2.6.0 and seldon-core | {
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"@chensun click<9,>=7.1.1 works for me with kfp and seldon-core",
"Please reopen, i can still see \r\nhttps://github.com/kubeflow/pipelines/blob/7b4f28379d4204da3ad38ce376d2157cc3454356/sdk/python/requirements.txt#L22\r\n\r\nand \r\n\r\nhttps://github.com/kubeflow/pipelines/blob/7b4f28379d4204da3ad38ce376d2157cc3... | 2021-09-30T17:46:04 | 2021-10-06T08:20:18 | 2021-10-06T05:50:09 | MEMBER | null | ### Environment
* KFP version: 1.7
* KFP SDK version: 1.8.3
* All dependencies version:
```
[~]$ pip list | grep kfp
kfp 1.8.3
kfp-pipeline-spec 0.1.11
kfp-server-api 1.7.0
```
kfp==1.8.3 collides with tensorflow==2.6 because it requires
https://github.com/kubeflow/pipelines/blob/220d79df66e31bbd93c409fb361e0463bde4aeac/sdk/python/setup.py#L56
while tensorflow needs
```
Warning!!! Possibly conflicting dependencies found:
* tensorflow==2.6.0
- typing-extensions [required: ~=3.7.4, installed: 3.10.0.2]
```
https://github.com/tensorflow/tensorflow/blob/421fba8888bb8f8724bc2e35ca2fdcde16e1bfe5/tensorflow/tools/pip_package/setup.py#L90
is `'typing-extensions>=3.7.4,<4;python_version<"3.9"'` not enough?
The same goes for seldon-core==1.11.* and package click and absl-py
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
kfp 1.8.2 requires absl-py<=0.11,>=0.9, but you have absl-py 0.13.0 which is incompatible.
kfp 1.8.2 requires click<8,>=7.1.1, but you have click 8.0.1 which is incompatible.
kfp 1.8.2 requires typing-extensions<4,>=3.10.0.2, but you have typing-extensions 3.7.4.3 which is incompatible.
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6649 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6649/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6649/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6649/events | https://github.com/kubeflow/pipelines/issues/6649 | 1,012,228,255 | I_kwDOB-71UM48VWCf | 6,649 | [bug] Kubeflow VolumeOp (PVC) not delete after Pipeline execution. | {
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"Possible related: https://github.com/kubeflow/pipelines/issues/3938",
"Hi @peterhaddad3121 ,\r\n\r\nto GC the resources created by `ResourceOp`s you should set the `set_owner_reference` argument to `True`. It's this one: https://github.com/kubeflow/pipelines/blob/5892bf97d08bbb51501d640cabfc310370a9f457/sdk/pyth... | 2021-09-30T14:07:23 | 2022-03-02T15:05:11 | null | NONE | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
The first step of a Pipeline is creating a VolumeOp to be attached to a couple of KF components in a Pipeline.
After the pipeline finishes and all Component pods are Completed then deleted, the PVC created does not delete.
### What happened:
I edited the deployment of ml-pipeline-persistenceagent to include:
```
- name: TTL_SECONDS_AFTER_WORKFLOW_FINISH
value: "60"
```
### What did you expect to happen:
The PVC of the VolumeOp created to be deleted. All pods are deleted after pipeline completion, the PVC still exists and no deletion timestamp exists.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
I saw all the corresponding issues, but this is related to using ArgoCD. This is using the Python SDK to run and create PIpelines.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
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<!-- /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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"cc @IronPan would you like to follow up on this?",
"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"
] | 2021-09-29T15:58:49 | 2022-03-02T18:05:16 | null | NONE | null | ## Summary
When launching a flex template, the URL is incorrect.
It is `https://dataflow.googleapis.com/v1b3/projects/ostk-gcp-mlcouponoffer-prod/locations/us-west3/templates:launch?gcsPath=`
It *should* be `https://dataflow.googleapis.com/v1b3/projects/ostk-gcp-mlcouponoffer-prod/locations/us-west3/flexTemplates:launch?gcsPath=`
Notice `templates` vs `flexTemplates` at the end of the URL
### What steps did you take
Code to generate the pipeline
```
from typing import NamedTuple
import kfp
from kfp import dsl
from kfp.v2 import compiler
from kfp.v2.dsl import (Artifact, Dataset, Input, InputPath, Model, Output,
OutputPath, ClassificationMetrics, Metrics, component)
from kfp.v2.google.client import AIPlatformClient
from kfp.components import load_component_from_url
from google.cloud import aiplatform
dataflow_template_op = load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/google-cloud/google_cloud_pipeline_components/experimental/dataflow/launch_flex_template/component.yaml'
)
```
### What happened:
The vertex AI pipeline exits with error
```
"googleapiclient.errors.HttpError: <HttpError 400 when requesting https://dataflow.googleapis.com/v1b3/projects/ostk-gcp-mlcouponoffer-prod/locations/us-west3/templates:launch?gcsPath=gs%3A%2F%2Fcoupon-offer-recommendation%2Ftemplates%2Fpublish-coupons-0.1.0-SNAPSHOT.json&validateOnly=false&alt=json returned "Invalid template file gs://coupon-offer-recommendation/templates/publish-coupons-0.1.0-SNAPSHOT.json. Please specify a correct Google-provided template path from https://cloud.google.com/dataflow/docs/guides/templates/provided-templates or create your own template following https://cloud.google.com/dataflow/docs/guides/templates/creating-templates">
```
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Vertex AI
* KFP version:
1.8.2
### 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 -->
<!-- /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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"node_id": "MDU6TGFiZWwyMTU3NjM0MjA0"... | open | false | null | [] | null | [
"Hi,\r\nAFAIK we can leverage `ExitHandler `to tear down resources.\r\nhttps://kubeflow-pipelines.readthedocs.io/en/latest/_modules/kfp/dsl/_ops_group.html#ExitHandler",
"@midhun1998 Thank you for the comment :)\r\nYes I checked `ExitHandler` but this could only handle the `ContainerOp`. So If we use `ExitHandler... | 2021-09-29T12:36:19 | 2022-03-02T18:05:14 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
There are `ResourceOp` to use some k8s resources(pvc, configmap, secret, ...) in kubeflow pipeline.
However, Is it possible to create and tear down such resources from the start of pipeline and the end of the pipeline ?
Also, Is it possible to tear down such resources no matter what pipeline succeeded or not (like `try ~ finally`)?
I couldn't find existing methods for this feature yet.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
I'd like to use for control pvc's lifecycle to sync with kfp run and control some other custom resources.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Not yet
---
<!-- 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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"Now my GCP function is able to list pipelines. \r\n\r\nI had to use the following service account: KF-NAME-user@KF-PROJECT.iam.gserviceaccount.com . \r\n\r\nThis message appears in the logs but it's just a warning message *No such file or directory: '/var/run/secrets/kubernetes.io/serviceaccount/namespace'*"
] | 2021-09-29T00:15:44 | 2021-09-30T15:55:48 | 2021-09-30T15:55:48 | NONE | null | I get the following error:
_"Failed to authorize with API resource references: Failed to authorize with API resource references: PermissionDenied: User 'myServiceAccount@appspot.gserviceaccount.com' is not authorized with reason: (request: ResourceAttributes{Namespace:namespace1,Verb:list,Group:pipelines.kubeflow.org,Version:v1beta1,Resource:experiments,Subresource:,Name:,_
Code
```
def main(data, context):
client = kfp.Client(host=HOST, client_id=CLIENT_ID,namespace=NAMESPACE)
logging.info(client.list_experiments(namespace=NAMESPACE))
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https://api.github.com/repos/kubeflow/pipelines/issues/6640 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6640/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6640/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6640/events | https://github.com/kubeflow/pipelines/issues/6640 | 1,010,224,654 | I_kwDOB-71UM48Ns4O | 6,640 | [bug] sample v2 test enables cache for all pipelines with v1 mode | {
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] | 2021-09-28T21:24:24 | 2021-09-29T04:49:48 | 2021-09-29T04:49:48 | CONTRIBUTOR | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
### What happened:
sample v2 test enables cache for all pipelines with v1 mode
### What did you expect to happen:
sample v2 test should disable cache for all pipelines with v1 mode
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
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### 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 -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6637 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6637/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6637/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6637/events | https://github.com/kubeflow/pipelines/issues/6637 | 1,010,111,330 | I_kwDOB-71UM48NRNi | 6,637 | [sdk] ValueError: compiler cannot find the model-export-path | {
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"Traceback (most recent call last):\r\n File \"/usr/local/bin/dsl-compile\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/main.py\", line 104, in main\r\n not args.disable_type_check,\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/m... | 2021-09-28T19:10:19 | 2022-03-02T10:06:38 | null | NONE | null | ### What steps did you take
I am building a scikit-learn model and want to deploy the model on aiplatform.
### What happened:
When I compile the pipeline, I am receiving the following error:
"ValueError: compiler cannot find the model-export-path"
### Environment:
model_file_path = f"{model_dir}/model.joblib"
with tf.io.gfile.GFile(model_file_path, mode="wb") as training_file:
joblib.dump(model, training_file)
I am pointing the model_file_path in the deployment process but it asks for model-export-path | {
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"followers_url": "https://api.githu... | null | [] | 2021-09-28T18:46:34 | 2021-10-06T02:03:08 | 2021-10-06T02:03:08 | NONE | null | When the input for a component is of type float and the value passed in is 0 the component gets it as None. Is this expected? If so what all are the scenarios that this would happen in. Following is a sample pipeline that I used on Google Vertex AI.
### Environment
* KFP version: 1.8.2
* All dependencies version:
kfp 1.8.2
kfp-pipeline-spec 0.1.11
kfp-server-api 1.4.1
### Steps to reproduce
```
from kfp.v2 import dsl
@dsl.component
def test_func(
a: float, b: float, c: float = 0.0
) -> NamedTuple("ExampleOutputs", [("sum", float), ("product", float)]):
"""Example function that demonstrates how to return multiple values."""
print(f"a: {a}")
print(f"b: {b}")
print(f"c: {c}")
@dsl.pipeline(
name="test-kfp-v2",
description="Test kfp v2 pipeline",
pipeline_root="",
)
def pipeline(
a: float = 0.0,
):
test_func(a, 0.0)
```
Actual
a: None
b: None
c: None
Expected
a: 0
b: 0
c: 0
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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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"
] | 2021-09-28T18:10:11 | 2023-01-18T09:00:14 | 2023-01-18T09:00:14 | CONTRIBUTOR | null | - [ ] Add caching doc for v2 mode. Part of https://github.com/kubeflow/pipelines/issues/6156
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https://api.github.com/repos/kubeflow/pipelines/issues/6628 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6628/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6628/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6628/events | https://github.com/kubeflow/pipelines/issues/6628 | 1,009,475,412 | I_kwDOB-71UM48K19U | 6,628 | [sdk] Cannot refer to the loop parameter within the grandchild task | {
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* KFP version:
Vertex Pipelines
* KFP SDK version:
1.8.2
### Steps to reproduce
Run the following pipeline on Vertex Pipelines:
```
from kfp import components
from kfp import dsl
import kfp.v2.compiler as compiler
@components.create_component_from_func
def print_op(msg: str):
print(msg)
@dsl.pipeline(
name='pipeline-with-loops-and-conditions',
pipeline_root=PIPELINE_ROOT,
)
def my_pipeline(text_parameter: str = '[{"p_a": -1, "p_b": ["hello", "world"]}, {"p_a": 2, "p_b": ["nihao", "shijie"]}]'):
with dsl.ParallelFor(text_parameter) as item:
with dsl.Condition(item.p_a != "-1"):
with dsl.ParallelFor(item.p_b) as item_p_b:
print_op(item_p_b)
compiler.Compiler().compile(pipeline_func=my_pipeline,
package_path='loops-and-conditions.json')
```
### Expected result
Expect no error
### Actual results
Get HTTP 400 error with message:
`Component input parameter 'pipelineparam--text_parameter-loop-item' is not defined`
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https://api.github.com/repos/kubeflow/pipelines/issues/6623 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6623/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6623/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6623/events | https://github.com/kubeflow/pipelines/issues/6623 | 1,008,158,549 | I_kwDOB-71UM48F0dV | 6,623 | [feature] Cache pod updates to change node-selector/tolerations. | {
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... | closed | false | null | [] | null | [
"This issue is a duplicate of another issue that has already been merged: https://github.com/kubeflow/pipelines/issues/4777\r\nClosing.",
"Reopening this issue, looks like there is a bug in the patchoperations path.\r\n```\r\n\t\t\tif pod.Spec.Affinity != nil {\r\n\t\t\t\tpatches = append(patches, patchOperation{... | 2021-09-27T13:32:21 | 2021-11-01T03:50:26 | 2021-10-14T23:12:38 | 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?
The ability to override node-selector, and toleration for components that are pulling from the cache.
### What is the use case or pain point?
Currently when a component is pulled from cache it gets scheduled with the same tolerations and node-selectors. In the case of a GPU machine this can result in a expensive machine being spun up just to get the cache. A secondary benefit is that the cache pod could be configured to run on a cluster system resource preventing the need to spin up a machine at all for the cache to execute "echo 'This step output is taken from cache.'".
### Is there a workaround currently?
I am not familiar with the cache server once, I have the familiarity I will work on identifying a workaround for my team.
---
<!-- 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/6620 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6620/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6620/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6620/events | https://github.com/kubeflow/pipelines/issues/6620 | 1,007,152,589 | I_kwDOB-71UM48B-3N | 6,620 | Is it possible to use a local path to stage a file for a kubeflow pipeline? | {
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"Hi,\r\n\r\nCan this help you https://github.com/kubeflow/pipelines/issues/2977 ?",
"Yup. This seems relevant.",
"Looks like it's only accessible via the kfp CLI though? From reading it it looks like it also has to do with specific file types, not arbitrary ones.",
"I think you can add your csv file into dock... | 2021-09-25T17:38:48 | 2022-04-02T02:44:32 | 2021-10-08T00:52:59 | NONE | null | Hi,
I have a local csv file that I want to use to kick start my pipeline. Is there a way to "stage" that file using kubeflow? I can't quite make sure if it's possible to use InputPath as a pipeline parameter or not. Am I supposed to upload my file to GCS and then pass the GCS URI along instead?
Thanks | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6619 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6619/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6619/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6619/events | https://github.com/kubeflow/pipelines/issues/6619 | 1,007,137,951 | I_kwDOB-71UM48B7Sf | 6,619 | [feature] Question about passing complex objects using pickle | {
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"Hi @drubinstein, sorry for the late reply.\r\n\r\nFor complex object such as numpy array, you can pass them by file. Here're some docs about passing by file:\r\nhttps://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#passing-parameters-by-file\r\nor the v2 way if you're using Vertex Pipe... | 2021-09-25T16:32:52 | 2022-04-17T07:27:10 | null | NONE | null | ### Feature Area
/area backend
/area sdk
/area samples
### What feature would you like to see?
May already be handled, but I notice that in [_data_passing.py](https://github.com/kubeflow/pipelines/blob/dec03067ca1f89f1ca23c7397830d60201448fa6/sdk/python/kfp/components/_data_passing.py#L129) there's a `Base64Pickle` Converter. Would it be possible to provide some example code in the documentation that shows how to say return an object that will get pickled and then unpickled when used in a following step of a pipeline? Alternatively (and preferred), would it be possible to automatically detect complex objects and automatically pickle/unpickle them (though I think you could instead have everything that gets passed between python components go through pickle instead)?
### What is the use case or pain point?
From what I've read and tried, KFP currently does not support more complex objects such as a numpy array or a datetime as a component arguments, parameters, return values etc. The current best way to handle that would be to convert them to a string and then parse it at the beginning of the next component step. If objects were pickled between components and then unpickled before being the python function component was called, then I could have more accurate types and reduce boilerplate code.
### Is there a workaround currently?
Currently, I pass strings around and parse them at the beginning of my components. For example instead of having
```
def foo(day=datetime.datetime) -> str:
# something is done
return "bar"
```
I'll do something closer to:
```
def foo(day=str) -> str:
day = datetime.datetime.strptime(day, "%Y-%m-%d")
# something is done
return "bar"
```
at the beginning of all my functions.
---
<!-- 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/6615 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6615/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6615/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6615/events | https://github.com/kubeflow/pipelines/issues/6615 | 1,006,798,105 | I_kwDOB-71UM48AoUZ | 6,615 | [frontend] Log tab issue with archived_logs and cache | {
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"Hello @boarder7395 , the pod log will not be cached, so it is expected not seeing the log. If you want to see this feature, maybe adding a feature request?",
"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 y... | 2021-09-24T20:20:25 | 2022-03-02T18:05:15 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow
* KFP version:
1.0.4
### Steps to reproduce
Run a pipeline that uses the cache for some component with archive logs configured.
### Expected result
When the pod is garbage collected after pulling from the cache the logs tab should either display "Pulled from cache." or
the logs of the original components run. How the logic is in the version of kfp I am using (1.0.4) the archive logs are pulled from prefix/{workflow.name}/{pod.name} and when the component was pulled from cache this file does not exist so the UI
displays "Failed to retrieve pod logs.".
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/6614 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6614/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6614/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6614/events | https://github.com/kubeflow/pipelines/issues/6614 | 1,006,794,529 | I_kwDOB-71UM48Anch | 6,614 | [feature] V2 DSL Load_component_from_url Dataflow | {
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"cc @IronPan would you like to add `command` to the component?",
"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"
] | 2021-09-24T20:15:02 | 2022-03-02T18:05:17 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
/area components
### What feature would you like to see?
Currently, it looks like loading the dataflow yaml components does not support v2. Also, the other google pipeline component seem to no longer use loading from url and instead use the google_cloud_pipeline_components library. It would be nice if the dataflow jobs were in that library as well
### What is the use case or pain point?
I have looked and I don't see a load_component_from_url under `import kfp.v2.component` so I have been using the old `import kfp.component`. I also was not able to find a yaml file for the `launch_flex_template` for v2.
```
dataflow_template_op = load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/google-cloud/google_cloud_pipeline_components/experimental/dataflow/launch_flex_template/component.yaml'
)
```
```
/Users/.../lib/python3.8/site-packages/kfp/components/_components.py:174: FutureWarning: Container component must specify command to be compatible with KFP v2 compatible mode and emissary executor, which will be the default executor for KFP v2.https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/
warnings.warn(
```
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6612 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6612/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6612/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6612/events | https://github.com/kubeflow/pipelines/issues/6612 | 1,006,632,197 | I_kwDOB-71UM47__0F | 6,612 | [backend] DataProc Cluster creation fails with "Invalid value at 'cluster' (cluster_name), Starting an object on a scalar field" | {
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"u... | closed | false | null | [] | null | [
"I figured out the issue and it was on my side. need to specify the cluster object using `cluster=object`"
] | 2021-09-24T16:25:08 | 2021-09-28T15:49:38 | 2021-09-28T15:49:01 | NONE | null | ### Environment
* KFP version:
1.3.0
### Steps to reproduce
Trying to create dataproc cluster in python using component.yaml and providing arugments as cluster object.
```import kfp.components as comp
dataproc_create_cluster_op = comp.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/1.6.0/components/gcp/dataproc/create_cluster/component.yaml')
dataproc_create_cluster_op(project_id=project_id, region=region, cluster=cluster)```
where cluster is a json object like
```{
"projectId": "demo",
"clusterName": "demo",
"config": {
"configBucket": "demo",
"gceClusterConfig": {}
}
....
}```
### Expected result
Dataproc cluster to be created.
### Error
```File "/ml/kfp_component/google/dataproc/_client.py", line 34, in create_cluster
body = cluster
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 851, in execute
raise HttpError(resp, content, uri=self.uri)
googleapiclient.errors.HttpError: <HttpError 400 when requesting
https://dataproc.googleapis.com/v1/projects/demo/regions/demo/clusters?requestId=12345&alt=json
returned "Invalid value at 'cluster' (cluster_name), Starting an object on a scalar field">```
<!-- 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/6598 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6598/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6598/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6598/events | https://github.com/kubeflow/pipelines/issues/6598 | 1,004,964,138 | I_kwDOB-71UM475okq | 6,598 | [feature] Pass additional options to docker on local runner | {
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"cc @chensun "
] | 2021-09-23T03:10:49 | 2021-09-24T14:58:37 | 2021-09-24T14:58:37 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
Pass additional docker options to local runner. This might be implemented using `ExecutionMode`, e.g.:
```python
ExecutionMode("docker", docker_options=["-v", "~/.config:/root/.config"])
```
### What is the use case or pain point?
My specific use case is authenticating to google cloud from local kfp tasks. But rather than trying to enumerate the exact docker flags that should be exposed, it makes more sense to me to allow a list of addition options for `docker run`.
Note: this might also solve #6496; cc @etsyamitra
### Is there a workaround currently?
Fork the sdk?
---
<!-- 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/6589 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6589/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6589/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6589/events | https://github.com/kubeflow/pipelines/issues/6589 | 1,002,776,825 | I_kwDOB-71UM47xSj5 | 6,589 | Annoying log that looks like an error | {
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"https://github.com/kubeflow/pipelines/pull/6590"
] | 2021-09-21T16:03:16 | 2021-09-24T17:21:54 | 2021-09-24T17:21:54 | CONTRIBUTOR | null | https://github.com/kubeflow/pipelines/blob/7875b68654a69ca761cb0ba4a920a30925a0e94b/sdk/python/kfp/_client.py#L203
When /var/run/secrets/kubernetes.io/serviceaccount/namespace file is missing, we log an INFO (cool) but also the error stack trace, which makes it look like an error (annoying) | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6588 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6588/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6588/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6588/events | https://github.com/kubeflow/pipelines/issues/6588 | 1,002,355,871 | I_kwDOB-71UM47vryf | 6,588 | [sdk] ParallelFor does not apply parallelism on the right template. | {
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"I think the initial design and implementation of parallelism on `dsl.ParallelFor` didn't match everyone's expectation on how it should work. Thus, there was a subsequent work: https://github.com/kubeflow/pipelines/pull/4199",
"also affected by this. ",
"was this fixed?",
"Here is hack:\r\n```\r\ndef fix_para... | 2021-09-21T12:13:54 | 2023-01-09T08:34:35 | null | NONE | null | ### Environment
* KFP SDK version: `1.8.2`
* All dependencies version:
```bash
kfp 1.8.2
kfp-pipeline-spec 0.1.10
kfp-server-api 1.7.0
```
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
```python
#pipeline.py
import kfp
@kfp.components.func_to_container_op
def dummy_op(arg):
pass
@kfp.components.func_to_container_op
def other_op(arg):
pass
@kfp.dsl.pipeline(name="For loop pipeline")
def pipeline(args: list):
with kfp.dsl.ParallelFor(args, parallelism=2) as item:
task1 = dummy_op(item)
task2 = other_op(item)
task2.after(task1)
```
### Expected result
The pipelines runs but compiling this pipeline with `dsl-compile --py pipeline.py --output ./pipeline.yaml` puts the `parallelism: 2` on a supposedly wrong template. Thus, when running this pipeline with 4 items in `args`, all 4 task are scheduled at once instead of keeping a maximum parallelism at 2. In the compiled YAML I was expecting to see `parallelism: 2` within the `entrypoint` template (the one with the `withParam` section).
Actually, when manually moving `parallelism: 2` to the entrypoint template, it works as intended.
See below
Actual output:
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: for-loop-pipeline-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.2, pipelines.kubeflow.org/pipeline_compilation_time: '2021-09-27T18:40:10.602539',
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"name": "args", "type": "JsonArray"}],
"name": "For loop pipeline"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.2}
spec:
entrypoint: for-loop-pipeline
templates:
- name: dummy-op
container:
args: [--arg, '{{inputs.parameters.args-loop-item}}']
command:
- sh
- -ec
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |
def dummy_op(arg):
pass
import argparse
_parser = argparse.ArgumentParser(prog='Dummy op', description='')
_parser.add_argument("--arg", dest="arg", type=str, required=True, default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = dummy_op(**_parsed_args)
image: python:3.7
inputs:
parameters:
- {name: args-loop-item}
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.2
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: "true"
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"args": ["--arg", {"inputValue": "arg"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf
\"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
"def dummy_op(arg):\n pass\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Dummy
op'', description='''')\n_parser.add_argument(\"--arg\", dest=\"arg\", type=str,
required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs
= dummy_op(**_parsed_args)\n"], "image": "python:3.7"}}, "inputs": [{"name":
"arg"}], "name": "Dummy op"}', pipelines.kubeflow.org/component_ref: '{}',
pipelines.kubeflow.org/arguments.parameters: '{"arg": "{{inputs.parameters.args-loop-item}}"}'}
- name: for-loop-1
parallelism: 2
inputs:
parameters:
- {name: args-loop-item}
dag:
tasks:
- name: dummy-op
template: dummy-op
arguments:
parameters:
- {name: args-loop-item, value: '{{inputs.parameters.args-loop-item}}'}
- name: other-op
template: other-op
dependencies: [dummy-op]
arguments:
parameters:
- {name: args-loop-item, value: '{{inputs.parameters.args-loop-item}}'}
- name: for-loop-pipeline
dag:
tasks:
- name: for-loop-1
template: for-loop-1
arguments:
parameters:
- {name: args-loop-item, value: '{{item}}'}
withParam: '{{workflow.parameters.args}}'
- name: other-op
container:
args: [--arg, '{{inputs.parameters.args-loop-item}}']
command:
- sh
- -ec
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |
def other_op(arg):
pass
import argparse
_parser = argparse.ArgumentParser(prog='Other op', description='')
_parser.add_argument("--arg", dest="arg", type=str, required=True, default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = other_op(**_parsed_args)
image: python:3.7
inputs:
parameters:
- {name: args-loop-item}
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.2
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: "true"
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"args": ["--arg", {"inputValue": "arg"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf
\"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
"def other_op(arg):\n pass\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Other
op'', description='''')\n_parser.add_argument(\"--arg\", dest=\"arg\", type=str,
required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs
= other_op(**_parsed_args)\n"], "image": "python:3.7"}}, "inputs": [{"name":
"arg"}], "name": "Other op"}', pipelines.kubeflow.org/component_ref: '{}',
pipelines.kubeflow.org/arguments.parameters: '{"arg": "{{inputs.parameters.args-loop-item}}"}'}
arguments:
parameters:
- {name: args}
serviceAccountName: pipeline-runner
```
Applying this diff fixes the issue:
```diff
--- pipeline.yaml 2021-09-27 18:40:10.608158969 +0200
+++ fixed.yaml 2021-09-27 18:42:43.469024168 +0200
@@ -50,3 +50,2 @@
- name: for-loop-1
- parallelism: 2
inputs:
@@ -68,2 +67,3 @@
- name: for-loop-pipeline
+ parallelism: 2
dag:
```
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
Am I somehow using `ParallelFor` in a wrong way ?
<!-- 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/6587 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6587/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6587/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6587/events | https://github.com/kubeflow/pipelines/issues/6587 | 1,002,267,470 | I_kwDOB-71UM47vWNO | 6,587 | [feature] Add multi-model support for training on Sagemaker | {
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"node_id": "MDU6TGFiZWwxMjg5NTg... | closed | false | null | [] | null | [
"cc @RedbackThomson @surajkota ",
"@jpvandervelden Thanks for creating the issue.\r\n\r\nLooks like you need support for [multiModel parameter](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html#sagemaker-Type-ContainerDefinition-Mode) in the [model component](https://github.co... | 2021-09-21T11:20:45 | 2021-10-22T13:57:46 | 2021-10-22T13:57:46 | 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?
A way to train Sagemaker models so that multiple models can be run on one Sagemaker endpoint. AWS Sagemaker facilitates this by providing a mme branch
(https://github.com/aws/sagemaker-scikit-learn-container/tree/mme) for the scikit-learn container (https://github.com/aws/sagemaker-scikit-learn-container).
### What is the use case or pain point?
When using Sagemaker out of the box one trained model will be deployed on one endpoint. When you need to deploy multiple models this means operating multiple endpoints. More endpoints mean more money spent. Using the multi-model approach reduces the spend on running the inference models.
### Is there a workaround currently?
Not that I know of...
The mme approach needs you to provide a custom built container (BYOC) that's built on the mme branch of https://github.com/aws/sagemaker-scikit-learn-container. This container doesn't contain the training script itself.
In Kubeflow you provide a docker that contains the training script. Is there a way to create a custom version of amazon/aws-sagemaker-kfp-components?
---
<!-- 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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"Since they have the same name, the last one will be the one that is exposed when using the `Client`. Thus I think it should be fine to remove the one on L661. ",
"@NikeNano - agreed - not sure on how proceed to a PR for this 🤷🏾♂️ \r\n\r\nI'm unable to create a remote branch with the change. I'm following the ... | 2021-09-21T00:26:21 | 2021-09-22T08:26:47 | 2021-09-22T08:26:47 | CONTRIBUTOR | null | Seems like there are two versions of this function within the same module:
https://github.com/kubeflow/pipelines/blob/7875b68654a69ca761cb0ba4a920a30925a0e94b/sdk/python/kfp/_client.py#L661
https://github.com/kubeflow/pipelines/blob/7875b68654a69ca761cb0ba4a920a30925a0e94b/sdk/python/kfp/_client.py#L1324
Should one of these be deprecated/removed/renamed? | {
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"@Bobgy am I missing something?",
"Could we use the health endpoint to validate this `/apis/v1beta1/healthz` , https://github.com/kubeflow/pipelines/blob/7875b68654a69ca761cb0ba4a920a30925a0e94b/sdk/python/kfp/_client.py#L394-L399",
"/assign \r\nI will make a method and add PR to validate if connect to a `kubef... | 2021-09-20T22:41:41 | 2022-03-02T18:05:31 | null | MEMBER | null | Currently, there does not seem to be a method on [`kfp.Client()`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html) which can reliably confirm that the `url` provided is actually a Kubeflow Pipelines API.
_NOTE: methods like `Client().list_pipelines()` return `{'next_page_token': None, 'pipelines': None, 'total_size': None}` when run against non-kubeflow apis (which is indistinguishable from there being no pipelines in the cluster)._
__What is the best way to determine that a `kfp.Client()` instance is valid?__ | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6581 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6581/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6581/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6581/events | https://github.com/kubeflow/pipelines/issues/6581 | 999,801,553 | I_kwDOB-71UM47l8LR | 6,581 | [bug] How to use Sidecar in Vertex pipeline? | {
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"I think the way you'd want to do this for Vertex (although I'm not sure if sidecars are supported 🤔 ):\r\n\r\n```python\r\nmain_op = kfp.components.load_component_from_text('''\r\n name: main_op\r\n implementation:\r\n container:\r\n image: x\r\n ''')\r\nmain_task = main_op()\r\n\r\nsidecar_t... | 2021-09-18T00:16:25 | 2021-09-24T01:29:00 | 2021-09-24T01:27:53 | NONE | null | ### What steps did you take
In kfp pipeline I can use the following code to create a containerOp and add a Sidecar to it:
```
sidecar_task = dsl.Sidecar(name='x', image='x')
main_task = dsl.ContainerOp(name='x', image='x', sidecars=[sidecar_task])
```
When I switch to Vertex pipeline, since `dsl.ContainerOp` is deprecated I need to create a component like this:
```
main_op = kfp.components.load_component_from_text('''
name: main_op
implementation:
container:
image: x
''')
main_task = main_op()
```
I wonder how can I add a sidecar to it. Thank you.
### What happened:
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### 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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"@zijianjoy \r\nI talked with sdk side and vertex side, the agreement is to implement disable/enable caching level at client side first.\r\nLater when we have a clear goal of caching CUJ, we can talk about implementing this in server side which means change pipeline job proto.\r\nSDK already implemented such logic ... | 2021-09-16T21:00:05 | 2022-09-13T10:15:07 | 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?
The ability to enable or disable (v2) caching for a pipeline run _via the UI_.
### What is the use case or pain point?
When developing pipelines, it is possible to end up in a position where the component "succeeds" (does not error), but returns erroneous results. Once this has happened, correcting the component code and re-executing does not suffice, as the previous (incorrect) results are cached.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
We separate authoring pipelines from executing them, so typically our executions are via the Vertex Pipelines UI, by uploading the compiled JSON.
One workaround would be to extend the pipeline code to support executing the pipeline from code, which does allow the caching to be disabled. This change would have to be made to all our pipelines, and is onerous.
<!-- Without this feature, how do you accomplish your task today? -->
Either move the input files and change the components to bust the cache, or execute the pipeline using Python.
Neither of these approaches are ideal, and neither would be easily available to non-coders when they execute the pipeline (people who can upload a given JSON file and set parameters, but not make code changes).
---
<!-- 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/6576 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6576/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6576/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6576/events | https://github.com/kubeflow/pipelines/issues/6576 | 997,841,423 | I_kwDOB-71UM47edoP | 6,576 | [sdk] KeyError when using several ParallelFor which depends on the same function component. | {
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"The following workaround works.\r\n\r\n```py\r\nimport uuid\r\n\r\n\r\ndef _create_component_from_func(func, *args, **kwargs):\r\n # Don't override func.__name__ that causes an execution error.\r\n func._component_human_name = f\"{func.__name__}_{uuid.uuid4().hex}\"\r\n return comp.create_component_from... | 2021-09-16T06:53:34 | 2021-09-28T07:10:10 | 2021-09-28T07:10:10 | NONE | null | ### Environment
* KFP version: I'm using Vertex Pipelines
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.8.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- 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.
-->
Compile the following pipeline function:
```py
def range_fn(stop: int) -> list:
return list(range(stop))
range_op = comp.create_component_from_func(
func=range_fn,
base_image="python:alpine",
)
range_op_2 = comp.create_component_from_func(
func=range_fn,
base_image="python:alpine",
)
def second_fn(msg: str, i: int):
print(f"{i}: {msg}")
second_op = comp.create_component_from_func(
func=second_fn,
base_image="python:alpine",
)
@dsl.pipeline(
name="hello-pipeline",
)
def hello_pipeline() -> None:
parallel_num = 5
name = "Foo"
range_task = range_op(stop=parallel_num)
with dsl.ParallelFor(range_task.output) as worker_id:
second_task = second_op(msg=f"hello {name}", i=worker_id)
name = "Bar"
range_task_2 = range_op(stop=parallel_num)
with dsl.ParallelFor(range_task_2.output) as worker_id:
second_task = second_op(msg=f"hello {name}", i=worker_id)
```
I ran into the following error:
```
File "/Users/hrsma2i/hello-pipeline/hello_pipeline/aiplatform_client_wrapper.py", line 40, in create_run_from_pipeline_func
compiler.Compiler().compile(
File "/Users/hrsma2i/Library/Caches/pypoetry/virtualenvs/hello-pipeline-37sr9i4z-py3.9/lib/python3.9/site-packages/kfp/v2/compiler/compiler.py", line 1177, in compile
pipeline_job = self._create_pipeline_v2(
File "/Users/hrsma2i/Library/Caches/pypoetry/virtualenvs/hello-pipeline-37sr9i4z-py3.9/lib/python3.9/site-packages/kfp/v2/compiler/compiler.py", line 1129, in _create_pipeline_v2
pipeline_spec = self._create_pipeline_spec(
File "/Users/hrsma2i/Library/Caches/pypoetry/virtualenvs/hello-pipeline-37sr9i4z-py3.9/lib/python3.9/site-packages/kfp/v2/compiler/compiler.py", line 920, in _create_pipeline_spec
inputs, outputs = self._get_inputs_outputs(
File "/Users/hrsma2i/Library/Caches/pypoetry/virtualenvs/hello-pipeline-37sr9i4z-py3.9/lib/python3.9/site-packages/kfp/v2/compiler/compiler.py", line 320, in _get_inputs_outputs
upstream_op = pipeline.ops[param.op_name]
KeyError: 'range-fn 2'
```
Redefining the `range_fn` as another function `range_fn_2` works, but the alias `range_fn_2 = range_fn` doesn't work.
### Expected result
<!-- What should the correct behavior be? -->
successful compilation
### 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/6574 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6574/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6574/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6574/events | https://github.com/kubeflow/pipelines/issues/6574 | 996,990,727 | I_kwDOB-71UM47bN8H | 6,574 | [sdk] KFP V2 pipeline fails with 'no module named ensurepip' | {
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"u... | closed | false | null | [] | null | [
"cc @chensun ",
":+1: I meet the same issue. Running with the default image is ok, but the error pops up when I switch to my own image where there is no ensurepip (but pip is pre-installed).\r\n\r\nusing system python 3.6 on ubuntu 18.04. Then I installed `python3.6-venv` package and call `python -m ensurepip` b... | 2021-09-15T11:51:42 | 2021-11-11T17:16:39 | 2021-11-11T17:16:39 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
With KF 1.3 but KFP bumped to 1.7
KFP version: 1.7
* KFP SDK version: 1.7.1
### Steps to reproduce
Build simple kFP V2 one-component hello-world pipeline.
### Expected result
Pipeline should run
### Materials and Reference
Output logs:
I0915 11:43:08.161494 1 env.go:30] cannot find launcher configmap: name="kfp-launcher" namespace="observability"
I0915 11:43:08.161671 1 launcher.go:145] PipelineRoot defaults to "minio://mlpipeline/v2/artifacts".
I0915 11:43:08.161832 1 cache.go:143] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
I0915 11:43:08.161839 1 cache.go:120] Connecting to cache endpoint ml-pipeline.kubeflow:8887
I0915 11:43:08.285842 1 launcher.go:194] enable caching
I0915 11:43:08.353559 1 object_store.go:285] Cannot detect minio-service in the same namespace, default to minio-service.kubeflow:9000 as MinIO endpoint.
/root/pyenv/bin/python3: No module named ensurepip
/root/pyenv/bin/python3: No module named ensurepip
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/6562 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6562/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6562/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6562/events | https://github.com/kubeflow/pipelines/issues/6562 | 996,134,599 | I_kwDOB-71UM47X87H | 6,562 | [feature] Add option to allow VolumeOp to re-use PVC | {
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"u... | closed | false | null | [] | null | [] | 2021-09-14T15:10:29 | 2021-09-23T05:43:35 | 2021-09-23T05:43:35 | MEMBER | null | ### Feature Area
/area sdk
### What feature would you like to see?
VolumeOp should allow re-using of existing PVC.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
When we have a VolumeOp component in a recurring-run pipeline the component creates new PVC every time a recurring run happens. However, In a use case where the subsequent runs should use the same PVC as the first run, the VolumeOp fails to handle this case.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
I understand we can create spec using `V1PersistentVolumeClaim` and pass it as `k8s_resource` argument but this seems to defeat the purpose of VolumeOp in the first place.
<!-- Without this feature, how do you accomplish your task today? -->
If this seems to be a useful feature I would be happy to create a PR.
---
<!-- 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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"@munagekar, For my education, what's your use case to include Argo placeholders in some \"name\"?",
"@chensun \r\n\r\n- prefix multiple resources created(svc,pvc,depolyment associated with a workflow ) with {{workflow.name}} to link all relevant resources together.\r\n- avoid a resource-name-conflict if the same... | 2021-09-14T10:10:37 | 2022-03-02T21:05:10 | null | CONTRIBUTOR | null | ### What steps did you take
Named a k8s resource as`xxx-{{workflow.uid}}`. It got converted to xxx-workflow-uid. This breaks argo workflow templating.
https://github.com/kubeflow/pipelines/blob/5d82fa7dc8dfe381a8759358b92cc94ef0a0f137/sdk/python/kfp/compiler/_k8s_helper.py#L20-L37.
### What did you expect to happen:
k8s resource names should allow argo workflow templating.
### Anything else you would like to add:
`{{.+}}` should be considered a special case and should not be affected by name sanitization.
### 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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"Deploy kubeflow with master branch. Verifies task api is already available.\r\n\r\n"
] | 2021-09-13T18:10:52 | 2021-09-13T18:12:44 | 2021-09-13T18:12:44 | CONTRIBUTOR | null | ### Feature Area
expose task API via Istio authorization policy. This is part of https://github.com/kubeflow/pipelines/issues/5680#issuecomment-913918171
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
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---
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| {
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https://api.github.com/repos/kubeflow/pipelines/issues/6554 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6554/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6554/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6554/events | https://github.com/kubeflow/pipelines/issues/6554 | 994,588,191 | MDU6SXNzdWU5OTQ1ODgxOTE= | 6,554 | [feature][v2] Allow pipelines to use per steps caching | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | open | false | null | [] | null | [
"We will consider adding this support in KFP v2, but will not support this in KFP v2 compatible mode. Do you want the staleness support? Or just simply enable/disable per-step caching?",
"Seems fine to me. Staleness support would certainly be very useful, but I guess I could find a workaround with only per-step c... | 2021-09-13T08:12:07 | 2022-04-21T00:30:16 | null | NONE | null | ### Feature Area
/area backend
### What feature would you like to see?
In v2 compatible mode, I would like to have a way to configure the caching per components like `task.execution_options.caching_strategy.max_cache_staleness = "P7D"` in v1.
### What is the use case or pain point?
I have some pipelines which must run every hour but some of its components takes hours to execute and their outputs does not change regularly. I want to cache their outputs for one week but this doesn't seems currently possible with v2 compatible mode.
### Is there a workaround currently?
Keep using v1 execution mode.
---
<!-- 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/6553 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6553/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6553/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6553/events | https://github.com/kubeflow/pipelines/issues/6553 | 994,375,137 | MDU6SXNzdWU5OTQzNzUxMzc= | 6,553 | [feature] Expose argo synchronization options | {
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"@Bobgy @capri-xiyue: if this request makes sense, let me know if there's a good place to add these options in the dsl, and I can write up a patch.",
"Hello @jmcarp , what is the CUJ for setting limitation of synchronization on 1)workflow level, 2) step level?",
"Hi @zijianjoy, the typical use case in either ca... | 2021-09-13T02:13:55 | 2023-09-01T09:43:28 | 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. -->
Argo includes useful synchronization features for workflows and templates: https://argoproj.github.io/argo-workflows/synchronization/. It would be helpful to expose these in kfp without having to manipulate manifests as yaml.
I'd be happy to work on this if the request makes sense. It seems like workflow and template level synchronization options would live on the pipeline config and container op classes respectively, but let me know if there's a better place to put them.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Allow users to set a semaphore on a workflow or a step.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Edit generated yaml.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/6552 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6552/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6552/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6552/events | https://github.com/kubeflow/pipelines/issues/6552 | 993,691,090 | MDU6SXNzdWU5OTM2OTEwOTA= | 6,552 | [bug] Pipeline version creation failed | {
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"Hello @yousefis , would you like to check if the inverse-proxy is healthy and whether your backend service has received this request and logs? Thank you!",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for you... | 2021-09-11T00:11:59 | 2022-03-02T18:05:35 | null | NONE | null | Issue in creating the kubeflow pipeline.
Pipeline version creation failed
<!DOCTYPE html>
<html lang=en>
<meta charset=utf-8>
<meta name=viewport content="initial-scale=1, minimum-scale=1, width=device-width">
<title>Error 524 ()!!1</title>
<style>
*{margin:0;padding:0}html,code{font:15px/22px arial,sans-serif}html{background:#fff;color:#222;padding:15px}body{margin:7% auto 0;max-width:390px;min-height:180px;padding:30px 0 15px}* > body{background:url(//www.google.com/images/errors/robot.png) 100% 5px no-repeat;padding-right:205px}p{margin:11px 0 22px;overflow:hidden}ins{color:#777;text-decoration:none}a img{border:0}@media screen and (max-width:772px){body{background:none;margin-top:0;max-width:none;padding-right:0}}#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54.png) no-repeat;margin-left:-5px}@media only screen and (min-resolution:192dpi){#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) no-repeat 0% 0%/100% 100%;-moz-border-image:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) 0}}@media only screen and (-webkit-min-device-pixel-ratio:2){#logo{background:url(//www.google.com/images/logos/errorpage/error_logo-150x54-2x.png) no-repeat;-webkit-background-size:100% 100%}}#logo{display:inline-block;height:54px;width:150px}
</style>
<a href=//www.google.com/><span id=logo aria-label=Google></span></a>
<p><b>524.</b> <ins>That’s an error.</ins>
<p> <ins>That’s all we know.</ins> | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6547 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6547/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6547/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6547/events | https://github.com/kubeflow/pipelines/issues/6547 | 993,566,380 | MDU6SXNzdWU5OTM1NjYzODA= | 6,547 | [backend] Retries fail silently for runs with compressed or offloaded node statuses | {
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"u... | closed | false | null | [] | null | [] | 2021-09-10T20:17:28 | 2021-09-17T00:12:44 | 2021-09-17T00:12:44 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? standalone
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: n/a
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
- Start a run with at least one failed task that includes enough nodes that the workflow object populates `status.compressedNodes` rather than `status.nodes`.
- Retry the run
- Failed nodes aren't retried, but no error is raised
### Expected result
Failed nodes are retried.
### Materials and Reference
Argo compresses node statuses when they're too big, and if compression isn't enough, they're optionally offloaded to the database: https://argoproj.github.io/argo-workflows/offloading-large-workflows/. The kfp retry logic doesn't appear to check compressed or offloaded nodes, so when there are too many node statuses, retries don't appear to have any effect.
We should be able to use `DecompressWorkflow` to handle compressed node statuses: https://github.com/argoproj/argo-workflows/blob/2e174bd4c585ccf72e34c8f72703a0950a67460c/workflow/packer/packer.go#L29. But to handle offloaded status, I think we'd either need to talk to the argo database directly, or use the argo api. I don't see argo persistence or offloaded statuses enabled in the helm or kustomize manifests in this repo, so maybe it's good enough to handle compressed node statues and error on offloaded statuses for now.
Thoughts @Bobgy?
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https://api.github.com/repos/kubeflow/pipelines/issues/6540 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6540/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6540/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6540/events | https://github.com/kubeflow/pipelines/issues/6540 | 992,971,877 | MDU6SXNzdWU5OTI5NzE4Nzc= | 6,540 | [sdk] Unable to run pipeline from Codelab | {
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"@ritaarex If I'm not mistaken, you set `region` to `'eu'` when you initialize the client?\r\n```\r\napi_client = AIPlatformClient(\r\n project_id=PROJECT_ID,\r\n region=REGION,\r\n)\r\n```\r\n`eu` is not a valid region, Vertex Pipelines currently support the following regions in Europe:\r\n- London (europe-w... | 2021-09-10T07:37:33 | 2021-09-24T09:09:07 | 2021-09-24T09:09:07 | NONE | null | Hi,
I am following the codelab [Intro to Vertex AI pipelines](https://codelabs.developers.google.com/vertex-pipelines-intro#0), but I am having trouble. At the final step, when I try to run the pipeline, I get an error.
Can anyone help?
Thanks!!
### Environment
* KFP SDK version: 1.8.0
* All dependencies version:
kfp 1.8.0
kfp-pipeline-spec 0.1.10
kfp-server-api 1.7.0
### Steps to reproduce
```
from typing import NamedTuple
import kfp
from kfp import dsl
from kfp.v2 import compiler
from kfp.v2.dsl import (Artifact, Dataset, Input, InputPath, Model, Output,
OutputPath, ClassificationMetrics, Metrics, component)
from kfp.v2.google.client import AIPlatformClient
from google.cloud import aiplatform
from google_cloud_pipeline_components import aiplatform as gcc_aip
PATH=%env PATH
%env PATH={PATH}:/home/jupyter/.local/bin
REGION="eu"
PIPELINE_ROOT = f"{BUCKET_NAME}/pipeline_root/"
@component(base_image="python:3.9", output_component_file="first-component.yaml")
def product_name(text: str) -> str:
return text
@component(packages_to_install=["emoji"])
def emoji(
text: str,
) -> NamedTuple(
"Outputs",
[
("emoji_text", str), # Return parameters
("emoji", str),
],
):
import emoji
emoji_text = text
emoji_str = emoji.emojize(':' + emoji_text + ':', use_aliases=True)
print("output one: {}; output_two: {}".format(emoji_text, emoji_str))
return (emoji_text, emoji_str)
@component
def build_sentence(
product: str,
emoji: str,
emojitext: str
) -> str:
print("We completed the pipeline, hooray!")
end_str = product + " is "
if len(emoji) > 0:
end_str += emoji
else:
end_str += emojitext
return(end_str)
@dsl.pipeline(
name="hello-world",
description="An intro pipeline",
pipeline_root=PIPELINE_ROOT,
)
def intro_pipeline(text: str = "Vertex Pipelines", emoji_str: str = "sparkles"):
product_task = product_name(text)
emoji_task = emoji(emoji_str)
consumer_task = build_sentence(
product_task.output,
emoji_task.outputs["emoji"],
emoji_task.outputs["emoji_text"],
)
compiler.Compiler().compile(
pipeline_func=intro_pipeline, package_path="intro_pipeline_job.json"
)
api_client = AIPlatformClient(
project_id=PROJECT_ID,
region=REGION,
)
response = api_client.create_run_from_job_spec(
job_spec_path="intro_pipeline_job.json",
# pipeline_root=PIPELINE_ROOT # this argument is necessary if you did not specify PIPELINE_ROOT as part of the pipeline definition.
)
```
### Error message
```
---------------------------------------------------------------------------
HttpError Traceback (most recent call last)
/tmp/ipykernel_20628/314143335.py in <module>
1 response = api_client.create_run_from_job_spec(
----> 2 job_spec_path="intro_pipeline_job.json",
3 #pipeline_root=PIPELINE_ROOT # this argument is necessary if you did not specify PIPELINE_ROOT as part of the pipeline definition.
4 )
~/.local/lib/python3.7/site-packages/kfp/v2/google/client/client.py in create_run_from_job_spec(self, job_spec_path, job_id, pipeline_root, parameter_values, enable_caching, cmek, service_account, network, labels)
356 return self._submit_job(
357 job_spec=job_spec,
--> 358 job_id=job_id,
359 )
360
~/.local/lib/python3.7/site-packages/kfp/v2/google/client/client.py in _submit_job(self, job_spec, job_id)
234 request = self._api_client.projects().locations().pipelineJobs().create(
235 body=job_spec, pipelineJobId=job_id, parent=self._parent)
--> 236 response = request.execute()
237 job_id = _extract_job_id(response['name'])
238 if job_id:
~/.local/lib/python3.7/site-packages/googleapiclient/_helpers.py in positional_wrapper(*args, **kwargs)
132 elif positional_parameters_enforcement == POSITIONAL_WARNING:
133 logger.warning(message)
--> 134 return wrapped(*args, **kwargs)
135
136 return positional_wrapper
~/.local/lib/python3.7/site-packages/googleapiclient/http.py in execute(self, http, num_retries)
913 callback(resp)
914 if resp.status >= 300:
--> 915 raise HttpError(resp, content, uri=self.uri)
916 return self.postproc(resp, content)
917
HttpError: <HttpError 404 when requesting https://eu-aiplatform.googleapis.com/v1beta1/projects/arex-data/locations/eu/pipelineJobs?pipelineJobId=hello-world-20210909105450&alt=json returned "Not Found">
```
### Expected result
Running the pipeline should generate a link to view the pipeline run in your console.
### 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/6536 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6536/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6536/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6536/events | https://github.com/kubeflow/pipelines/issues/6536 | 991,858,599 | MDU6SXNzdWU5OTE4NTg1OTk= | 6,536 | [doc] how to set resource request/limit | {
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"https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#specify-machine-type is a good example for how to document this",
"/assign @chensun \r\ncan you keep track of this request?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no furth... | 2021-09-09T06:37:35 | 2022-03-02T21:05:21 | 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. -->
Documentation on kubeflow.org about how to set cpu request, cpu limit, memory request, memory limit, gpu request, gpu limit etc.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
This is a FAQ in kubeflow pipelines slack channel.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
We have an example in https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_spec/resource_spec.py, but the example is not easily searchable on Google, therefore this question is asked many times.
---
<!-- 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/6526 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6526/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6526/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6526/events | https://github.com/kubeflow/pipelines/issues/6526 | 990,908,073 | MDU6SXNzdWU5OTA5MDgwNzM= | 6,526 | [backend] Caching leads to unexpected prefix error | {
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"Hi @jonasdebeukelaer, that's a bug I introduced, I believe it should be fixed by https://github.com/kubeflow/pipelines/pull/6415 as a side effect. Can you verify KFP SDK 1.8.0?",
"@Bobgy yes seems that fixed it thanks, sorry didn't see a new version had been released!",
"No worries, thank you for verifying the... | 2021-09-08T09:03:31 | 2021-09-13T02:29:57 | 2021-09-12T21:59:16 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? standalone on azure with minio object storage
* KFP version: `1.7.0`
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
```
kfp 1.7.1
kfp-pipeline-spec 0.1.9
kfp-server-api 1.7.0
```
### Steps to reproduce
Run a pipeline twice with the second step getting an artifact from the first step.
Second run will fail on something like
```
Failed to execute component: failed to download input artifact "model" from remote storage URI "minio://mlpipeline/v2/artifacts/pipeline/my pipeline/3341543c-cb07-464b-b6a8-4d58d86fb209/train-3/model": URI "minio://mlpipeline/v2/artifacts/pipeline/my pipeline/3341543c-cb07-464b-b6a8-4d58d86fb209/train-3/model" does not have expected bucket prefix "minio://mlpipeline/v2/artifacts/pipeline/my pipeline/78bfc8c3-6707-474e-9b20-b6132830c060"
(I think this part is unrelated but leaving in in case it's useful)
time="2021-09-07T16:12:03.327Z" level=error msg="cannot save artifact /tmp/outputs/predictions/data" argo=true error="stat /tmp/outputs/predictions/data: no such file or directory"
Error: exit status 1
```
where `78bfc8c3-6707-474e-9b20-b6132830c060` is he current pipeline id and `3341543c-cb07-464b-b6a8-4d58d86fb209` is the previous pipeline run id.
### Expected result
caching works as expected and the new pipeline is able to access its cached results.
### Materials and Reference
error happens in line 100 in `object_store.go`.
---
<!-- 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/6517 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6517/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6517/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6517/events | https://github.com/kubeflow/pipelines/issues/6517 | 990,271,910 | MDU6SXNzdWU5OTAyNzE5MTA= | 6,517 | [feature] Allow KFP to specify Minio instance when configuring pipeline root | {
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"This can be achieved via https://github.com/kubeflow/pipelines/issues/5753 too if implemented.",
"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",
"What needs t... | 2021-09-07T19:15:41 | 2023-06-29T04:35:28 | 2022-08-04T22:56:03 | CONTRIBUTOR | null | ### Feature Area
<!-- /area backend -->
### What feature would you like to see?
Currently when user configure pipeline root with minio, user can't change the MinIO instance. Kubeflow Pipelines can only use the Minio instance deployed with itself. Allow KFP to specify Minio instance other than the minio instance deployed with KFP when configuring pipeline root
### 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/6512 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6512/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6512/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6512/events | https://github.com/kubeflow/pipelines/issues/6512 | 989,668,245 | MDU6SXNzdWU5ODk2NjgyNDU= | 6,512 | [sdk] nodeSelector specification not being set correctly in pipeline spec | {
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"u... | closed | false | null | [] | null | [] | 2021-09-07T07:13:11 | 2021-09-09T22:26:51 | 2021-09-09T22:26:51 | NONE | null | ### Environment
* KFP version:
1.3
* KFP SDK version:
1.8.0
* All dependencies version:
kfp 1.8.0
kfp-pipeline-spec 0.1.10
kfp-server-api 1.6.0
### Steps to reproduce
python samples/core/resource_spec/runtime_resource_request_gpu.py
Check the nodeSelector spec in the yaml file generated at the path samples/core/resource_spec/runtime_resource_request_gpu.py.yaml
nodeSelector: {} -> this specification is wrong.
### Expected result
nodeSelector should have been generated at run time.
### Materials and Reference
---
<!-- 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/6511 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6511/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6511/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6511/events | https://github.com/kubeflow/pipelines/issues/6511 | 989,502,529 | MDU6SXNzdWU5ODk1MDI1Mjk= | 6,511 | Where is the changelog for the `kfp` PyPi package? | {
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"cc @chensun \r\n\r\nCurrently we don't have CHANGELOG, defer to Chen to determine the direction of documentation",
"@thesuperzapper We have the changelog for SDK: https://github.com/kubeflow/pipelines/blob/master/sdk/RELEASE.md\r\n\r\n> How are kfp PyPi releases related to pipelines releases?\r\n\r\nKFP SDK is r... | 2021-09-07T00:49:00 | 2021-09-23T05:00:36 | 2021-09-23T05:00:36 | MEMBER | null | I often see releases for the [`kfp` PyPi releases](https://pypi.org/project/kfp/#history) which are not aligned with [`kubeflow/pipelines` releases](https://github.com/kubeflow/pipelines/releases), for example the latest versions are currently:
- [`kfp` == `1.8.0`](https://pypi.org/project/kfp/1.8.0/)
- [`kubeflow/pipelines` == `1.7.0`](https://github.com/kubeflow/pipelines/releases/tag/1.7.0).
__So I have two questions:__
1. Is there a public changelog for the `kfp` PyPi package (similar to the main [CHANGELOG.md](https://github.com/kubeflow/pipelines/blob/master/CHANGELOG.md))?
2. How are `kfp` PyPi releases related to pipelines releases? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6510 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6510/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6510/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6510/events | https://github.com/kubeflow/pipelines/issues/6510 | 989,157,591 | MDU6SXNzdWU5ODkxNTc1OTE= | 6,510 | Shared memory issue in Kubeflow when training on GPU cluster | {
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"Hello @mikel-brostrom , this doesn't seem to be Kubeflow Pipelines related. This is more related to kubernetes because the component runs directly on k8s. Can you try figuring out how much GPU you need on training component and find solution using general k8s approach?",
"I fixed it by creating a pipelinevolme ... | 2021-09-06T13:08:56 | 2021-12-24T06:01:00 | 2021-09-16T23:05:23 | NONE | null | ### What steps did you take
1. I deployed a pipeline to Kubeflow from my local computer
2. The pipeline starts and
* The fetch component runs successfully
* The split component run successfully
* BUT, the train component gives an error. More specifically:
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
### What I tried
1. I ssh:d into my GPU worker pool and increased the smh by:
```bash
sudo mount -o remount,size=8G /dev/shm
```
without any luck.
Any thoughts? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6506 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6506/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6506/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6506/events | https://github.com/kubeflow/pipelines/issues/6506 | 988,092,803 | MDU6SXNzdWU5ODgwOTI4MDM= | 6,506 | [feature] Use `pipelines.kubeflow.org/engine_mode` pod label to differentiate between different kfp engine like "v2" and "v2_compatible" | {
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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"
] | 2021-09-03T20:51:46 | 2022-03-02T21:05:23 | null | CONTRIBUTOR | null | ### Feature Area
Use `pipelines.kubeflow.org/engine_mode` pod label to differentiate between different kfp engine like "v2" and "v2_compatible
<!-- /area backend -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6503 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6503/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6503/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6503/events | https://github.com/kubeflow/pipelines/issues/6503 | 987,486,814 | MDU6SXNzdWU5ODc0ODY4MTQ= | 6,503 | pipeline parameters,CustomPythonPackageTrainingJobRunOp, NoneType object is not subscriptable | {
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"cc @SinaChavoshi ",
"Getting this error now on changing to python_module_name. Have put the code in #6426 \r\n\r\nKeyError Traceback (most recent call last)\r\n<ipython-input-9-57017d2a36e9> in <module>\r\n 2 pip_package_path=PIPELINE_DISPLAY_NAME + \".json\"\r\n 3 comp... | 2021-09-03T07:17:30 | 2021-09-27T15:52:08 | 2021-09-27T15:52:07 | NONE | null | Hi Sina,
This is not the case.
I am already passing python_module_name as str.
But still getting the error.
Sample for reference:-
PROJECT_ID = 'xyz'
LOCATION = 'abc'
PIPELINE_ROOT_PATH = 'gs://test'
MODEL_DISPLAY_NAME='custom-test1-template'
PIPELINE_DISPLAY_NAME = 'custom-test-pipeline'
CONTAINER_URI='us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0-23:latest'
STAGING_BUCKET='gs://test1'
BASE_OUTPUT_DIR='gs://test2/custom/'
PYTHON_PACKAGE_GCS_URI='gs://my-bucket/trainer-0.1.tar.gz'
PYTHON_MODULE_NAME='trainer.task'
ENDPOINT_DISPLAY_NAME='custom-pipeline-endpoint'
MODEL_SERVING_CONTAINER_IMAGE_URI='us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest'
TRAINING_JOB_NAME='custom-pipeline-job'
REPLICA_COUNT=1
MACHINE_TYPE='n1-standard-4'
def pipeline(
project: str = PROJECT_ID,
location: str = LOCATION,
display_name: str = TRAINING_JOB_NAME,
python_package_gcs_uri: str = PYTHON_PACKAGE_GCS_URI,
python_module_name: str = PYTHON_MODULE_NAME,
container_uri: str = CONTAINER_URI,
staging_bucket: str = STAGING_BUCKET,
base_output_dir: str = BASE_OUTPUT_DIR,
model_serving_container_image_uri: str = MODEL_SERVING_CONTAINER_IMAGE_URI,
replica_count: int = REPLICA_COUNT,
machine_type: str =MACHINE_TYPE ,
model_display_name: str = MODEL_DISPLAY_NAME,
):
training_job_run_op = gcc_aip.CustomPythonPackageTrainingJobRunOp(
display_name=display_name,
python_package_gcs_uri=python_package_gcs_uri,
python_module=python_module_name,
container_uri=container_uri,
project=project,
location=location,
staging_bucket=staging_bucket,
base_output_dir=base_output_dir,
model_serving_container_image_uri=model_serving_container_image_uri,
replica_count=replica_count,
machine_type=machine_type,
)
Note:- I am not able to reopen this issue.
_Originally posted by @cah-parita-desai in https://github.com/kubeflow/pipelines/issues/6426#issuecomment-912281873_ | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6502 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6502/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6502/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6502/events | https://github.com/kubeflow/pipelines/issues/6502 | 987,485,253 | MDU6SXNzdWU5ODc0ODUyNTM= | 6,502 | I believe this issue was fixed by #6473 . Closing as fixed, this is not the case please reopen this bug. | {
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"@SinaChavoshi :- Wer you able to look into this further?",
"thank you for the report! reopened https://github.com/kubeflow/pipelines/issues/6426 for you"
] | 2021-09-03T07:15:24 | 2021-09-10T00:42:57 | 2021-09-10T00:42:57 | NONE | null | I believe this issue was fixed by #6473 . Closing as fixed, this is not the case please reopen this bug.
_Originally posted by @SinaChavoshi in https://github.com/kubeflow/pipelines/issues/6426#issuecomment-911878051_ | {
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"Tracking action items:\r\n\r\n- [x] https://github.com/kubeflow/pipelines/pull/6467 \r\n- [x] https://github.com/kubeflow/pipelines/pull/6501\r\n- [ ] TODO: Check formatting in CI.",
"/lgtm",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no furt... | 2021-09-03T00:29:35 | 2022-03-02T21:05:09 | null | COLLABORATOR | null | Currently, the KFP SDK code isn’t formatted consistently. One noticeable inconsistency is that some files have 2-space indentation (conforms to Google internal style) while others have 4-space indentation (conforms to [PEP8](https://www.python.org/dev/peps/pep-0008/#indentation) and [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md#34-indentation)).
To have a clean, consistent code base, we should pick a formatting style and stick with it.
Based some evaluation of both Google's internal style and the public [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md), we decided on the following [YAPF](https://github.com/google/yapf/) configuration:
```
[style]
based_on_style = yapf
indent_width = 4
split_before_named_assigns = true
column_limit = 80
```
The new style config only applies to KFP SDK code. While for components, component authors can pick their own style.
/area sdk
/cc @neuromage @ji-yaqi
/cc @IronPan @SinaChavoshi
/cc @Bobgy | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6496 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6496/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6496/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6496/events | https://github.com/kubeflow/pipelines/issues/6496 | 986,937,655 | MDU6SXNzdWU5ODY5Mzc2NTU= | 6,496 | Expose docker entrypoint on kfp local runner | {
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... | open | false | null | [] | null | [
"@chensun Hello Chen, would you like to help looking at this issue? Thank you!\r\n\r\ncc @lynnmatrix ",
"Do it make sens to add it to the entrypoint if you want to change it for different use cases? Could you drop the entrypoint and instead incoporate it as part of the command @etsyamitra ?\r\n",
"That would be... | 2021-09-02T16:26:47 | 2022-03-02T18:05:32 | null | NONE | null | ### Feature Area
kfp local runner through docker exec environment
### What feature would you like to see?
Being able to set docker --[entrypoint](https://docs.docker.com/engine/reference/run/#entrypoint-default-command-to-execute-at-runtime) which running the component.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Removing the inconsistencies between running through docker and kfp locally with more powerful features.
Creating different images just for having separate entrypoint seems like too much overhead, it would be ideal to pass the entrypoint to the docker itself for each component.
### Is there a workaround currently?
Bake it into the image
---
<!-- 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/6495 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6495/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6495/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6495/events | https://github.com/kubeflow/pipelines/issues/6495 | 986,748,937 | MDU6SXNzdWU5ODY3NDg5Mzc= | 6,495 | [sdk] compilation failed when reusing operator definition on inner For loop | {
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"This is fixed by https://github.com/kubeflow/pipelines/pull/6600\r\nPending KFP SDK 1.8.3 release (by today)."
] | 2021-09-02T13:43:21 | 2021-09-28T20:59:22 | 2021-09-28T20:59:22 | NONE | null | ### Environment
* KFP version: Not important
* KFP SDK version: 1.7.* and 1.8.*
* All dependencies version:
kfp 1.7.1
kfp-pipeline-spec 0.1.10
kfp-server-api 1.7.0
### Steps to reproduce
The following script fails to build to compile the pipeline when using kfp==1.7.* but succeeds when using kfp=1.6*
```python
import kfp
from kfp.components import create_component_from_func
from kfp.compiler import Compiler
from typing import List
def get_iterator() -> List[str]:
return ["a", "b"]
def print_output(a: str, b: str) -> None:
print(a, b)
@kfp.dsl.pipeline(name="test_pipeline")
def pipeline_func():
op = create_component_from_func(get_iterator)()
print(op.name)
with kfp.dsl.ParallelFor(op.output) as loop_var:
op2 = create_component_from_func(get_iterator)()
print(op2.name)
with kfp.dsl.ParallelFor(op2.output) as loop_var2:
create_component_from_func(print_output)(a=loop_var, b=loop_var2)
Compiler().compile(pipeline_func, "test.yaml")
```
with the following error
> Traceback (most recent call last):
> File "test/test_kfp_version_compat.py", line 24, in <module>
> Compiler().compile(pipeline_func, "test.yaml")
> File "/Users/victor.bellon/.virtualenvs/continuous-elas-meta/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 1061, in compile
> self._create_and_write_workflow(
> File "/Users/victor.bellon/.virtualenvs/continuous-elas-meta/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 1114, in _create_and_write_workflow
> workflow = self._create_workflow(
> File "/Users/victor.bellon/.virtualenvs/continuous-elas-meta/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 950, in _create_workflow
> workflow = self._create_pipeline_workflow(
> File "/Users/victor.bellon/.virtualenvs/continuous-elas-meta/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 717, in _create_pipeline_workflow
> templates = self._create_dag_templates(pipeline, op_transformers)
> File "/Users/victor.bellon/.virtualenvs/continuous-elas-meta/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 653, in _create_dag_templates
> inputs, outputs = self._get_inputs_outputs(
> File "/Users/victor.bellon/.virtualenvs/continuous-elas-meta/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 272, in _get_inputs_outputs
> upstream_op = pipeline.ops[param.op_name]
> KeyError: 'get-iterator 2'
### Expected result
The pipeline should compile properly
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6493 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6493/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6493/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6493/events | https://github.com/kubeflow/pipelines/issues/6493 | 986,310,043 | MDU6SXNzdWU5ODYzMTAwNDM= | 6,493 | [frontend] <Unable to configure minio accesskey using manifests> | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | open | false | null | [] | null | [
"Hello @Hmr-ramzi , currently there is no good way to programmatically update minio access key in multi-user mode.\r\n\r\nYou can change minio access key using kubectl following this https://github.com/kubeflow/pipelines/blob/a35812429c9e57b29e7b559814f299af8c2fd305/manifests/kustomize/base/installs/multi-user/pipe... | 2021-09-02T07:45:07 | 2022-03-02T21:05:15 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Using kubeflow manifests
* KFP version:
1.5.1
### Steps to reproduce
By providing an access key to minio that is different than minio123 the frontend is unable to access minio
### Expected result
Once the backend is configured with the right access key this is also assumed to be available for the frontend when we deploy kubeflow with the kubeflow manifests
### Materials and Reference
https://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/frontend/server/configs.ts
---
<!-- 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/6492 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6492/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6492/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6492/events | https://github.com/kubeflow/pipelines/issues/6492 | 986,103,270 | MDU6SXNzdWU5ODYxMDMyNzA= | 6,492 | [backend] API performance regression in 1.5 | {
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"u... | closed | false | null | [] | null | [
"Hi @jmcarp, thank you for filing this report!\r\nIs it possible also trying to reproduce in the latest 1.7.0 release?\r\n\r\nI don't have any ideas at the moment, so welcome any contributions\r\n"
] | 2021-09-02T04:23:06 | 2021-09-08T10:48:07 | 2021-09-08T10:48:07 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Standalone
* KFP version:
1.5.1
* KFP SDK version:
n/a
### Steps to reproduce
- Upgrade from 1.4.1 to 1.5.1.
- Refresh the runs page several times.
- API latency is mostly fast (~500ms) but sometimes jumps to >30s.
<img width="875" alt="Screen Shot 2021-09-01 at 11 28 10 PM" src="https://user-images.githubusercontent.com/1633460/131781384-e752bb4f-dd96-4aa8-95d2-46491b1218fd.png">
Note: after downgrading to 1.4.1, API performance was consistently fast (~500ms).
<img width="876" alt="Screen Shot 2021-09-02 at 12 21 53 AM" src="https://user-images.githubusercontent.com/1633460/131781405-2e3cd16d-9cd0-4130-b6a9-68c44924d952.png">
### Expected result
API performance should be unchanged.
### 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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"u... | open | false | null | [] | null | [
"@yanniszark @elikatsis @kimwnasptd \r\n\r\nHello Arrikto, is this an expected behavior for Kale needing this permission to get argo workflow? Would you like to consider adding permission for Kale to access Pipelines?\r\n",
"Any updates or thoughts on this?",
"@zijianjoy @elikatsis A fix for this issue was foun... | 2021-09-01T13:51:57 | 2022-03-02T18:05:37 | null | CONTRIBUTOR | null | We are trying to setup Kale in Kubeflow v1.3.0 with KFP 1.7 in multi-user mode.
Kale can access the KFP API and perform compile, upload and start a run.
However, the run always fails on the first user-made component (the kale-marshall-volume step succeds). The error message indicates that the user service-account doesn't have access to the workflow. Pipelines uploaded through the UI or using REST calls to the KFP API runs successfully. Only pipelines uploaded by Kale fails. See logs below.
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Using KubeFlow v1.3 on-prem manifests. Multi-user installation.
* KFP version:
1.7
* KFP SDK version:
1.7.1 (1.7.2 has a mismatch with kfp-server-api)
### Steps to reproduce
Create notebook from web UI.
Create single cell notebook with print statement.
Annotate as pipeline step and hit compile and run.
### Expected result
Pipelines produced by Kale should work.
### Materials and Reference
Logs from the failing component:
```
2021-09-01 07:33:55 Kale mlmdutils:90 [INFO] ---------- Initializing MLMD context... ----------
2021-09-01 07:33:55 Kale mlmdutils:91 [INFO] Connecting to MLMD...
2021-09-01 07:33:55 Kale mlmdutils:93 [INFO] Successfully connected to MLMD
2021-09-01 07:33:55 Kale mlmdutils:94 [INFO] Getting step details...
2021-09-01 07:33:55 Kale mlmdutils:95 [INFO] Getting pod name...
2021-09-01 07:33:55 Kale mlmdutils:97 [INFO] Successfully retrieved pod name: kale-test-2lq4m-2937332039
2021-09-01 07:33:55 Kale mlmdutils:98 [INFO] Getting pod namespace...
2021-09-01 07:33:55 Kale mlmdutils:101 [INFO] Successfully retrieved pod namespace: jars
2021-09-01 07:33:55 Kale mlmdutils:102 [INFO] Getting pod...
2021-09-01 07:33:55 Kale mlmdutils:104 [INFO] Successfully retrieved pod
2021-09-01 07:33:55 Kale mlmdutils:105 [INFO] Getting workflow name from pod...
2021-09-01 07:33:55 Kale mlmdutils:109 [INFO] Successfully retrieved workflow name: kale-test-2lq4m
2021-09-01 07:33:55 Kale mlmdutils:110 [INFO] Getting workflow...
Traceback (most recent call last):
File "/tmp/tmp.pGfFr6iV77", line 35, in <module>
_outputs = hej(**_parsed_args)
File "/tmp/tmp.pGfFr6iV77", line 3, in hej
_kale_mlmdutils.init_metadata()
File "/root/pyenv/lib/python3.7/site-packages/kale/common/mlmdutils.py", line 537, in init_metadata
mlmd_instance = MLMetadata()
File "/root/pyenv/lib/python3.7/site-packages/kale/common/mlmdutils.py", line 112, in __init__
self.pod_namespace)
File "/root/pyenv/lib/python3.7/site-packages/kale/common/workflowutils.py", line 73, in get_workflow
ARGO_WORKFLOWS_PLURAL, name)
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/api/custom_objects_api.py", line 1484, in get_namespaced_custom_object
return self.get_namespaced_custom_object_with_http_info(group, version, namespace, plural, name, **kwargs) # noqa: E501
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/api/custom_objects_api.py", line 1605, in get_namespaced_custom_object_with_http_info
collection_formats=collection_formats)
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 353, in call_api
_preload_content, _request_timeout, _host)
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 184, in __call_api
_request_timeout=_request_timeout)
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 377, in request
headers=headers)
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/rest.py", line 243, in GET
query_params=query_params)
File "/root/pyenv/lib/python3.7/site-packages/kubernetes/client/rest.py", line 233, in request
raise ApiException(http_resp=r)
kubernetes.client.exceptions.ApiException: (403)
Reason: Forbidden
HTTP response headers: HTTPHeaderDict({'Audit-Id': '55fcf2fc-2f05-4286-9e95-cb916b633246', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'X-Content-Type-Options': 'nosniff', 'X-Kubernetes-Pf-Flowschema-Uid': '6b4284f3-6c8a-4ff6-9175-c9cc05603c3b', 'X-Kubernetes-Pf-Prioritylevel-Uid': 'c15d461f-7449-4812-903b-59f654fe74ee', 'Date': 'Wed, 01 Sep 2021 07:33:55 GMT', 'Content-Length': '387'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"workflows.argoproj.io \"kale-test-2lq4m\" is forbidden: User \"system:serviceaccount:jars:default-editor\" cannot get resource \"workflows\" in API group \"argoproj.io\" in the namespace \"jars\"","reason":"Forbidden","details":{"name":"kale-test-2lq4m","group":"argoproj.io","kind":"workflows"},"code":403}
```
Any help would be greatly appreciated!
---
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/6486 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6486/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6486/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6486/events | https://github.com/kubeflow/pipelines/issues/6486 | 984,906,920 | MDU6SXNzdWU5ODQ5MDY5MjA= | 6,486 | [backend] Unsupported resource type: PIPELINE | {
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"u... | open | false | null | [] | null | [
"Hello @rubenaranamorera , would you like to try using ResourceType_PIPELINE_VERSION? You can use this or get the `PipelineID` from `PipelineSpec` as specified in https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#/definitions/apiPipelineSpec for the same goal.",
"Using \... | 2021-09-01T09:11:17 | 2022-03-02T21:05:14 | null | NONE | null | When calling the api with an APIResourceReference:
```
apiResourceReference := &job_model.APIResourceReference{
Key: &job_model.APIResourceKey{
ID: pipelineOk.Payload.ID,
Type: job_model.APIResourceTypePIPELINE,
},
Name: pipelineOk.Payload.Name,
Relationship: job_model.APIRelationshipCREATOR,
}
```
I'm getting an error:
`"error": "[POST /apis/v1beta1/jobs][400] CreateJob default &{Code:3 Details:[0xc0035a7920] Error:Create job failed: Error to convert resource references.: Failed to convert reference type: Invalid input error: Unsupported resource type: PIPELINE}"}`
I think it is because "PIPELINE" is missing in https://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/backend/src/apiserver/common/const.go#L73-L86
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"Hello @sujaykulkarn , currently this is not supported by KFP. cc @chensun ",
"I think the ask is to support additional artifact repositories, which needs to be supported by KFP runtime first. \r\n\r\n@Bobgy to comment.",
"I am interested in more user feedback before adding these features, because you can also ... | 2021-08-31T14:09:02 | 2022-03-02T21:05:16 | null | NONE | null | Hi, I was trying to understand how to create HTTP, git and other artifacts using SDK which are supported by Argo Workflows. (https://argoproj.github.io/argo-workflows/configure-artifact-repository/) please help with your inputs on how to create pipelines using these artifacts. Thanks | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6469 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6469/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6469/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6469/events | https://github.com/kubeflow/pipelines/issues/6469 | 982,470,760 | MDU6SXNzdWU5ODI0NzA3NjA= | 6,469 | v2 - downloading/uploading artifacts should be backoff retried | {
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"the error is expected, because network is not always stable.\r\nIn these cases, we should have a backoff retry to handle the flakiness of network.",
"Reassigned it to James Wu to further discuss the priority and the assignee",
"This issue has been automatically marked as stale because it has not had recent act... | 2021-08-30T07:13:26 | 2022-04-17T06:27:41 | null | CONTRIBUTOR | null | v2 sample test flaky when downloading/uploading artifacts with error message:
```
net/http: TLS handshake timeout
```
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/issues/6468#issuecomment-908093370_ | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6465 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6465/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6465/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6465/events | https://github.com/kubeflow/pipelines/issues/6465 | 981,976,289 | MDU6SXNzdWU5ODE5NzYyODk= | 6,465 | [bug] Unhandled SageMaker training job status 'stopped' causing infinite loop | {
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"color": "db1203",
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... | closed | false | null | [] | null | [
"Thanks for adding this issue. You are more than welcome to contribute a fix @benhyy. ",
"/area components/aws/sagemaker",
"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 has... | 2021-08-29T02:33:01 | 2023-01-09T21:50:03 | 2023-01-09T21:50:02 | NONE | null | ### What steps did you take
Code gets stuck in infinite loop is SageMaker training job gets stopped (unhandled use case)
### What happened:
https://github.com/kubeflow/pipelines/blob/master/components/aws/sagemaker/train/src/sagemaker_training_component.py#L57-L66
Above code only caters for training job status `Completed` or `Failed`, so if the training job status is marked as `Stopped`, it causes an infinite loop in below code
https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/components/aws/sagemaker/common/sagemaker_component.py#L197-L201
### What did you expect to happen:
Training job status `stopped` to be catered for
### Environment:
### Anything else you would like to add:
### 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/6457 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6457/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6457/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6457/events | https://github.com/kubeflow/pipelines/issues/6457 | 981,497,708 | MDU6SXNzdWU5ODE0OTc3MDg= | 6,457 | [bug] Problem starting a pipeline run with REST api | {
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"u... | open | false | null | [] | null | [
"Hello @mlu4679 , you can refer to https://www.kubeflow.org/docs/components/pipelines/multi-user/#how-are-resources-separated to specify resource, you can use either the resource `NAMESPACE ` or `EXPERIMENT` in resource filter. Because these two fields allow multi-user mode to learn which namespace to run your pipe... | 2021-08-27T18:36:37 | 2022-03-02T21:05:13 | null | NONE | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
I am trying to use the KFP REST API in order to create a new run of a pipeline that already exists.
Using the python requests package, I am authenticating myself in order to use POST and GET requests with the API. Currently, the GET requests work as intended, however, when I try to use a POST request to start a new pipeline run, I run into an error. A snippet of the code I am using is below:
```
runURL = f"{HOST}/pipeline/apis/v1beta1/runs"
inputobject2={"description":"","name":"gcd_test3","pipeline_spec":
{"parameters":[{"name":"a","value":"70"},{"name":"b","value":"35"},{"name":"c","value":"42"},{"name":"d","value":"14"}]},
"resource_references":[{"key":{"id":experimentID,"type":"EXPERIMENT"},"relationship":"OWNER"},
{"key":{"id":pipelineid,"type":"PIPELINE_VERSION"},"relationship":"CREATOR"}],"service_account":""}
headers2 = {
"Content-Type": "application/json"
}
output = session.post(runURL, data=inputobject2, headers = headers2)
pp.pprint(output)
pp.pprint(output.json())
```
### What happened:
I get a response 400 error, with the following text:
```
<Response [400]>
{'code': 3,
'details': [{'@type': 'type.googleapis.com/api.Error',
'error_details': 'Invalid input error: ListRuns must filter by '
'resource reference in multi-user mode.',
'error_message': 'ListRuns must filter by resource reference in '
'multi-user mode.'}],
'error': 'Invalid input error: ListRuns must filter by resource reference in '
'multi-user mode.',
'message': 'Invalid input error: ListRuns must filter by resource reference '
'in multi-user mode.'}
```
### What did you expect to happen:
I expect that a new pipeline run should start, and that I should be able to view the details of the run in the KFP UI.
### Environment:
<!-- Please fill in those that seem relevant. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
1.0.0
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.2.0
kfp-server-api 1.2.0
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
As said earlier, using get requests to retrieve information such as all the pipelines that already exist works fine, it is just the HTTPS POST commands that do not work.
### 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 prioritize the issues with the most 👍.
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"KFP SDK v2 issue: https://github.com/kubeflow/pipelines/issues/6574\r\n",
"Hello, we love the v2 so far but we would highly appreciate to be able to compare runs like we used to on v1 (eg: be able to compare the metrics of each of them in one go instead of having to open each of them manually).\r\nThis is our ma... | 2021-08-26T07:52:43 | 2022-04-14T11:57:07 | null | CONTRIBUTOR | null | Documentation: www.kubeflow.org/docs/components/pipelines/sdk/v2/v2-compatibility/
Design: [bit.ly/kfp-v2-compatible](https://bit.ly/kfp-v2-compatible)
[New Features](https://docs.google.com/document/d/139pweX3PtCv96DLeyVYzZpcf3mB1Z0MFvjSEmu4D3tg/edit?pli=1#heading=h.jmnajnr9e0zj)
Breaking Changes and Known Caveats: https://github.com/kubeflow/pipelines/issues/6133
For **feedbacks**, comment on this issue.
For **bug reports**, create a separate issue logging the details and comment about it here. | {
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"/assign @zijianjoy ",
"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"
] | 2021-08-26T00:16:38 | 2022-03-02T21:49:34 | null | CONTRIBUTOR | null | ### Feature Area
<!-- /area frontend -->
### What feature would you like to see?
I would like to use the Google IAP as an auth proxy for the KFP UI. As a secondary security measure, Google recommends verifying the JWT header from IAP at the application layer: https://cloud.google.com/iap/docs/signed-headers-howto. As discussed in https://kubeflow.slack.com/archives/CE10KS9M4/p1629833793108400, we can do this in KFP using a custom middleware that's used if the user enables IAP verification using environment variables. I'm planning to send in a draft PR for this.
### What is the use case or pain point?
The use case is to expose the KFP UI at a stable url (unlike the long, random urls used by AI Platform Pipelines) with a secure auth proxy in front.
### Is there a workaround currently?
It's possible to configure IAP without verifying headers at the application layer, but it would be more secure to verify headers.
---
<!-- 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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"cc @chensun ",
"cc @lynnmatrix, do you want to take a look?",
"I think this code already supports parameterizing the mounting location: \r\n\r\nhttps://github.com/kubeflow/pipelines/blob/a1ec227a24d71b7a697884d1e2d00a57583f376b/sdk/python/kfp/_local_client.py#L344-L351\r\n\r\nSo to change it, you just need to ... | 2021-08-25T16:19:58 | 2021-08-30T17:55:26 | 2021-08-30T17:55:26 | NONE | null | ### Feature Area
Have been using the kfp local runner feature using the docker execution environment.
......
run_pipeline_func_locally(
pipeline_func,
arguments={
"train_output_dir": TRAINER_OUTPUT_PATH,
},
execution_mode=LocalClient.ExecutionMode("docker"),
)
```
However this always traslated to docker running with mount on '/tmp'.
```
['docker', 'run', '-v', '**/tmp:/tmp**', '<image>, '--training_epochs', '1', '--model_export_dir', '/tmp/model/1629731723/']
```
This pollutes the '/tmp' location in local and adds additional steps to components to interact (i.e. move over artifacts and keep them ready in corresponding '/tmp' location.
Alternatively if the component needs to access remote storage it needs to be authenticated and mounting the custom path/configs would be the only relatively safe way to do so.
### What feature would you like to see?
Ability for users to pass parameters to the docker itself like '-v' mount options or '--entrypoint' shell options etc.
### What is the use case or pain point?
There is friction in using kfp local runner as it is through docker vs plain docker due to the feature gap.
### Is there a workaround currently?
Move everything over to '/tmp' to use it for staging.
---
<!-- 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/6433 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6433/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6433/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6433/events | https://github.com/kubeflow/pipelines/issues/6433 | 979,282,637 | MDU6SXNzdWU5NzkyODI2Mzc= | 6,433 | [sdk] Compilation problems in kfp==1.7.1 when creating two independent operators from the same python component. | {
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"cc @chensun ",
"@thomas-mueller can you provide a minimal reproduction sample?",
"@Bobgy, @chensun, here is my smallest example:\r\n\r\n```python\r\nimport functools\r\nimport kfp\r\nimport typing\r\n\r\ncreate_component_from_func = functools.partial(kfp.components.create_component_from_func, base_image=\"pyth... | 2021-08-25T15:12:21 | 2021-09-28T07:10:10 | 2021-09-28T07:10:10 | NONE | null | ### Environment
```
$ pip list | grep kfp
kfp 1.7.1
kfp-pipeline-spec 0.1.9
kfp-server-api 1.4.1
```
### Steps to reproduce
I have a python component function and call it twice in a pipeline with different parameter to create two independent operators from it. But during compilation of my pipeline, I get to following error:
```
Traceback (most recent call last):
File "C:\Users\t13muell\.conda\envs\dstk\Scripts\kf_pipeline_preprocessing-script.py", line 33, in <module>
sys.exit(load_entry_point('data-science-toolkit', 'console_scripts', 'kf_pipeline_preprocessing')())
File "d:\code\data_science_toolkit\data_science_toolkit\scripts\kf_pipeline_preprocessing.py", line 115, in kf_pipeline_preprocessing_cli
dry_run=args.dry_run,
File "d:\code\kubeflow\kubeflow_util\kubeflow_util\pipelines.py", line 309, in submit_pipeline
pipeline_func=pipeline_function, package_path=pipeline_package_file.name, type_check=False, pipeline_conf=pipeline_conf
File "C:\Users\t13muell\.conda\envs\dstk\lib\site-packages\kfp\compiler\compiler.py", line 1064, in compile
package_path=package_path)
File "C:\Users\t13muell\.conda\envs\dstk\lib\site-packages\kfp\compiler\compiler.py", line 1119, in _create_and_write_workflow
pipeline_conf)
File "C:\Users\t13muell\.conda\envs\dstk\lib\site-packages\kfp\compiler\compiler.py", line 954, in _create_workflow
pipeline_conf,
File "C:\Users\t13muell\.conda\envs\dstk\lib\site-packages\kfp\compiler\compiler.py", line 717, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "C:\Users\t13muell\.conda\envs\dstk\lib\site-packages\kfp\compiler\compiler.py", line 659, in _create_dag_templates
op_name_to_for_loop_op,
File "C:\Users\t13muell\.conda\envs\dstk\lib\site-packages\kfp\compiler\compiler.py", line 272, in _get_inputs_outputs
upstream_op = pipeline.ops[param.op_name]
KeyError: 'kf-comp-iterable-indices 2'
```
My python function is called `kf_comp_iterable_indices `. It all works with `pip install "kfp<1.7.1"` which results in version 1.7.0.
### Expected result
Correct execution of the two independent operators.
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"ping @Bobgy WDYT?",
"Hello @NikeNano , may I ask for the use case for this feature? For example, what kind of users need to configure TTL for pipeline? Usually it is the cluster admin who control the lifecycle of workflow. And can the pipeline creators themselves have the control over TTL in your mind?",
"My e... | 2021-08-25T13:28:13 | 2022-01-30T14:57:12 | 2022-01-30T14:57:12 | MEMBER | null | ### Feature Area
There have been multiple issues related to time to live(ttl) for pipelines, https://github.com/kubeflow/pipelines/issues/5545, https://github.com/kubeflow/pipelines/issues/5234. A common solution is to use `set_ttl_seconds_after_finish` which don't respect the persistence agent and can resolve in that data is lost for the users.
In order to allow users to
configure the ttl for pipelines the suggested solution is to set `TTL_SECONDS_AFTER_WORKFLOW_FINISH` [https://github.com/kubeflow/pipelines/issues/5234#issuecomment-805599988]
but this will set for all pipelines.
<!-- /area backend -->
<!-- /area sdk -->
### What feature would you like to see?
To support the persistence agent while allowing users to set time to live on pipeline level, I suggest that we add the option to the pipeline spec and
update the persistence agent to support this.
### What is the use case or pain point?
Today either the users have to set the same TTL for all pipelines(through `TTL_SECONDS_AFTER_WORKFLOW_FINISH` in the persistiance agent) or
use `set_ttl_seconds_after_finish` which might result in data lose.
<!-- 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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"@haoxins Yes, absolutely welcome a PR on this!",
"Just a heads up, Minio changed its license from Apache 2.0 to AGPL v3.There is a bit of controversy surrounding this decision:\r\n\r\nhttps://github.com/minio/minio/discussions/12157\r\nhttps://github.com/minio/minio/discussions/12158\r\nhttps://github.com/minio/... | 2021-08-25T12:38:42 | 2022-06-10T17:00:39 | 2022-06-10T17:00:39 | CONTRIBUTOR | null | ### Feature Area
/area components
Is there any reason not to upgrade the [**`minio`**](https://hub.docker.com/r/minio/minio/tags) to the latest version?
Is it open for others to submit PR for this? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6430 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6430/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6430/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6430/events | https://github.com/kubeflow/pipelines/issues/6430 | 979,046,935 | MDU6SXNzdWU5NzkwNDY5MzU= | 6,430 | Return minio log url when getting the status of a run using rest api call | {
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"node_id": "MDU6TGFiZWwyMTU3NjM0MjA0"... | open | false | null | [] | null | [
"Hello, in KFPv1, you can parse argo workflow yaml to get this URL. In KFPv2, we will plan for supporting this after Alpha release.",
"Hello, thank you for your reply. Can you please provide any example to get url using KFPv1?\r\nor any link which I can refer. \r\n\r\nAlso thanks for taking this into account for ... | 2021-08-25T11:45:59 | 2022-03-02T21:05:27 | null | NONE | null | Hello folks,
we see archived logs that are on the Inputs/Outputs tab under Output artifacts in the form of URI on Kubeflow UI.
Log URI: "minio://mlpipeline/artifacts/..../.../main.log"
We have a rest end point from kubeflow side where we get status for specific run id.
```http://kubeflow-hostt/ml-pipeline/apis/v1beta1/runs/runId```
we need the logs url appended in the output as a parameter of above call along with status success/failed.
This will be helpful as we are using this rest api endpoint in our service and want to return logs if the status comes out to be failed.
currently there is no way to trace the logs
It will be helpful if we can achieve
<!-- 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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"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",
"Hi @chensun ! This discussion is very dear to us, as many of our workflows do require us to run pipelines that declare volumes and use ... | 2021-08-25T10:54:12 | 2022-09-24T00:47:51 | null | CONTRIBUTOR | null | ### Environment
* KFP version: 1.7.0
* KFP SDK version: 1.7.0
* All dependencies version:
```
kfp 1.7.0
kfp-pipeline-spec 0.1.9
kfp-server-api 1.7.0
```
### Steps to reproduce
try to create a PVC using `kfp.dsl.VolumeOp`
end up with
```
Traceback (most recent call last):
File "/Users/jonas/repositories/traffic4cast-2021/pipeline/data_pipeline.py", line 27, in <module>
client.create_run_from_pipeline_func(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/_client.py", line 881, in create_run_from_pipeline_func
compiler.Compiler(mode=mode, launcher_image=launcher_image).compile(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1055, in compile
self._create_and_write_workflow(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1108, in _create_and_write_workflow
workflow = self._create_workflow(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 946, in _create_workflow
workflow = self._create_pipeline_workflow(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 713, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 677, in _create_dag_templates
v2_compat.update_op(op,
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/v2_compat.py", line 53, in update_op
op.add_volume_mount(
AttributeError: 'VolumeOp' object has no attribute 'add_volume_mount'
```
code sample:
```python
import kfp
import kfp.v2.dsl as dsl
@component(base_image='python:3')
def get_data():
print('dummy component')
@dsl.pipeline(
name='Data prep pipeline'
)
def pipeline():
pvc = kfp.dsl.VolumeOp(name='data-vol', resource_name='azure-disk-rw2',
volume_name='azure-disk', modes=[kfp.dsl.VOLUME_MODE_RWO], size='400G')
get_data_task = get_data()
get_data_task.add_pvolumes({'/mnt/data': pvc.volume})
client = kfp.Client(host='http://localhost:8081')
client.create_run_from_pipeline_func(
pipeline, arguments={}, run_name='data prep pipeline',
experiment_name='Default', enable_caching=False,
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE
)
```
and there are a number of other incompatibilities too.
### Expected result
The pipeline compiles, and a pvc would be created for an existing pv.
### Materials and Reference
Happy to help fix this issue if it's not too convoluted/some basic guidance can be given.
SDK v2 is shaping up nicely though, looking forward to it getting fleshed out fully 👌 .
---
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