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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 some features I need (eg: resource quota request / limit, packaging components..).\r\n\r\nI add here the simple code if it could provide any insight.\r\n```\r\nimport kfp\r\nimport kfp.dsl as dsl\r\nimport kfp.components as comp\r\n\r\ndef web_app(\r\n mlpipeline_ui_metadata_path: kfp.components.OutputPath(),\r\n ):\r\n\r\n import json\r\n\r\n metadata = {\r\n 'outputs': [{\r\n 'type': 'web-app',\r\n 'storage': 'inline',\r\n 'source': '<h1>Hello, World!</h1>',\r\n }]\r\n }\r\n\r\n with open(mlpipeline_ui_metadata_path, 'w') as metadata_file:\r\n json.dump(metadata, metadata_file)\r\n\r\n\r\ncreate_web_app_task = comp.create_component_from_func(\r\n func=web_app,\r\n output_component_file=\"web_app_component.yaml\",\r\n base_image=\"python:3.7\"\r\n)\r\n\r\n\r\[email protected](name=\"viz_sample\")\r\ndef the_pipeline():\r\n web_app_task = create_web_app_task()\r\n\r\n\r\ndef main():\r\n kfp.compiler.Compiler().compile(pipeline_func=the_pipeline, package_path=\"pipeline.yaml\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n2. In __V2 compatible mode___, if `package_root` is set to (directly) `gs://{bucket}`, visualization UI works fine. if `package_root` is set to MinIO gateway `minio://{bucket}` I have the same error as posted.\r\n\r\n",
"I've just found out that after creating a workspace (namespace), the `ml-pipeline-ui-artifact` doesn't have any MinIO config (eg: `MINIO_ACCESS_KEY`, `MINIO_SECRET_KEY`, ...). By (manually) adding some `env` into the `ml-pipeline-ui-artifact` deployments yaml, all the pipeline UI artifacts can be displayed.\r\n\r\nTaking a look at this `sync.py` https://github.com/kubeflow/manifests/blob/master/apps/pipeline/upstream/base/installs/multi-user/pipelines-profile-controller/sync.py#L267\r\n\r\nI don't see configmaps or secrets, which are created by the script, get mounted into the `ml-pipeline-ui-artifact` deployment.\r\n\r\n@zijianjoy could it be a bug or my setup is wrong?",
"> I've just found out that after creating a workspace (namespace), the `ml-pipeline-ui-artifact` doesn't have any MinIO config (eg: `MINIO_ACCESS_KEY`, `MINIO_SECRET_KEY`, ...). By (manually) adding some `env` into the `ml-pipeline-ui-artifact` deployments yaml, all the pipeline UI artifacts can be displayed.\r\n> \r\n> Taking a look at this `sync.py` https://github.com/kubeflow/manifests/blob/master/apps/pipeline/upstream/base/installs/multi-user/pipelines-profile-controller/sync.py#L267\r\n> \r\n> I don't see configmaps or secrets, which are created by the script, get mounted into the `ml-pipeline-ui-artifact` deployment.\r\n> \r\n> @zijianjoy could it be a bug or my setup is wrong?\r\n\r\nMounting the ENV vars is proposed in PR https://github.com/kubeflow/pipelines/pull/5864. \r\n\r\n",
"> 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.\r\n\r\nNote that private cluster deployment with Kubeflow is not supported for GKE. Refer to https://www.kubeflow.org/docs/distributions/gke/private-clusters/.\r\n\r\n> I add here the simple code if it could provide any insight.\r\n\r\nI don't seem to see an issue with your sample code here. I guess it is because of the permission issue as discussed in this thread. \r\n\r\n\r\nBesides, you can also check out this doc for using the service account in your Pod with SA token: https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#multi-user-mode",
"@zijianjoy I think the fix is the PR #5864 , I'm applying the patch while it's not merge yet.",
"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",
"As we released KFP 1.8.1, it should include the mentioned fix. Feel free to reopen if the issue persists."
] | 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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I_kwDOB-71UM49PDwS
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[feature] continueOn functionality available in Argo but not in KFP
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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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[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\"HyparameterTuningJobRunOp\" need to be renamed \"HyperparameterTuningJobRunOp\"\r\n\r\nThe doc stream need to be updated as well:\r\nparameter_spec -> parameters\r\nbase_output_directory seems to be mandatory as well\r\n",
"but I am still having other issues with \"TypeError: Object of type PipelineParam is not JSON serializable\". I am probably some argument with the wrong type.\r\nEDIT: typo on my side, with the modifications, everything is working finr",
"seems that everything was fixed with https://github.com/kubeflow/pipelines/commit/16c8928dc14c4aba09c1413885ca3febb2d5a86d"
] | 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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I_kwDOB-71UM49Kl6n
| 6,743 |
[custom-artefact-registry] Define artefact registry to be used when packages are installed
|
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] | 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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Side panel: Artifact: Artifact detail, Visualization
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Side panel: Execution: Input/Output, execution detail
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I_kwDOB-71UM49JhnN
| 6,738 |
[feature] Provides option to install python packages in component image
|
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] | 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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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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[backend] Error occured while trying to proxy to: ... central1.pipelines.googleusercontent.com
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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 status errors in the Deployments. In particular:\r\n```\r\nDeployment | metadata-grpc-deployment | Does not have minimum availability\r\nDeployment | ml-pipeline | Does not have minimum availability \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",
"You can check more about the log in `metadata-grpc-deployment`, there might explain the reason why you don't have a working backend.",
"Any updates on this issue?\r\nI am facing similar problem with \"Kubeflow on AWS\" eks1.24 + kf1.7\r\n\"http://ml-pipeline-ui-artifact.kubeflow:80\" in ml-pipeline-ui.log doesn't seem right:\r\n\r\n```\r\nET /apis/v1beta1/healthz\r\nGET /pipeline/artifacts/s3/XXXXXXXXXXXX/main.log?namespace=kubeflow\r\n[HPM] Router new target: /artifacts -> \"http://ml-pipeline-ui-artifact.kubeflow:80\"\r\nProxied artifact request: /pipeline/artifacts/s3/XXXXXXXXXXXX/main.log\r\n[HPM] Error occurred while trying to proxy request /pipeline/artifacts/s3/XXXXXXXXXXXX/main.log from istioingress.example.net to /artifacts (ENOTFOUND) (https://nodejs.org/api/errors.html#errors_common_system_errors)\r\n```\r\n\r\n"
] | 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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`StartedAt` and `FinishedAt` for executions are not available in MLMD
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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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I_kwDOB-71UM49HWVO
| 6,728 |
Ability to query cache server
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[
"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 pipeline? What is your use case for checking the existence of cache with cache key?",
"@zijianjoy We haven't moved to the V2 yet. (There are couple of issues related to separate namespaces that we need to resolve first - https://github.com/kubeflow/pipelines/issues/6133)\r\n\r\nI gave more color to what I am trying to do here: #6727",
"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"
] | 2021-10-13T14:38:30 | 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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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 currently adding caching to our pipeline.\r\n\r\nMy plan is to quickly solve the DAG locally, compute `cache key` for each step and then show to the end user (data scientists in this case) that these steps will be recomputed again so that they have an idea about the computation that is going to be performed. \r\n\r\nFor this I need two things:\r\n1. Compute cache key (this should be doable since all the info is in the graph generated by the `Compiler`)\r\n2. Query for these cache keys against cache server (as shown in #6728)\r\n",
"At the current point, you can rely on the condition `The task’s interface is defined as the combination of the pipeline task specification (base image, command, args), the pipeline task’s inputs (the name and id of artifacts, the name and value of parameters), and the pipeline task’s outputs specification(artifacts and parameters). ` to tell whether a pipeline step will be cached. Reference from: https://www.kubeflow.org/docs/components/pipelines/caching-v2/#what-is-step-caching. \r\n\r\nHowever, the cache key is an implementation detail, therefore exposing cache key calculation mechanism might not be stable and can potential break in each version. Plus that it is unknown to KFP whether a task will be cached before runtime. I would suggest to use the condition above to tell whether the pipeline step will be cached. Alternatively, just run the pipeline once to see the result is cached or not.",
"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"
] | 2021-10-13T14:19:08 | 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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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 i do something like this ?\r\nhttps://github.com/kubeflow/kfctl/issues/140#issuecomment-719894529",
"If you are using Google Cloud, you can set your email account in header `X-Goog-Authenticated-User-Email` field.\r\n\r\nYou can do this by using modHeader extension: https://modheader.com/guide/\r\n",
"I'm having the same issue as well on https://github.com/kubeflow/manifests/tree/v1.4.0",
"Would you like to try the multi-user mode approach here? https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#multi-user-mode",
"Yup tried it. It did not work either",
"@nschenone Would you like to follow the step in https://github.com/kubeflow/pipelines/blob/master/samples/contrib/kubeflow-e2e-mnist/kubeflow-e2e-mnist.ipynb to create `PodDefault` and apply this configuration when creating Notebook instance?\r\n\r\nPlus, can you share your code of creating kfp client and listing pipeline?",
"@zijianjoy sadly im using on-premise deployment (deployed on our own kubernets clusters )\r\nfor your latest answer, i added the manifest, and modified the configuration to include it while creating the jupyter \r\nCan i provide any other information that can help you locate the issue ?\r\nPS: apologies for the late reply ",
"Hi @zijianjoy I also tried the step you suggested and was unfortunately not able to get it to work. @rexad the only thing I've found so far that has worked is applying the K8s resource mentioned in this [comment](https://github.com/kubeflow/pipelines/issues/4440#issuecomment-871927424). It feels like a hack, but is the only thing that has worked so far",
"@rexad In my case, the issue was an outdated version of `kfp`. After running `pip install --upgrade kfp` and upgrading to `kfp==1.8.6` my issue was resolved via the method @zijianjoy mentioned in the e2e notebook example. ",
"Thank you @nschenone , which platform are you deploying your Kubeflow cluster? @rexad is using on-premise approach so I am wondering if your suggestion can help @rexad as well.\r\n\r\nHello @rexad , note that deploying directly using Kubeflow manifests is not supported: https://www.kubeflow.org/docs/started/installing-kubeflow/#install-the-kubeflow-manifests-manually. \r\n\r\nOne thing you can try is to install https://modheader.com/guide/\r\n\r\nThen add the request header: X-Goog-Authenticated-User-Email with value: `accounts.google.com:<user-email-address>`",
"@zijianjoy I am also installing on prem via the manifests like @rexad ",
"@nschenone after updating kfp it worked !!!! may i enquire how did you figure out the version was wrong, did a lot of digging couldn't see it ...\r\n@zijianjoy ouch so what should be used to install on premise ? whill check with our ops-team they might be able to help ",
"@rexad Lots of trial and error...and error...and error :) It is not explicitly stated anywhere, but I noticed in the notebook example that @zijianjoy linked that they are installing a newer version of `kfp` than I had.\r\n\r\nWhen I installed `kfp==1.8.6` and instantiated a client, there was a message printed about the token located at `/var/run/secrets/kubeflow/pipelines/token` which is where [this guide](https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#multi-user-mode) comes into play.",
"@nschenone yeah saw that guide and i already applied the manifests but the kfp update did the trick thanks.\r\nMay i enquire about the underlying infrastructure you are using for kubeflow ?",
"Glad to hear it. I'm using a local on-prem server and installing via the manifests. I've actually deployed my k8s cluster using [k3d](https://k3d.io/). Not exactly for prod but great for dev and testing",
"Glad that upgrading kfp SDK works out for you both! \r\n\r\nOn-prem server deployment can be used by advanced users. The other approach is to deploy via packaged distribution: https://www.kubeflow.org/docs/started/installing-kubeflow/#install-a-packaged-kubeflow-distribution. \r\n\r\nFeel free to close this issue as the original problem has been resolved.",
"I would also like to highlight this point: After trying many things for hours I also just did a (I imagined somehow that I already did this; apparently I did not): `!pip install --upgrade kfp` before running (dont forget to restart your kernel...)\r\n\r\n```\r\nimport kfp\r\nclient = kfp.Client()\r\nprint(client.list_experiments())\r\n```\r\n\r\nSo @zijianjoy 's suggestion actually works with the newest kfp version...\r\n\r\n\r\n> Would you like to try the multi-user mode approach here? https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#multi-user-mode\r\n\r\n",
"> I would also like to highlight this point: After trying many things for hours I also just did a (I imagined somehow that I already did this; apparently I did not): `!pip install --upgrade kfp` before running (dont forget to restart your kernel...)\r\n> \r\n> ```\r\n> import kfp\r\n> client = kfp.Client()\r\n> print(client.list_experiments())\r\n> ```\r\n> \r\n> So @zijianjoy 's suggestion actually works with the newest kfp version...\r\n> \r\n> > Would you like to try the multi-user mode approach here? https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#multi-user-mode\r\n\r\n@MarkusThill OMG This is insane. Thanks a lot.",
"Just as you created an allow for the pipelines you also have to do this for experiments if you want to run list_experiments() when you have multi-user deployment:\r\n\r\napiVersion: kubeflow.org/v1alpha1\r\nkind: PodDefault\r\nmetadata:\r\n name: access-ml-experiments\r\n namespace: <NAMESPACE>\r\nspec:\r\n desc: Allow access to Kubeflow Experiments\r\n selector:\r\n matchLabels:\r\n access-ml-experiments: \"true\"\r\n volumes:\r\n - name: volume-kf-experiments-token\r\n projected:\r\n sources:\r\n - serviceAccountToken:\r\n path: token\r\n expirationSeconds: 7200\r\n audience: experiments.kubeflow.org \r\n volumeMounts:\r\n - mountPath: /var/run/secrets/kubeflow/experiments\r\n name: volume-kf-experiments-token\r\n readOnly: true\r\n env:\r\n - name: KF_PIPELINES_SA_TOKEN_PATH\r\n value: /var/run/secrets/kubeflow/pipelines/token",
"I am also facing the same issue. Not able to list both experiments and pipelines.\r\n\r\n\r\nI have installed via manisfests on a on-prem local K8s cluster (RKE2).\r\nI would like to know, to which version, kfp has been updated. \r\n\r\nIn my case, \r\nkfp 2.0.1\r\nkfp-pipeline-spec 0.2.2\r\nkfp-server-api 2.0.0 "
] | 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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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 to fix it?\r\n\r\n> Could you share the code to reproduce?\r\n\r\nI think we can easy to reproduce it\r\n```Python\r\n@component()\r\ndef create_list() -> list:\r\n return list(range(10))\r\n\r\n\r\n@component()\r\ndef to_df(\r\n input_number: int,\r\n output_csv: Output[Dataset],\r\n):\r\n import pandas as pd\r\n\r\n pd.DataFrame([input_number]).to_csv(output_csv.path)\r\n\r\n\r\n@component()\r\ndef from_df(\r\n input_number: Input[Dataset],\r\n) -> int:\r\n import pandas as pd\r\n\r\n return int(pd.read_csv(input_number.path).iloc[0, 0])\r\n\r\n\r\n@pipeline(\r\n name=\"test_for\",\r\n description=\" test_for\",\r\n)\r\ndef parallel_for_test():\r\n list_step = create_list()\r\n\r\n with dsl.ParallelFor(list_step.output) as number:\r\n df_step = to_df(number)\r\n from_df_step = from_df(df_step.outputs[\"output_csv\"])\r\n``` \r\n\r\nhere we have 10 times df_step and each time there are different inputs but output.path for all steps is the same\r\n`[minio://mlpipeline/v2/artifacts/pipeline/test_for/10db378a-3320-4631-86ca-0cbe798e9cbe/to-df/output_csv](***/pipeline/artifacts/minio/mlpipeline/v2/artifacts/pipeline/test_for/10db378a-3320-4631-86ca-0cbe798e9cbe/to-df/output_csv?namespace=***)`\r\n\r\n\r\n "
] | 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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[backend] metadata-grpc-service mysql_real_connect failed (SSL enabled)
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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 - --metadata_store_server_config_file=/config\r\n command:\r\n - /bin/metadata_store_server\r\n```\r\n\r\nConfig:\r\n```\r\nconnection_config {\r\n mysql {\r\n host: \"...\"\r\n port: int\r\n database: \"...\"\r\n user: \"...\"\r\n password: \"...\"\r\n ssl_options {\r\n verify_server_cert: true\r\n capath: \"/etc/ssl/certs/my-cert.pem\"\r\n }\r\n }\r\n }\r\n```\r\n\r\nError:\r\n\r\n**2021-10-11 18:04:01.536992: 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.**\r\n\r\nIs there any example documentation regarding SSL setup for metadata-grpc as it pertains to kubeflow pipelines?\r\n",
"Hello @andrijaperovic , would you like to check the logs in `cloudsqlproxy` deployment? It should have more information about why the connection has failed.",
"@zijianjoy thank you for following up.\r\n\r\nI thought cloudsqlproxy is only applicable for gcp based on the kustomize template:\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/manifests/kustomize/env/gcp/cloudsql-proxy/cloudsql-proxy-deployment.yaml\r\n\r\nWe are running in an Azure (AKS) environment, so there is no cloudsqlproxy deployment in our KFP installation. ",
"I am not very familiar with SQL connection in Azure, the article I can find is https://docs.microsoft.com/en-us/azure/azure-sql/database/connectivity-architecture. \r\n\r\n@berndverst for more help on Azure.",
"@zijianjoy since this issue is due to a sub-component shipped from `google/ml-metadata` I've filed a corresponding ticket there:\r\nhttps://github.com/google/ml-metadata/issues/130\r\n\r\nHave reached out to @BrianSong to see whether the `google/ml-metadata` ticket can be reopened.\r\nAfter my discussion with @berndverst this issue can be addressed in `google/ml-metadata` only.",
"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'm experiencing this problem (or one that looks like it). The logs contain repeated\r\n\r\nW0213 18:24:37.372198 3635 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0213 18:24:37.372260 3637 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0213 18:24:37.372275 3638 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0213 18:24:37.372458 3639 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0213 18:24:37.372793 3621 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0213 18:24:37.465122 3622 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:",
"This causes Kubeflow Artifacts to fail:\r\nW0308 15:29:32.612677 247 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.613674 245 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.614279 233 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.614387 248 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.614480 236 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.616160 240 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.616427 244 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.616662 249 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.618607 201 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.618873 194 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.625411 211 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.625411 222 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.625516 208 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626050 228 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626132 183 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626195 217 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626196 215 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626279 207 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626308 220 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.626356 221 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.630316 246 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.630416 237 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.631172 232 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.631250 238 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.631335 241 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: \r\nW0308 15:29:32.631448 242 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error: ",
"I have a similar problem with kubeflow. `MySQL` pod is not running and so `grpc` and `ml-pipeline` pods can't connect to the database and are stuck in `CrashLoopBackOff`. \r\nDid you find a solution?",
"> I have a similar problem with kubeflow. `MySQL` pod is not running and so `grpc` and `ml-pipeline` pods can't connect to the database and are stuck in `CrashLoopBackOff`. Did you find a solution?\r\n\r\n+1",
"For me, when I ran the kubeflow deployment command again, the issue was fixed. Some time mysql pod gets deleted (did not know why) which caused this problem. So running the deployment command again fixed it\r\n",
"> \r\n\r\nHi @mohamedFaris47, Thanks for the kind reply. What's the meaning of `run the kubeflow command again`? BTW, do you refer to the [kubeflow installation doc](https://www.kubeflow.org/docs/components/pipelines/v2/installation/quickstart/) to deploy the KFP standalone instance?",
"I mean running the deployment command mentioned in the Kubeflow documentation [here](https://github.com/kubeflow/manifests#install-with-a-single-command)",
"> I mean running the deployment command mentioned in the Kubeflow documentation [here](https://github.com/kubeflow/manifests#install-with-a-single-command)\r\n\r\nI see, but the deployment still failed after running the command. The error ` mysqld: Can't read dir of '/etc/mysql/conf.d/' (OS errno 13 - Permission denied)` was encountered in the mysql pod.",
"I don't know the cause of this permission denial unfortunately. That's different from my case, I had only the mysql pod missing so redeploying got it fixed."
] | 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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I_kwDOB-71UM487AuP
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Shows Run details, Run parameters, a link to Pipeline definition if possible.
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[] | 2021-10-10T21:59:24 | 2021-10-15T06:21:37 | 2021-10-15T06:21:37 |
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I_kwDOB-71UM486__h
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Shows Run ID, Run name on title
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[] | 2021-10-10T21:43:21 | 2021-10-11T17:55:46 | 2021-10-11T17:55:45 |
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I_kwDOB-71UM486_98
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Rundetails Buttons: Retry, Clone Run, Terminate, Restore and Archive
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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 already supported? I believe other features are supported.",
"@zijianjoy I just check the `terminate` functionality and it's already supported after https://github.com/kubeflow/pipelines/pull/8963"
] | 2021-10-10T21:42:44 | 2023-03-17T23:05:38 | 2023-03-17T23:05:38 |
COLLABORATOR
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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.py",
"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"
] | 2021-10-08T20:05:40 | 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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[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 recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 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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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 advantage of pickling in the context of pipelines is that it ships the module in which the function is defined as compared to not pickling option which only ships the code for the function. This means that if have the following:\r\n\r\n```py\r\ndef foo():\r\n ...\r\n\r\ndef bar():\r\n foo()\r\n```\r\n\r\nand you change `foo` then you don't need to build your image again since when triggering the pipeline, `foo`s changes will be automatically included. \r\n\r\nBut like I said, overall I agree that pickling is not a great format since it changes very frequently. ",
"> @chensun I agree with you that pickling in general is not a good idea.\r\n> \r\n> The advantage of pickling in the context of pipelines is that it ships the module in which the function is defined as compared to not pickling option which only ships the code for the function. This means that if have the following:\r\n> \r\n> ```python\r\n> def foo():\r\n> ...\r\n> \r\n> def bar():\r\n> foo()\r\n> ```\r\n> \r\n> and you change `foo` then you don't need to build your image again since when triggering the pipeline, `foo`s changes will be automatically included.\r\n> \r\n> But like I said, overall I agree that pickling is not a great format since it changes very frequently.\r\n\r\nMaybe I missed something in your use case, but with function-based component, you never need to build an image. By default, the container image is `python:3.7`, and we inline the source code of the function as plain text in the container cmd line. If you set `use_code_pickling=True`, then we inline the pickled string of the function in the container cmd line. Either way, the function appears, in some form, in the container commands, so there's no difference for caching impact. \r\n\r\nThe visual difference in the container spec is like below:\r\n- When `use_code_pickling=True`\r\n\r\n```\r\ncontainer:\r\n image: python:3.7\r\n command:\r\n - ...\r\n - foo = pickle.loads(base64.b64decode(...))\r\n - ...\r\n```\r\n\r\n- When `use_code_pickling=False`\r\n```\r\ncontainer:\r\n image: python:3.7\r\n command:\r\n - ...\r\n - def foo(): \\n ....\r\n - ...\r\n```",
"@chensun For simple functions, the approach is equivalent. (simple = self contained)\r\n\r\nHowever, we have a complex codebase split across different modules. Researchers can (and do) change code in different modules such that the function code remains same, but the modules it is using are different now. Kubeflow (and fwiw python) cannot recursively go to all modules that function is using and copy their code as well. \r\n\r\nWhat `use_code_pickling` does is that it uses cloudpicking under the hood and to cloudpickling, you can tell which modules to include as part of the pickle. (by default it uses function's module). ",
"**update** I didn't realize that the latest `kfp` release was 1.8.12. I verified that 1.8.12 has the updated cloudpickle requirement. I was confused because the version tags on Github and CHANGELOG.md suggested that 1.8.0 should have had the changes.\r\n\r\n---\r\n\r\nHello KFP team,\r\n\r\nThis PR is listed as included in 1.8.0 [according to the changelog](https://github.com/kubeflow/pipelines/blob/master/CHANGELOG.md#180-2022-02-16), but I'm getting some package conflicts between kfp 1.8.1 and other packages that depend on cloudpickle>=2.\r\n\r\nIt seems that kfp 1.8.1 still depends on `cloudpickle>=1.3.0,<2`. See:\r\n```shell\r\n$ pip download --no-deps kfp==1.8.1\r\nCollecting kfp==1.8.1\r\n Using cached kfp-1.8.1.tar.gz (248 kB)\r\n Preparing metadata (setup.py) ... done\r\nSaved ./kfp-1.8.1.tar.gz\r\nSuccessfully downloaded kfp\r\n\r\n$ tar xzf kfp-1.8.1.tar.gz\r\n\r\n$ grep cloudpickle kfp-1.8.1/setup.py\r\n 'cloudpickle>=1.3.0,<2',\r\n```\r\n\r\nHere's what I originally ran that led to this problem:\r\n```shell\r\n$ python -m venv pickledemo\r\n$ source pickledemo/bin/activate\r\n$ pip install -U pip\r\n$ pip install kfp==1.8.1 apache-beam==2.38.0\r\n[...]\r\nERROR: Cannot install apache-beam==2.38.0 and kfp==1.8.1 because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n kfp 1.8.1 depends on cloudpickle<2 and >=1.3.0\r\n apache-beam 2.38.0 depends on cloudpickle<3 and >=2.0.0\r\n\r\nTo fix this you could try to:\r\n1. loosen the range of package versions you've specified\r\n2. remove package versions to allow pip attempt to solve the dependency conflict\r\n\r\nERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\r\n```\r\n\r\nCould you please advise? Thanks!"
] | 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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I_kwDOB-71UM48wiAK
| 6,697 |
Pushing csv/json/parquet files to Big Query using Kubeflow component
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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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[feature] Removes docker dependencies in test infra related scripts
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open
| false | null |
[] | null |
[
"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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| 1,019,208,238 |
I_kwDOB-71UM48v-Iu
| 6,695 |
10/06/21 Postsubmit test failure due to ai-platform models reaches quota limit
|
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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\r\nwhile IFS=' ' read -r model version\r\ndo\r\n gcloud ai-platform versions delete $version --model $model --quiet\r\n gcloud ai-platform models delete $model --quiet\r\ndone < models.txt\r\n```",
"A follow up action item would be identifying which tests are leaking the models and fix the test or sample.\r\n\r\nA previous example is we had a notebook running as part of sample test, and it's model deletion commands was commented out: https://github.com/kubeflow/pipelines/pull/4916\r\n\r\n/cc @capri-xiyue @zijianjoy\r\n\r\n",
"Took a quick look, based on the naming of the models, it's likely leaked by the same notebook fixed by #4916.\r\n\r\nLikely it's because sometimes tests canceled or failed in the middle, so it didn't reach the model deleting command, and over almost a year since the last batch cleanup, we accumulated another 100 of models.\r\n",
"Thank you Chen for fixing and explaining this issue!",
"Duplicates https://github.com/kubeflow/pipelines/issues/2356"
] | 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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I_kwDOB-71UM48nJZT
| 6,687 |
SDK: Drop support for Python 3.6
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[] | 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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I_kwDOB-71UM48mNVS
| 6,686 |
POC for Runtime Pipeline DAG
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] | 2021-10-05T18:26:07 | 2021-10-11T05:45:23 | 2021-10-11T05:45:23 |
COLLABORATOR
| null | null |
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| 1,016,135,303 |
I_kwDOB-71UM48kP6H
| 6,685 |
[sdk] failure launching Dataproc jobs
|
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[] | null |
[
"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.py. The callstack I posted is also in google contrib code, somewhere in the common container used for a lot of google API calls.",
"@ckchow @zijianjoy I am facing the same error. Any workaround? thanks.",
"Falling back to `gcr.io/ml-pipeline/ml-pipeline-gcp:1.6.0` worked.",
"Fixed by https://github.com/kubeflow/pipelines/pull/7364"
] | 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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I_kwDOB-71UM48jkVR
| 6,681 |
Vertex AI Pipeline - Container OP `set_cpu_limit` does not work with parameter_values nor at runtime
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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 @chensun \r\nThanks a lot for your feedback. \r\n\r\nI work for one of the biggest Google Cloud partners, we get this request regularly from our customers, at least once every 2 weeks. \r\nParameterizing the machine type (CPU and memory) can be really useful if you use the same pipeline just for different datasets and or hyperparameters (This way there is no need to re-compile). \r\n\r\nChanging those hyperparameters also might require bigger machines. \r\nFor example, if you increase the batch size. \r\n\r\nCurrently, a re-compile of the pipeline is required. Would be useful if we could do this via parameter as well. ",
"in this line, would be nice also if when a task throws an kfp error for being out of memory, that \r\na) you can play with the memory-limit as a parameter as SaschaHeyer request, just re-runing the task, not the whole pipeline (though if cache is enabled this maybe is already solved)\r\nb) does the upscale automatically and re-runs the task again",
"@SaschaHeyer Thanks for the context!",
"> in this line, would be nice also if when a task throws an kfp error for being out of memory, that \r\na) you can play with the memory-limit as a parameter as SaschaHeyer request, just re-runing the task, not the whole pipeline (though if cache is enabled this maybe is already solved) \r\n\r\nYes, caching would help here if the upstream doesn't have any changes on their inputs.\r\n\r\n> b) does the upscale automatically and re-runs the task again\r\n\r\nThis might create some surprise billing issue :) ",
"> This might create some surprise billing issue :)\r\n\r\nyep, in case implemented, there should be an `autoscale: bool = False` argument in the function `kfp.v2.compiler.Compiler().compile`\r\nBut agree that option b): auto-scaling could have some dramatic problems for the user in terms of money money that option a) doesn't have.\r\n",
"Huge +1 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",
"are plans to support this? or is explored in another ticket?",
"Hi \r\nare there any updates? This would be a huge benefit for re-using pipelines without the need to re-compile them. ",
"> Hi are there any updates? This would be a huge benefit for re-using pipelines without the need to re-compile them.\r\n\r\n+++\r\n\r\nI agree with @SaschaHeyer, we are building reusable pipeline templates with only data changing and depending on the data size, we would want to be able to configure the CPU and Memory for each of the components through pipeline params or any other way."
] | 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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Side Panel for KFPv2 Run
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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"
] | 2021-10-05T00:49:10 | 2023-01-18T08:43:48 | 2023-01-18T08:43:48 |
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Open a pipeline run using run ID
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Support runtime drawing of sub-DAG.
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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.",
"@amrobbins Can you try using `parallelFor`? https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html?#kfp.dsl.ParallelFor\r\n\r\nThis issue is for tracking KFPv2 UI implementation, sorry that it is not obvious from title",
"The reason parallel for won't work for my use case is because it is a for loop over a static set of items, and I want to read from the a table to get a dynamic set of items for which to launch operations.\r\n\r\nIf this functionality doesn't exist, is it possible for me to create a feature request?",
"ok, I see the parallel for does look like it will work for my use case. Thanks for the suggestion.",
"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",
"Recently Reactflow has supported sub flow: https://reactflow.dev/docs/guides/sub-flows/",
"It is completed: https://github.com/kubeflow/pipelines/pull/8326"
] | 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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I_kwDOB-71UM48i2dW
| 6,673 |
Draw static graph first, then update static graph with runtime MLMD execution and artifact, and edge.
|
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| 6,672 |
Upload pipeline spec to backend
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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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I_kwDOB-71UM48iUQm
| 6,669 |
10/04/2021 Presubmit kubeflow-pipelines-tfx-python36 failing
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[] | 2021-10-04T21:00:10 | 2021-10-04T21:32:17 | 2021-10-04T21:32:17 |
COLLABORATOR
| null |
```
+ 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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| 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/7b4f28379d4204da3ad38ce376d2157cc3454356/sdk/python/requirements.in#L29"
] | 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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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/python/kfp/dsl/_resource_op.py#L104\r\n\r\nThis essentially ties the CR to the workflow and makes it to get deleted once the agent GCs the workflow.",
"> \r\n> \r\n> Hi @peterhaddad3121 ,\r\n> \r\n> to GC the resources created by `ResourceOp`s you should set the `set_owner_reference` argument to `True`. It's this one:\r\n> \r\n> https://github.com/kubeflow/pipelines/blob/5892bf97d08bbb51501d640cabfc310370a9f457/sdk/python/kfp/dsl/_resource_op.py#L104\r\n> \r\n> This essentially ties the CR to the workflow and makes it to get deleted once the agent GCs the workflow.\r\n\r\nthere is something wrong with RBAC \r\n\r\n```\r\ncannot set blockOwnerDeletion if an ownerReference refers to a resource you can't set finalizers on\r\n```",
"@elikatsis @zijianjoy \r\n\r\nset_owner_resources needs https://github.com/kubeflow/pipelines/pull/6622 to set the finalizers for blockowner deletion\r\n\r\n```\r\n resource_component = kfp.dsl.VolumeOp(\r\n name = \"create-pvc\",\r\n resource_name = 'pvc', # name=\"{{workflow.name}}-%s\" % resource_name\r\n modes = kfp.dsl.VOLUME_MODE_RWO,\r\n size = '1Gi',\r\n #storage_class = 'ultra-high',\r\n set_owner_reference = True # https://github.com/kubeflow/pipelines/issues/6649#issuecomment-938509228\r\n )\r\n```\r\n\r\nor you get something like this\r\n```\r\nError from server (Forbidden): error when creating \\\"/tmp/manifest.yaml\\\": persistentvolumeclaims \\\"big-data-pipeline-vm2ps-artefacts\\\" is forbidden: cannot set blockOwnerDeletion if an ownerReference refers to a resource you can't set finalizers on: ,\r\n```\r\n\r\n",
"Please close, since with #6622 merged you can set the owner references properly.",
"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"
] | 2021-09-30T14:07:23 | 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 -->
<!-- /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://github.com/kubeflow/pipelines/issues/6645
| 1,011,141,438 |
I_kwDOB-71UM48RMs-
| 6,645 |
[bug] GCP component launch_flex_template wrong URL
|
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open
| false | null |
[] | null |
[
"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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| 1,010,903,173 |
I_kwDOB-71UM48QSiF
| 6,644 |
[feature] Is there any feature to control lifecycle of resource used in kf pipeline ?
|
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[
"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`, we should implement the tear down code in such kf component.\r\nI'm not sure this is the only possible solution",
"cc @chensun to see the best solution for this.\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"
] | 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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| 1,010,330,639 |
I_kwDOB-71UM48OGwP
| 6,642 |
How can I run a pipeline when a file is uploaded to a GCP bucket (Kubeflow 1.3 multi-user)?
|
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[] | null |
[
"Now my GCP function is able to list pipelines. \r\n\r\nI had to use the following service account: [email protected] . \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 '[email protected]' 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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| 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)?
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* 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 -->
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|
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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/main.py\", line 91, in compile_pyfile\r\n _compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/main.py\", line 68, in _compile_pipeline_function\r\n kfp.compiler.Compiler().compile(pipeline_func, output_path, type_check)\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py\", line 1038, in compile\r\n package_path=package_path)\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py\", line 1092, in _create_and_write_workflow\r\n pipeline_conf)\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py\", line 938, in _create_workflow\r\n pipeline_conf,\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py\", line 713, in _create_pipeline_workflow\r\n templates = self._create_dag_templates(pipeline, op_transformers)\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py\", line 667, in _create_dag_templates\r\n condition_params,\r\n File \"/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py\", line 387, in _get_dependencies\r\n raise ValueError('compiler cannot find the ' + upstream_op_name)\r\nValueError: compiler cannot find the model-export-path\r\n",
"@nurcandurak can you please share the complete pipeline code you were compiling? also which SDK version were you using?",
"I use kfp= 1.6.1\r\n\r\nAnd the code block is: \r\nfrom sklearn.linear_model import LinearRegression\r\nimport joblib\r\n\r\nmodel = LinearRegression()\r\nmodel.fit(training_data_set[feature_columns], training_data_set[target_col])\r\n\r\nmodel_file_path = f\"{model_dir}/model.joblib\"\r\n\r\n## Then there is ai_platform deploy operation, THere is no serving operation.\r\ndef aiplatform_deploy_op(\r\n model_uri: str,\r\n project_id: str,\r\n model_id: str,\r\n version_id: str,\r\n aiplatform_runtime_version: str,\r\n python_version: str,\r\n machine_type: str,\r\n model_logging: bool = False,\r\n region: str = \"us-central1\",\r\n framework: str = \"SCIKIT-LEARN\",",
"@nurcandurak KFP 1.6.1 is 6 months old, can you please try the latest sdk and see if the issue still exists. \r\nIf yes, can you please provide a complete code to repro this?\r\nThe code you show seems like they're part of a component implementation?\r\n```\r\nfrom sklearn.linear_model import LinearRegression\r\nimport joblib\r\n\r\nmodel = LinearRegression()\r\nmodel.fit(training_data_set[feature_columns], training_data_set[target_col])\r\n\r\nmodel_file_path = f\"{model_dir}/model.joblib\"\r\n```\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"
] | 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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I_kwDOB-71UM48NL6x
| 6,636 |
[sdk] Getting None as value when float 0 is passed in
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[] | 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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| 1,010,058,578 |
I_kwDOB-71UM48NEVS
| 6,635 |
[docs]v2 Add docs for v2 mode
|
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] | 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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I_kwDOB-71UM48NCQh
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Show summary card with ID, Upload time, description, source, version. Version can be selected.
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[] | 2021-09-28T18:01:03 | 2021-09-30T18:20:32 | 2021-09-30T18:20:32 |
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I_kwDOB-71UM48K19U
| 6,628 |
[sdk] Cannot refer to the loop parameter within the grandchild task
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[] | 2021-09-28T08:58:15 | 2021-09-29T18:27:09 | 2021-09-29T18:27:09 |
COLLABORATOR
| null |
### Environment
* 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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I_kwDOB-71UM48F0dV
| 6,623 |
[feature] Cache pod updates to change node-selector/tolerations.
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[] | 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{\r\n\t\t\t\t\tOp: OperationTypeRemove,\r\n\t\t\t\t\tPath: \"spec/affinity\",\r\n\t\t\t\t})\r\n\t\t\t}\r\n\t\t\tif pod.Spec.NodeSelector != nil {\r\n\t\t\t\tpatches = append(patches, patchOperation{\r\n\t\t\t\t\tOp: OperationTypeRemove,\r\n\t\t\t\t\tPath: \"spec/nodeSelector\",\r\n\t\t\t\t})\r\n\t\t\t}\r\n```\r\nShould be\r\n```\r\n\t\t\tif pod.Spec.Affinity != nil {\r\n\t\t\t\tpatches = append(patches, patchOperation{\r\n\t\t\t\t\tOp: OperationTypeRemove,\r\n\t\t\t\t\tPath: \"/spec/affinity\",\r\n\t\t\t\t})\r\n\t\t\t}\r\n\t\t\tif pod.Spec.NodeSelector != nil {\r\n\t\t\t\tpatches = append(patches, patchOperation{\r\n\t\t\t\t\tOp: OperationTypeRemove,\r\n\t\t\t\t\tPath: \"/spec/nodeSelector\",\r\n\t\t\t\t})\r\n\t\t\t}\r\n```\r\n",
"Hello @boarder7395 , since you found the issue and fix, would you like to create a PR to fix this? ",
"I will also look into it, because i get `time=\"2021-10-11T13:55:07.889Z\" level=warning msg=\"Non-transient error: Internal error occurred: error in remove for path: 'spec/nodeSelector': Unable to remove nonexistent key: nodeSelector: missing value\"` if i do not set CACHE_NODE_RESTRICTIONS for\r\nhttps://github.com/NikeNano/pipelines/blob/62814cde289712dad774d1dbd2550832269680a7/backend/src/cache/server/mutation.go#L164",
"@zijianjoy I have made the fix and the fix has been merged in I believe: #6656\r\n\r\n@juliusvonkohout That is the error I was seeing as well, the PR I mentioned above should fix the issue.",
"KFP backend 1.7.1 has been released: https://github.com/kubeflow/pipelines/releases/tag/1.7.1. This has included the fix in this issue. Would you like to give it a try?"
] | 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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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 dockerimage(In the dockerfile you can use \"add\" to add your csv file).\r\nOn the pipeline , you can try this code:\r\n\r\n import sys\r\n sys.path.append(\"your csv file path\")\r\nI use this method to my local npz file on the pipeline , and it worked for me.\r\n\r\n\r\n\r\n\r\n\r\n",
"Looks like @mike0355 has already provided a solution. Please feel free to continue discussion if not worked.",
"if the data is too big ?"
] | 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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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 Pipelines\r\nhttps://www.kubeflow.org/docs/components/pipelines/sdk-v2/v2-component-io/\r\n\r\nGenerally speaking pickling isn't a great idea. Technically it works for small object like dataframe, however you would be passing some unreadable string between components. If you have a large numpy array, the pickle data could be too large to be passed as string value, because components are eventually containerized apps, string values are passed into a container using command line, so it subjects to the limit of command line characters.",
"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"
] | 2021-09-25T16:32:52 | 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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I_kwDOB-71UM48AoUZ
| 6,615 |
[frontend] Log tab issue with archived_logs and cache
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open
| false | null |
[] | null |
[
"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 your contributions.\n"
] | 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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| 1,006,794,529 |
I_kwDOB-71UM48Anch
| 6,614 |
[feature] V2 DSL Load_component_from_url Dataflow
|
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open
| false | null |
[] | null |
[
"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(
```
---
<!-- 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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| 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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[] | 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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I_kwDOB-71UM47_v46
| 6,611 |
When click SubDAG, shows its parameters and artifacts, with link to its internal layer.
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I_kwDOB-71UM47_v4R
| 6,610 |
When click Artifact, shows its type, name, upstream execution.
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I_kwDOB-71UM475okq
| 6,598 |
[feature] Pass additional options to docker on local runner
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closed
| false | null |
[] | null |
[
"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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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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I_kwDOB-71UM47vryf
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[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_parallelism(source_pipeline_path, parallelism = 10, target_pipeline_path = None):\r\n \"\"\"\r\n limits the number of parallel tasks\r\n Args:\r\n source_pipeline_file(str) - path to the source pipeline yaml to be edited.\r\n parallelism(int) - parallelisim to use\r\n target_pipeline_path (str) - target path, default same as source.\r\n \r\n Returns:\r\n None - edits the source file.\r\n \"\"\"\r\n # see https://github.com/kubeflow/pipelines/issues/6588\r\n\r\n with open(source_pipeline_path,'rt') as f:\r\n data = yaml.load(f, Loader=SafeLoader)\r\n pipeline_name = json.loads(data['metadata']['annotations']['pipelines.kubeflow.org/pipeline_spec'])['name']\r\n pipeline_index = [i for i, t in enumerate(data['spec']['templates']) if t['name']==pipeline_name][0]\r\n data['spec']['templates'][pipeline_index]['parallelism']=parallelism\r\n target_pipeline_path = target_pipeline_path or source_pipeline_path\r\n with open(target_pipeline_path,'wt') as f:\r\n yaml.dump(data, f)\r\n```"
] | 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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| 1,002,267,470 |
I_kwDOB-71UM47vWNO
| 6,587 |
[feature] Add multi-model support for training on Sagemaker
|
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[] | 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.com/kubeflow/pipelines/tree/master/components/aws/sagemaker/model).\r\n\r\nIf this is what you are looking for, please use the `secondary_containers` argument to specify your model parameters like the docker image containing inference code, associated artifacts, and environment map that the inference code uses when the model is deployed for predictions. The format of input would be \r\n\r\n```\r\n[ \r\n { \r\n \"ContainerHostname\": \"string\",\r\n \"Environment\": { \r\n \"string\" : \"string\" \r\n },\r\n \"Image\": \"string\",\r\n \"Mode\": \"MultiModel\",\r\n \"ModelDataUrl\": \"string\",\r\n \"MultiModelConfig\": { \r\n \"ModelCacheSetting\": \"string\"\r\n }\r\n }\r\n]\r\n```\r\nYou can find the details about these parameters [here](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html). This example should help you to [understand the end to end workflow](https://docs.aws.amazon.com/sagemaker/latest/dg/create-multi-model-endpoint.html#create-multi-model-endpoint-sdk). Note that parameters related to both the endpoint config and endpoint APIs are in the [deploy component](https://github.com/kubeflow/pipelines/tree/master/components/aws/sagemaker/deploy).\r\n\r\nPlease let us know if you have any more questions\r\n\r\nPlease assign `/area components/aws/sagemaker` for issues related to Amazon SageMaker Components for Kubeflow Pipelines\r\n\r\n/area components/aws/sagemaker",
"Thanks @surajkota! I will give this a try.",
"@surajkota We got it working using this suggestion. Thanks for your help!"
] | 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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I_kwDOB-71UM47sy8Z
| 6,586 |
Potentially duplicated function list_pipeline_versions()?
|
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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 contribution readme [CONTRIBUTION.md](https://github.com/kubeflow/pipelines/blob/master/CONTRIBUTING.md) but it doesn't say much re: creating upstream branches.\r\n\r\nOr am I supposed to fork, fix, and send a patch? \r\n\r\nOr is there a core dev team that's taking care of it? ",
"Workflow would be along the lines: \r\n1) Fork the repository: https://github.com/kubeflow/pipelines\r\n2) Create a branch and update the code\r\n3) Make a pull request to https://github.com/kubeflow/pipelines from your forked repo. \r\n\r\nYou are more than welcome to make a PR to Kubeflow pipelines :) someone in the core team have to approve the PR, you can see who are part of the core team after you make a PR in the comments from the bot. ",
"@NikeNano - thanks - I think I've done it right? hahaha (I'm a little new to forks and upstream contribution workflows like this one - typically worked within an org).",
"> @NikeNano - thanks - I think I've done it right? hahaha (I'm a little new to forks and upstream contribution workflows like this one - typically worked within an org).\r\n\r\nLooks great! Had the same experience when I started to contribute to open source :)"
] | 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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method to check if SDK is talking to a real KFP API
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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 `kubeflow pipelines` or not. ",
"Or do we actually need to have a separate functionality in the sdk for this @thesuperzapper ?",
"We will call `get_kfp_healthz` api every time when we initialize client. Maybe we can check the response of `get_kfp_healthz` api to check whether it connects to a real KFP API. Refer to [code](https://github.com/kubeflow/pipelines/blob/7875b68654a69ca761cb0ba4a920a30925a0e94b/sdk/python/kfp/_client.py#L196)",
"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"
] | 2021-09-20T22:41:41 | 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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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_task = dsl.Sidecar(name='x', image='x')\r\nmain_task.add_sidecar(sidecar_task)\r\n``` \r\n\r\nhttps://github.com/kubeflow/pipelines/blob/8b55b1632e4fd626b32bce7fd88582e1fdfea5dc/sdk/python/kfp/dsl/_container_op.py#L1070\r\n\r\nDoes this work for you?",
"Sidecar is not supported on Vertex Pipelines.\r\n",
"We don't have the plan to support sidecar in vertex pipeline in the near future."
] | 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 -->
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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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I_kwDOB-71UM47hfSg
| 6,578 |
[feature] Ability to disable caching for a particular pipeline run via the UI
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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 in https://github.com/kubeflow/pipelines/blob/55a2fb5c20011b01945c9867ddff0d39e9db1964/sdk/python/kfp/v2/google/client/client.py#L140-L152, you can follow such logic in UI.",
"@capri-xiyue Sounds good on providing the ability to disable pipeline level caching on UI side, once we updated pipeline job proto to support this field. But question: currently when you create a run from pipeline template, UI doesn't use the PipelineJob payload itself, instead UI will create a run using pipeline_version_id for PIPELINE_VERSION resource_reference. So I guess it should be backend which exposes caching configuration first?",
"> @capri-xiyue Sounds good on providing the ability to disable pipeline level caching on UI side, once we updated pipeline job proto to support this field. But question: currently when you create a run from pipeline template, UI doesn't use the PipelineJob payload itself, instead UI will create a run using pipeline_version_id for PIPELINE_VERSION resource_reference. So I guess it should be backend which exposes caching configuration first?\r\n\r\nCan UI manipulate the template before UI call backend?",
"Discussed offline, backend needs to expose cache configuration first and then front end can disable caching for a particular pipeline run via the UI",
"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 activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"We will cover this and more in https://github.com/kubeflow/pipelines/pull/8177 hopefully we can present it tomorrow in the KFP meeting"
] | 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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I_kwDOB-71UM47edoP
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[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_func(func=func, *args, **kwargs)\r\n\r\n\r\ndef func_op(func, *args, **kwargs):\r\n # Instantiate an operation right before calling it\r\n # because the suffix must be dynamically generated.\r\n op = _create_component_from_func(\r\n func=func,\r\n base_image=\"python:alpine\",\r\n )\r\n task = op(*args, **kwargs)\r\n return task\r\n\r\n\r\n@pipeline()\r\ndef hello_pipelien():\r\n\t# ...\r\n range_task = func_op(func=range_fn, stop=parallel_num)\r\n with dsl.ParallelFor(range_task.output) as worker_id:\r\n\t\t# ...\r\n```",
"/assign @chensun "
] | 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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| 996,990,727 |
I_kwDOB-71UM47bN8H
| 6,574 |
[sdk] KFP V2 pipeline fails with 'no module named ensurepip'
|
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[
"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` but it complains\r\n\r\n```\r\nensurepip is disabled in Debian/Ubuntu for the system python.\r\n\r\nPython modules for the system python are usually handled by dpkg and apt-get.\r\n\r\n apt-get install python-<module name>\r\n\r\nInstall the python-pip package to use pip itself. Using pip together\r\nwith the system python might have unexpected results for any system installed\r\nmodule, so use it on your own risk, or make sure to only use it in virtual\r\nenvironments.\r\n```",
"Another related issue is that in a different image I need to use a conda environment. I think a general solution can be a fully customizable command to install the packages.",
"Or the only reasonable solution is that the user should be able to choose which ever image they like.",
"@zijianjoy @chensun What has changed in this regard since KFP SDK V1 pipelines? The same image that works perfectly for V1 fails for V2.",
"> @zijianjoy @chensun What has changed in this regard since KFP SDK V1 pipelines? The same image that works perfectly for V1 fails for V2.\r\n\r\n@ConverJens, the change is we install `kfp` package into the component for the executor and build-in artifact types support. The alternative would be inline the code into the container command line as mentioned in https://github.com/kubeflow/pipelines/issues/6116#issuecomment-885506281\r\n\r\nWe will look into making `kfp` installation an option that you could opt-out. Before that is ready, can you use an alternative image that has pip installed? ",
"> 👍 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\n> using system python 3.6 on ubuntu 18.04. Then I installed `python3.6-venv` package and call `python -m ensurepip` but it complains\r\n> \r\n> ```\r\n> ensurepip is disabled in Debian/Ubuntu for the system python.\r\n> \r\n> Python modules for the system python are usually handled by dpkg and apt-get.\r\n> \r\n> apt-get install python-<module name>\r\n> \r\n> Install the python-pip package to use pip itself. Using pip together\r\n> with the system python might have unexpected results for any system installed\r\n> module, so use it on your own risk, or make sure to only use it in virtual\r\n> environments.\r\n> ```\r\n\r\n@jiasenwu Did this fail your component execution? or was it only warnings? ",
"@chensun Thanks for the response!\r\n\r\nI have pip installed and I have already preinstalled both kfp and other pypi packages. So this is happening with an image that already has pip installed but I'm not using a virtual environment.",
"> > +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> > using system python 3.6 on ubuntu 18.04. Then I installed `python3.6-venv` package and call `python -m ensurepip` but it complains\r\n> > ```\r\n> > ensurepip is disabled in Debian/Ubuntu for the system python.\r\n> > \r\n> > Python modules for the system python are usually handled by dpkg and apt-get.\r\n> > \r\n> > apt-get install python-<module name>\r\n> > \r\n> > Install the python-pip package to use pip itself. Using pip together\r\n> > with the system python might have unexpected results for any system installed\r\n> > module, so use it on your own risk, or make sure to only use it in virtual\r\n> > environments.\r\n> > ```\r\n> \r\n> @jiasenwu Did this fail your component execution? or was it only warnings?\r\n\r\nThis is an error. Ubuntu doesn't allow me to call ensurepip. I end up doing a monkey patch to this function `kfp.v2.components.component_factory._get_packages_to_install_command`\r\n\r\n```python\r\ndef patched_get_packages_to_install_command(package_list=None):\r\n result = []\r\n if package_list:\r\n install_packages_command = (\r\n \"PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet \\\r\n --no-warn-script-location {}\"\r\n ).format(\" \".join([repr(str(package)) for package in package_list]))\r\n result = [\r\n \"sh\",\r\n \"-c\",\r\n ' ({install_packages} || {install_packages} --user) && \"$0\" \"$@\"'.format(\r\n install_packages=install_packages_command\r\n ),\r\n ]\r\n return result\r\n```",
"@chensun @jiasenwu \r\nI feel that there must be something fundamentally broken about the way package installations is now handled in kfp v2.\r\n\r\nInstalling additional requirements worked great in kfp v1 using the same base image for the pipeline so it seems as some other change in how packages are now being installed is the culprit.",
"Closing the issue as this has been patricianly mitigated by the above linked PR."
] | 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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I_kwDOB-71UM47X87H
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[feature] Add option to allow VolumeOp to re-use PVC
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[] | 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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[bug] Sanitize Names kfp.dsl doesn't allow the usage of argo template variables
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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 pipeline is executed twice simultaneously by making the name dependent on unique-id associated with the workflow.\r\n- previously this also prevented caching with VolumeOp by making the name unique. [REF](https://github.com/kubeflow/pipelines/issues/5257#issuecomment-813350932) ",
"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"
] | 2021-09-14T10:10:37 | 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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expose task API via Istio authorization policy
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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. -->
### 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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[feature][v2] Allow pipelines to use per steps caching
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"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 caching if this is hard to implement in v2.",
"cc @capri-xiyue for the request on enable/disable per-step caching in KFP v2.",
"Hello @zijianjoy @capri-xiyue,\r\nI see this issue is still labelled as \"needs more info\", can I do something to help you ?\r\nIs this feature still planned for v2 ?\r\nHave a good day 🙂",
"@Itega Would you like to describe how you want to configure the cache staleness? For example, do you want to configure this when building pipeline template? or configure staleness across the whole KFP deployment? \r\n\r\nDescribing the user journey will be helpful.",
"@zijianjoy Sorry for the late answer.\r\nI'd like to configure cache staleness when building the pipeline template so I can have different cache staleness configured for different steps in a pipeline.\r\n\r\nI have a pipeline which should run every hour, this pipeline has a step which takes more than one hour to execute. I want to cache this step for one week as its result does not change often.\r\nSome other steps in this pipeline should be cached for one day and some others should not be cached.\r\n\r\nI'd like to configure this in the python template of my pipeline as I can do in v1 but any solution will do while I can do what's described above !\r\n\r\nLet me know if this is unclear or if you need more informations !",
"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"
] | 2021-09-13T08:12:07 | 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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[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 case is to prevent concurrent execution of tasks that could otherwise change some shared resource. For example, if there's a race condition between tasks, or between multiple runs of the same task, it's helpful to use argo synchronization to prevent those tasks from running concurrently.\r\n\r\nThis is a useful argo feature, and it would be helpful to have it in kfp as well.",
"> Hi @zijianjoy, the typical use case in either case is to prevent concurrent execution of tasks that could otherwise change some shared resource. For example, if there's a race condition between tasks, or between multiple runs of the same task, it's helpful to use argo synchronization to prevent those tasks from running concurrently.\r\n> \r\n> This is a useful argo feature, and it would be helpful to have it in kfp as well.\r\n\r\n\r\nHi @jmcarp , in your use case, can you add synchronization logic for the shared resources? I think when it comes to shared resources, it also makes sense that the synchronization logic is added to the shared resources instead of the workflows.",
"I *can* implement my own synchronization, but that's the kind of feature I expect the workflow engine to provide--it's easy to get wrong, so ideally it's implemented once at the workflow layer and not independently by each user of the workflow tool. That's why tools like argo workflows and apache airflow include synchronization primitives, for example.",
"I see a 👍 from @Bobgy. Would you accept a patch for this?",
"I think that sounds like a fair argument, but regarding project status, we are transitioning to v2, so it's better reconsider this feature request after other critical features are ready.",
"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",
"Is there any update on that feature? We are currently using kfp 1.8 and are evaluating possibilities of migration towards v2.x. However, we rely heavily on Argo synchronization features, which we were able to utilize by patching Argo YAML that Kubeflow compiler was outputting. With IR YAML this will become impossible, since we won't have direct control over Argo YAML",
"@Bobgy hi. Since 2.0 is officially released for quite some time, should this feature that allows semaphore usage get more attention?"
] | 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! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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[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 your contributions.\n"
] | 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>
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<title>Error 524 ()!!1</title>
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<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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MDU6SXNzdWU5OTM1NjYzODA=
| 6,547 |
[backend] Retries fail silently for runs with compressed or offloaded node statuses
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[] | 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?
<!-- 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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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-west2)\r\n- Belgium (europe-west1)\r\n- Netherlands (europe-west4)\r\n\r\nref: https://cloud.google.com/vertex-ai/docs/general/locations#available_regions",
"Hi,\n\nThanks for this, it helped a lot.\nBest regards,\n\nRita\n\nOn Wed, 22 Sept 2021 at 23:45, Chen Sun ***@***.***> wrote:\n\n> @ritaarex <https://github.com/ritaarex> If I'm not mistaken, you set\n> region to 'eu' when you initialize the client?\n>\n> api_client = AIPlatformClient(\n> project_id=PROJECT_ID,\n> region=REGION,\n> )\n>\n> eu is not a valid region, Vertex Pipelines currently support the\n> following regions in Europe:\n>\n> - London (europe-west2)\n> - Belgium (europe-west1)\n> - Netherlands (europe-west4)\n>\n> ref:\n> https://cloud.google.com/vertex-ai/docs/general/locations#available_regions\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/kubeflow/pipelines/issues/6540#issuecomment-925353447>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ASRD3NBAPYDYLHG5DNFVBULUDJFAVANCNFSM5DYYFDQA>\n> .\n> Triage notifications on the go with GitHub Mobile for iOS\n> <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675>\n> or Android\n> <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.\n>\n>\n",
"@ritaarex, no problem. I'm closing the issue assuming your issue is resolved. Thanks!"
] | 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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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 further activity occurs. Thank you for your contributions.\n"
] | 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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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 new version!"
] | 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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[
"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 to be implemented to support this? maybe I can work on that",
"@dvaldivia It would be awesome if you could look into this! Ping @Bobgy ",
"/assign @chensun @zijianjoy ",
"Users can now specify the Minio instance to use by using a KFP v2 deployment (alpha) and KFP SDK v2 (beta).",
"@connor-mccarthy do you have an example?",
"@dvaldivia \r\nThis is specified via the `pipeline_root` argument in this example: https://www.kubeflow.org/docs/components/pipelines/sdk-v2/v2-compatibility/#compiling-and-running-pipelines-in-v2-compatibility-mode. The example is v2 compatible mode, but this applies to KFP v2 also.\r\n\r\n`pipeline_root`:\r\n```\r\npipeline_root: (Optional.) The root path where this pipeline’s outputs are stored. This can be a MinIO, Google Cloud Storage, or Amazon Web Services S3 URI. You can override the pipeline root when you run the pipeline.\r\n```",
"@connor-mccarthy - \r\nDefault location for minio PipelineRoot - \"minio://mlpipeline/v2/artifacts\"\r\nAssume I have installed minio in cluster through operator - \r\nminio.ns-1.svc.cluster.local:80\r\n\r\nHow can i specify it's custom location ?",
"You can specify it when creating a pipeline with the `@kfp.dsl.pipeline` decorator:\r\n\r\n```python\r\nfrom kfp import dsl\r\n\r\[email protected](pipeline_root='gs://my-pipeline-root/example-pipeline')\r\ndef my_pipeline():\r\n ...\r\n```",
"> You can specify it when creating a pipeline with the `@kfp.dsl.pipeline` decorator:\r\n> \r\n> ```python\r\n> from kfp import dsl\r\n> \r\n> @dsl.pipeline(pipeline_root='gs://my-pipeline-root/example-pipeline')\r\n> def my_pipeline():\r\n> ...\r\n> ```\r\n\r\nrun_result = kfp.Client().create_run_from_pipeline_func(\r\n pipeline_func, \r\n experiment_name=experiment_name, \r\n run_name=run_name,\r\n arguments=arguments,\r\n mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE,\r\n pipeline_root='minio.ns-1://mlpipeline/v2/artifacts'\r\n)\r\n\r\nI tried that but its not working ",
"@ashujain2, can you please provide an error message to help with debugging?",
"> pipeline_root='minio.ns-1://mlpipeline/v2/artifacts'\r\n\r\n```\r\ntime=\"2022-08-11T18:29:46.802Z\" level=info msg=\"capturing logs\" argo=true\r\nI0811 18:29:46.846397 28 cache.go:143] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.\r\nI0811 18:29:46.846416 28 cache.go:120] Connecting to cache endpoint ml-pipeline.kubeflow:8887\r\nF0811 18:29:46.955301 28 main.go:50] Failed to execute component: parse bucket config failed: unrecognized pipeline root format: \"minio.cserver-minio.svc.cluster.local://ns-1/artifacts/v2/pipeline/example-metrics-viz/b29c9ad8-9123*********\"\r\ntime=\"2022-08-11T18:29:46.956Z\" level=error msg=\"cannot save artifact /tmp/outputs/metrics/data\" argo=true error=\"stat /tmp/outputs/metrics/data: no such file or directory\"\r\ntime=\"2022-08-11T18:29:46.956Z\" level=error msg=\"cannot save artifact /tmp/outputs/output_text_path/data\" argo=true error=\"stat /tmp/outputs/output_text_path/data: no such file or directory\"\r\nError: exit status 1\r\n```\r\n\r\n\r\nBasically , I have setup minio and kubeflow into same EKS cluster along with Kubeflow with an overall aim to achieve to segregate artifacts and metadata per namespace per bucket .\r\n \r\nminIO Endpoint- minio.cserver-minio.svc.cluster.local\r\nInto this hosted dedicated minio sever -I have couple of buckets created like ns-1, ns-2 ....\r\n\r\nSomehow , I was able to achieve it by implementing steps written here - ### https://blog.min.io/how-to-kubeflow-minio/\r\n\r\nArtifacts are going into dedicated minio bucket but pipeline O/p's are going to default minio .\r\n \r\n\r\n\r\n\r\n",
"I have raised a specific proposal for achieving this in: \r\n- https://github.com/kubeflow/pipelines/issues/6517"
] | 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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MDU6SXNzdWU5ODk2NjgyNDU=
| 6,512 |
[sdk] nodeSelector specification not being set correctly in pipeline spec
|
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[] | 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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MDU6SXNzdWU5ODk1MDI1Mjk=
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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 released independently. It's meant to be backward compatible with KFP backend except otherwise noticed.",
"@chauhang perhaps we should link this changelog on [the `kfp` PyPi](https://pypi.org/project/kfp/)?",
"> @chauhang perhaps we should link this changelog on [the `kfp` PyPi](https://pypi.org/project/kfp/)?\r\n\r\nThat's a good idea. Will do so."
] | 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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MDU6SXNzdWU5ODkxNTc1OTE=
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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 in my dsl.pipeline by:\r\n\r\n`smh_vol = kfp.dsl.PipelineVolume(name=\"shm-vol\", empty_dir={\"medium\": \"Memory\"})`\r\n\r\nand then adding the volume to my training task by:\r\n\r\n`train_task.add_pvolumes({\"/dev/shm\": smh_vol})`\r\n\r\nCould anybody explain why this works??\r\n\r\n",
"But yes @zijianjoy, your are right. This is more Kubernetes related than Kubeflow",
"hi @mikel-brostrom, I had the same issue. \r\nI followed your solution by adding two lines in my dsl pipeline, \r\nbut it's still not working. \r\ndo you have any idea? and do you have any references for these two lines?\r\nthanks. \r\n"
] | 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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[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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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 compiler.Compiler().compile(\r\n----> 4 pipeline_func=pipeline, package_path=pip_package_path\r\n 5 )\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in compile(self, pipeline_func, package_path, pipeline_name, pipeline_parameters, type_check)\r\n 1137 pipeline_parameters = {\r\n 1138 param.name: param for param in args_list_with_defaults\r\n-> 1139 }\r\n 1140 # Update pipeline parameters override if there were any.\r\n 1141 pipeline_parameters_override = pipeline_parameters_override or {}\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in _create_pipeline_v2(self, pipeline_func, pipeline_name, pipeline_parameters_override)\r\n 1070 pipeline_func: Pipeline function with @dsl.pipeline decorator.\r\n 1071 pipeline_name: The name of the pipeline. Optional.\r\n-> 1072 pipeline_parameters_override: The mapping from parameter names to values.\r\n 1073 Optional.\r\n 1074 \r\n\r\n<ipython-input-7-72f66fff9826> in pipeline(project, location, display_name, python_package_gcs_uri, python_module_name, container_uri, staging_bucket, base_output_dir, model_serving_container_image_uri, replica_count, machine_type, model_display_name)\r\n 38 model_serving_container_image_uri=model_serving_container_image_uri,\r\n 39 replica_count=replica_count,\r\n---> 40 machine_type=machine_type,\r\n 41 )\r\n 42 \r\n\r\n/opt/conda/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/utils.py in component_yaml_generator(**kwargs)\r\n 531 additional_args = []\r\n 532 for key, args in args_to_serialize.items():\r\n--> 533 for arg_key, value in args.items():\r\n 534 additional_args.append(f'--{key}.{arg_key}')\r\n 535 additional_args.append(value)\r\n\r\nKeyError: 'python_module_name'",
"Duplicate of #6426 , I'm closing this issue and reopening the original issue. "
] | 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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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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[SDK] Standardize code style using YAPF and docformatter.
|
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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 further activity occurs. Thank you for your contributions.\n"
] | 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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MDU6SXNzdWU5ODY5Mzc2NTU=
| 6,496 |
Expose docker entrypoint on kfp local runner
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[
"@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 ideal, however it seems like it's not [parameterized](https://github.com/kubeflow/pipelines/blob/a1ec227a24d71b7a697884d1e2d00a57583f376b/sdk/python/kfp/_local_client.py#L344-L351) at the moment. \r\ncould you please clarify how could we provide the entrypoint as a part of the command?",
"kfp local runner is contributed by a community contributor, and is supported as an Alpha feature. \r\nFeel free to send PRs for feature requests like this.",
"> parameterized\r\n\r\nThe` ENTRYPOINT` specifies a command that will always be executed when the container starts while the `CMD` specifies arguments that will be fed to the ENTRYPOINT(if left empty the default entrypoint for docker `/bin/sh -c ` will be used. ) Thus could you skip the `ENTRYPOINT` in your use cases and rely on the `CMD` to include the differences in terms of `ENTRYPOINT`?\r\n\r\nAn example pipelines would be: \r\n\r\n```python\r\nimport kfp.components as comp\r\nfrom kfp import LocalClient, run_pipeline_func_locally\r\n\r\n\r\n# Create a simple component using only bash commands. The output of this component\r\n# can be passed to a downstream component that accepts an input with the same type.\r\ncreate_step_write_lines = comp.load_component_from_text(\"\"\"\r\nname: write_lines\r\ndescription: Writes text to a file.\r\n\r\ninputs:\r\n- {name: text, type: String}\r\n\r\noutputs:\r\n- {name: data, type: Data}\r\n\r\nimplementation:\r\n container:\r\n image: busybox\r\n command:\r\n - sh\r\n - -c\r\n - |\r\n mkdir -p \"$(dirname \"$1\")\"\r\n echo \"$0\" > \"$1\"\r\n args:\r\n - {inputValue: text}\r\n - {outputPath: data}\r\n\"\"\")\r\n\r\n\r\ndef pipeline():\r\n write_lines_step = create_step_write_lines(\r\n text='one\\ntwo\\nthree\\nfour\\nfive\\nsix\\nseven\\neight\\nnine\\nten')\r\n\r\nif __name__ == \"__main__\":\r\n\r\n out = run_pipeline_func_locally(\r\n pipeline,\r\n {},\r\n execution_mode=LocalClient.ExecutionMode(\"docker\"),\r\n )\r\n print(out.get_output_file(\"write-lines\"))\r\n\r\n\r\n```\r\n\r\nwhere you could change the command to account for different `entrypoints` to the container, https://www.kubeflow.org/docs/components/pipelines/reference/component-spec/#implementation. Maybe you could give an example on how you like to use this feature @etsyamitra. I would be happy to work on the implementation when I understand the use case better. ",
"Does this solve your problem @etsyamitra otherwise please add an example so I better understand what you like to achieve. ",
"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"
] | 2021-09-02T16:26:47 | 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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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. -->
---
<!-- 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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| 986,310,043 |
MDU6SXNzdWU5ODYzMTAwNDM=
| 6,493 |
[frontend] <Unable to configure minio accesskey using manifests>
|
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[
"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/pipelines-profile-controller/sync.py#L346-L357, based on your user namesapce.\r\n\r\nWelcome contribution to design and implement this for a better usage!",
"@zijianjoy Thanks for your response. Unfortunately, I changing the secret in the user namespace and also this does not get it to work. I can change the part related to the backend but the frontend according to my tests only works with accesskey minio123 and if i use a different accesskey (even updated in the secret in the user namespace) it responds with a complain from Minio that the key is not valid. From what i see here is that these credentials are hard coded in the frontend. [link](https://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/frontend/server/configs.ts)",
"@Hmr-ramzi the credentials are indeed hard-coded, but they can be overriden by setting appropriate environment variables. Take a look at the platform-agnostic Helm Chart for Kubeflow Pipelines: https://github.com/getindata/helm-charts/tree/main/charts/kubeflow-pipelines which sets custom MinIO accesskey and secretkey via values:\r\nhttps://github.com/getindata/helm-charts/blob/c17c10622ed6ba0f324d8a5432c615bf20864d1b/charts/kubeflow-pipelines/templates/pipeline.yaml#L589 in the `metadata-ui` _Deployment_ and other places where it's necessary to work.\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"
] | 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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[backend] API performance regression in 1.5
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[
"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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| 985,181,216 |
MDU6SXNzdWU5ODUxODEyMTY=
| 6,487 |
[backend] Kale pipelines always fails
|
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[] | 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 found here: https://github.com/kubeflow-kale/kale/issues/352.\r\n\r\nHowever, should this be fixed permanently on the KFP side or is Kale doing something strange by needing 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"
] | 2021-09-01T13:51:57 | 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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[backend] Unsupported resource type: PIPELINE
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[
"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 \"PIPELINE_VERSION\" leads me to another issue:\r\n\r\n`\"error\": \"[GET /apis/v1beta1/pipelines][400] ListPipelines default &{Code:3 Details:[0xc0033f85d0] Error:Invalid input error: Invalid resource references for pipelines. ListPipelines requires filtering by namespace.}\"}`\r\n\r\nIt seems that the only accepted resource type here is the namespace:\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/backend/src/apiserver/server/pipeline_server.go#L209",
"@rubenaranamorera In the original issue description, you are calling `POST /apis/v1beta1/jobs` , but in the new issue, you are calling `GET /apis/v1beta1/pipelines`. I think specifying `PIPELINE_VERSION` doesn't make sense in ListPipelines API, because if you know the pipeline version ID, you can get the exact pipeline entry directly by using `GetPipeline` API, you don't need to list pipelines. Is this understanding correct?\r\n\r\ncc @capri-xiyue for the backend related question on accepting namespace resource type only.",
"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"
] | 2021-09-01T09:11:17 | 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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Which all artifact types can be created using the pipeline sdk which are supported by argo workflows?
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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 download the artifact you want in a component.\nMore detailed use-case that explains why these are useful are helpful.\n\nAnd please thumbs up on the issue, if you are interested too.",
"Hi @chensun, by KFP runtime you mean argo controller right? if that's the case it supports these extra artifacts, I tried manually editing the workflow YAML and deployed it in the Kubeflow pipelines UI and it works. But manual editing of workflow is difficult so if we have support for the same using KFP SDK it will be useful. \r\n@Bobgy Yes I agree with you, for general if KFP SDK supports these features then the user who has stored their input artifacts in a say git-repo, or an HTTP artifactory repo can easily intake those artifacts and also there are N number of opportunities which comes with this feature. ",
"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"
] | 2021-08-31T14:09:02 | 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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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 activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 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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| 6,465 |
[bug] Unhandled SageMaker training job status 'stopped' causing infinite loop
|
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[] | 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 been resolved in https://github.com/kubeflow/pipelines/pull/8336. Please use the latest version of components and let us know if you face any problems. Thanks",
"/close",
"@surajkota: Closing this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/6465#issuecomment-1376375518):\n\n>/close\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>"
] | 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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| 981,497,708 |
MDU6SXNzdWU5ODE0OTc3MDg=
| 6,457 |
[bug] Problem starting a pipeline run with REST api
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[
"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 pipeline.\r\n\r\nReference:\r\nhttps://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#/definitions/apiResourceReference",
"Hi @zijianjoy\r\nIn the documentation, they use the object ApiResourceReference to specify the resource, is it possible to convert it to a dictionary to pass it along in my POST request? The sample seems to still use the kfp client in order to create a run/experiment. ",
"Hello @mlu4679 \r\n\r\nDoes this look like something you are looking for? https://github.com/kubeflow/website/issues/2916",
"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"
] | 2021-08-27T18:36:37 | 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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| 6,451 |
KFP SDK v2 compatibility mode feedback
|
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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 main use of pipelines, to compare their metrics. \r\nThanks and keep up the good work.",
"Tracking issue mentioned by @AlexandreBrown in https://github.com/kubeflow/pipelines/issues/6912",
"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",
"@Bobgy https://github.com/kubeflow/pipelines/issues/7561",
"cc @chensun @zijianjoy "
] | 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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[feature] Validate IAP JWT header
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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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| 979,454,685 |
MDU6SXNzdWU5Nzk0NTQ2ODU=
| 6,436 |
Upgrade MLMD proto to 1.2.0 or above to get filter_query
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[] | 2021-08-25T17:17:51 | 2021-08-28T23:59:23 | 2021-08-28T23:59:23 |
COLLABORATOR
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MDU6SXNzdWU5Nzk0MDAxOTE=
| 6,435 |
Allow custom mounting for kfp local runner
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[] | null |
[
"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 set `pipeline_root` when initialize the client: \r\n\r\nhttps://github.com/kubeflow/pipelines/blob/a1ec227a24d71b7a697884d1e2d00a57583f376b/sdk/python/kfp/_local_client.py#L171-L178",
"Just realize `run_pipeline_func_locally` is a wrapper function which does not expose the `pipeline_root` parameter. \r\n\r\nhttps://github.com/kubeflow/pipelines/pull/6462 should fix the issue.",
"@chensun Thank you so much for working on it, would there be any way to expose the docker --entrypoint as well? It could be either at pipeline level or at component level to keep it custom."
] | 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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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=\"python:3.7-buster\")\r\n\r\n\r\n@create_component_from_func\r\ndef kf_comp_ranges(n1: int, n2: int) -> typing.NamedTuple(\"kf_comp_ranges\", [(\"r1\", str), (\"r2\", str)]):\r\n import json\r\n return json.dumps(list(range(n1))), json.dumps(list(range(n2)))\r\n\r\n\r\n@create_component_from_func\r\ndef kf_comp_indices(iterable: str) -> typing.List[int]:\r\n import json\r\n return list(range(len(json.loads(iterable))))\r\n\r\n\r\n@create_component_from_func\r\ndef kf_comp_item(iterable: str, index: int) -> int:\r\n import json\r\n return json.loads(iterable)[index]\r\n\r\n\r\[email protected](name=\"test\", description=\"test\")\r\ndef kf_pipeline(n1: int, n2: int):\r\n ranges = kf_comp_ranges(n1=n1, n2=n2)\r\n\r\n indicies1 = kf_comp_indices(iterable=ranges.outputs[\"r1\"])\r\n indicies2 = kf_comp_indices(iterable=ranges.outputs[\"r2\"])\r\n\r\n # without these loops, compiling would work.\r\n with kfp.dsl.ParallelFor(indicies1.output) as index1:\r\n item1 = kf_comp_item(iterable=ranges.outputs[\"r1\"], index=index1)\r\n with kfp.dsl.ParallelFor(indicies2.output) as index2:\r\n item2 = kf_comp_item(iterable=ranges.outputs[\"r2\"], index=index2)\r\n\r\n\r\nkfp.compiler.Compiler().compile(pipeline_func=kf_pipeline, package_path=\"test.yaml\", type_check=False)\r\n```\r\n\r\nThe code works until `kfp==1.7.0`. Versions 1.7.1 and 1.7.2 give errors like\r\n```\r\nTraceback (most recent call last):\r\n File \"test.py\", line 40, in <module>\r\n kfp.compiler.Compiler().compile(pipeline_func=kf_pipeline, package_path=\"test.yaml\", type_check=False)\r\n File \"C:\\Users\\t13muell\\.conda\\envs\\dstk\\lib\\site-packages\\kfp\\compiler\\compiler.py\", line 1064, in compile\r\n package_path=package_path)\r\n File \"C:\\Users\\t13muell\\.conda\\envs\\dstk\\lib\\site-packages\\kfp\\compiler\\compiler.py\", line 1119, in _create_and_write_workflow\r\n pipeline_conf)\r\n File \"C:\\Users\\t13muell\\.conda\\envs\\dstk\\lib\\site-packages\\kfp\\compiler\\compiler.py\", line 954, in _create_workflow\r\n pipeline_conf,\r\n File \"C:\\Users\\t13muell\\.conda\\envs\\dstk\\lib\\site-packages\\kfp\\compiler\\compiler.py\", line 717, in _create_pipeline_workflow\r\n templates = self._create_dag_templates(pipeline, op_transformers)\r\n File \"C:\\Users\\t13muell\\.conda\\envs\\dstk\\lib\\site-packages\\kfp\\compiler\\compiler.py\", line 659, in _create_dag_templates\r\n op_name_to_for_loop_op,\r\n File \"C:\\Users\\t13muell\\.conda\\envs\\dstk\\lib\\site-packages\\kfp\\compiler\\compiler.py\", line 272, in _get_inputs_outputs\r\n upstream_op = pipeline.ops[param.op_name]\r\nKeyError: 'kf-comp-indices 2'\r\n```",
"/assign @chensun "
] | 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! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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| 979,151,391 |
MDU6SXNzdWU5NzkxNTEzOTE=
| 6,432 |
[feature] Allow time to live to be set on pipelines individually while supporting the persistance agent to do the GC.
|
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[] | null |
[
"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 experience is that I had some pipelines which I wanted to clean up quicker(especially recurring runs) and didn't wanted to set the same default for all pipelines(only have experience from smaller team where we have to do it all ourself). \r\n\r\nAnother use case is to release expensive hard where described here: https://github.com/kubeflow/pipelines/issues/5234#issue-821046204. Maybe this can be solved in other ways though?\r\n",
"As far as I understand it, latest releases of KFP come with an updated release of Argo, that as update the way to specify a TTL in workflow (see https://argoproj.github.io/argo-workflows/fields/#ttlstrategy).\r\nI made a PR to correct it : https://github.com/kubeflow/pipelines/pull/7141\r\n",
"As I understand this is now resolved so will close this. "
] | 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? -->
---
<!-- 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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MDU6SXNzdWU5NzkwOTgyMjQ=
| 6,431 |
[components] Upgrade minio to the latest version
|
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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/minio/discussions/12156",
"Thanks, we don't allow AGPL, so if upgrade, we can only choose a version before it was changed to AGPL.",
"We might need to consider a MinIO alternative, so that people can keep getting security fixes.",
"> We might need to consider a MinIO alternative, so that people can keep getting security fixes.\r\n\r\nWhat alternative are there out there I am only aware of [rook](https://github.com/rook/rook)/[ceph](https://github.com/ceph/ceph). What are we looking for when it comes to replacing Minio except the license?",
"I think [SeaweedFS](https://github.com/chrislusf/seaweedfs) could be another alternative.",
"It seems that [NooBaa](https://www.noobaa.io/) could be another alternative using an Apache License. The Datashim project uses it in their [tutorials](https://github.com/datashim-io/datashim/wiki/Deployment-and-Usage-of-S3-Object-Stores) with BackingStore [PV-Pool](https://github.com/noobaa/noobaa-operator/blob/master/doc/backing-store-crd.md#pv-pool-type) to setup an S3 Object Store. The other BackingStores could potentially replace Minio's Gateway functionality.",
"@MatthiasCarnein just a bit curious about the License details.\r\n\r\nBecause this project uses argo-workflows, and argo-workflows uses MinIO v7 APIs, https://github.com/argoproj/argo-workflows/blob/master/go.mod#L35\r\nIs this also influence Kubeflow pipelines?\r\n",
"@haoxins I'm not a lawyer but from my understanding the Minio Client SDK for Go is a separate piece of software that uses the Apache 2.0 license: https://github.com/minio/minio-go/blob/master/LICENSE\r\n\r\n",
"Minio alternatives are also discussed here: https://github.com/kubeflow/pipelines/issues/7878"
] | 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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MDU6SXNzdWU5NzkwNDY5MzU=
| 6,430 |
Return minio log url when getting the status of a run using rest api call
|
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[] | 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 the next release",
"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"
] | 2021-08-25T11:45:59 | 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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MDU6SXNzdWU5NzkwMDE1NDQ=
| 6,429 |
[sdk] VolumeOp support for sdk v2
|
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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 persistent storage. We have seen many customers using this and enterprise use cases. I wouldn't want this discussion to becomes stale.\r\n\r\nWe have been working with KFP v1 until now but we want to to transition to V2 as well. Can I ask what is the state wrt supporting VolumeOps in V2, if you are planning on doing something about it or if you are open to contributions?"
] | 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 👌 .
---
<!-- 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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