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"Feel free to contribute to `kfp[kubernetes]` to extend the capability of VolumeOps."
] | 2023-07-05T17:35:30 | 2023-07-13T22:43:53 | null | NONE | null | ### Feature Area
/area backend
/area sdk
### What feature would you like to see?
To continue discussion from https://github.com/kubeflow/pipelines/issues/8570, the resourceOp was deprecated in v2, and although `kfp[kubernetes]` [supports](https://www.kubeflow.org/docs/components/pipelines/v2/platform-specific-features/) the removed volume types, there is no longer obvious support or design plan for resourceOp being used in the context of creating a custom resource definition. Any custom operator that would have worked in v1 via resourceOp no longer has support. For our use case, we were testing version 1 (and required this ability) and version 2 has become not possible for us to use. I suspect there is equivalent need if you ask across the community.
### What is the use case or pain point?
Running custom resources / controllers that can handle scoped work to work with KubeFlow pipelines.
### Is there a workaround currently?
I have not found a workaround - I am looking to use Kueue or back to basic Argo (the first might be a good direction, the second adds some complexity in that logs / statuses aren't easy to pass forward to the running Argo pod without a bit of hacks.
---
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"/assign @Linchin ",
"Hi @andre-lx, thank you for bringing up this issue. I tried the same pipeline on a newly deployed 2.0.0 cluster, and the run finished without issue. looking at the log you provided, we have \r\n\r\n> github.com/kubeflow/pipelines/backend/src/v2/metadata.(*Client).PublishExecution(0xc000b29920, {0x20a4878, 0xc000058040}, 0x0, 0x0, {0x0, 0x0, 0xc000b60000?}, 0x4)\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/v2/<b>metadata/client.go:388</b> +0x69\r\n\r\nThe metadata client seems to come from [version 2.0.0-rc.2](https://github.com/kubeflow/pipelines/blob/2.0.0-rc.2/backend/src/v2/metadata/client.go#L388) instead of [version 2.0.0](https://github.com/kubeflow/pipelines/blob/2.0.0/backend/src/v2/metadata/client.go#L412). Could you double check if you applied the manifest of version 2.0.0? Try apply the manifest again ([here](https://github.com/kubeflow/pipelines/blob/e03e31219387b587b700ba3e31a02df486aa364f/backend/src/v2/metadata/client.go#L412)) and see if the issue persists.\r\n\r\n\r\n",
"Also, could you let me know which way you used to deploy KFP, standalone or via kubeflow?",
"Hi @Linchin, I just checked and we are using the following image: \r\nhttps://github.com/kubeflow/pipelines/blob/e03e31219387b587b700ba3e31a02df486aa364f/manifests/kustomize/base/metadata/base/kustomization.yaml#L10-L12\r\n\r\nThe deployment was done using the follwing file: https://github.com/kubeflow/pipelines/blob/2.0.0/manifests/kustomize/env/platform-agnostic-multi-user/kustomization.yaml\r\n\r\nThanks",
"Hi @andre-lx @Linchin \r\nSame issue we are also facing. Did you get a chance to fix it?",
"> Hi @andre-lx @Linchin Same issue we are also facing. Did you get a chance to fix it?\r\n\r\nI had to revert it to 1.8.5 for now. ",
"+ @chensun for visibility of this issue ",
"I have the same error. Here are the details.\r\n\r\n1. Running in standalone mode\r\n2. Running in virtual cluster (everything is working but cannot run pipelines)\r\n3. All pods are working\r\n4. I can upload and run pipelines on UI, but the pod is failing\r\n5. Using the pipelines version 2.0.0\r\n6. Generating the pipeline with the command below\r\nkfp dsl compile --py v2/hello_world.py --output hello_world.pipeline.json\r\n"
] | 2023-07-05T14:53:10 | 2023-08-08T17:08:21 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Manifests
* KFP version:
2.0.0
* KFP SDK version:
```
kfp 2.0.1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0
```
### Steps to reproduce
Hello, we are trying the migration from pipelines 1.8.5 to 2.0.0 but after the apply we are aheving some issues.
Running the "hello world" example from the jupyerlab:
```python
from kfp import dsl
import kfp
from kfp import dsl
@dsl.component
def say_hello(name: str) -> str:
hello_text = f'Hello, {name}!'
print(hello_text)
return hello_text
@dsl.pipeline
def hello_pipeline(recipient: str) -> str:
hello_task = say_hello(name=recipient)
return hello_task.output
from kfp import compiler
compiler.Compiler().compile(hello_pipeline, 'pipeline.yaml')
from kfp.client import Client
client = Client()
run = client.create_run_from_pipeline_package(
'pipeline.yaml',
arguments={
'recipient': 'World',
},
)
```
Or running the generated `pipeline.yaml` from the result directly though the UI, we always get the following error on the third pod that is started:
```
time="2023-07-05T14:19:23.912Z" level=info msg="capturing logs" argo=true
time="2023-07-05T14:19:23.945Z" level=info msg="capturing logs" argo=true
I0705 14:19:23.966873 51 launcher_v2.go:90] input ComponentSpec:{
"inputDefinitions": {
"parameters": {
"name": {
"parameterType": "STRING"
}
}
},
"outputDefinitions": {
"parameters": {
"Output": {
"parameterType": "STRING"
}
}
},
"executorLabel": "exec-say-hello"
}
I0705 14:19:23.967498 51 cache.go:139] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
I0705 14:19:23.967512 51 cache.go:116] Connecting to cache endpoint ml-pipeline.kubeflow:8887
panic: runtime error: invalid memory address or nil pointer dereference
[signal SIGSEGV: segmentation violation code=0x1 addr=0x0 pc=0x941c29]
goroutine 1 [running]:
github.com/kubeflow/pipelines/backend/src/v2/metadata.(*Client).PublishExecution(0xc000b29920, {0x20a4878, 0xc000058040}, 0x0, 0x0, {0x0, 0x0, 0xc000b60000?}, 0x4)
/go/src/github.com/kubeflow/pipelines/backend/src/v2/metadata/client.go:388 +0x69
github.com/kubeflow/pipelines/backend/src/v2/component.(*LauncherV2).publish(0x1d3c167?, {0x20a4878?, 0xc000058040?}, 0x1?, 0x1?, {0x0?, 0x1a51660?, 0xc0006a63a0?}, 0xc73bb0?)
/go/src/github.com/kubeflow/pipelines/backend/src/v2/component/launcher_v2.go:266 +0x9b
github.com/kubeflow/pipelines/backend/src/v2/component.(*LauncherV2).Execute.func2()
/go/src/github.com/kubeflow/pipelines/backend/src/v2/component/launcher_v2.go:144 +0x65
github.com/kubeflow/pipelines/backend/src/v2/component.(*LauncherV2).Execute(0xc00028e540, {0x20a4878, 0xc000058040})
/go/src/github.com/kubeflow/pipelines/backend/src/v2/component/launcher_v2.go:156 +0x91e
main.run()
/go/src/github.com/kubeflow/pipelines/backend/src/v2/cmd/launcher-v2/main.go:98 +0x3ed
main.main()
/go/src/github.com/kubeflow/pipelines/backend/src/v2/cmd/launcher-v2/main.go:47 +0x19
time="2023-07-05T14:19:24.950Z" level=info msg="sub-process exited" argo=true error="<nil>"
Error: exit status 2
time="2023-07-05T14:19:25.918Z" level=info msg="sub-process exited" argo=true error="<nil>"
Error: exit status 2
```
The service `ml-pipeline.kubeflow:8887` exists.
Everything works great on version 1.8.5.
If you need the logs from the others two pods please let me know. I also check the logs in all the kubeflow services and I can't find any issue.
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"/cc @jlyaoyuli \r\n/assign @zijianjoy ",
"@axel7083 This is a good point for improving the latency of rendering run list. There are multiple approaches to solve this problem:\r\n\r\n1. Like you suggested, we might be able to extend `/pipeline/apis/v2beta1/runs` List call to include experiment name and run detail. However, it requires API changes and potentially database table-join action, it might not be ideal to include too many data in `/pipeline/apis/v2beta1/runs` List call, which is going to slow down this call because the amount of data transmit is too large for one call.\r\n2. We can simplify by reducing repeated experiment query, by using `useQuery()` function and enable caching.\r\n3. We can also adopt React Suspense, https://react.dev/reference/react/Suspense, so we can load the run list itself fairly quickly, while waiting for the rest of API calls to complete. Once all API calls return, run list page doesn't need to re-render.\r\n\r\n@jlyaoyuli is doing some improvements to frontend API call performance currently. \r\n\r\nI will open up the discussion and welcome more ideas to improve frontend page performance."
] | 2023-07-04T07:55:55 | 2023-07-14T00:02:46 | null | CONTRIBUTOR | null | ### Description
When we navigate to the runs page, listing all the runs. The page take a lot of times to loads.
The frontend make a request to `/pipeline/apis/v2beta1/runs` then returns a list of run, containing each of them a `run-id` and a `pipeline_version_id`, for each run, the frontend will query the run details, and pipeline version details, to get their names.
On our cluster, it can take up to 4 seconds for 10 runs. Maybe we should includes in the `/pipeline/apis/v2beta1/runs` endpoint an optional query parameter to add directly the names to avoid making multiple requests.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9700 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9700/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9700/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9700/events | https://github.com/kubeflow/pipelines/issues/9700 | 1,786,617,694 | I_kwDOB-71UM5qfZ9e | 9,700 | Unable to run sidecar with kubeflow | {
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"Hi @robbiedow, the documentation you linked is not the official documentation. Please use [this](https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/) for documentation. At this point, KFP v2 does not support sidecar. We will use #9768 to track the feature request."
] | 2023-07-03T18:12:50 | 2023-07-27T22:59:37 | null | NONE | null | Hi,
I am trying to run a sidecar in a kubeflow component, and I can't figure out how to do it. I found some [documentation](https://kf-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.Sidecar) that indicates there is a class called kfp.dsl.Sidecar, but this example indicates this is not the case.
**I Ran:**
```
import kfp
echo = kfp.dsl.Sidecar(
name="echo",
image="nginx:1.13",
command=["nginx", "-g", "daemon off;"],
)
```
**I got the error:**
`AttributeError: module 'kfp.dsl' has no attribute 'Sidecar'`
I also tried implementing [this example](https://github.com/kubeflow/pipelines/blob/master/samples/core/sidecar/sidecar.py) from the kubeflow pipelines repo, but the line `from kfp.deprecated import dsl, compiler` does not import. I kept getting the error `AttributeError: module 'kfp.dsl' has no attribute <SomePackage>` for various packages. After pip installing the ones that were missing, I finally got `AttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'` which I don't know how to resolve.
It seems that the GitHub example and documentation are both for Kubeflow v1 anyways, and I am looking for a solution that works in v2. Is there a way to implement a sidecar on v2?
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"Hi @chensun @jlyaoyuli can I look into this issue?"
] | 2023-06-30T20:26:04 | 2023-07-26T01:56:41 | null | COLLABORATOR | null | null | {
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"@chensun @zijianjoy I think this is very important before cutting the 2.0.0 for the backend.",
"/assign @zijianjoy "
] | 2023-06-29T00:18:35 | 2023-07-13T22:46:18 | 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 -->
## Related Issues
- https://github.com/kubeflow/pipelines/issues/6517
# The Problem
Currently, we are hardcoding a few important configs in KFP V2's launcher and cache code.
This is making it difficult for distributions to support KFP v2 in a generic way.
__Launcher - The minio credentials secret is hard-coded as `mlpipeline-minio-artifact`:__
https://github.com/kubeflow/pipelines/blob/35f750703dcf4d1073a90708375459d1491085dd/backend/src/v2/objectstore/object_store.go#L292
__Launcher - The minio credentials secret keys are hard-coded as `accesskey` and `secretkey`:__
https://github.com/kubeflow/pipelines/blob/35f750703dcf4d1073a90708375459d1491085dd/backend/src/v2/objectstore/object_store.go#L324-L325
__Launcher - The minio endpoint is hard-coded as `minio-service.kubeflow:9000`:__
https://github.com/kubeflow/pipelines/blob/35f750703dcf4d1073a90708375459d1491085dd/backend/src/v2/objectstore/object_store.go#L291
__Cache - The `ml-pipeline` endpoint is hard-coded as `ml-pipeline.kubeflow:8887`:__
https://github.com/kubeflow/pipelines/blob/35f750703dcf4d1073a90708375459d1491085dd/backend/src/v2/cacheutils/cache.go#L26
# The Solution
We want to allow the above configs to be changed from their defaults, preferably on a per-namespace basis.
I propose we add these configs to the `ConfigMap/kfp-launcher` which already exists in each profile namespace to set `defaultPipelineRoot`.
For example, a new `ConfigMap/kfp-launcher` in a profile namespace might look like this:
```yaml
data:
defaultPipelineRoot: "minio://mlpipeline/v2/artifacts"
## minio endpoint config
minioEndpoint: "minio.example.com:9000"
## minio auth configs
minioAuthSecret: "mlpipeline-minio-artifact"
minioAuthSecretAccessKeyKey: "access_key"
minioAuthSecretAccessKeyKey: "access_key"
## ml pipeline endpoint config
mlPipelineEndpoint: "ml-pipeline.kubeflow:8887"
```
The question now becomes how to propagate these configs to the `kfp-launcher` container in each workflow Pod.
I think there are two options:
- OPTION 1: have the `kfp-launcher` container read the `ConfigMap/kfp-launcher`
- [when it executes](https://github.com/kubeflow/pipelines/blob/dbebbde2001f7d2a7372a8abfd9c5c5a0eaea2b8/backend/src/v2/component/launcher_v2.go#L132), it can pass the minio configs down as it calls [`objectstore.OpenBucket`](https://github.com/kubeflow/pipelines/blob/dbebbde2001f7d2a7372a8abfd9c5c5a0eaea2b8/backend/src/v2/component/launcher_v2.go#L163)
- setting `mlPipelineEndpoint` has to be done before it runs [`cacheutils.NewClient()`](https://github.com/kubeflow/pipelines/blob/dbebbde2001f7d2a7372a8abfd9c5c5a0eaea2b8/backend/src/v2/component/launcher_v2.go#L115)
- OPTION 2: read the `ConfigMap/kfp-launcher` in [`RootDAG` at the same time as we read the `defaultPipelineRoot`](https://github.com/kubeflow/pipelines/blob/eadeb3096b9cb3d3c8495491a461154b973b4e2a/backend/src/v2/driver/driver.go#L146)
- (this approach reduces the number of API calls to read `ConfigMap/kfp-launcher`)
# Other Thoughts
We could make this slightly more advanced to allow for "selecting" credentials based on the bucket & key-prefix being used, because not all pipelines have to use the default [bucket_root](https://kubeflow-pipelines.readthedocs.io/en/latest/source/client.html?h=root#kfp.client.Client.run_pipeline.pipeline_root), so might need different credentials.
For example, we could extend `ConfigMap/kfp-launcher` like this:
```yaml
data:
defaultPipelineRoot: "minio://my-bucket/v2/artifacts"
## minio endpoint config
minioEndpoint: "minio.example.com:9000"
## ml pipeline endpoint config
mlPipelineEndpoint: "ml-pipeline.kubeflow:8887"
## bucket auth configs (as a YAML string)
bucketAuth: |
## list of "auth providers" for minio
minio:
## specifies auth for objects under "minio://my-bucket/v2/artifacts/"
- bucketName: "my-bucket"
keyPrefix: "v2/artifacts/"
authFromSecret:
secretName: "my-secret-in-profile-namespace"
accessKeyKey: "access_key"
secretKeyKey: "secret_key"
## similar, but for s3 buckets
s3: []
## similar, but for gcs buckets
gcs: []
```
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. | {
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"Hello @tleewongjaro-agoda , the ability to control whether a user has permission to take KFP actions, is same as configuring role and rolebinding in kubernetes, the reference to each action is mapped to RBAC configuration: https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/common/const.go#L18-L41. \r\n\r\nThe proposed behavior is going to be a customization you can perform yourself, the KFP itself doesn't enforce such behavior to all users."
] | 2023-06-27T03:35:53 | 2023-07-31T23:58:55 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Multi-user
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
v1.6.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
Login with another user and obtain executed run url from another user, then hit `retry`. The run will be executed on the owner's namespace with owner's credentials.
Though, I suspect it might have been from our istio authorization policy? If that's the case, then I'd like to ask for advice in setting it up.
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
Should prevent other users (aside from owner) to execute retry on a run.
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9683 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9683/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9683/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9683/events | https://github.com/kubeflow/pipelines/issues/9683 | 1,774,862,639 | I_kwDOB-71UM5pykEv | 9,683 | [feature] simplify collecting scalar metrics | {
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"I made a quick POC to show how we could simply the process of getting those metrics. See [kfp-metrics-collector](https://github.com/axel7083/kfp-metrics-collector)"
] | 2023-06-26T14:00:13 | 2023-06-27T13:09:09 | 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 -->
## Description
In the current implentation, it is very hard to collect scalar metrics for a given run. It requires internally many interaction with `metadata-grpc-service.kubeflow`.
<details>
<summary><h3>Code Example for fetching manually metrics by run-id</></summary>
For example, let's have a simple pipeline, with 2 components producing each one an artefact `System.Metrics`.
- `component-a` produces `metrics-b` with one metric called `result` bein an integer
- `component-b` produces `metrics-b` with two metrics called `result` and `example` being both double values
## Getting the metrics
Since there is no restriction on the naming of output artefact, when we want to display a metric, to identify it properly we need to provide the following information:
- The execution artifact (here component-a or component-b),
- The name of artifact (here `metrics-a` or `metrics-b`)
- The name of the metric inside, with the value, and the type (here, result, result or example).
In the current implementation, here is how we can fetch all of those information based on a run-id:
```python
def get_scalar_metrics(
logger: logging.Logger,
mlmd_store: metadata_store.MetadataStore,
run_id: str
) -> Dict[str, Any]:
logger.debug(f'Getting metadata context for run_id {run_id}.')
context = mlmd_store.get_context_by_type_and_name(
context_name=run_id,
type_name='system.PipelineRun'
)
logger.debug(f'Getting artifact with context id: {context.id}.')
artifacts = mlmd_store.get_artifacts_by_context(context_id=context.id)
executions = mlmd_store.get_executions_by_context(context_id=context.id)
events = mlmd_store.get_events_by_execution_ids(execution_ids=[execution.id for execution in executions])
logger.debug(f'Found {len(artifacts)} artifacts.')
metrics = {}
artifacts_map = {artifact.id: artifact for artifact in artifacts}
for execution in executions:
execution_events = [event for event in events if event.execution_id == execution.id]
display_name = execution.custom_properties.get('display_name', None)
if display_name is None:
continue
display_name = display_name.string_value
metrics[display_name] = {}
for event in execution_events:
artifact = artifacts_map.get(event.artifact_id)
artifact_name = artifact.custom_properties.get('display_name', None)
if artifact_name is None:
continue
artifact_name = artifact_name.string_value
metrics[display_name][artifact_name] = extract_custom_properties(artifact.custom_properties)
return metrics
```
With the previous code, and the proper RBAC configured we can get:
```json
{
"component-a": {
"metric-a": [{
"name": "result",
"value": 8,
"type": "integer_value"
}
]
},
"component-b": {
"metric-b": [{
"name": "result",
"value": 8.5,
"type": "double_value"
}, {
"name": "example",
"value": 15,
"type": "double_value"
},
]
},
}
```
</details>
### What feature would you like to see?
For many usage exposing the metrics in a simpler way would be very interesting. Being able to access them from the SDK would probably be an advantage.
### What is the use case or pain point?
For our usage, we want to be able to monitor our recurring training and getting alert from specific metrics generated by Kubeflow Pipelines. But this could be extended to other use case. As I am also experimenting with [katib](https://github.com/kubeflow/katib) exposing a simpler way to get those metrics would ease the code required for custom metrics collectors.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9682 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9682/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9682/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9682/events | https://github.com/kubeflow/pipelines/issues/9682 | 1,772,795,936 | I_kwDOB-71UM5pqrgg | 9,682 | [feature] Add ability to specify node affinity & toleration using KFP V2 | {
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"Additionally would be great to have the ability to set requests/limits for custom resources. `cpu`, `memory` and `nvidia.com/gpu` are obviously staples and cover most of the necessary resource requests/limits, but being able to use and experiment with other custom resources (e.g. to make GPU sharing between containers possible) is a big plus too. So in addition to the above, would like to see `add_resource_request()` and `add_resource_limit()` back in the new versions of KFP SDK.",
"Hello @AlexandreBrown , thanks for proposing this. The node selector is already supported. https://www.kubeflow.org/docs/components/pipelines/v2/platform-specific-features/\r\nFor the node affinity and toleration is awaiting contributors!",
"Hello @connor-mccarthy, as I have a high need for this feature, I already have an implementation. I would love to contribute it. How should we proceed? Can I open a PR or do we need to do a Design Review first? (CLA is already submitted)",
"Hi, @mcrisafu! Thanks for your interest in contributing this.\r\n\r\nI think this feature is sufficiently large to be deserving of a [design](https://github.com/kubeflow/pipelines/blob/master/CONTRIBUTING.md#step-4-design-review). Please feel free to start there. You can add the doc to this issue. From there, we can decide whether it makes sense to discuss at an upcoming KFP community meeting [[Meeting](https://www.kubeflow.org/docs/components/pipelines/v2/community-and-support/#kubeflow-pipelines-community-meeting), [Agenda](http://bit.ly/kfp-meeting-notes)].",
"Hi @connor-mccarthy, thank you for your feedback. [Here](https://docs.google.com/document/d/1Zd4js046FV-Ee_FQHkzWG0LkdriyHZzv5Ug1Y48bF1k/edit?usp=sharing) the requested design doc.",
"@Linchin, when you have the chance, could you please take a look at this FR for the KFP BE?",
"@mcrisafu Thank you for writing the design doc, which includes the general idea. Could you expand it to include more implementation details, ambiguities, potential challenges etc.?",
"I would like to draw some attention to this topic. There is another issue referencing this one (https://github.com/kubeflow/pipelines/issues/9768) where the implementation of tolerations and affinity is mentioned as part of a bigger plan. @cjidboon94 and @Linchin interacted with that one, but I think @connor-mccarthy and @AlexandreBrown not yet.\r\n\r\nWe really need this feature and I believe a lot of people do. Node selection, toleration and affinity settings are essential parts of effective pod scheduling in Kubernetes.\r\n\r\nI offered help in that thread and could go ahead and start implementing these features, however I found this thread where @mcrisafu mentioned: \"_...I already have an implementation_\".\r\n\r\nI would love to contribute, but of course there's no point doing duplicated work. Therefore, I ask:\r\n- Is there anything blocking the progress of this feature?\r\n- Is the implementation fully done or, if not, how advanced is it?\r\n- Can we help you in any way to accelerate the process?\r\n\r\nWe are happy to jump into code review, testing or the implementation itself, should it still be missing some (or several) parts.",
"Thank you, @schrodervictor. I haven't had the time yet to update the document. I also believe that the suggestion from @cjidboon94 in #9768 is much better than my own \"implementation.\"\r\n\r\nWe have decided not to migrate to KFP v2 just yet, as we have several issues beyond just toleration. We would greatly appreciate having #9768 implemented. Unfortunately, I don't understand the code base (and Go) well enough to do this myself.\r\n\r\nFrom my perspective, it would be better to prioritize pushing the other feature instead of going with my sub-optimal hack. However, if you're still interested in the code, please take a look at this [commit](https://github.com/kubeflow/pipelines/compare/master...mcrisafu:pipelines:feat(backend)--k8s-tolerations-Fixes-%239682).",
"A related PR: https://github.com/kubeflow/pipelines/pull/9913"
] | 2023-06-24T15:48:09 | 2023-08-28T20:54:34 | 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?
A core production use case of KFP involves being able to run CPU and GPU workload on specific nodegroups that are more powerful and different from the nodegroup where Kubeflow is installed and usually they will have autoscaling as well.
In order to achieve this, we used to be able to simply specify which component would run on which node using node affinity + tolerations. This is no longer possible in KFP v2 yet I feel like such a core feature should be supported.
### What is the use case or pain point?
The existing [set_accelerator_type](https://kubeflow-pipelines.readthedocs.io/en/sdk-2.0.1/source/dsl.html?h=add_#kfp.dsl.PipelineTask.set_accelerator_type.accelerator) is far from being flexible enough to allow such use case. Here is a small list of examples that shows that `set_accelerator_type` is not flexible enough to support production use cases :
- Does not work if the GPU is not one of the few (3) supported GPU : `NVIDIA_TESLA_K80`, `TPU_V3` or `cloud-tpus.google.com/v3`. Otherwise we must use the generic `nvidia.com/gpu` which is not precise hence defeating the purpose of selecting an accelerator.
- If you have 2 nodegroups with the same GPU but one should be reserved for inference and one should be reserved for pipeline exectuion (eg: training) then there is no way to cover such distinction purely based on `set_accelerator_type('nvidia.com/gpu')`.
- this method is only meant to be used for GPU but it is common to want to run CPU workload on specific nodegroups, reasons could include nodegroup isolation (to run workload that wont affect the nodegroup where Kubeflow core pods run) or to allow for more powerful CPU nodegroups to be used for pipeline while Kubeflow would remain on cheaper instances.
### Is there a workaround currently?
Users can try to use external tools such as [Kyverno](https://kyverno.io/) to create mutating rules that a webhook can use to add a toleration and/or node affinity/node selector based on some predefined criteria such as a label name and value.
It's still a pain since it makes it way more involved than being able to use `.add_node_affinity()` and `.add_toleration()` on a component. In fact, we can't even add a label using kfp sdk anymore so matching has to be done on labels that are present by chance (we have no control to explicitly ensure their presence).
Also even using Kyverno, some cases might be hard or impossible to cover. For instance, if you have 2 kubeflow components, both will have the same labels yet you'd like one to run on a less expensive GPU nodegroup and only have one of the two run on a more powerful GPU nodegroup, then in that case since the pods have the same labels, the only way to specify which nodegroup it should run on is at component definition time (via KFP sdk) yet this is not supported currently in KFP v2.
Given that Kubeflow's main goal is to lower the barrier to run ML on kubernetes, I believe this workaround goes against such goal and should not be the only solution that is available to people. It would be in everyone's best interest if the KFP SDK adds back the `add_node_affinity()` and `add_toleration()` so that data scientists/ML specialists can easily specify where to run each component instead of relying on more advanced MLOps solutions that require more and more Kubernetes knowledge.
<!-- Don't delete message below to encourage users to support your feature request! -->
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"Hello @marlonmBoti, we suggest to use KFP SDK 2.0 and see if the error is still existing.",
"> Hello @marlonmBoti, we suggest to use KFP SDK 2.0 and see if the error is still existing.\r\n\r\nThanks, @jlyaoyuli "
] | 2023-06-23T20:44:05 | 2023-07-14T14:35:07 | null | NONE | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.8.16
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
I've been facing a problem in which I can't run a pipeline due to some error triggered by a parameter Output[Metrics]. Here's the detailed problem:
I have a component dedicated to log the metrics of my ML model. In this component I import Output and Metrics from kfp.v2.dsl, like this example:
```
def log_metrics(scalar_metrics: Output[Metrics]):
scalar_metrics.log_metric("accuracy", 0.9)
```
Whenever I try to run the pipeline I got this error message:
```
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INVALID_ARGUMENT
details = "Can't find producer subtask 'condition-improve-visualization-2' in the DAG tasks."
debug_error_string = "UNKNOWN:Error received from peer ipv6:%5B2800:3f0:4004:806::200a%5D:443 {created_time:"2023-06-22T14:05:50.586657-03:00", grpc_status:3, grpc_message:"Can\'t find producer subtask \'condition-improve-visualization-2\' in the DAG tasks."}"
β¦
raise exceptions.from_grpc_error(exc) from exc
google.api_core.exceptions.InvalidArgument: 400 Can't find producer subtask 'condition-improve-visualization-2' in the DAG tasks.
```
After some trial and error, I've noticed that when I call the component inside a condition group, I get the aforementioned error. See the excerpt below:
```
@dsl.pipeline(name="ds-cons-churn-blz")
def pipeline(
project_id: str = "ml-tools-developer",
env: str = "dev",
):
improve_visualization_op = improve_visualization().set_display_name(
"Improve visualization"
)
with Condition(
improve_visualization_op.output == "true", "Improve Visualization"
):
log_metrics_op = log_metrics()
```
Nonetheless, if I remove the component of the condition group, the error disappears. See the excerpt below:
```
@dsl.pipeline(name="ds-cons-churn-blz")
def pipeline(
project_id: str = "ml-tools-developer",
env: str = "dev",
):
improve_visualization_op = improve_visualization().set_display_name(
"Improve visualization"
)
with Condition(
improve_visualization_op.output == "true", "Improve Visualization"
):
log_metrics_op = log_metrics()
```
After contacting Google Support, they advised me to change the signature of the condition to the following:
`with Condition(improve_visualization_op.output == "true", "condition-improve-visualization-1")`
It looks like there is a bug with the KFP SDK when the task within a condition writes log_metrics.
### Expected result
<!-- What should the correct behavior be? -->
The pipeline should run without any problems.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
Sample Code
```
import argparse
import os
from pathlib import Path
import yaml
from google.cloud import aiplatform
# kubeflow imports
from kfp.v2 import compiler, dsl
from kfp.v2.dsl import Condition, component, Output, Metrics
ENV = "dev"
"""
Pipeline Definition
"""
@component
def improve_visualization() -> str:
return "true"
@component
def log_metrics(scalar_metrics: Output[Metrics]):
scalar_metrics.log_metric("accuracy", 0.9)
@dsl.pipeline(name="ds-cons-churn-blz")
def pipeline(
project_id: str = "ml-tools-developer",
env: str = "dev",
):
global ENV
improve_visualization_op = improve_visualization().set_display_name(
"Improve visualization"
)
with Condition(
improve_visualization_op.output == "true", "condition-improve-visualization-1"
):
log_metrics_op = log_metrics()
"""
Compile and Run
"""
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="dev")
parser.add_argument("--run", action="store_true")
parser.add_argument("--build-dir", "-b", type=Path, default="./build")
args = parser.parse_args()
ENV = args.env
pipeline_name = "churn"
# create build dir
os.makedirs(args.build_dir, exist_ok=True)
# compile pipeline
build_path = str(args.build_dir / "churn.json")
compiler.Compiler().compile(pipeline_func=pipeline, package_path=build_path)
if args.run:
project_id = "ml-tools-developer"
pipeline_root = "gs://{}/executions".format("vertex-ai-pipelines-dev")
job = aiplatform.PipelineJob(
display_name=pipeline_name,
template_path=build_path,
pipeline_root=pipeline_root,
enable_caching=False,
parameter_values=None,
)
job.submit()
```
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9679 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9679/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9679/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9679/events | https://github.com/kubeflow/pipelines/issues/9679 | 1,770,990,195 | I_kwDOB-71UM5pjypz | 9,679 | Where is `UserContainer` for v2? Is it deprecated completely? | {
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} | [] | open | false | null | [] | null | [] | 2023-06-23T07:48:48 | 2023-06-23T07:48:48 | null | NONE | null | I was previously using `UserContainer` to create my init containers as follows:
```
@property
def INIT_CONTAINER(self) -> dsl.UserContainer:
return dsl.UserContainer(
name="git-sync",
image="k8s.gcr.io/git-sync:v3.1.1",
mirror_volume_mounts=True,
)
```
However, I notice that in v2 it's been deprecated. What can I use in v2 to implement the same functionality? I've tried using things like `ContainerSpec` but I'm not sure if it's the same thing.
https://github.com/kubeflow/pipelines/blob/536d93a1bffe035bf5222e7a48faf59d3b053800/sdk/python/kfp/deprecated/dsl/_container_op.py#L685 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9678 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9678/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9678/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9678/events | https://github.com/kubeflow/pipelines/issues/9678 | 1,770,548,356 | I_kwDOB-71UM5piGyE | 9,678 | cannot save parameter /tmp/outputs/condition | {
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"same issue on my kfp deployment, any solution?",
"I haven't found any yet! I've moved away from Kubeflow Pipelines because there doesn't seem to be much support for it, and a lot of what I needed is being deprecated in v1 to v2.",
"I'm having the same issue +1",
"/assign @chensun \r\nWill it be related to https://github.com/kubeflow/pipelines/issues/7629?"
] | 2023-06-23T00:07:53 | 2023-08-31T22:45:21 | null | NONE | null | I've created a KubeFlow pipelines deployment on GKE with the manifests directly from the repository (the ones in the tutorial combined with code examples here led to validation errors). You can see the exact steps I'm taking to deploy the cluster, install KubeFlow, generate the compile / compile / install here: https://github.com/converged-computing/flux-operator-component/tree/add/component#kubeflow-on-gke
The issue I run into is on a run - I don't see any evidence that my workflow is running, but rather a weird error message about a parameter path in `/tmp/outputs` not existing. I also see that although I haven't yet defined any outputs, there is an automatic addition of a metadata one?
```console
I0622 23:49:18.534358 17 main.go:224] output ExecutorInput:{
"inputs": {
"parameterValues": {
"command": "echo hello world",
"image": "ghcr.io/flux-framework/flux-restful-api:latest",
"local": true,
"name": "hello-world-run-123",
"namespace": "kubeflow",
"nnodes": 2,
"project": "llnl-flux"
}
},
"outputs": {
"outputFile": "/tmp/kfp_outputs/output_metadata.json"
}
}
```
And the error
```console
time="2023-06-22T23:49:19.209Z" level=info msg="sub-process exited" argo=true error="<nil>"
time="2023-06-22T23:49:19.209Z" level=info msg="/tmp/outputs/pod-spec-patch -> /var/run/argo/outputs/parameters//tmp/outputs/pod-spec-patch" argo=true
time="2023-06-22T23:49:19.210Z" level=info msg="/tmp/outputs/cached-decision -> /var/run/argo/outputs/parameters//tmp/outputs/cached-decision" argo=true
time="2023-06-22T23:49:19.210Z" level=error msg="cannot save parameter /tmp/outputs/condition" argo=true error="open /tmp/outputs/condition: no such file or directory"
```
I am new to KubeFlow so I apologize for my ignorance! I really like the idea, conceptually, and hope that the implementation is able to empower me to build components that work - I'm still trying to get my feet wet for this basic development setup. Thanks! | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9675 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9675/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9675/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9675/events | https://github.com/kubeflow/pipelines/issues/9675 | 1,768,564,665 | I_kwDOB-71UM5paie5 | 9,675 | [feature] Migration from @material-ui to @mui | {
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"/cc @zijianjoy ",
"I think we already have this functionality for grouping list:\r\n\r\n\r\n"
] | 2023-06-21T21:57:37 | 2023-06-22T23:09:23 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
We are planning to introduce hierarchy Run List view to accommodate use-cases like Resume Training KF Run should be under Main Train KF Run also Analysis KF Run should be under respective Train KF Run.
- Train 1
- -- Resume Train
- -- Analysis Run 1
- -- Retry Resume Train
- Train 2
- -- Resume Train
- ....
As Frontend uses very old `@materila-ui` package it's difficult to implement this view. Planning to use this package https://material-table.com/#/docs/features/tree-data, but it doesn't work as it depends on @mui package.
Any plans to upgrade @material-ui to @mui?
### 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? -->
No
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9673 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9673/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9673/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9673/events | https://github.com/kubeflow/pipelines/issues/9673 | 1,767,390,068 | I_kwDOB-71UM5pWDt0 | 9,673 | [feature] filter.proto support for json or nested comparaison | {
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"@axel7083: The label(s) `area/-->, area/-->` cannot be applied, because the repository doesn't have them.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/9673):\n\n>### Feature Area\r\n>\r\n>\r\n>\r\n>/area backend -->\r\n>/area sdk -->\r\n>\r\n>\r\n>\r\n>\r\n>### What feature would you like to see?\r\n>\r\n>For example when listing runs, I would like to be able to filter based on the pipeline_id, or pipeline_version_id. Those field are nested inside the pipeline_version_reference property. And the following synthax is not supported:\r\n>\r\n>```python\r\n>filter = json.dumps({\"predicates\": [{\"operation\": \"EQUALS\", \"key\": \"pipeline_version_reference.pipeline_id\",\"stringValue\": \"<pipeline-id>\",}]})\r\n>kubeflow_client.list_runs(filter=filter)\r\n>```\r\n>\r\n>### Is there a workaround currently?\r\n>\r\n>Listing all the runs, and filtering manually on the client-side.\r\n>\r\n>---\r\n>\r\n>\r\n>Love this idea? Give it a π. \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>"
] | 2023-06-21T11:35:30 | 2023-06-21T11:35:47 | 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?
For example when listing runs, I would like to be able to filter based on the pipeline_id, or pipeline_version_id. Those field are nested inside the pipeline_version_reference property. And the following synthax is not supported:
```python
filter = json.dumps({"predicates": [{"operation": "EQUALS", "key": "pipeline_version_reference.pipeline_id","stringValue": "<pipeline-id>",}]})
kubeflow_client.list_runs(filter=filter)
```
### Is there a workaround currently?
Listing all the runs, and filtering manually on the client-side.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9672 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9672/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9672/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9672/events | https://github.com/kubeflow/pipelines/issues/9672 | 1,766,865,800 | I_kwDOB-71UM5pUDuI | 9,672 | [feature] Option to provide pipeline parameters as a JSON file or some other key-value mapping. | {
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"Hi @seanswyi, we do support dictionary-typed input parameters in v2: https://www.kubeflow.org/docs/components/pipelines/v2/data-types/parameters/#input-parameters\r\n",
"@chensun Thanks, just noticed. I'll try to use that and re-open the issue if needed."
] | 2023-06-21T06:42:12 | 2023-06-23T07:39:22 | 2023-06-23T07:39:22 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
As far as I'm concerned when creating a pipeline using a Python function, you can specify the pipeline parameters so that you can adjust them in the Kubeflow UI. However, I thought that it would be convenient if we could provide the parameters as a dictionary or something rather than type out every single parameter in the Python code.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Being able to manage many pipeline parameters and keep track of runs and their argument values.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Not exactly a workaround. The pipeline that I want to run has a total of 30ish parameters and I'm currently finding myself manually typing them out in the function definition.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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] | null | [] | 2023-06-21T00:53:44 | 2023-07-20T22:54:39 | null | NONE | null | ModeBatchPredictJob documentation states bigquery_output_table as a valid output, stating that "Artifact tracking the batch prediction job output. This is only available if bigquery_output_table is specified."
However, bigquery_output_table is not an input of the function.
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"This has been fixed. Thanks!"
] | 2023-06-21T00:23:44 | 2023-07-20T22:54:07 | 2023-07-20T22:54:07 | NONE | null | [This page](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) appears to be updated for v2.0.0,
however, the text "Please see the [Google Cloud Pipeline Components API reference documentation] " links to v1.0.41 documentation.
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] | null | [] | 2023-06-20T10:49:13 | 2023-06-29T22:59:54 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
Using SDK:
```
aiplatform.init(
project=project_id, location=location, experiment=vertex_experiment_id
)
pipeline = aiplatform.pipeline_jobs.PipelineJob(
display_name="pipeline_job",
enable_caching=enable_caching,
template_path=template_path,
parameter_values=parameter_values,
pipeline_root=pipeline_root,
encryption_spec_key_name=encryption_spec_key_name,
)
pipeline.submit(
service_account=service_account,
network=network,
experiment=vertex_experiment_id,
)
return pipeline
```
* 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: 1.8.22
<!-- 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.
-->
```
partial_table_reference = {
"projectId": str(project_id),
"datasetId": str(dataset_id),
}
split_train_data = (
BigqueryQueryJobOp(
project=project_id,
location=project_location,
query=split_train_query,
job_configuration_query=dict(
destinationTable=dict(tableId=train_table, **partial_table_reference),
writeDisposition="WRITE_TRUNCATE",
),
)
.set_display_name("Split train data")
)
```
### Expected result
<!-- What should the correct behavior be? -->
Pipeline should execute and generate the bigquery table.
```
RuntimeError: BigQquery Job failed. Cannot retrieve the job name. Request:{'configuration': {'query': {'destinationTable': {'tableId': 'train_data_tf_training_{{pipelineparam:op=;name=data_semantic_version}}_8385687113001074688', 'projectId': '{{pipelineparam:op=;name=project_id}}', 'datasetId': '{{pipelineparam:op=;name=dataset_id}}'}, 'writeDisposition': 'WRITE_TRUNCATE', 'query': 'SELECT *\nFROM\n `features.preprocessed_chicago_taxi_trips` AS t\nWHERE\n MOD(ABS(FARM_FINGERPRINT(TO_JSON_STRING(t))),\n 10) IN (0, 1, 2, 3, 4, 5, 6, 7)', 'useLegacySql': False}, 'labels': {'vertex-ai-pipelines-run-billing-id': '8385687113001074688'}}, 'jobReference': {'location': 'europe-west1'}}; Response: {'error': {'code': 400, 'message': "Invalid project ID '{{pipelineparam:op=;name=project_id}}'. Project IDs must contain 6-63 lowercase letters, digits, or dashes. Some project IDs also include domain name separated by a colon. IDs must start with a letter and may not end with a dash.", 'errors': [{'message': "Invalid project ID '{{pipelineparam:op=;name=project_id}}'. Project IDs must contain 6-63 lowercase letters, digits, or dashes. Some project IDs also include domain name separated by a colon. IDs must start with a letter and may not end with a dash.", 'domain': 'global', 'reason': 'invalid'}], 'status': 'INVALID_ARGUMENT'}}.
```
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area sdk
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"Hi @Princy-Malhotra , if you click the ` test-train-split ` component, do you see any log?",
"> Hi @Princy-Malhotra , if you click the ` test-train-split ` component, do you see any log?\r\n\r\nNo @chensun , there are no logs for that component. Logs are empty. Here are some screenshots attached:\r\n\r\n\r\n",
"Hi @chensun , did you find a solution for this? (Asking since it's a bit important) "
] | 2023-06-19T18:34:37 | 2023-07-03T16:44:55 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
I used Standalone deployment as mentioned here: [https://www.kubeflow.org/docs/components/pipelines/v2/installation/quickstart/#:~:text=Step%201%3A%20Deploy%20a%20KFP%20standalone%20instance%20into%20your%20cluster](url)
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0-rc.2
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 2.0.0-rc.2
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Given below is the code that I am trying to execute:
```
from kfp import dsl
from typing import NamedTuple
from kfp import compiler
from kfp.dsl import component, Input, Output, Dataset
@component(
base_image='python:3.11',
packages_to_install=['pandas','numpy','scikit-learn']
)
def fetch_process_data(processed_X:Output[Dataset], processed_y:Output[Dataset]):
from sklearn.datasets import fetch_openml
import pandas as pd
df,y= fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
# Preprocess the data
df.to_csv(processed_X.path, index=False)
y.to_csv(processed_y.path, index=False)
@component(
base_image='python:3.11',
packages_to_install=['pandas','scikit-learn']
)
def test_train_split(fetched_X:Input[Dataset],fetched_Y:Input[Dataset],X_train:Output[Dataset], X_test:Output[Dataset], y_train:Output[Dataset], y_test:Output[Dataset]):
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.DataFrame(pd.read_csv(fetched_X.path))
y = pd.DataFrame(pd.read_csv(fetched_Y.path))
X_train1, X_test1, y_train1, y_test1 = train_test_split(df, y, train_size=0.8)
X_train1.to_csv(X_train.path, index=False)
X_test1.to_csv(X_test.path, index=False)
y_train1.to_csv(y_train.path, index=False)
y_test1.to_csv(y_test.path, index=False)
@component(
base_image='python:3.11',
packages_to_install=['pandas','scikit-learn']
)
def dec_tree(X_train: Input[Dataset], y_train: Input[Dataset])->float:
import pandas as pd
X_train = pd.read_csv(X_train.path)
y_train = pd.read_csv(y_train.path)
# Fit a decision tree model and return it's accuracy
@dsl.pipeline(
name='process_data'
)
def process_data()->NamedTuple('Outputs',[('pro_X', Dataset), ('pro_y', Dataset)]):
from collections import namedtuple
ans1 = fetch_process_data()
process_out = namedtuple('Outputs', ['pro_X', 'pro_y'])
return process_out(ans1.outputs['processed_X'], ans1.outputs['processed_y'])
@dsl.pipeline(
name='pipe_main',
description='An example pipeline',
)
def pipe_main():
ans = process_data()
split_data = test_train_split(fetched_X=ans.outputs['pro_X'], fetched_Y=ans.outputs['pro_y'])
accu = dec_tree(X_train=split_data.outputs['X_train'], y_train=split_data.outputs['y_train'])
if __name__=='__main__':
print('entry')
compiler.Compiler().compile(pipeline_func=pipe_main,package_path='p3y.yaml')
```
I then uploaded the yaml file on the localhost and created a run.
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
The run should have completed successfully. But, the run is failing in the middle at test-train-split component. I think the error is somewhere where we are passing the results of the process_data() pipeline to the test-train-split component. I am not able to find where the error is. A screenshot of the run is attached below:
The Sub DAG for process data pipeline is:
The logs do not show any error, only warnings.
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
I referred to the official documentation for Kubeflow v2. Here's the link : [https://www.kubeflow.org/docs/components/pipelines/v2/](url)
### 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 π. | {
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```
Failed to build test images:
STEP TEMPLATE PODNAME DURATION MESSAGE
β integration-test-bkwmd integration-test child 'integration-test-bkwmd-1801226946' failed
βββ¬ββ build-api-integration-test-image build-image No more retries left
β βββ build-api-integration-test-image(0) build-image integration-test-bkwmd-2355310860 2m Error (exit code 6)
β βββ build-api-integration-test-image(1) build-image integration-test-bkwmd-2824937097 1m Error (exit code 6)
βββ build-basic-e2e-tests-image(0) build-image integration-test-bkwmd-616792187 4m
βββ build-frontend-integration-tests-image(0) build-image integration-test-bkwmd-406757411 3m
βββ build-initialization-test-image build-image No more retries left
βββ build-initialization-test-image(0) build-image integration-test-bkwmd-3701586361 2m Error (exit code 6)
βββ build-initialization-test-image(1) build-image integration-test-bkwmd-10760444 1m Error (exit code 6)
```
and the error traced to curl command:
```
integration-test-bkwmd-10760444: curl: (6) getaddrinfo() thread failed to start
integration-test-bkwmd-2824937097: curl: (6) getaddrinfo() thread failed to start
integration-test-bkwmd-616792187: Unpacking libpython3.7-minimal:amd64 (3.7.3-2+deb10u4) over (3.7.3-2+deb10u3) ...
integration-test-bkwmd-2824937097: The command '/bin/sh -c curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz' returned a non-zero code: 6
integration-test-bkwmd-10760444: The command '/bin/sh -c curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz' returned a non-zero code: 6
```
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"> Will components built for KFPv1 be compatible with KFPv2?\r\n\r\nGenerally speaking, yes. KFP v2 aims to be backward compatible with v1 components for the critical paths. \r\n\r\nTo add one more documentation to your existing list, components and pipelines are now compiled/persisted in the same manner:\r\nhttps://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/"
] | 2023-06-16T16:59:26 | 2023-06-22T23:15:25 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area samples
/area components
### What feature would you like to see?
Samples and documentation on component specification, build and package instructions for KFPv2 components similar to https://www.kubeflow.org/docs/components/pipelines/v1/sdk/component-development/#creating-a-component-specification so that users can simply use `load_component_from_file/url` to use a component.
### What is the use case or pain point?
We are investigating KFPv2 support for [SageMaker components](https://github.com/kubeflow/pipelines/tree/master/components/aws/sagemaker#amazon-sagemaker-components-for-kubeflow-pipelines) so that users can simply use `load_component_from_file/url` and run their workload. This is general ask since all the components might need to make changes to support KFPv2
I have looked at following documentation:
- https://www.kubeflow.org/docs/components/pipelines/v2/load-and-share-components/
- https://www.kubeflow.org/docs/components/pipelines/v2/components/container-components/
- https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/
Will components built for KFPv1 be compatible with KFPv2?
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9627 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9627/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9627/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9627/events | https://github.com/kubeflow/pipelines/issues/9627 | 1,759,360,492 | I_kwDOB-71UM5o3bXs | 9,627 | [Pipeline backend] <In few scenarios, on pipeline terminate, the pipeline doesn't show the right status> | {
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"Thank you @mChowdhury-91. This is covered by #8966. Let's keep that for tracking the issue."
] | 2023-06-15T18:42:15 | 2023-07-20T22:59:33 | 2023-07-20T22:59:33 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Standalone deployment
* KFP SDK version: 1.8.14
### Steps to reproduce
There are 2 scenarios:
1. For long running Kubeflow pipelines, the terminate pipeline action doesn't update the right status. The job was complete, but the status show running and even on terminate nothing happens..
2. Sometimes, the terminate button doesn't change the status. Although the pod is terminated and "Completed", the kubeflow UI still shows the pipeline as running..
### Expected result
On terminate, once the pipeline pod is killed and in "Completed" status, the UI should also reflect back the failure staus
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9622 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9622/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9622/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9622/events | https://github.com/kubeflow/pipelines/issues/9622 | 1,756,937,228 | I_kwDOB-71UM5ouLwM | 9,622 | Error (exit code 1): failed to put file: Get "https://s3.domain/location?": dial tcp: i/o timeout | {
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kfp==1.8.13
kfp-pipeline-spec==0.1.16
kfp-server-api==1.8.3
### Steps to reproduce
[Try out this example](https://github.com/kubeflow/examples/blob/master/financial_time_series/tensorflow_model/run_preprocess.py)
I am receiving the below logs, only change from example is instead txt file, I am storing a JSON file.
### LOGS
*time="2023-06-14T13:16:52.835Z" level=info msg="/blob_path.json -> /var/run/argo/outputs/parameters//blob_path.json" argo=true
time="2023-06-14T13:16:52.835Z" level=info msg="/blob_path.json -> /var/run/argo/outputs/artifacts/blob_path.json.tgz"
argo=true
time="2023-06-14T13:16:52.835Z" level=info msg="Taring /blob_path.json"*
After logs, if I check in Kubeflow UI, the error received as shown below
### Error
**Error (exit code 1): failed to put file: Get "https://s3.domain/location?": dial tcp: i/o timeout**
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https://api.github.com/repos/kubeflow/pipelines/issues/9621 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9621/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9621/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9621/events | https://github.com/kubeflow/pipelines/issues/9621 | 1,756,016,553 | I_kwDOB-71UM5oqq-p | 9,621 | [feature] <Bagging pipeline with Node-red control> | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-06-14T04:08:37 | 2023-09-12T07:41:41 | 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?
Bagging is an Ensemble learning technique that allows partition of a whole data set into different sub-set of dataset for seperate training processes and aggregation of a final prediction result given different balloting strategy. Bagging is a useful technique normally seen in federated learning for many industrial applications such as IT security and medical services where privacy is an issue. Our bagging pipeline has data separation, decision tree training and balloting steps. Each is wrapped into pipeline components. A complete pipeline docker file is constructed and a yaml file is conbined within a Node-red custom node for easy deployment.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Bagging pipeline requires a lot of man power to construct including data separation and different AI training procedures. An automation feature can save developer or deployer alike a lot of labor. A GUI is handy to have for easy deployment and testing of different data partition schemes and different training algorithm.
<!-- It helps us understand the benefit of this feature for your use case. -->
We use breast cancer data set for our illustration.
### Is there a workaround currently?
none to my knowledge. Currently, we have an initial draft if all the codes and readme file in the following repository:
https://github.com/Lance410247/kubeflow-node-red
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9614 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9614/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9614/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9614/events | https://github.com/kubeflow/pipelines/issues/9614 | 1,755,220,268 | I_kwDOB-71UM5onoks | 9,614 | [sdk]: credentials type as str instead of ServiceAccountTokenVolumeCredentials | {
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"Hi @axel7083!\r\nThank you. If you would like, you are very welcome for send a short PR and cc @connor-mccarthy."
] | 2023-06-13T15:59:01 | 2023-07-20T23:01:07 | null | CONTRIBUTOR | null | ## Description
When creating a kfp Client and providing the argument `credentials`, the typing indicates `str` but is suppose to be `kfp.client.ServiceAccountTokenVolumeCredentials`.
https://github.com/kubeflow/pipelines/blob/a9ac0b95bcc69c6214ca4e5e3208c816096f0377/sdk/python/kfp/client/client.py#L153 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9613 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9613/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9613/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9613/events | https://github.com/kubeflow/pipelines/issues/9613 | 1,754,862,362 | I_kwDOB-71UM5omRMa | 9,613 | [feature] Plotting Scalar Metrics | {
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"@zpChris you have been working on the CompareV2, any feedback on this idea ?\r\n",
"I think it would make reading those scalar metrics easy in many circumstances, depending on the range and quantity of the data. It might be tricky to display different variables in this format - whether they would be on the same histogram with a color legend (while paying attention to accessibility concerns), or if each variable would have its own separate histogram.\r\n\r\nI was working on this CompareV2 page last summer, though I am not currently working on this area.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-06-13T13:12:23 | 2023-09-12T07:41:42 | 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?
I would like to be able to plot for example getting an Histograms when comparing runs based on chosen scalar metrics.
### What is the use case or pain point?
It would make easier to visualize the results.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9612 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9612/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9612/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9612/events | https://github.com/kubeflow/pipelines/issues/9612 | 1,754,799,914 | I_kwDOB-71UM5omB8q | 9,612 | [bug] ModelBatchPredictOp component is not using SA of pipeline to run itself | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-06-13T12:41:13 | 2023-09-12T07:41:44 | null | NONE | null | ### Environment
google-api-core 1.34.0
google-cloud-aiplatform 1.25.0
google-cloud-core 2.3.2
google-cloud-logging 3.5.0
google-cloud-pipeline-components 1.0.44
kfp 1.8.22
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
python 3.8
* How do you deploy Kubeflow Pipelines (KFP)?
Running Vertex AI pipeline;
### Steps to reproduce
1. In my veretx ai pipeline I'm using the ModelBatchPredict component:
```python
batch_prediction_train_task = ModelBatchPredictOp(project=PROJECT_ID,
unmanaged_container_model = import_unmanaged_model_inf_train_task.outputs["artifact"],
location = REGION,
job_display_name="Batch_prediction_custom_model_after_training",
gcs_source_uris=[f"{MODEL_DIR}/df_output.jsonl"],
instances_format="jsonl",
predictions_format="jsonl",
gcs_destination_output_uri_prefix = get_latest_model_task.outputs["latest_model_uri"],
machine_type="n1-standard-32",
manual_batch_tuning_parameters_batch_size = 125
).set_display_name("Batch prediction custom model after training")
```
2. for the ModelBatchPredictOp I am using a model with custom container (both training and predictions). The problem is that if I don't pass explicitly the credentials to the prediction image, the batch prediction job from the component will fail. I can see in the logs that it is failing due to the fact that in there is not enough permissions to run... It is weird, because all other components are running perfectly, as I am running the pipeline with a dedicated SA; at the same time, if I will explicitly pass the credentials of the same SA that I'm using for the pipeline, directly to serving docker image - the batchprediction job will be executed with no problem; also, I tried to give all same right to the standard compute service account, because I thought it might be the case that the batchprediciton job is running by default with it, but it did not help - job failed with same permissions related issues;
the code to launch the pipeline with SA specified:
```python
pipeline_job = aip.PipelineJob(
display_name=DISPLAY_NAME,
template_path=PIPELINE_LOCAL_PATH,
pipeline_root=PIPELINE_ROOT,
parameter_values=pipeline_params,
enable_caching=False,
labels=_conf["query_labels"][CURRENT_ENV],
)
pipeline_job.submit(service_account = SA_vertex, experiment=EXPERIMENT_NAME)
```
The SA_vertex that is used in pipeline had those permissions/roles:
- Artifact Registry Reader
- BigQuery Data Editor
- BigQuery User
- Dataflow Worker
- Logs Viewer
- Logs Writer
- Storage Object Admin
- Vertex AI user
The standard compute SA has such permissions/roles:
- AI Platform Admin
- AI Platform Service Agent
- Artifact Registry Reader
- BigQuery Data Editor
- BigQuery User
- Dataflow Worker
- Editor
- Logging Admin
- Logs Viewer
- Logs Writer
- Monitoring Admin
- Service Account Admin
- Storage Object Admin
- Vertex AI user
- Viewer
### Expected result
ModelBatchPredictOp component should be running with no problem without explicitly passing the credentials to the image; It should use the SA passed to the pipeline;
### Some comments
overall, from the documentation it is quite hard to understand how exactly BatchPrediction works in details; could you explain what exact identity batchprediction job should normally use?
also, it is hard to debug, because if batchPrediction is not launching because there is not enough permissions in the identity that component is using, we also can not get any logs from google-cloud-logging, because it also uses the component identity, which is not "sufficient", if not explicitly passing the credentials;
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area backend
/area components
###
---
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"I migrate to kfp 2.0.0 and the issue is still here. The issue is probably linked to https://github.com/kubeflow/pipelines/issues/9603"
] | 2023-06-13T09:44:47 | 2023-08-28T20:54:18 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? manifests
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0-rc.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Description
On some pipeline (need investigation), the logs are not displayed, nor the inputs outputs.
The problem go further, pipeline with one component, which failed, will not allows to display the logs
See below the Investigation section for more details
<details>
<summary><h3>Steps to reproduce</h3></summary>
```shell
# Setup cluster
kind create cluster --image kindest/node:v1.25.8
# Clone/Checkout pipeline
git clone https://github.com/kubeflow/pipelines && cd pipelines
git checkout tags/2.0.0-rc.1
# Deploy kubeflow pipeline
kustomize build manifests/kustomize/cluster-scoped-resources | kubectl apply -f -
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kustomize build manifests/kustomize/env/dev | kubectl apply -f -
```
Here is an example of very simple pipeline, with a single component
`component.yaml`
```
name: python version
description: will display the version used
implementation:
container:
image: python:3.10
command:
- python
args:
- "--version"
```
`pipeline.py`
```
from kfp.components import load_component_from_file
import kfp.dsl as dsl
hello_op = load_component_from_file("components/hello-world/component.yaml")
@dsl.pipeline
def hello_pipeline(
name: str = 'Kevin'
):
task = hello_op()
task.set_display_name('Salutation !')
task_2 = hello_op()
task_2.after(task)
```
</details>
### Expected result
Being able to see the logs, inputs/outputs of the first task.
<details>
<summary><h4>Screenshots</h4></summary>
The actual result is :
</details>
### Materials and Reference
Here is the compiled pipeline I showed the code:
[pipeline.yaml.txt](https://github.com/kubeflow/pipelines/files/11733375/pipeline.yaml.txt)
### Investigation
The problems seems to be linked to the `set_display_name` value set. When I used "Salutation !" it was not able to display the content properly.
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9610 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9610/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9610/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9610/events | https://github.com/kubeflow/pipelines/issues/9610 | 1,753,713,570 | I_kwDOB-71UM5oh4ui | 9,610 | [backend] using large number of file watchers / instances | {
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"@kubeflow/wg-pipeline-leads",
"Hello @thesuperzapper, can you provide more information to prove that the issue (open too many files) is attributed by pipeline?"
] | 2023-06-12T22:51:53 | 2023-06-29T22:59:07 | null | MEMBER | null | Kubeflow Pipelines seems to be using a very large number of file watchers & instances, this has caused issues like:
- https://github.com/kubeflow/manifests/issues/2087
Currently, the only solution is to increase the max, with these values in `/etc/sysctl.conf`:
- `fs.inotify.max_user_instances = 1280`
- `fs.inotify.max_user_watches = 655360`
I am not certain which Pod is opening all these file watchers, but I think it might be `Deployment/ml-pipeline` (that is, `gcr.io/ml-pipeline/api-server`) because it fails to start with the `"too many open files"` error. However, it also might be that `ml-pipeline` will fail if there are too many open files, but it's not causing the large number of open files.
I am not sure if we can do something about how many of these file watchers we use, to get it down to a more typical number.
I would appreciate someone taking a look.
---
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"@RansSelected, closing as duplicate of canonical bog #8881."
] | 2023-06-12T14:01:30 | 2023-06-12T16:31:27 | 2023-06-12T16:31:27 | NONE | null | Hello @connor-mccarthy ,
Unfortunately I'm still experiencing same issue, parent_model is ignored;
I'm getting the parent like this:
``` python
_ID = 0
try:
models = aip.Model.list(project = PROJECT_ID, location = REGION , order_by = "create_time desc", filter=("display_name={}").format(CURRENT_USE_CASE))
models = [model for model in models if "default" in model.version_aliases]
PARENT_MODEL_URI = models[_ID].uri
PARENT_MODEL_RES_NAME = models[_ID].resource_name
except Exception as e:
print(f"No parent model exists, {e}")
PARENT_MODEL_URI = None
PARENT_MODEL_RES_NAME = None
```
then passing to pipeline as parameter:
```python
pipeline_params = {
"parent_model_uri": PARENT_MODEL_URI,
"parent_model_res_name": PARENT_MODEL_RES_NAME,
}
```
```python
pipeline_job = aip.PipelineJob(
parameter_values=pipeline_params,...
```
Then in pipeline, i'm getting the parent_model:
```python
# Get the Vertex AI model resource
get_model_task = GetVertexModelOp(model_resource_name=parent_model_res_name).set_display_name("Get parent model")
```
and finally passing as argument parent_model:
```python
model_upload_op = ModelUploadOp(
parent_model = get_model_task.outputs["model"], ...
```
_Originally posted by @RansSelected in https://github.com/kubeflow/pipelines/issues/8881#issuecomment-1576346799_
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"If I recall correctly, we have fixed this issue in the latest version. Can you try 2.0.0?",
"I migrate to 2.0.0 (`gcr.io/ml-pipeline/frontend:2.0.0`) and the problem still persist.\r\nI made the following steps:\r\n- Migrate from `gcr.io/ml-pipeline/frontend:2.0.0-rc.1` to `gcr.io/ml-pipeline/frontend:2.0.0`\r\n- Cleared my browser cache (to be sure)\r\n\r\nThe problem appear on pipeline v2",
"> If I recall correctly, we have fixed this issue in the latest version. Can you try 2.0.0?\r\n\r\nAfter some investigation, I found out, that the meta data execution was not updated. The frontend is not using the `https://{host}/pipeline/apis/v2beta1/runs/{run-id}` for displaying the current state. It is using execution metadata. And it does not seems to be updated properly. What do you think @chensun "
] | 2023-06-09T14:02:11 | 2023-07-10T08:39:05 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? manifests
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: [2.0.0-rc.1](https://github.com/kubeflow/pipelines/releases/tag/2.0.0-rc.1)
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
The `example 2` is the task failed. However it does not appear in the graph.
- You can refresh the page, the task will never be shown as failed.
- The state in the tab shows "running".
When checking the `https://{host}/pipeline/apis/v2beta1/runs/{run-id}` we get the information that the task is indeed failed. As shown in the example, here is an extract of the response:
```json
...
"run_details": {
"task_details": [{
"run_id": "{run-id}",
"task_id": "{task-id}",
"display_name": "example 2",
"create_time": "...",
"start_time": "...",
"end_time": "...",
"state": "FAILED",
"state_history": [{
"update_time": "2023-06-09T13:41:58Z",
"state": "RUNNING"
}, {
"update_time": "2023-06-09T13:42:18Z",
"state": "FAILED"
}
],
"child_tasks": [{
"pod_name": "..."
}
]
},
...
]
}
...
```
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9600 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9600/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9600/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9600/events | https://github.com/kubeflow/pipelines/issues/9600 | 1,749,013,104 | I_kwDOB-71UM5oP9Jw | 9,600 | [feature] <Quantum AI kubeflow pipeline with Node-red control> | {
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"This is maybe going over my head, but maybe what you could do at the moment is using Kubeflow to interact with the IBM SDKs and use their APIs to control a quantum computer. Maybe a cool first pr will be a cool Kubeflow pipeline example interacting with that kind of service. Let me know if that workaround works for you.\r\n\r\n",
"Dear David,\r\n\r\nGreat to hear you are interested in our project. At this moment we have built the whole integration flow from Node-red to kubeflow pipeline. The whole kubeflow pipeline on quantum AI is fully tested and proven feasible. However, our quantum AI features joining qiskit neural network simulation, rather real quantum computer. We will do an initial PR on kubeflow pipeline ASAP. Please help review the PR and give us your opinions. "
] | 2023-06-09T02:53:22 | 2023-06-26T07:52:52 | 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?
A GUI front-end that allows users to trigger a quantum AI flow without having to do the manual configuration of traditional data to/from quantum data, and adapt a specific quantum circuit. The flow can take any traditional data for quantum AI training. A slection of quantum circuit is ready for rapid testing of a real world AI big data test case.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
The use case can be a simple mnist CNN classification problem or a complicated virus detection problem using PSO swarm algorithm, e.tc.
<!-- It helps us understand the benefit of this feature for your use case. -->
Quantum AI features a mass parallel calculation capability which can expedite the AI training process for many industrial applications. However, its setup process is inherent complicated. Generally, It not only requires conversion to/from traditional data to quantum data, but also the design of complex quantum circuit. We wrap the whole procesures into a kubeflow pipeline, and show the control flow can be run on a Node-red GUI front end. The test case runs a mnist data set CNN classification application and the accuracy can reach a %96 high with short one half training time compared to traditional AI. We are seting up the PR and will have a local repository for all the code and readme file illustrating the complete processes.
### Is there a workaround currently?
None to my knowledge
<!-- Without this feature, how do you accomplish your task today? -->
One have to go through a laborious set up and conversion procedures of traditional data to/from quantum data. It generally requires some professional understanding of the quantum simulator such as IBM Qiskit.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9593 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9593/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9593/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9593/events | https://github.com/kubeflow/pipelines/issues/9593 | 1,747,421,576 | I_kwDOB-71UM5oJ4mI | 9,593 | [frontend] tqdm logs not displayed correctly | {
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] | closed | false | null | [] | null | [] | 2023-06-08T08:56:46 | 2023-06-22T23:48:11 | 2023-06-22T23:48:11 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? manifests
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: [2.0.0-rc.1](https://github.com/kubeflow/pipelines/releases/tag/2.0.0-rc.1)
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
Here is an example of kubeflow components:
```yaml
name: Example
description: Pass
implementation:
container:
image: python:alpine3.18
command:
- /bin/sh
args:
- "-c"
- "pip install tqdm && seq 9999999 | tqdm --bytes | wc -l"
```
A simple pipeline
```python
from kfp.components import load_component_from_file
import kfp.dsl as dsl
example_op = load_component_from_file("example.yaml")
@dsl.pipeline
def example():
example_op()
```
### Expected result
When fetching logs from the pod using **kubectl**, we get the following logs:
However in the kubeflow logs tab:
The logs are not returning in lines.
In the logs endpoint (`pipeline/k8s/pod/logs?podname=...`) we get:
## Investigation
Using notepad++, we can see the differences between the ends of lines:
CR = Carriage Return (\r)
LF = Line Feed (\n)
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9590 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9590/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9590/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9590/events | https://github.com/kubeflow/pipelines/issues/9590 | 1,746,638,449 | I_kwDOB-71UM5oG5Zx | 9,590 | [sdk] ParallelFor not resolving iterable object correctly when used with DataprocPySparkBatchOp | {
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"I am really hoping there is some easy subscriptable way to access the value. I have tried using an 2 arrays, 1 of integers and 1 with the values, parallelfor over the integer, using it to index the array. However, a parallelfor seems to iterate in a different way, it seems to 'print' from the loop only once, even though it is \"looping\" several times. So that was a no go. There is something going on that I don't fully understand with parallelfor."
] | 2023-06-07T20:36:03 | 2023-06-22T22:56:10 | null | NONE | null | ### Environment
Google Cloud Platform:
* KFP version: 1.0.29 (forced runtime version)
* KFP SDK version: Google Provided (not sure how it works in the cloud, all I needed was a pip install on my vertex notebook)
* All dependencies version:
kfp 1.8.20
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
Using a simple array:
MARKET_LIST = ['CA','US','KR','JP','BN','MY','SG']
I want to create a series of components that will spawn for each market in the MARKET_LIST and process them in parallel to save time. The market is a parameter that will be passed to each component. This works very well, until we try to use market (iteration variable from list) as a parameter to a DataprocPySparkBatchOp. When using an iterated object as a parameter to a DataprocPySparkBatchOp, we don't get the value of the iterate, we get the python object definition.
### Expected result
For example, I want to use the market as part of a pathing resolution to a bucket in google storage.
when using this template for pathing:
path = f'gs://my-bucket/{market}/some-file.ext'
With a market value of 'CA' for the first iteration, I expect to see:
gs://my-bucket/CA/some-file.ext
Instead, I see:
gs://my-bucket/{{pipelineparam:op=;name=loop-item-param-1}}/some-file.ext
### Materials and Reference
Here is the code from the actual loop to help
# define the pipeline
@kfp.v2.dsl.pipeline(name=PIPELINE_NAME)
def my_pipeline(project: str = PROJECT_ID):
MARKET_LIST = ['CA','US','KR','JP','BN','MY','SG']
with ParallelFor(MARKET_LIST) as market:
# dynamically generate args
viewViewArgs = [
'--project', PROJECT_ID,
'--transaction_location', f'gs://{STAGING_BUCKET}/{market}/pdp-view-processed/',
'--category_location', f'gs://{DATA_BUCKET}/{market}/catalog/categories.parquet',
'--item_location', f'gs://{DATA_BUCKET}/{market}/catalog/items.parquet',
'--output_location', f'gs://{RECOMMENDATION_BUCKET}/recommendation/final/{market}/view-view-new/',
'--min_lift', '1.1',
'--min_confidence', '0.01',
'--rec_count', '20',
'--min_frequency', '5',
'--app_name', f'view-view-{market}']
# create operator with dynamically generated args
viewView = DataprocPySparkBatchOp(
project = PROJECT_ID,
location = LOCATION,
container_image = DOCKER_IMAGE,
subnetwork_uri = SUBNETWORK_URI,
service_account = SERVICE_ACCOUNT,
batch_id = BATCH_ID,
main_python_file_uri = f"gs://some-bucket/view-view.py",
runtime_config_version = '1.0.29', # required since Friday, Feb 11, 2023 'upgrade'
jar_file_uris = JAR_FILES,
args = viewViewArgs
).set_display_name("view-view")
## another sample
## conclusion
Keep in mind that this resolves just fine if you are sending it as a parameter to a component. It resolves and the component operates as expected.
I've tried several things, including making the list a json object and calling the value, but the same result happens. Googling Kubeflow mostly brings me to here (to git references), I do find some examples out in the wild, but not enough to find a usable solution. Now this could be simple, there could be a subscriptable way of obtaining the value from the iterable object, but I can not find the docs to support subscripting kubeflow parallelfor iterable object or references to it, or, how it will behave with the DataprocPySparkBatchOp.
My first time attempting to do something like this, with Kubeflow, and with git!
Thanks!
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9586 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9586/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9586/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9586/events | https://github.com/kubeflow/pipelines/issues/9586 | 1,746,269,228 | I_kwDOB-71UM5oFfQs | 9,586 | [backend] PutParentContexts API is called multiple times using same primary key in V2 | {
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"cc @chensun ",
"Looks like this issue actually already exists without upgrading MLMD version. \r\n\r\n",
"PR is merged, thus marking this issue as fixed."
] | 2023-06-07T16:23:33 | 2023-06-26T17:57:55 | 2023-06-26T17:57:55 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
KFP v2
* MLMD
Upgrade MLMD to 1.13 version or above
### Steps to reproduce
Create a KFP V2 Pipeline Run which has sub-DAG.
The problem is that such run will fail in newer MLMD version, because the following MLMD API call is made multiple times in single pipeline run: https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/metadata/client.go#L264. As the function which wraps this API call is `GetPipeline`: https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/metadata/client.go#L246, it naturally means that the user of this function didn't expect any post or put action within it.
In the older MLMD version, it is fine because the same API call will overwrite the same row in MLMD. But in newer MLMD version, it will fail because such primary key (parent context ID and child context ID) already exists.
### Expected result
Check for the existence of Parent-child context relationship before calling `PutParentContexts`.
---
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"I found out there is an argoexecutor which runs alongside the Python container. The cluster logs indicated that it was being OOMKilled. I found a config in the cluster that specified memory settings for argoexecutor so I increased the max from 512MB to 1024MB. This solved the issue.\r\n\r\nI still think it would be helpful to provide a bit more information via the UI to help debug these kind of issues",
"Curious if this also affects v2. @Linchin to help follow up."
] | 2023-06-07T10:52:07 | 2023-08-28T20:54:22 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Not sure
* KFP version:
Version: 1.8.5
* KFP SDK version:
```
$ pip list | grep kfp
kfp 1.8.20
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
```
### Steps to reproduce
Run the code in the listing below. I observer OOM. I observer the following logs:
```
time="2023-06-07T10:47:13.665Z" level=info msg="capturing logs" argo=true
Waiting before...
Waiting after...
time="2023-06-07T10:49:15.563Z" level=info msg="/tmp/outputs/output/data -> /var/run/argo/outputs/artifacts/tmp/outputs/output/data.tgz" argo=true
time="2023-06-07T10:49:15.563Z" level=info msg="Taring /tmp/outputs/output/data"
time="2023-06-07T10:49:26.391Z" level=info msg="archived 2 files/dirs in /tmp/outputs/output/data"
```
Then I observe state `This step is in Error state with this message: OOMKilled (exit code 137)`.
### Expected result
No OOM. I specify that the pod should have atleast 8GB free memory. I expect that the processes involved in zipping and transferring a 500MB file should not exceed the 8GB limit.
### Materials and Reference
```python
from kfp import dsl
from kfp.components import func_to_container_op
from kfp.components import OutputPath, InputPath
@func_to_container_op
def create_large_output_dir(
output_path: OutputPath(str), # type: ignore
):
import os
import time
print("Waiting before...")
time.sleep(60)
os.system(f"mkdir -p {output_path}")
os.system(f"tail --bytes 500M /dev/urandom > {output_path}/out.txt")
print("Waiting after...")
time.sleep(60)
@func_to_container_op
def ingest_large_input_dir(
input_path: InputPath(str), # type: ignore
):
import os
os.system(f"ll {input_path}")
@dsl.pipeline(name="metrics-visualization-pipeline")
def pipeline():
task = create_large_output_dir()
task.set_memory_request("8G")
ingest_large_input_dir(input=task.outputs["output"])
from kfp import Client
client = # the client
client.create_run_from_pipeline_func(
pipeline,
arguments={},
experiment_name="walter-experiment",
run_name="walter-run",
)
```
---
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"Hi @ifuwang, could you please explain what exactly needs to be done on KFP to achieve this feature? Thank you.",
"@Linchin thanks for your enquiry. What this feature needs is a Node-red front end interacting with a Kubeflow pipeline modules. The Node-red front end originally comes with GUIs specifically geared toward IOT applications. For Node-red nodes to communicate with the Kubeflow pipeline, a KFP server API is needed to interact with the Node-red custom Nodes. We have initially implemented a few custom nodes that communicate with the kubeflow pipeline containers. They are a random forest pipeline node, a decision tree pipeline node, and a logistic regression pipeline node as can be seen in the following site:\r\nhttps://github.com/NightLightTw/kubeflow-Node-REDοΌWhat is missing currently is an integration canvas showing fusion of this AI pipeline node with the IOT operation nodes. This will then complete the targeted feature to complete the AIOT application scenario. We hope to present this feature within the kubeflow suite. Please let us know what and where is the best way to present?"
] | 2023-06-07T03:18:04 | 2023-06-30T02:07:25 | null | NONE | null | ### Feature Area
We implemented a kserve pipeline specifically for the IOT Rasberry PI chipset. The pipeline takes in a regular input data, be a text or image, and feeds into a trained model. e.g. H5 file, from other AI training processes. The pipeline will take an input from Rasberry PI sensor, and deliver an output back to the PI chipset for proper response such as playing an audio or beeping a buzzer. A node-red GUI frontend is constructed with a custom node which can trigger the execution of such a kserve pipeline. The message node in the node-red pallet can be dragged to the canvas and connected to the kserve pipeline node will show the inference result.
<!-- 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 node-red GUI frontend is constructed with a custom node which can trigger the execution of such a kserve pipeline. The message node in the node-red pallet can be dragged to the canvas and connected to the kserve pipeline node. Eventually, it shows the inference result which can be a text or an action on the PI sensor.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
A medical care use case can be tested here. The completed kserve pipeline can detect whether the patients entering a hospital is wearing a mask or not. The node-red custom node trigger a Rasberry PI chip camera sensor and feed it to the kserve model and get feedback from the inference result. The result then transfer back to the Rasberry PI for proper response.
<!-- It helps us understand the benefit of this feature for your use case. -->
The medical use case is helpful for saving hospital human labors. Additionally, this pipeline can be useful for easy configuration of any IOT development where a desired input can be led to the model for inference. The inference result can be fed back to the PI for response. The benefit of such pipeline is easy and fast deployment of a trained model for relevant IOT devices.
### Is there a workaround currently?
None to my knowledge.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"After some investigation, it turns out that the status of the run comes from the report from argo that is then [relayed by the persistence agent to the backend](https://github.com/kubeflow/pipelines/blob/db1e0e213c83780619dea3eabbc5231450ba62c5/backend/src/agent/persistence/client/pipeline_client.go#L79). Since this is an argo behavior, we will keep it like this."
] | 2023-06-06T17:08:08 | 2023-06-14T20:31:57 | 2023-06-14T20:31:57 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
KFP standalone
* KFP version:
2.0.0-rc.1
* KFP SDK version:
2.0.0-rc.1
### Steps to reproduce
Run this pipeline:
https://github.com/kubeflow/pipelines/blob/27d8f7a651631df1afc59b18cb387b7d7dd87636/sdk/python/test_data/pipelines/pipeline_with_exit_handler.py
https://github.com/kubeflow/pipelines/blob/27d8f7a651631df1afc59b18cb387b7d7dd87636/sdk/python/test_data/pipelines/pipeline_with_exit_handler.yaml
### Expected result
Expect pipeline to show failed status.
### Actual result
Pipeline shown as succeeded even the inner task failed.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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"Hi @chensun, thank you so much for catching this! I think this was caused due to a [decision we made earlier about resolving default parameters](https://github.com/kubeflow/pipelines/blob/c0640d7a033b6fa2133b92c2a459d9eb3dabee32/backend/src/v2/driver/driver.go#L824)(PR #9184). In the past, we would add the default parameter values into the `executorInput` when there is no user input, but this will cause the default value to show up at UI as an input, which is somewhat inaccurate. So we decided to not add default values to `executorInput` and let components resolve themselves. I can add some logic to add the default values in the launcher, if they are not already present. This should resolve the bug without undoing the previous decision."
] | 2023-06-05T21:31:09 | 2023-06-13T18:25:05 | 2023-06-13T18:25:04 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
KFP standalone
* KFP version:
2.0.0rc.1
* KFP SDK version:
2.0.0rc.1
### Steps to reproduce
Run the following pipeline:
https://github.com/kubeflow/pipelines/blob/63936c07735521b1244e1e528a9baf80d40b296d/sdk/python/test_data/pipelines/xgboost_sample_pipeline.py
https://github.com/kubeflow/pipelines/blob/63936c07735521b1244e1e528a9baf80d40b296d/sdk/python/test_data/pipelines/xgboost_sample_pipeline.yaml
### Expected result
Expect pipeline to run successfully.
### Actual result
Pipeline failed due to input parameter with default value not resolved at runtime:
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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.",
"Well that's not very nice..."
] | 2023-06-05T20:44:10 | 2023-09-05T07:41:22 | null | NONE | null | ### Feature Area
<!-- /area backend -->
### What feature would you like to see?
Caching of output of tasks once task completes and not only once the entire pipeline is finished.
### What is the use case or pain point?
Waiting for entire pipeline to finish before starting a new pipeline so some early output artefacts can be used in the subsequent pipeline.
### Is there a workaround currently?
Waiting longer...
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9570 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9570/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9570/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9570/events | https://github.com/kubeflow/pipelines/issues/9570 | 1,742,499,789 | I_kwDOB-71UM5n3G_N | 9,570 | AttributeError: module 'kfp_server_api' has no attribute 'ApiPipelineSpec' | {
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"@Princy-Malhotra, do you have the opportunity to recompile the pipeline with with the latest release of the KFP SDK, 2.0.0-rc.1? This issue is resolved with that version.",
"> @Princy-Malhotra, do you have the opportunity to recompile the pipeline with with the latest release of the KFP SDK, 2.0.0-rc.1? This issue is resolved with that version.\n\nHey @connor-mccarthy, thanks for the help. However, I upgraded the KFP version to 2.0.0b12 and this solved the issue for me."
] | 2023-06-05T19:52:38 | 2023-06-11T21:56:12 | 2023-06-11T21:56:12 | NONE | null | ### Environment
* KFP version: 2.0.0b4
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 2.0.0-rc.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version: kfp 2.0.0b4
kfp-pipeline-spec 0.1.16
kfp-server-api 2.0.0a6
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
I downloaded your .py file from here: https://github.com/kubeflow/pipelines/blob/master/samples/v2/lightweight_python_functions_v2_pipeline/lightweight_python_functions_v2_pipeline.py
I saved it under the name "sample" and compiled it using the command: python3 sample.py
It compiled fine and a .yaml file was created with the same name as the .py file. Now, when I uploaded and run this yaml file on the kubeflow localhost for creating a run, I got the error as shown below:
What should I do to resolve this issue?
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
It should run without errors and give the output
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9568 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9568/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9568/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9568/events | https://github.com/kubeflow/pipelines/issues/9568 | 1,741,988,674 | I_kwDOB-71UM5n1KNC | 9,568 | How to import containerized Python component after building it? | {
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"I recommend importing the Python component from the module in which it is defined. Loading the YAML also works, however.",
"@connor-mccarthy Forgive me for being dense, but by \"importing the Python component from the module in which it is defined\" do you mean in the `component.py` file? (referencing the example layout above)\r\n\r\nFor the `pipeline.py` file, what does the import look like? The docs example make it seem like the pipeline function is being defined in a completely flat file structure (i.e. same or sibling module as component) is that correct? \r\n\r\nA folder structure I encounter for python-based components looks like this:\r\n\r\n```\r\nmy-repo/\r\nβββ components\r\nβΒ Β βββ bar\r\nβΒ Β βΒ Β βββ component.py\r\nβΒ Β βββ foo\r\nβΒ Β βββ component.py\r\nβββ pipeline.py\r\n```\r\n\r\nWhat does the import look now if I were to to convert `foo` and `bar` into containerized Python components? Do I build two separate containerized components? could I build a single one containing both? \r\n\r\nAlso, I'm unclear on how these are working under the hood. Do we need the `component_metadata` folder? Does importing from the YAML contained their work? Is it the \"IR\" YAML? Should that be what is committed to source control? ",
"Certainly not dense! These are good question and the docs should clarify this more. I will work on an update to the docs. In the meantime, the following take your questions one-by-one and summarizes how to think about Containerized Python Components beneath:\r\n\r\n> by \"importing the Python component from the module in which it is defined\" do you mean in the component.py file?\r\n\r\nFor this, I mean to import from `bar/component.py` and `foo/component.py` into `pipeline.py`.\r\n\r\n> For the pipeline.py file, what does the import look like?\r\n\r\nFor your specific project layout, the import would be `from components.foo.component import my_component` (replacing `my_component` with whatever the symbol is for your component object`).\r\n\r\n> The docs example make it seem like the pipeline function is being defined in a completely flat file structure (i.e. same or sibling module as component) is that correct?\r\n\r\nEither works! You'll just need to update the path of the imports to align with how your source code is laid out. This is consistent with normal Python module/object discovery; nothing special is required for KFP.\r\n\r\n> Do we need the component_metadata folder? Does importing from the YAML contained their work?\r\n\r\nYou do not strictly need the `component_metadata` for importing your components into a pipeline. You can load the [IR YAML](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/#ir-yaml) instead of importing the Python object if you'd like, but I recommend importing the Python object to maintain a Pythonic experience end-to-end.\r\n\r\n> Should that be what is committed to source control?\r\n\r\nWhile this is up to you and your organization, I recommend committing at least the Python component/pipeline definitions to source control and somehow pinning the version of the KFP SDK used to compile your components/pipelines. This enables you to reliably reproduce compilation, so there is no need to version the YAML directly.\r\n\r\n---\r\n\r\nIn general, it is best to think of [Containerized Python Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/) as a convenience mechanism for building images usable in [Lightweight Python Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/lightweight-python-components/). Usage has two parts: (1) you [build the component image](https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/#3-build-the-component) and push the image then (2) you use the component, which is essentially now a Lightweight Python Component that references the image you just built/pushed. That's all a Containerized Python Component is.\r\n\r\nAs for imports and project layout, this is up to you! You'll be subject to all the normal Python rules when importing objects across modules.\r\n\r\nPlease feel free to re-open if this answer is insufficient."
] | 2023-06-05T15:08:32 | 2023-07-05T17:56:56 | 2023-07-05T17:56:56 | CONTRIBUTOR | null | This might be a really dumb question, but given a file layout of something similar to the docs example:
```
pipeline.py
components/
component.py
util.py
```
And then running the build command:
```
kfp component build src/ --component-filepattern component.py
```
How would I import the component in `pipeline.py`? Do I import the generated YAML in `component_metadta/` or do I import the function from the module? It's not clear to me from the docs what is expected for referencing the component.
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https://api.github.com/repos/kubeflow/pipelines/issues/9567 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9567/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9567/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9567/events | https://github.com/kubeflow/pipelines/issues/9567 | 1,741,655,305 | I_kwDOB-71UM5nz40J | 9,567 | How to Apply node affinity and toleration in kubeflow components deployment? | {
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"```python\r\nfrom kfp import dsl\r\nfrom kubernetes.client import V1Toleration\r\n\r\ndef apply_toleration(toleration: str, container_op: dsl.ContainerOp) -> None:\r\n container_op.add_toleration(\r\n V1Toleration(\r\n key=toleration,\r\n operator=\"Equal\",\r\n value=\"true\",\r\n effect=\"NoSchedule\",\r\n )\r\n )\r\n\r\ndef add_node_selector(label_name: str, label_value: str, container_op: dsl.ContainerOp) -> None:\r\n container_op.add_node_selector_constraint(label_name=label_name, label_values=label_value)\r\n \r\n```\r\n\r\nhere you go :)\r\n",
"Thank you for your reply!\r\nCan you please tell me specifically in which file do I need to put this piece of code? I am using kustomize option for deployment of kubelfow v1.7. Plus I am using \"rds-s3\" deployment option. [[Reference](https://awslabs.github.io/kubeflow-manifests/docs/deployment/rds-s3/guide/#20-set-up-rds-s3-and-configure-secrets:~:text=%5BRDS%20and%20S3%5D%20Deploy%20both%20RDS%20and%20S3)]\r\n\r\nYour quick response would be highly appreciated.\r\n@tClelford",
"Those are examples to demonstrate how tolerations and node selectors work in KFP rather than code you should copypaste -- apply_toleration, for example, is applying a toleration that you might not want. You should apply the tolerations and node selectors as part of your component definition. \r\n\r\nAlso, I think you'd be better off using stackoverflow or something for this sort of thing, you're much more likely to get responses.\r\n\r\n\r\nEdit to add:\r\n\r\nI think I might have misunderstood your question - I'm talking about how to apply tolerations and node selectors when defining a pipeline, if you're talking about how you control which nodes the various bits of the kubeflow backend run on, this is no help at all :D",
"Got it, Thank you @tClelford \r\nBut actually I am using Kustomize for deployment of whole Kubeflow components stack. And applying a patch to kustomization. Here's my patch.yaml\r\n```apiVersion: apps/v1\r\nkind: Not-imp \r\nmetadata:\r\n name: not-important\r\nspec:\r\n template:\r\n spec:\r\n affinity:\r\n nodeAffinity:\r\n requiredDuringSchedulingIgnoredDuringExecution:\r\n nodeSelectorTerms:\r\n - matchExpressions:\r\n - key: dedicated\r\n operator: In\r\n values:\r\n - kubeflow\r\n tolerations:\r\n - key: \"dedicated\"\r\n operator: \"Equal\"\r\n value: \"kubeflow\"\r\n effect: \"NoSchedule\"\r\n````\r\n\r\nAnd updated the kustomization ( `kubeflow-manifests/deployments/rds-s3/kustomization.yaml` ) file:\r\n\r\n```\r\napiVersion: kustomize.config.k8s.io/v1beta1\r\nkind: Kustomization\r\n\r\nbases:\r\n - ./base\r\nresources:\r\n # Configured for AWS RDS and AWS S3\r\n\r\n # AWS Secret Manager\r\n - ../../awsconfigs/common/aws-secrets-manager\r\n # Kubeflow Pipelines\r\n - ../../awsconfigs/apps/pipeline\r\n # Katib\r\n - ../../awsconfigs/apps/katib-external-db-with-kubeflow\r\npatches:\r\n - path: ./patch.yaml\r\n target:\r\n kind: Deployment\r\n - path: ./patch.yaml\r\n target:\r\n kind: Job\r\n - path: ./patch.yaml\r\n target:\r\n kind: StatefulSet\r\n```\r\n\r\nIf anyone else can help me with this, would be quite helpful for me.",
"@TalhaNaeem101\r\nThe terraform deployment of Kubeflow on AWS uses helm to manage the deployments. For the rds s3 deployment you can modify `charts/apps/kubeflow-pipelines/rds-s3` for deployments using irsa and `charts/apps/kubeflow-pipelines/rds-s3-static` for deployments using static credentials.\r\n\r\nAre you encountering any problems while applying the node tolerations when using kustomize?",
"I am currently facing a similar issue while configuring affinity and toleration for the pipelines v1 backend. In my use case, I need to select specific node pools for a Workflow/Notebook to run on. I have successfully achieved this for the notebooks (by following this pullrequest https://github.com/kubeflow/kubeflow/pull/5237), but I'm encountering difficulties when it comes to the workflows. Does someone know how to configurate affinity and toleration on Workflow resources?"
] | 2023-06-05T12:17:18 | 2023-06-29T11:42:28 | null | NONE | null | Hey,
I have been using kubeflow for 3-4 months. I was using kubeflow v1.5, managed its deployment with kusotmize.
Now, after upgrading the kubeflow version to [v1.7](https://awslabs.github.io/kubeflow-manifests/release-v1.7.0-aws-b1.0.1/docs/), as we get [terraform deployment option](https://awslabs.github.io/kubeflow-manifests/docs/deployment/rds-s3/guide-terraform/) with this version. But I could not find a way to pass configuration for [node affinity ](https://kubernetes.io/docs/tasks/configure-pod-container/assign-pods-nodes-using-node-affinity/)and [toleration](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/) for kubeflow components.
Please help me out if there's some way to pass configuration for node affinity and tolerations to the manifests of kubeflow components. | {
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] | null | [] | 2023-06-04T15:03:03 | 2023-06-20T18:33:43 | 2023-06-20T18:33:43 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? **Google Cloud Vertex AI**
* KFP version: N/A
* KFP SDK version: **2.0.0rc1**
* Python 3.9.10
### Steps to reproduce
I'm following the tutorial for Containerized Python Components: https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/
I have these files as in the example:
```
src/
βββ my_component.py
βββ math_utils.py
```
Install `kfp` using
```
virtualenv venv
source venv/bin/activate
pip install kfp[all]==2.0.0rc1 docker==6.1.3 click==7.1.2
```
The command to build the containerized python component is:
`kfp component build src/ --component-filepattern my_component.py --no-push-image`
This command to build the componet fails with the error: `'list' object has no attribute 'get'`
Using `click==8.1.3`, it works fine:
```
pip install kfp[all]==2.0.0rc1 docker==6.1.3 click==8.1.3
```
Now it works.
### Expected result
Build docker image.
### Materials and reference
Containerized Python Components Tutorial: https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/
### Labels
/area sdk
/area components
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9565 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9565/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9565/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9565/events | https://github.com/kubeflow/pipelines/issues/9565 | 1,739,975,578 | I_kwDOB-71UM5nteua | 9,565 | [backend] default cache image missing tag, causing pull every time | {
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"@kubeflow/wg-pipeline-leads I believe this is a critical issue as it affects every Kubeflow Pipelines deployment."
] | 2023-06-03T23:02:05 | 2023-08-28T20:54:43 | null | MEMBER | null | Currently the default `cacheImage` is set to `gcr.io/google-containers/busybox` (with no tag).
When an image has no tag, Kubernetes will default to using `latest` with a `pullPolicy` of `Always`, meaning large pipelines might be pulling this image a crazy number of times. This is likely the cause of the previous issue which resulted in us changing the default from `alpine` (which became subject to the DockerHub rate limits, and started failing for many users): https://github.com/kubeflow/pipelines/issues/4099
For reference, [the only thing the container actually needs to be able to do is run `echo`](https://github.com/kubeflow/pipelines/blob/23739e9be38a3229911ea5ee0c4d8389ba84f607/backend/src/cache/server/mutation.go#L177).
__This default of `gcr.io/google-containers/busybox` is hard-coded in a few places:__
- [in the cache server code](https://github.com/kubeflow/pipelines/blob/23739e9be38a3229911ea5ee0c4d8389ba84f607/backend/src/cache/server/mutation.go#L168-L173)
- [in the manifests](https://github.com/kubeflow/pipelines/blob/23739e9be38a3229911ea5ee0c4d8389ba84f607/manifests/kustomize/base/installs/generic/pipeline-install-config.yaml#L64)
- [in the cache API sever config](https://github.com/kubeflow/pipelines/blob/23739e9be38a3229911ea5ee0c4d8389ba84f607/backend/src/apiserver/config/config.json#L21)
__We should do two things:__
1. Push an updated "cache image" to GCR (the current `latest` tag is actually [very outdated](https://console.cloud.google.com/gcr/images/google-containers/global/busybox), from 2016)
2. Set the default tag to something other than `latest`
---
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"/assign @zijianjoy ",
"Thank you @andre-lx , the concern makes sense in the case of high amount of namespaces. I am reading the past design decision in https://docs.google.com/document/d/1YNxKUbJLnBRL7DbPn76fsShkQx5Q5jTc-iXfLmLt1FU/edit. The concern is over-granting permission to a single service account. If you would like, I think the current workaround is to avoid creating visualization server and artifact fetcher by modifying profile controller. It will remove the feature of downloading artifact and tensorboard, but I think it can mitigate the issue in short term.",
"Hi @zijianjoy \r\n\r\nThanks for the quick around. \r\n\r\nThis is indeed one possible solution on the short term and we will be using it. \r\n\r\nUnfortunately, this removed the possibility to download artifacts as you mentioned and the artifacts page does not load, so I hope this gets solved in a future release.\r\n\r\nThanks again, AndrΓ©",
"> Thank you @andre-lx , the concern makes sense in the case of high amount of namespaces. I am reading the past design decision in https://docs.google.com/document/d/1YNxKUbJLnBRL7DbPn76fsShkQx5Q5jTc-iXfLmLt1FU/edit. The concern is over-granting permission to a single service account. If you would like, I think the current workaround is to avoid creating visualization server and artifact fetcher by modifying profile controller. It will remove the feature of downloading artifact and tensorboard, but I think it can mitigate the issue in short term.\r\n\r\n@zijianjoy Luckily that is not true. You can easily disable the deprecated visualization server and switch the ml-pipeline ui to not use the resource hogging artifact proxy. It can use minio directly by changing one environment variable. So both components are unnecessary.\r\n\r\nTo make this secure only the namespace parameter has to be enforced in the UI as explained here https://github.com/kubeflow/pipelines/issues/8406#issuecomment-1640918121",
"> > Thank you @andre-lx , the concern makes sense in the case of high amount of namespaces. I am reading the past design decision in [docs.google.com/document/d/1YNxKUbJLnBRL7DbPn76fsShkQx5Q5jTc-iXfLmLt1FU/edit](https://docs.google.com/document/d/1YNxKUbJLnBRL7DbPn76fsShkQx5Q5jTc-iXfLmLt1FU/edit). The concern is over-granting permission to a single service account. If you would like, I think the current workaround is to avoid creating visualization server and artifact fetcher by modifying profile controller. It will remove the feature of downloading artifact and tensorboard, but I think it can mitigate the issue in short term.\r\n> \r\n> @zijianjoy Luckily that is not true. You can easily disable the deprecated visualization server and switch the ml-pipeline ui to not use the resource hogging artifact proxy. It can use minio directly by changing one environment variable. So both components are unnecessary.\r\n> \r\n> To make this secure only the namespace parameter has to be enforced in the UI as explained here [#8406 (comment)](https://github.com/kubeflow/pipelines/issues/8406#issuecomment-1640918121)\r\n\r\nHi @juliusvonkohout. This make sense. \r\n\r\nFor now, and for version 1.8.5 there are any workarround to fix this issue? Use the artifacts without the two pods per namespace?\r\n\r\nThanks\r\n",
"@andre-lx I can help with the open source implementation, but solving this for a single user is more of a paid consulting question ;-). If you want that, reach out on slack. As a hint: it is doable in Kubeflow 1.7 but still as insecure as the current situation. You can put this on the agenda for the next KFP meeting or order consulting."
] | 2023-06-02T11:03:33 | 2023-07-20T09:27:49 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow deployment
* KFP version:
1.8.2
### Steps to reproduce
I didn't find any reference for this issue.
At this moment, 2 pods are created for each namespace. The pipelines are running smoothly, but this 2 pods per namespace are making our Kubernetes cluster a lot more expensive.
Imagine the following scenario:
- 500 users, each one with their own namespace, 2 pods per namespace = 1000 pods
- each node runs up to 100 pods
You will get up to 10 nodes, only to have these 2 pods per user. Even if the user does not use the pipelines.
A practical example:
```cli
> kubectl get pods --no-headers -A -o wide | grep ip-xx-xx-xx-xx.xx-west-2.compute.internal | wc -l
110
> kubectl get pods --no-headers -A -o wide | grep ip-xx-xx-xx-xx.xx-west-2.compute.internal | grep ml-pipeline | wc -l
100
```
My question is, what can we do to improve this costs. For example, there are any way of not creating the pods, or creating the pods only when they are necessary.
### Expected result
Since this takes a lot of unnecessary resources, should exist a way of improving this.
Thanks
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} | [] | 2023-06-02T05:54:03 | 2023-06-02T10:49:09 | 2023-06-02T10:49:08 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Standalone KFP
* KFP version:
2.0.0-rc.1
* KFP SDK version:
N/A
### Steps to reproduce
While doing some random debugging, I found the following error log in api-server pod:
```
I0602 05:14:55.623705 7 error.go:278] Error 1406: Data too long for column 'StateHistory' at row 1
InternalServerError: Failed to update or insert tasks. Query: INSERT INTO tasks (UUID,Namespace,PipelineName,RunUUID,PodName,MLMDExecutionID,CreatedTimestamp,StartedTimestamp,FinishedTimestamp,Fingerprint,Name,ParentTaskUUID,State,StateHistory,MLMDInputs,MLMDOutputs,ChildrenPods,Payload) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?),(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?),(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?),(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?),(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?),(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?),(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) ON DUPLICATE KEY UPDATE UUID=VALUES(UUID),Namespace=VALUES(Namespace),PipelineName=VALUES(PipelineName),RunUUID=VALUES(RunUUID),PodName=VALUES(PodName),MLMDExecutionID=VALUES(MLMDExecutionID),CreatedTimestamp=VALUES(CreatedTimestamp),StartedTimestamp=VALUES(StartedTimestamp),FinishedTimestamp=VALUES(FinishedTimestamp),Fingerprint=VALUES(Fingerprint),Name=VALUES(Name),ParentTaskUUID=VALUES(ParentTaskUUID),State=VALUES(State),StateHistory=VALUES(StateHistory),MLMDInputs=VALUES(MLMDInputs),MLMDOutputs=VALUES(MLMDOutputs),ChildrenPods=VALUES(ChildrenPods),Payload=VALUES(Payload). Args: [a05e339f-c5b8-4cc7-ad07-4464a3d671b8 kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s 1685667943 1685667943 1685682894 conditional-execution-pipeline-with-exit-handler-flj2s FAILED [{"UpdateTimeInSec":1685667944,"State":"RUNNING"},{"UpdateTimeInSec":1685667964,"State":"FAILED"},{"UpdateTimeInSec":1685680898,"State":"RUNNING"},{"UpdateTimeInSec":1685680908,"State":"FAILED"},{"UpdateTimeInSec":1685681065,"State":"RUNNING"},{"UpdateTimeInSec":1685682895,"State":"FAILED"}] ["conditional-execution-pipeline-with-exit-handler-flj2s-1811741639"] {"UUID":"a05e339f-c5b8-4cc7-ad07-4464a3d671b8","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685667943,"FinishedTimestamp":1685682894,"Fingerprint":"","Name":"conditional-execution-pipeline-with-exit-handler-flj2s","ParentTaskId":"","State":"FAILED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667944,"State":"RUNNING"},{"UpdateTimeInSec":1685667964,"State":"FAILED"},{"UpdateTimeInSec":1685680898,"State":"RUNNING"},{"UpdateTimeInSec":1685680908,"State":"FAILED"},{"UpdateTimeInSec":1685681065,"State":"RUNNING"},{"UpdateTimeInSec":1685682895,"State":"FAILED"}],"ChildrenPods":["conditional-execution-pipeline-with-exit-handler-flj2s-1811741639"],"Payload":""} 112a791d-8891-4c80-bf2d-8c3b3d5addd1 kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s-1155479742 1685667943 1685682894 1685682894 print-op-driver RUNTIME_STATE_UNSPECIFIED [{"UpdateTimeInSec":1685667964,"State":"RUNTIME_STATE_UNSPECIFIED"}] ["conditional-execution-pipeline-with-exit-handler-flj2s-1301759341"] {"UUID":"112a791d-8891-4c80-bf2d-8c3b3d5addd1","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s-1155479742","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685682894,"FinishedTimestamp":1685682894,"Fingerprint":"","Name":"print-op-driver","ParentTaskId":"","State":"RUNTIME_STATE_UNSPECIFIED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667964,"State":"RUNTIME_STATE_UNSPECIFIED"}],"ChildrenPods":["conditional-execution-pipeline-with-exit-handler-flj2s-1301759341"],"Payload":""} c162f2ed-c450-4b02-ac00-041449cfc3d9 kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s-1255015604 1685667943 1685682884 1685682888 exit-handler-1-driver FAILED [{"UpdateTimeInSec":1685667954,"State":"PENDING"},{"UpdateTimeInSec":1685667964,"State":"FAILED"},{"UpdateTimeInSec":1685680898,"State":"PENDING"},{"UpdateTimeInSec":1685680908,"State":"FAILED"},{"UpdateTimeInSec":1685681065,"State":"PENDING"},{"UpdateTimeInSec":1685682895,"State":"FAILED"}] ["conditional-execution-pipeline-with-exit-handler-flj2s-3251969059"] {"UUID":"c162f2ed-c450-4b02-ac00-041449cfc3d9","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s-1255015604","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685682884,"FinishedTimestamp":1685682888,"Fingerprint":"","Name":"exit-handler-1-driver","ParentTaskId":"","State":"FAILED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667954,"State":"PENDING"},{"UpdateTimeInSec":1685667964,"State":"FAILED"},{"UpdateTimeInSec":1685680898,"State":"PENDING"},{"UpdateTimeInSec":1685680908,"State":"FAILED"},{"UpdateTimeInSec":1685681065,"State":"PENDING"},{"UpdateTimeInSec":1685682895,"State":"FAILED"}],"ChildrenPods":["conditional-execution-pipeline-with-exit-handler-flj2s-3251969059"],"Payload":""} f3aee305-3c6b-4afc-89c3-442e6747ebc3 kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s-1301759341 1685667943 1685682894 1685682894 print-op RUNTIME_STATE_UNSPECIFIED [{"UpdateTimeInSec":1685667964,"State":"RUNTIME_STATE_UNSPECIFIED"}] {"UUID":"f3aee305-3c6b-4afc-89c3-442e6747ebc3","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s-1301759341","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685682894,"FinishedTimestamp":1685682894,"Fingerprint":"","Name":"print-op","ParentTaskId":"","State":"RUNTIME_STATE_UNSPECIFIED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667964,"State":"RUNTIME_STATE_UNSPECIFIED"}],"ChildrenPods":null,"Payload":""} 7d7a1b13-3fd7-4f34-a6c9-0b1dc806915f kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s-1811741639 1685667943 1685667943 1685667948 root-driver SUCCEEDED [{"UpdateTimeInSec":1685667944,"State":"PENDING"},{"UpdateTimeInSec":1685667954,"State":"SUCCEEDED"}] ["conditional-execution-pipeline-with-exit-handler-flj2s-669961186"] {"UUID":"7d7a1b13-3fd7-4f34-a6c9-0b1dc806915f","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s-1811741639","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685667943,"FinishedTimestamp":1685667948,"Fingerprint":"","Name":"root-driver","ParentTaskId":"","State":"SUCCEEDED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667944,"State":"PENDING"},{"UpdateTimeInSec":1685667954,"State":"SUCCEEDED"}],"ChildrenPods":["conditional-execution-pipeline-with-exit-handler-flj2s-669961186"],"Payload":""} 959cbf73-e675-48c7-ac25-39d2817aaa59 kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s-3251969059 1685667943 1685682894 1685682894 exit-handler-1 RUNTIME_STATE_UNSPECIFIED [{"UpdateTimeInSec":1685667964,"State":"RUNTIME_STATE_UNSPECIFIED"}] ["conditional-execution-pipeline-with-exit-handler-flj2s-1155479742"] {"UUID":"959cbf73-e675-48c7-ac25-39d2817aaa59","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s-3251969059","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685682894,"FinishedTimestamp":1685682894,"Fingerprint":"","Name":"exit-handler-1","ParentTaskId":"","State":"RUNTIME_STATE_UNSPECIFIED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667964,"State":"RUNTIME_STATE_UNSPECIFIED"}],"ChildrenPods":["conditional-execution-pipeline-with-exit-handler-flj2s-1155479742"],"Payload":""} a1bad563-f9c5-418e-b553-de2011ff0a98 kubeflow 1140fd28-645e-44df-baa6-6c5847527f94 conditional-execution-pipeline-with-exit-handler-flj2s-669961186 1685667943 1685667953 1685682894 root FAILED [{"UpdateTimeInSec":1685667954,"State":"RUNNING"},{"UpdateTimeInSec":1685667964,"State":"FAILED"},{"UpdateTimeInSec":1685680898,"State":"RUNNING"},{"UpdateTimeInSec":1685680908,"State":"FAILED"},{"UpdateTimeInSec":1685681065,"State":"RUNNING"},{"UpdateTimeInSec":1685682895,"State":"FAILED"}] ["conditional-execution-pipeline-with-exit-handler-flj2s-1255015604"] {"UUID":"a1bad563-f9c5-418e-b553-de2011ff0a98","Namespace":"kubeflow","PipelineName":"","RunId":"1140fd28-645e-44df-baa6-6c5847527f94","PodName":"conditional-execution-pipeline-with-exit-handler-flj2s-669961186","MLMDExecutionID":"","CreatedTimestamp":1685667943,"StartedTimestamp":1685667953,"FinishedTimestamp":1685682894,"Fingerprint":"","Name":"root","ParentTaskId":"","State":"FAILED","StateHistoryString":"","MLMDInputs":"","MLMDOutputs":"","ChildrenPodsString":"","StateHistory":[{"UpdateTimeInSec":1685667954,"State":"RUNNING"},{"UpdateTimeInSec":1685667964,"State":"FAILED"},{"UpdateTimeInSec":1685680898,"State":"RUNNING"},{"UpdateTimeInSec":1685680908,"State":"FAILED"},{"UpdateTimeInSec":1685681065,"State":"RUNNING"},{"UpdateTimeInSec":1685682895,"State":"FAILED"}],"ChildrenPods":["conditional-execution-pipeline-with-exit-handler-flj2s-1255015604"],"Payload":""}]
github.com/kubeflow/pipelines/backend/src/common/util.NewInternalServerError
```
Further inspection reveals that the column has the size of `varchar(255)` instead of the designed `longtext`:
```
mysql> describe tasks;
+-------------------+--------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------------------+--------------+------+-----+---------+-------+
| UUID | varchar(255) | NO | PRI | NULL | |
| Namespace | varchar(255) | NO | | NULL | |
| PipelineName | varchar(255) | NO | | NULL | |
| RunUUID | varchar(255) | NO | MUL | NULL | |
| PodName | varchar(255) | NO | | NULL | |
| MLMDExecutionID | varchar(255) | NO | | NULL | |
| CreatedTimestamp | bigint | NO | | NULL | |
| StartedTimestamp | bigint | YES | | 0 | |
| FinishedTimestamp | bigint | YES | | 0 | |
| Fingerprint | varchar(255) | NO | | NULL | |
| Name | varchar(255) | YES | | NULL | |
| ParentTaskUUID | varchar(255) | YES | | NULL | |
| State | varchar(255) | YES | | NULL | |
| StateHistory | varchar(255) | YES | | NULL | |
| MLMDInputs | longtext | YES | | NULL | |
| MLMDOutputs | longtext | YES | | NULL | |
| ChildrenPods | longtext | YES | | NULL | |
| Payload | longtext | YES | | NULL | |
+-------------------+--------------+------+-----+---------+-------+
18 rows in set (0.00 sec)
```
---
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<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
desert
<!-- 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! -->
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"/assign @chensun ",
"Because the visualization is using Tensorboard, and the base image is tensorflow:\r\nhttps://github.com/kubeflow/pipelines/blob/b339e93914a1417b376e53f7318d896cc3942088/backend/Dockerfile.visualization#L21 \r\n\r\nVisualization server is not currently used by v2, it's kept to maintain v1 compatibility. We're still evaluating whether to support Tensorboard visualization in v1. ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-06-02T01:29:51 | 2023-09-09T07:41:23 | null | MEMBER | null | The `gcr.io/ml-pipeline/visualization-server` image is by far the largest container in Kubeflow pipelines at `1.3 GB`, what is making it so large, and how can we improve it?
- https://console.cloud.google.com/gcr/images/ml-pipeline/GLOBAL/visualization-server
This is especially important because a `Deployment/ml-pipeline-visualizationserver` is created in each profile namespace, so it's a big waste of resources if not truly needed.
---
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"We are hoping to be able to upgrade MLMD when its next minor version is available (1.14.0)"
] | 2023-06-01T23:14:59 | 2023-06-22T21:00:45 | null | MEMBER | null | While we updated our clients for MySQL 8.0 in https://github.com/kubeflow/pipelines/pull/8351, we still depend on [`google/ml-metadata`](https://github.com/google/ml-metadata), which does not fully support MySQL 8.0. In MySQL 8.0, the `default-authentication-plugin` was [changed from `mysql_native_password` to `caching_sha2_password`](https://dev.mysql.com/blog-archive/upgrading-to-mysql-8-0-default-authentication-plugin-considerations/), but `ml-metadata` does not support `caching_sha2_password` auth:
- https://github.com/google/ml-metadata/issues/178
Furthermore, we are [running a very old version of `ml-metadata`](https://github.com/kubeflow/pipelines/blob/0587a3fd352f8e4743c226fd7dcd6a3f28adf09c/manifests/kustomize/base/metadata/base/metadata-grpc-deployment.yaml#L25), specifically version `1.5.0` from 2021, but [`1.13.1` exists](https://github.com/google/ml-metadata/releases), as raised here: https://github.com/kubeflow/pipelines/issues/5439#issuecomment-1572728997
---
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"/assign @zijianjoy ",
"Can you try this installation approach instead?\r\nhttps://www.kubeflow.org/docs/components/pipelines/v1/installation/localcluster-deployment/#deploying-kubeflow-pipelines",
"@zijianjoy When I used the commands in your link, everything was installed correctly, and none of the pods were crashing - however, I could not run my KFP v2 pipelines. The first component would hang, and there were no logs.\r\n\r\nI was able to get everything working using the following:\r\n\r\nexport PIPELINE_VERSION=2.0.0-rc.2\r\nkubectl apply -k \"github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION\"\r\nkubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io\r\nkubectl apply -k \"github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic?ref=$PIPELINE_VERSION\"\r\n\r\nI used \"platform-agnostic\" instead of \"platform-agnostic-pns\".",
"I got [same error](https://github.com/kubeflow/pipelines/issues/9719) on latest version 2.0.0 too when install by\r\n\r\n```shell\r\nexport PIPELINE_VERSION=2.0.0\r\nkubectl apply --kustomize=\"github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=${PIPELINE_VERSION}\"\r\nkubectl wait crd/applications.app.k8s.io --for=condition=established --timeout=60s\r\nkubectl apply --kustomize=\"github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=${PIPELINE_VERSION}\"\r\n```\r\n\r\nBased on @keithpij method:\r\n\r\n```shell\r\nexport PIPELINE_VERSION=2.0.0\r\nkubectl apply --kustomize=\"github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=${PIPELINE_VERSION}\"\r\nkubectl wait crd/applications.app.k8s.io --for=condition=established --timeout=60s\r\nkubectl apply --kustomize=\"github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic?ref=${PIPELINE_VERSION}\"\r\n```\r\n\r\nThis will install without `proxy-agent` and `controller-manager` π\r\n\r\nI think it would be great to mention it in the document at https://www.kubeflow.org/docs/components/pipelines/v2/installation/quickstart/\r\n\r\n---\r\n\r\nRegarding `platform-agnostic-pns` - Process Namespace Sharing (PNS) which also mentioned [here](https://www.kubeflow.org/docs/components/pipelines/v1/installation/localcluster-deployment/#deploying-kubeflow-pipelines)\r\n\r\n> Note: Process Namespace Sharing (PNS) is not mature in Argo yet - for more information go to [Argo Executors](https://argoproj.github.io/argo-workflows/workflow-executors/) and reference βpns executorsβ in any issue you may come across when using PNS.\r\n\r\nI found in [ArgoCD document](https://argoproj.github.io/argo-workflows/workflow-executors/#process-namespace-sharing-pns), it shows:\r\n\r\n\r\n\r\nSo I think we should not use `platform-agnostic-pns` any more."
] | 2023-06-01T14:20:48 | 2023-07-10T05:58:17 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
I deployed KFP to the K8S cluster that comes with Docker Desktop. I used the following commands:
export PIPELINE_VERSION=2.0.0-rc.1
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
* KFP version: Version: 2.0.0-rc.1
* KFP SDK version: I ran: pip install kfp --pre
kfp 2.0.0rc1
kfp-pipeline-spec 0.2.2
kfp-server-api 2.0.0rc1
### Steps to reproduce
I am seeing the proxy-agent pod and the controller-manager pod stuck in the CrashLoopBackOff state. When I look at the logs of the proxy-agent I see the output below. I am unable to look at the logs of the controller-manager. When I try I get a message saying "logs are hidden because volume is too excessive". The KFP UI seems to be working fine and I can submit a pipeline and get it to run. I just need to understand these errors in case I see them in another environment.
+++ dirname /opt/proxy/attempt-register-vm-on-proxy.sh
++ cd /opt/proxy
++ pwd
+ DIR=/opt/proxy
++ kubectl get configmap inverse-proxy-config -o json
++ jq -r '.data.Hostname // empty'
+ HOSTNAME=
+ [[ -n '' ]]
+ [[ ! -z '' ]]
++ curl http://metadata.google.internal/computeMetadata/v1/instance/zone -H 'Metadata-Flavor: Google'
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0curl: (6) Could not resolve host: metadata.google.internal
<!--
Specify how to reproduce the problem.
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"/close\r\nby #9544 and #9545",
"@Tomcli: Closing this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/9543#issuecomment-1572696175):\n\n>/close\r\n>by #9544 and #9545\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>"
] | 2023-05-31T23:03:28 | 2023-06-01T20:02:48 | 2023-06-01T20:02:47 | 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?
The Arrikto team added new enhancement for the k8s client update in python. The new fix is only in the python kubernetes version 26.1. So we want to loosen the python kubernetes version requirement on the kfp sdk so it can be installed in the same python environment.
https://github.com/kubernetes-client/python/commit/392a8c1d0767ce534b121b3b0553e5b1297e430e
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
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<!-- 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 -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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<!-- 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:
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* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
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### Materials and reference
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run, job, pipeline, pipeline version, etc.
Similar to https://www.kubeflow.org/docs/components/pipelines/v1/concepts/
<!-- 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 -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π. | {
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* 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:
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To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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] | 2023-05-31T17:15:45 | 2023-06-03T00:28:04 | 2023-06-03T00:28:03 | COLLABORATOR | null | null | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-31T02:41:12 | 2023-08-29T07:41:21 | null | NONE | null | ### Feature Area
You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev. Although it is a powerfull tool for AI applications, many coding procedures including proper library infusion and grammer editting etc. are needed. It is a nice thing to have an automation process to package all these operations into a kubeflow pipeline. Hence we propose a YOLO kubeflow pipeline as a convenient tool for major ML operations for object detection.
<!-- 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?
Further, it takes many laborious procedures to build a practical YOLO pipelines, e.g. docker image editing and YAML file compilation etc. A graphical UI can provide a rapid deployment medium for ML developer especially YOLO, professional/non professional alike. Here we propose a Node-red frontend which can exercise an YOLO operation without starting from scratch. Node-red is a graphical user interface that features drag and drop operations on its pallet panel for easy customization of YOLO functionality.
<!-- 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?
none to my knowledge
<!-- Without this feature, how do you accomplish your task today? -->
it takes many laborious procedures to build a practical YOLO pipelines, e.g. docker image editing and YAML file compilation etc.
Here we propose a Node-red frontend which can exercise an kubeflow YOLO pipeline operation without starting from scratch. Node-red is a graphical user interface that features drag and drop operations on its pallet and canvas panel for easy customization of YOLO functionality.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9532 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9532/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9532/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9532/events | https://github.com/kubeflow/pipelines/issues/9532 | 1,733,245,362 | I_kwDOB-71UM5nTzmy | 9,532 | [feature] Kubeflow-Node-Red | {
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"Hi @NightLightTw, thank you for requesting this feature! Kubeflow intends to work with different platforms. It would be great if you could bring this up in the Kubeflow community meeting. \r\nHere are the Kubeflow manifest: https://github.com/kubeflow/manifests",
"Dear @Linchin π Thank you for replying our feature request. I will be glad to attend your group meeting to introduce the new integration feature of Node-red with Kubeflow. Please give us the web site and time of the meeting. We will attend on time. ",
"I just wanted to link my response to the other issue with what I think your next steps should be:\r\n- https://github.com/kubeflow/manifests/issues/2499#issuecomment-1699863245"
] | 2023-05-31T01:38:28 | 2023-08-30T21:26:16 | 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 -->
It takes many laborious procedures to build a practical ML pipelines including pipeline docker file compilation and edit. Laborious manual operations on kubeflow package are required to observe trainning results.
### What feature would you like to see?
Kube-node-red is aiming to integrate Kubeflow/Kubebeters with node-red, leveraging node-red's low-code modules, and using Kubeflow resources (e.g. Kubeflow pipeline, Kserve) to enhance its AI/ML ability.
### What is the use case or pain point?
Although kubeflow pipeline is a convenient tool for major ML operations such training and testting, it takes many laborious procedures to build a practical ML pipelines, not to mention switching between various ML algorithms and data sets. A graphical UI can provide a rapid deployment medium for ML developer, professional/non professional alike, to exercise an ML operation without starting from scratch. The following site provide some completed integration suites and examples for illustration of this op.
https://github.com/NightLightTw/kubeflow-Node-RED
### Is there a workaround currently?
No workaround as far as I know.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9524 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9524/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9524/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9524/events | https://github.com/kubeflow/pipelines/issues/9524 | 1,731,878,606 | I_kwDOB-71UM5nOl7O | 9,524 | [backend] Cache Debugging QA | {
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"Hi @WalterSmuts, thank you for bringing up this question! We do allow cache for tasks, and you can check out the design doc here: https://docs.google.com/document/d/1_Hy1_KvuKh-heydg8qIyXYTiMfNPxePx4BHGpZT4xMk/edit?resourcekey=0-PTtkRPF5xpS3lD8dssqX1Q\r\nHope this info helps you understand the issue! Please let me know if you have any more questions.",
"This document is not publicly accessible. I've requested permission but am still waiting. \r\n\r\nI'm more interested in ways of *debugging* and *introspecting* the process. How do I understand why a *specific* task was cached or not cached. Can I see this information in the logs?",
"> I'm more interested in ways of debugging and introspecting the process. How do I understand why a specific task was cached or not cached? Can I see this information in the logs?\r\n\r\nWould be amazing to at least see the generated hash value for the task. \r\nI'm currently using KPF 2.0.0 on Vertex AI and the cache works correctly only 50% of the time ..."
] | 2023-05-30T09:29:00 | 2023-08-28T20:55:10 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? unsure
* KFP version: 1.8.5
* KFP SDK version: 1.8.20
### Not a bug.
This is not a bug. Only a question. I tried joining the kubeflow slack but the link on the readme does not allow you to sign-in if you've not already got an account with https://kubeflow.slack.com/.
### Question
How do I debug tasks that are not cached? I expect it to be cached because all inputs come from cached tasks and no changes has been made. Simply re-running the pipeline without changing *anything* results in another execution of my task. I am sure I haven't set `max_cache_staleness` for this task. I'm using code-pickling (could be the culprit).
I'm hoping there is some way I can introspect the decisions on why using a cache wasn't considered. If that's not possible perhaps some tips on what kubeflow considers when deciding to use a cached output of a previous run.
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"We need more work on the illustration."
] | 2023-05-28T05:19:51 | 2023-05-31T01:38:08 | 2023-05-31T01:37:26 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Although kubeflow pipeline is a convenient tool for major ML operations such training and testing, it takes many laborious procedures to build a practical ML pipelines, not to mention switching between various ML algorithms and data sets. A graphical UI can provide a rapid deployment medium for ML developer, professional/non professional alike, to exercise an ML operation without starting from scratch. The following site provide some completed integration suites and examples for illustration of this op.
<!-- Provide a description of this feature and the user experience. -->
https://github.com/NightLightTw/kubeflow-Node-RED
### 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?
No workaround as far as I know.
<!-- Without this feature, how do you accomplish your task today? -->
it takes many laborious procedures to build a practical ML pipelines including pipeline docker file compilation and edit. Laborious manual operations on kubeflow package are required to observe trainning results.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. | {
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<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Kube-node-red is aiming to integrate Kubeflow/Kubebeters with node-red, leveraging node-red's low-code modules, and using Kubeflow resources (e.g. Kubeflow pipeline, Kserve) to enhance its AI/ML ability.
### What is the use case or pain point?
Although kubeflow pipeline is a convenient tool for major ML operations such training and testting, it takes many laborious procedures to build a practical ML pipelines, not to mention switching between various ML algorithms and data sets. A graphical UI can provide a rapid deployment medium for ML developer, professional/non professional alike, to exercise an ML operation without starting from scratch. The following site provide some completed integration suites and examples for illustration of this op.
https://github.com/NightLightTw/kubeflow-Node-RED
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
No workaround as far as I know.
<!-- Without this feature, how do you accomplish your task today? -->
it takes many laborious procedures to build a practical ML pipelines including pipeline docker file compilation and edit. Laborious manual operations on kubeflow package are required to observe trainning results.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a π. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9520 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9520/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9520/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9520/events | https://github.com/kubeflow/pipelines/issues/9520 | 1,728,304,375 | I_kwDOB-71UM5nA9T3 | 9,520 | [sdk] Remove trailing whitespace in the generated YAML | {
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"SGTM, @zzmao. Thanks for catching this."
] | 2023-05-26T22:23:09 | 2023-06-15T22:57:00 | null | COLLABORATOR | null | Trailing whitespace are observed in the generated YAML files:
We could remove them by simply apply `rstripe()`.
@connor-mccarthy If this looks good to you, I can create a PR.
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"Hello @ianmaddox , would you like to try 2.0.0-rc.1 manifests to see if the problem is fixed? https://github.com/kubeflow/pipelines/releases/tag/2.0.0-rc.1",
"I followed the install instruction there and it all seems to have fallen apart. Here's the tail end of the upgrade output;\r\n```\r\nInstalling collected packages: kfp-server-api\r\n Attempting uninstall: kfp-server-api\r\n Found existing installation: kfp-server-api 1.8.5\r\n Uninstalling kfp-server-api-1.8.5:\r\n Successfully uninstalled kfp-server-api-1.8.5\r\nERROR: 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.\r\nkfp 1.8.20 requires kfp-server-api<2.0.0,>=1.1.2, but you have kfp-server-api 2.0.0rc1 which is incompatible.\r\nSuccessfully installed kfp-server-api-2.0.0rc1\r\n```\r\n\r\nAll commands now result in an error about ApiGetHealthzResponse:\r\n```\r\n\r\n % kfp --version\r\nTraceback (most recent call last):\r\n File \"/home/ian/bin/miniconda3/bin/kfp\", line 5, in <module>\r\n from kfp.__main__ import main\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/__init__.py\", line 25, in <module>\r\n from ._client import Client\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/_client.py\", line 96, in <module>\r\n class Client(object):\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/_client.py\", line 394, in Client\r\n def get_kfp_healthz(self) -> kfp_server_api.ApiGetHealthzResponse:\r\nAttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'. Did you mean: 'V2beta1GetHealthzResponse'?\r\n\r\n\r\n % kfp --help\r\nTraceback (most recent call last):\r\n File \"/home/ian/bin/miniconda3/bin/kfp\", line 5, in <module>\r\n from kfp.__main__ import main\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/__init__.py\", line 25, in <module>\r\n from ._client import Client\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/_client.py\", line 96, in <module>\r\n class Client(object):\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/_client.py\", line 394, in Client\r\n def get_kfp_healthz(self) -> kfp_server_api.ApiGetHealthzResponse:\r\nAttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'. Did you mean: 'V2beta1GetHealthzResponse'?\r\n\r\n % kfp --namespace $NAMESPACE pipeline list\r\nTraceback (most recent call last):\r\n File \"/home/ian/bin/miniconda3/bin/kfp\", line 5, in <module>\r\n from kfp.__main__ import main\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/__init__.py\", line 25, in <module>\r\n from ._client import Client\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/_client.py\", line 96, in <module>\r\n class Client(object):\r\n File \"/home/ian/bin/miniconda3/lib/python3.10/site-packages/kfp/_client.py\", line 394, in Client\r\n def get_kfp_healthz(self) -> kfp_server_api.ApiGetHealthzResponse:\r\nAttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'. Did you mean: 'V2beta1GetHealthzResponse'?\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."
] | 2023-05-24T00:00:52 | 2023-08-25T07:41:36 | null | NONE | null | ### Environment
Ubuntu 22.04
Kind 0.19.0
kubectl Client Version: v1.27.1
kubectl Kustomize Version: v5.0.1
kubectl Server Version: v1.27.1
kfp 2.0.0-alpha.7 manifests
* How do you deploy Kubeflow Pipelines (KFP)?
From full manifest deployment
* KFP version:
user_local
* KFP SDK version:
kfp 1.8.20
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
Execute `kfp list` with a namespace, or `kfp run` or `kfp recurring-run` with a namespace
```
% kfp pipeline list
Failed to automatically set namespace.
+--------------------------------------+-----------------+---------------------------+
| Pipeline ID | Name | Uploaded at |
+======================================+=================+===========================+
| 0c9e8389-5810-4d3a-a686-ab3b90462951 | test-mk2 | 2023-05-23T23:37:03+00:00 |
+--------------------------------------+-----------------+---------------------------+
% kfp --namespace $NAMESPACE pipeline list
(404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Audit-Id': '4a0c9900-f5fb-42e6-987d-f9d2eae56919', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Tue, 23 May 2023 23:56:29 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
% kfp --namespace $NAMESPACE recurring-run list
(404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Audit-Id': '75b0fb55-aa97-4f39-ad96-1f7564cbc70a', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Tue, 23 May 2023 23:59:57 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
% kfp --namespace $NAMESPACE run list
(404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Audit-Id': 'e9d63939-6c3b-459b-b1ae-d9eafc2df1f7', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Tue, 23 May 2023 23:59:39 GMT', 'Content-Length': '198'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"services \"ml-pipeline\" not found","reason":"NotFound","details":{"name":"ml-pipeline","kind":"services"},"code":404}
```
### Expected result
Pipeline, run, and recurring run commands work. These commands all worked in the standalone kfp install we had last week.
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-23T17:29:24 | 2023-08-28T20:55:16 | null | COLLABORATOR | null | ### Feature Area
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/area backend
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### What feature would you like to see?
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### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
---
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] | 2023-05-23T17:27:03 | 2023-08-28T20:55:36 | null | COLLABORATOR | null | ### Feature Area
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/area backend
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### 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? -->
---
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/area backend
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<!-- /area components -->
### 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? -->
---
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<!-- /area components -->
### What feature would you like to see?
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<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
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---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-23T17:22:28 | 2023-08-28T20:55:49 | null | COLLABORATOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
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---
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Using `2.0.0-rc.1` tag with kubeflow manifest v1.7.0 leads to missing resources in profile namespace. (Missing `mlpipeline-minio-artifact` secrets, `metadata-grpc-configmap` configmap, etc.. It makes the pipelines crashes)
## Environment
To show the problem, we will create a k8s cluster using kind, with a kubernetes version 1.25.8.
We will clone the manifests repository (1.7.0), deploy kubeflow without the pipeline extension. Then clone the pipeline repository (2.0.0-rc1), and deploy the manifests files.
### Steps to reproduce
#### Create the cluster
```sh
# Create k8s cluster
kind create cluster --image kindest/node:v1.25.8
# Wait for all nodes to be ready
kubectl wait --for=condition=Ready node --all
```
#### Setup kubeflow
```sh
# git clone and checkout manifests v1.7.0
git clone https://github.com/kubeflow/manifests
cd manifests
git checkout tags/v1.7.0
# Comment the line related to Kubeflow Pipelines deployement files (You can comment additional components if you do not want to deploy everything)
sed -i '/# Kubeflow Pipelines/{n;s/^/#/}' ./manifests/example/kustomization
# Deploy the kubeflow stuff
while ! kustomize build example | awk '!/well-defined/' | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
# clone and checkout pipelines 2.0.0-rc1
git clone https://github.com/kubeflow/pipelines
git checkout tags/2.0.0-rc.1
cd pipelines
kustomize build manifests/kustomize/env/cert-manager/platform-agnostic-multi-user | kubectl apply -f -
```
#### Create Profile resource
Now we can create a profile
```sh
kubectl apply -f - <<EOF
apiVersion: kubeflow.org/v1beta1
kind: Profile
metadata:
name: example-debug
spec:
owner:
kind: User
name: user@example.com
EOF
```
Now list the secrets in our new profile
```sh
kubectl get secrets -n example-debug
```
We do not get anything
### Expected result
We should get `mlpipeline-minio-artifact`. But also the `metadata-grpc-configmap` configmap
Not having the secrets properly set in the profile namespace leads to errors like https://github.com/kubeflow/pipelines/issues/7129
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https://api.github.com/repos/kubeflow/pipelines/issues/9476 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9476/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9476/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9476/events | https://github.com/kubeflow/pipelines/issues/9476 | 1,720,650,437 | I_kwDOB-71UM5mjwrF | 9,476 | Standardize release tags | {
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"@chensun @connor-mccarthy Looks like in retagging the SDK releases, the tag `2.0.0b6` was missed and deleted. We no longer see `2.0.0b6` or `sdk-2.0.0b6` tag. Can you please check?",
"> @chensun @connor-mccarthy Looks like in retagging the SDK releases, the tag `2.0.0b6` was missed and deleted. We no longer see `2.0.0b6` or `sdk-2.0.0b6` tag. Can you please check?\r\n\r\n@kuldeepjain Yes, I did experience some errors on a few old tags, and ignored them because I didn't think anyone would actually need those old tags. Any reason you're interested in this specific tag?",
"@chensun We were using that tag, is it possible to restore that to `sdk-2.0.0b6`?\r\ncc: @rachitchauhan43 @deepk2u ",
"@kuldeepjain is there anything blocking you from moving to a newer version. In fact, all 2.0 alpha and beta versions prior to 2.0.0b16 are subjects to a dependency breakage which affects all Python lightweight components at runtime. So you should probably upgrade to 2.0.0b16 or later.\r\nContext: https://github.com/kubeflow/pipelines/issues/9300"
] | 2023-05-22T22:01:54 | 2023-05-25T22:47:13 | 2023-05-25T22:47:13 | COLLABORATOR | null | ## Issue:
While investigating a recent [upgrade test failure](https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/9473/kubeflow-pipeline-upgrade-test/1660746693114597376), I discovered that it's upgrade upgrading from the last release (which is supposed to be 2.0.0-rc.1 at this moment)
Further more, the tag [v2.0.0b2](https://github.com/kubeflow/pipelines/releases/tag/v2.0.0b2) belongs to an almost one-year old SDK release, instead of the recent backend release [2.0.0-beta.2](https://github.com/kubeflow/pipelines/releases/tag/2.0.0-beta.2) -- the runtime releases and the SDK releases have been competing with each other on git tag names:
A few examples:
The following tags are for SDK releases:
- [1.8.22](https://github.com/kubeflow/pipelines/tree/1.8.22)
- [2.0.0b17](https://github.com/kubeflow/pipelines/tree/2.0.0b17)
- [sdk-2.0.0-rc.1](https://github.com/kubeflow/pipelines/tree/sdk-2.0.0-rc.1)
while the followings are for runtime releases:
- [1.8.5](https://github.com/kubeflow/pipelines/tree/1.8.5)
- [2.0.0-beta.2](https://github.com/kubeflow/pipelines/tree/2.0.0-beta.2)
- [2.0.0-rc.1](https://github.com/kubeflow/pipelines/tree/2.0.0-rc.1)
and there are two outliers whose tag names starts with a `v`:
```bash
$ git tag --sort=v:refname | tail -30
2.0.0-alpha.2
2.0.0-alpha.3
2.0.0-alpha.4
2.0.0-alpha.5
2.0.0-alpha.6
2.0.0-alpha.7
2.0.0-beta.0
2.0.0-beta.1
2.0.0-beta.2
2.0.0-rc.1
2.0.0b1
2.0.0b2
2.0.0b3
2.0.0b4
2.0.0b5
2.0.0b6
2.0.0b7
2.0.0b8
2.0.0b9
2.0.0b10
2.0.0b11
2.0.0b12
2.0.0b13
2.0.0b14
2.0.0b15
2.0.0b16
2.0.0b17
sdk-2.0.0-rc.1
v1.7.0-alpha.3
v2.0.0b2
```
This is causing issue that the upgrade test was preparing from the wrong starting version because of the sorting: https://github.com/kubeflow/pipelines/blob/3bd2a8d0e5e1aaa51ffbd5ae116615730437ed3b/test/upgrade-tests.sh#L76
## Proposal:
I propose the following AIs to solve the issue:
1. [DONE] Delete the `v1.7.0-alpha.3` and `v2.0.0b2` tag, so they don't mess up with tag sorting.
1. [#9478] Going forward, for SDK releases, we always prefix tag name with `sdk-`.
1. [DONE] Retag recent SDK releases (from 2.0.0b1 to 2.0.0b17) to include the `sdk-` prefix, so that `2.0.0-rc.1` becomes the latest tag without `sdk-` prefix.
1. [#9477] Change the upgrade test script to exclude tags starting with `sdk-` prefix.
@connor-mccarthy @Linchin @jlyaoyuli @gkcalat What do you think?
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"Yes its required since we should be having some way to make a proper status update at the end of the pipeline. ",
"/assign @Linchin ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-18T23:33:11 | 2023-08-28T20:55:31 | null | MEMBER | null | ### Feature Area
Specifically, support functionality exposed by `task.ignore_upstream_outputs` in the KFP SDK (SDK implementation https://github.com/kubeflow/pipelines/pull/8838).
<!-- 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?
Enable exit tasks to consume outputs of tasks enclosed by the exit handler.
Specifically, the following should be runnable:
```python
@dsl.pipeline()
def my_pipeline(text: str = 'message'):
task = fail_op(message=text)
print_op(message=task.output).ignore_upstream_failure()
```
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"Not sure if this handles all cases but to get around the issue we change the regex in objectstore.go to:\r\n```\r\n\tbucketPattern := regexp.MustCompile(`(^[a-z][a-z0-9]+:///?)([^/?]+)([\\?][^/]+)?/([^?]*)?$`)\r\n```\r\nAnd then change what groups are pulled for prefix. Seems to work for the couple of cases we tried. Basically grab the query string first and then grab prefix. ",
"Oh and one other thing we had to changes in the launcher was that it does a prefix check between the artifact uri and the default bucket. If it is using a cached artifact from a previous run it always thinks it is a non default bucket because it is including the run id in the prefix. Which works (treating it like a non-default bucket for a cached object) most of the time, but in this case also drops the query parameters when it recreates the bucketconfig. It should realize that is still the default bucket. So the logic is wrong basically for non-default bucket checking. Haven't looked whether these issues exist in the v2 version of the launcher.... ",
"Hi @cchanley2003, thank you for reporting the issue! Looks like you are using KFP 1.7.x. I recommend upgrading to `2.0.0-rc.1` and see if the issue persists. Also, could you please help me understand why changing the regex pattern can help solve the issue? Is the current regex\r\n```\r\nbucketPattern = regexp.MustCompile(`(^[a-z][a-z0-9]+:///?)([^/?]+)(/[^?]*)?(\\?.+)?$`)\r\n```\r\nnot recognizing something in your uri?\r\n",
"So a few notes. First why are we doing this:\r\n\r\nNeed custom endpoints for s3 buckets in certain regions such as gov cloud. The query string is supported by golangs blob syntax.\r\n\r\nWhy does the new regex work? This assumes the query string is in the middle of the url and the prefix is after. I think the original regex assumes query string is at the end. But this can never happen since ml-metadata (or something related to that) always appends the bucket with pipeline/run_name/run_id. So there is no way to insert the query string at the end of the pipelineRoot. \r\n\r\nAs far as I can tell once you query string is added to the original regex it ends up dropping the prefix (in this case the pipeline/run_name/run_id that is added to every root bucket.\r\n\r\nHopefully this makes sense. \r\n\r\nAs for upgrading we are running kubeflow 1.7 (see https://www.kubeflow.org/docs/releases/kubeflow-1.7/) which references an alpha version of kfp 2.0.0. ",
"One other note of something we ran into is that if cache hit occurs the query string beaks as well. This is because when a cache hit occurs the source artifact is considered to be a non-default bucket. It considers it a non-default bucket because the run_id is different due to the postfix that was added. But when creating the non-default bucket it doesn't carry over the query string. \r\n\r\nSo either the run_id shouldn't be considered when deciding if this is a non-default bucket or the query string needs to be used when recreating the bucket. ",
"Thank you for explaining, I see why the regex isn't working. Although you are using kfp 2.0.0-alpha.7, the error log seems to use a much older version of launcher:\r\n```\r\nI0518 17:46:27.707799 17 launcher.go:144] PipelineRoot defaults to \r\n```\r\nThe file `launcher.go` and the message have been removed long ago.",
"Yeah that was pipeline compiled with a 1.8 sdk. I'll retry with 2.0.0 sdk",
"So I attempted to use the kfp2.0.0rc1 sdk. Was unable to compile the pipeline. I then attempted to compile this [pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/v2/lightweight_python_functions_v2_pipeline/lightweight_python_functions_v2_pipeline.py) and got similar error. \r\n```\r\nAttributeError: '_FieldProperty' object attribute '__doc__' is read-only\r\n```\r\nAlso the rc1 launcher doesn't seem to be compatible with a pipeline compiled with 1.8. So not sure how to test rc1 launcher?"
] | 2023-05-18T18:22:45 | 2023-08-28T20:58:10 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Self hosted
* KFP version:
kubeflow 1.7
* KFP SDK version:
kfp sdk 1.8.19
### Steps to reproduce
1. Added the following the kfp-launcher configmap:
```
data:
defaultPipelineRoot: s3://<bucket>?region=<region>&endpoint=<endpoint>443
```
2. Tested with sample data passing (everything worked and everything went into s3) using parameters passing only. Wrote the following test for artifact passing:
```
from kfp import ( dsl, compiler)
from kfp.v2.dsl import component
from kfp.v2.dsl import (
Input,
Output,
Dataset,
)
@component
def write_data(output: Output[Dataset]):
with open(output.path, "w") as writer:
writer.write("Hello World")
@component
def read_data(input: Input[Dataset]):
with open(input.path, "r") as reader:
print(reader.read())
@dsl.pipeline(
name="data-passing-pipeline",
description="A pipeline that passes data between components",
)
def test_data_passing():
data = write_data()
read_data(data.outputs["output"])
if __name__ == "__main__":
# execute only if run as a script
compiler.Compiler(mode=dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
test_data_passing, "datapassing_V2.yaml")
```
3. Compile and run the pipeline.
* Write data successfully writes to bucket/pipeline/run name/run id/write_data/output
* Fails to pass the artifact to the next level.
Logs:
write_data:
```
time="2023-05-18T17:46:27.683Z" level=info msg="capturing logs" argo=true
I0518 17:46:27.707799 17 launcher.go:144] PipelineRoot defaults to "s3://<bucket>?region=us-gov-west-1&endpoint=s3.us-gov-west-1.amazonaws.com:443".
I0518 17:46:27.707962 17 cache.go:143] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
I0518 17:46:27.707970 17 cache.go:120] Connecting to cache endpoint ml-pipeline.kubeflow:8887
I0518 17:46:28.311175 17 launcher.go:193] enable caching
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
[KFP Executor 2023-05-18 17:46:39,713 INFO]: Looking for component `write_data` in --component_module_path `/tmp/tmp.FWpikJlbBK/ephemeral_component.py`
[KFP Executor 2023-05-18 17:46:39,713 INFO]: Loading KFP component "write_data" from /tmp/tmp.FWpikJlbBK/ephemeral_component.py (directory "/tmp/tmp.FWpikJlbBK" and module name "ephemeral_component")
I0518 17:46:39.791419 17 launcher.go:860] ExecutorOutput: {
"artifacts": {
"output": {
"artifacts": [
{
"name": "output",
"uri": "s3://<bucket>/write-data/output",
"metadata": {
"s3_region": ""
}
}
]
}
}
}
I0518 17:46:42.977145 17 object_store.go:249] uploadFile(localFilePath="/s3/<bucket>s/write-data/output", blobFilePath="write-data/output")
time="2023-05-18T17:46:43.270Z" level=info msg="/tmp/outputs/output/data -> /var/run/argo/outputs/artifacts/tmp/outputs/output/data.tgz" argo=true
time="2023-05-18T17:46:43.270Z" level=info msg="Taring /tmp/outputs/output/data"
```
read_data
```
time="2023-05-18T17:47:04.086Z" level=info msg="capturing logs" argo=true
I0518 17:47:04.111076 18 launcher.go:144] PipelineRoot defaults to "s3://<bucket>?region=us-gov-west-1&endpoint=s3.us-gov-west-1.amazonaws.com:443".
I0518 17:47:04.111241 18 cache.go:143] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
I0518 17:47:04.111249 18 cache.go:120] Connecting to cache endpoint ml-pipeline.kubeflow:8887
I0518 17:47:04.299847 18 launcher.go:193] enable caching
F0518 17:47:07.879874 18 main.go:50] Failed to execute component: failed to download input artifact "input" from remote storage URI "s3://<bucket>/write-data/output": failed to list objects in remote storage "write-data/output": blob (code=NotFound): NoSuchKey: The specified key does not exist.
status code: 404, request id: 8TSYRFXKQAVT04R9, host id: cIgr/PDL5WrVcBp1yvm+icUoYnWnInLMaueFYHZ0OWSQod0OYrdg+p2hRUzPaw8puaUbzkEcvF0=
```
### Expected result
The pipelines successfully runs.
---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-18T14:46:54 | 2023-08-17T07:41:40 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* KFP SDK version:
1.8.14; 2.0.0b17
### Steps to reproduce
Batch prediction operator ModelBatchPredictOp from google_cloud_pipeline_components (in both v1.* and the v2.0.0 beta branches)
Does not allow to pass `model_monitoring_objective_config` and `model_monitoring_alert_config` args to the `create` function of the BatchPredictionJob class.
### Expected result
interface of ModelBatchPredictOp component is in sync with `BatchPredictionJob.create`
### Materials and reference
https://github.com/kubeflow/pipelines/blob/2.0.0b17/components/google-cloud/google_cloud_pipeline_components/v1/batch_predict_job/component.py#L32
https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.BatchPredictionJob#google_cloud_aiplatform_BatchPredictionJob_create
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area components
---
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"@gkcalat I can look to submit the changes if the above suggestion makes sense",
"Hi @CosyOranges,\r\n\r\nSounds good to me. Please, assign your PR to @chensun and @Linchin.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-18T10:11:34 | 2023-08-21T07:41:37 | null | NONE | null | ### Feature Area
We make **heavy** use of Kubeflow's caching feature.
However, it doesn't play very well with some of the config you can set in Argo's `workflow-controller-configmap`. We run a large amount of pipelines at scale and as the number of pipelines increased the load on our EKS-Control plane also increased to the point where ~24k containers existed in one of our clusters and pretty much brought the entire cluster to a stand-still.
- After some debugging the root cause was found to be the interplay between the above configmap and the `cache-server`.
- To improve things we altered the `workflow-controller-configmap` to the following:
- ```
spec:
parallelism: 50 # Parallelism limits the max total parallel pods that can execute at the same time in a workflow
# PodGC describes how to delete completed pods as they complete: https://argoproj.github.io/argo-workflows/fields/#podgc
podGC:
strategy: OnPodSuccess # Strategy is the strategy to use. One of "OnPodCompletion", "OnPodSuccess", "OnWorkflowCompletion", "OnWorkflowSuccess"
# TTLStrategy limits the lifetime of a Workflow that has finished execution depending on if it Succeeded or Failed
ttlStrategy:
secondsAfterSuccess: 60 # Time to live after workflow is successful
secondsAfterFailure: 10800 # Time to live after workflow fails
# secondsAfterCompletion: 604800 # Time to live after workflow is completed, replaces ttlSecondsAfterFinished
- This caused an issue with caching: Pods were being cleaned up before the `workflow` itself was marked as completed and so the pod was **not** added to the cache-db
The root cause of this problem is the following in `backend/src/cache/server/watcher.go`
```
func isPodCompletedAndSucceeded(pod *corev1.Pod) bool {
return pod.ObjectMeta.Labels[ArgoCompleteLabelKey] == "true" && pod.Status.Phase == corev1.PodSucceeded
}
```
- This means the watcher won't deem a pod ready to be cached until the entire pipeline has completed.
### What feature would you like to see?
Ideally the above `isPodCompletedAndSucceeded` would simply check the whether or no the pod itself is successful, if yes we don't care about the rest of the pipeline and that pod/tasks metadata can be added to the cache-db
### What is the use case or pain point?
This would greatly increase the scalability of caching and also help to reduce the load on the Control Plane for people running pipelines at large scales as they will be able to clean pods up at a greater rate and still benefit from caching.
### Is there a workaround currently?
Not really, we've currently made the above change to our Fork of Kubeflow and are running this in our clusters
---
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"The underlying issue is because `gopkg.in/yaml.v2` library implements the problematic YAML 1.1 spec, and threats \"y\" as boolean by design: https://github.com/go-yaml/yaml/issues/214\r\nThis has been fixed in `gopkg.in/yaml.v3`"
] | 2023-05-18T04:01:27 | 2023-05-24T08:57:21 | 2023-05-24T08:57:21 | NONE | null |
* How did you deploy Kubeflow Pipelines (KFP)?
Use https://github.com/kubeflow/manifests to deploy kubeflow.
```bash
git clone https://github.com/kubeflow/manifests.git
cd manifests
git checkout v1.7-branch
```
Modify `manifests/apps/pipeline/upstream/base/pipeline/kustomization.yaml`:
```
...
images:
- name: gcr.io/ml-pipeline/api-server
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/persistenceagent
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/scheduledworkflow
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/frontend
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/viewer-crd-controller
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/visualization-server
newTag: 2.0.0-rc.1
```
Then, use one command to apply these manifest:
```bash
while ! kustomize build example | awk '!/well-defined/' | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
```
* KFP version:
version: 2.0.0-rc.1
* KFP SDK version:
version: 2.0.0-rc.1
### Steps to reproduce
1. Create two sample pipeline (v2)
- pipeline - **pipeline-add(x,y)**
```python
from kfp import dsl
from kfp import compiler
@dsl.component
def square(x: float) -> float:
return x ** 2
@dsl.component
def add(x: float, y: float) -> float:
return x + y
@dsl.component
def square_root(x: float) -> float:
return x ** .5
@dsl.pipeline
def pythagorean(a: float, b: float) -> float:
a_sq_task = square(x=a)
b_sq_task = square(x=b)
sum_task = add(x=a_sq_task.output, y=b_sq_task.output)
return square_root(x=sum_task.output).output
compiler.Compiler().compile(pythagorean, 'pipeline-add(x,y).yaml')
```
- pipeline - **pipeline-add(x,z)**
```python
from kfp import dsl
from kfp import compiler
@dsl.component
def square(x: float) -> float:
return x ** 2
@dsl.component
def add(x: float, z: float) -> float:
return x + z
@dsl.component
def square_root(x: float) -> float:
return x ** .5
@dsl.pipeline
def pythagorean(a: float, b: float) -> float:
a_sq_task = square(x=a)
b_sq_task = square(x=b)
sum_task = add(x=a_sq_task.output, z=b_sq_task.output)
return square_root(x=sum_task.output).output
compiler.Compiler().compile(pythagorean, 'pipeline-add(x,z).yaml')
```
Note: The key difference of these two pipelines is the input parameter's naming for **add()** component. _add(x,y)_ v.s _add(x,z)_!!
2. Click "Pipelines" menu, then click "Upload pipeline" for these two testing pipeline IR yaml
3. Verify "Pipeline Spec" for _add(x,y)_
You can find at line#6, the input parameter of "y" has changed to "true" which cause the pipeline fail.
Line#227 also indicating the same situation.
Then I try to review the IR yaml that generate by compiler "pipeline-add(x,y).yaml", and the format is correct. We can see two input parameters "x" and "y".
4. Verify "Pipeline Spec" for _add(x,z)_
I am suspect what cause the issue, so the other version pipeline just change the input parameters from "y" to "z". Checking the "Pipeline Spec" and the result is the same as "pipeline-add(x,z).yaml".
Base on this observation, I think the pipeline api backend might be the one which alter the "y" to "true".
### Expected result
"Pipeline Spec" representation & structure shoud be identical as IR yaml.
### Materials and reference
I put two version of IR yaml as attachment.
[pipeline-add.zip](https://github.com/kubeflow/pipelines/files/11504160/pipeline-add.zip)
### Labels
/area backend
---
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* How did you deploy Kubeflow Pipelines (KFP)?
Standalone KFP from `master`
### Steps to reproduce
Try to upload a pipeline with `%` + any other special character in the pipeline name
### Expected result
Creating a new pipeline and a new pipeline version
### Materials and Reference
/CC @jlyaoyuli
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a π.
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"It is actually possible for Markdown and HTML to define in the `component.yaml` the output in the following way:\r\n\r\n```yaml\r\noutputs:\r\n- {name: output-html, type: HTML, description: 'data analysis in html'}\r\n- {name: output-markdown, type: Markdown, description: 'data analysis in markdown'}\r\n```\r\n\r\nThen use them as `outputPath` to write in it. But for the `ClassificationMetrics` or `Metrics` I do not see how to it.\r\n\r\nThe documentation about the Visualisation I find is in [v2-sdk-use-sdk-visualization-apis](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/output-viewer/#v2-sdk-use-sdk-visualization-apis)\r\n\r\nIt should probably be updated with example with non-python based 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."
] | 2023-05-17T14:27:31 | 2023-08-22T07:41:56 | null | CONTRIBUTOR | null | ### Feature Area
/area components
### What feature would you like to see?
I do not know if it is already possible. I read the [Visualize Results in the Pipelines UI](https://www.kubeflow.org/docs/components/pipelines/v1/sdk/output-viewer/), but I do not see how to export visualization from components.
I would like to be able to have Output of type Markdown, ClassificationMetrics, HTML etc... from component defined as file (Non-Python).
### What is the use case or pain point?
Log metrics and provide visualization when using non-python based components.
### Is there a workaround currently?
If I want to have a markdown I need to add an OutputPath to my `component.yaml`, write the mardown I want to this one, and then sending this file to a python based component and write it in its `Output[Markdown]`.
---
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} | [] | 2023-05-17T06:09:50 | 2023-05-23T19:05:38 | 2023-05-23T19:05:37 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Use https://github.com/kubeflow/manifests to deploy kubeflow.
```bash
git clone https://github.com/kubeflow/manifests.git
cd manifests
git checkout v1.7-branch
```
Modify `manifests/apps/pipeline/upstream/base/pipeline/kustomization.yaml`:
```
...
images:
- name: gcr.io/ml-pipeline/api-server
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/persistenceagent
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/scheduledworkflow
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/frontend
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/viewer-crd-controller
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/visualization-server
newTag: 2.0.0-rc.1
```
Then, use one command to apply these manifest:
```bash
while ! kustomize build example | awk '!/well-defined/' | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
```
* KFP version:
version: 2.0.0-rc.1
### Steps to reproduce
1. Create sample pipeline (v2)
```python
from kfp import dsl
@dsl.component
def square(x: float) -> float:
return x ** 2
@dsl.component
def add(x: float, y: float) -> float:
return x + y
@dsl.component
def square_root(x: float) -> float:
return x ** .5
@dsl.pipeline
def pythagorean(a: float, b: float) -> float:
a_sq_task = square(x=a)
b_sq_task = square(x=b)
sum_task = add(x=a_sq_task.output, y=b_sq_task.output)
return square_root(x=sum_task.output).output
compiler.Compiler().compile(pythagorean, 'pipeline.yaml')
```
2. Click "Pipelines" menu, then click "Upload pipeline"
3. Click "Create experiment" button and input experiment name
4. Configure "Start a run" with pipeline input parameters and click "Start"
Use browser developer tools and find out UI call "/pipeline/apis/v2beta1/runs" and submit below JSON payload:
```json
{
"description": "",
"display_name": "Run of pythagorean (3d5ba)",
"experiment_id": "e86993e5-7338-49f0-a129-3f1aa1676a06",
"pipeline_version_reference": {
"pipeline_id": "165d1ce7-ed91-4def-ac7a-14aabbdb0704",
"pipeline_version_id": "77477a12-0a2e-4f1b-860c-2089216ea83f"
},
"runtime_config": {
"parameters": {
"a": 3,
"b": 4
}
},
"service_account": ""
}
```
5. After run is triggered and execution fail.
Checking the logs the error is "VaueError: Function `square` returned value of type<class int>; want type <class float> ...
### Expected result
UI should detect input parameters type and convert into right JSON number representation.
Pipeline API server should do the right type casting base on IR Yaml.
### Reference
The execution log of 1st task (square):
```
time="2023-05-17T05:52:35.570Z" level=info msg="capturing logs" argo=true
time="2023-05-17T05:52:35.602Z" level=info msg="capturing logs" argo=true
I0517 05:52:35.639643 34 launcher_v2.go:84] input ComponentSpec:{
"inputDefinitions": {
"parameters": {
"x": {
"parameterType": "NUMBER_DOUBLE"
}
}
},
"outputDefinitions": {
"parameters": {
"Output": {
"parameterType": "NUMBER_DOUBLE"
}
}
},
"executorLabel": "exec-square-2"
}
I0517 05:52:35.640545 34 cache.go:139] Cannot detect ml-pipeline in the same namespace, default to ml-pipeline.kubeflow:8887 as KFP endpoint.
I0517 05:52:35.640563 34 cache.go:116] Connecting to cache endpoint ml-pipeline.kubeflow:8887
I0517 05:52:35.777714 34 object_store.go:306] Cannot detect minio-service in the same namespace, default to minio-service.kubeflow:9000 as MinIO endpoint.
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead:
https://pip.pypa.io/warnings/venv
[KFP Executor 2023-05-17 05:53:02,601 INFO]: Looking for component `square` in --component_module_path `/tmp/tmp.NS1HEzRMO2/ephemeral_component.py`
[KFP Executor 2023-05-17 05:53:02,602 INFO]: Loading KFP component "square" from /tmp/tmp.NS1HEzRMO2/ephemeral_component.py (directory "/tmp/tmp.NS1HEzRMO2" and module name "ephemeral_component")
[KFP Executor 2023-05-17 05:53:02,603 INFO]: Got executor_input:
{
"inputs": {
"parameterValues": {
"x": 4
}
},
"outputs": {
"parameters": {
"Output": {
"outputFile": "/tmp/kfp/outputs/Output"
}
},
"outputFile": "/tmp/kfp_outputs/output_metadata.json"
}
}
Traceback (most recent call last):
File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/site-packages/kfp/components/executor_main.py", line 105, in <module>
executor_main()
File "/usr/local/lib/python3.7/site-packages/kfp/components/executor_main.py", line 101, in executor_main
executor.execute()
File "/usr/local/lib/python3.7/site-packages/kfp/components/executor.py", line 341, in execute
self._write_executor_output(result)
File "/usr/local/lib/python3.7/site-packages/kfp/components/executor.py", line 258, in _write_executor_output
func_output)
File "/usr/local/lib/python3.7/site-packages/kfp/components/executor.py", line 228, in _handle_single_return_value
f'Function `{self._func.__name__}` returned value of type {type(return_value)}; want type {origin_type}'
ValueError: Function `square` returned value of type <class 'int'>; want type <class 'float'>
F0517 05:53:02.719437 34 main.go:49] failed to execute component: exit status 1
Error: exit status 1
Error: exit status 1
```
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"Hello @erhwenkuo, thanks for reporting this issue! It should be fixed in our next release.",
"In addition, we would like to provide 2 alternative ways to create run using specific pipeline and experiment.\r\n1. Similar to what you observed, you can create experiment from experiment list and then create run in experiment UI and then choose the pipeline you want. The parameters will appear.\r\n2. Create a experiment first, and then click **Create run** in pipeline UI, you can choose the experiment you created before. The parameters will appear as well.\r\n\r\nPlease feel free let us know if you have any question!"
] | 2023-05-17T05:38:35 | 2023-05-19T19:02:49 | 2023-05-19T19:02:49 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Use https://github.com/kubeflow/manifests to deploy kubeflow.
```bash
git clone https://github.com/kubeflow/manifests.git
cd manifests
git checkout v1.7-branch
```
Modify `manifests/apps/pipeline/upstream/base/pipeline/kustomization.yaml`:
```
...
images:
- name: gcr.io/ml-pipeline/api-server
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/persistenceagent
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/scheduledworkflow
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/frontend
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/viewer-crd-controller
newTag: 2.0.0-rc.1
- name: gcr.io/ml-pipeline/visualization-server
newTag: 2.0.0-rc.1
```
Then, use one command to apply these manifest:
```bash
while ! kustomize build example | awk '!/well-defined/' | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
```
* KFP version:
version: 2.0.0-rc.1
### Steps to reproduce
1. Create sample pipeline (v2)
```python
from kfp import dsl
@dsl.component
def square(x: float) -> float:
return x ** 2
@dsl.component
def add(x: float, y: float) -> float:
return x + y
@dsl.component
def square_root(x: float) -> float:
return x ** .5
@dsl.pipeline
def pythagorean(a: float, b: float) -> float:
a_sq_task = square(x=a)
b_sq_task = square(x=b)
sum_task = add(x=a_sq_task.output, y=b_sq_task.output)
return square_root(x=sum_task.output).output
compiler.Compiler().compile(pythagorean, 'pipeline.yaml')
```
2. Click "Pipelines" menu, then click "Upload pipeline"
3. Click "Create experiment" button and input experiment name
4. Configure "Start a run" (issues is here!!)
### Expected result
"Start a run" UI should show pipeline input parameters according to pipeline IR Yaml. However, it is showing "This pipeline has no parameters".
But, if we click "Skip this step" and click "Create run" run on Experiment UI
The pipeline input parameters will appear on the UI (as show below)
<!-- Don't delete message below to encourage users to support your issue! -->
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"Found the fix here https://github.com/kubeflow/pipelines/pull/9437. Will wait for the next feature release :) "
] | 2023-05-15T20:18:21 | 2023-05-17T09:47:29 | 2023-05-17T09:47:29 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
It would be useful to relax the pyyaml requirement in the stable version of the kfp (1.8.21), similar to the pre-release version below
https://github.com/kubeflow/pipelines/blob/master/sdk/python/requirements.in#LL22C15-L22C15
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
The current implementation of kfp is more restrictive, and conflicts with dbt-core (https://github.com/dbt-labs/dbt-core/blob/main/core/setup.py#L72)
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
One could use the pre-release version, which is more flexible in terms of PyYAML
---
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https://api.github.com/repos/kubeflow/pipelines/issues/9418 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9418/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9418/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9418/events | https://github.com/kubeflow/pipelines/issues/9418 | 1,710,267,160 | I_kwDOB-71UM5l8JsY | 9,418 | [feature] support PipelineParameterChannel for SecretAsEnv | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-15T14:52:37 | 2023-08-17T07:41:42 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
When using `kubernetes.secret.use_secret_as_env` the name of the secret need to be hardcoded in the pipeline, and therefore cannot be provided by a PipelineParameterChannel.
### What is the use case or pain point?
Providing ease for adapting pipeline between namespace, which can have different name for secrets, while having the same content (S3 credentials, etc..)
### Is there a workaround currently?
Hardcoding the secret.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9406 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9406/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9406/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9406/events | https://github.com/kubeflow/pipelines/issues/9406 | 1,708,040,016 | I_kwDOB-71UM5lzp9Q | 9,406 | [sdk] client wait_for_run_completion function has an extra wait iteration | {
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* KFP version:
Version 2.0.0-beta.2
* KFP SDK version:
KFP SDK v2.0.0b16
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
The state of the pipeline is updated and still have to sleep for 1 iteration even though the state is completed.
https://github.com/kubeflow/pipelines/blob/96947e6fb971178e5a67b407b0d0ecb7192db9d4/sdk/python/kfp/client/client.py#L1387-L1393
### Expected result
The wait_for_run_completion should check the state right after retrieving it instead of sleep and validate in the next iteration.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9389 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9389/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9389/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9389/events | https://github.com/kubeflow/pipelines/issues/9389 | 1,705,802,589 | I_kwDOB-71UM5lrHtd | 9,389 | [sdk] Which versions work with which one | {
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"Sorry about the confusion. We have a tracking bug to document the compatibility matrix: https://github.com/kubeflow/pipelines/issues/8728\r\n\r\nShort answer:\r\n- `Kubeflow Pipelines 2.0.0-alpha.7` backend doesn't support the feature added via `kfp-kubernetes` or `platform_config` yet. But you can get k8s specific feature (volume, secrets, etc) working using the v1 SDK syntax. `kfp==1.8.*` should also work with this backend.\r\n- `Kubeflow Pipelines 2.0.0-Beta.2` supports `kfp-kubernetes` or `platform_config` features. For this backend, you need SDK `kfp>=2.0.0b17`.\r\n\r\n",
"Thanks you closing as I got the anwser expected "
] | 2023-05-11T13:09:37 | 2023-05-11T17:55:24 | 2023-05-11T17:55:23 | CONTRIBUTOR | null | We have some issues figuring out which version of the `sdk` goes with which `kfp-server-api`, and the extra `[kubernetes]` in kubeflow.
In Kubeflow/manifest, in the 1.7 release they are using `Kubeflow Pipelines 2.0.0-alpha.7` however they do not specify the sdk or kfp-server-api.
Based on some conversation in the kubeflow slack, a working setup is `kfp==2.0.0b12` and `kfp-server-api==2.0.0a6`. But this make impossible to use the kubernetes extra since
> kfp-kubernetes 0.0.1 requires kfp>=2.0.0-beta.13, but you have kfp 2.0.0b12 which is incompatible.
Moreover we can try to force using kfp-kubernetes, but got errors when trying to use secrets for instance.
> AttributeError: 'PipelineTask' object has no attribute 'platform_config'
I know @connor-mccarthy @chensun have helped people on the slack for related issues, and we would be grateful to have some clarification.
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https://api.github.com/repos/kubeflow/pipelines/issues/9386 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9386/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9386/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9386/events | https://github.com/kubeflow/pipelines/issues/9386 | 1,705,117,147 | I_kwDOB-71UM5logXb | 9,386 | [sdk] ExitHandler doesn't work when a input of ParallelFor is a list. | {
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"@rayuron can you expand how you modify the code to make it pass?\r\n\r\n> Materials and Reference\r\n> The compilation is successful, but `job.submit()` does not working with the following error.\r\n> *Converting the input of ParallelFor from a list to JSON as following makes it successful. `s = \"[{'a':1,'b':2},{'a':2,'b':2}]\"`\r\n\r\nCan you provide how you convert it, is it by `json.loads(s)`, for me I get the following error,\r\n` google.api_core.exceptions.InvalidArgument: 400 Component input parameter 'loop-item-param-1-subvar-a' is not defined.`"
] | 2023-05-11T05:55:33 | 2023-08-06T14:33:10 | null | NONE | null | ### Environment
* KFP version:
Vertex AI Pipelines
* KFP SDK version:
1.8.21
* All dependencies version:
kfp 1.8.21
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
Run the following pipeline on Vertex AI Pipelines
```python
from kfp.v2 import dsl
from kfp.v2 import compiler
from kfp.v2.dsl import ParallelFor
import google.cloud.aiplatform as aip
@dsl.component()
def exit_op(msg: str) -> None:
print(msg)
@dsl.component()
def print_op(msg: str) -> None:
print(msg)
@dsl.pipeline(name="exithandler-with-parallelfor")
def pipeline():
with dsl.ExitHandler(exit_op("exit")):
with ParallelFor([{'a':1,'b':2},{'a':2,'b':2}]) as item:
print_op(item.a)
print_op(item.b)
compiler.Compiler().compile(
pipeline_func=pipeline,
package_path="exithandler-with-parallelfor.json",
)
job = aip.PipelineJob(
project="project",
display_name="exit_pipeline",
template_path="exithandler-with-parallelfor.json",
pipeline_root="gs://bucket/pipeline_root",
enable_caching=False,
)
job.submit()
```
### Expected result
Expect no error
### Materials and Reference
The compilation is successful, but `job.submit()` does not working with the following error.
*Converting the input of ParallelFor from a list to JSON as following makes it successful.
`"[{'a':1,'b':2},{'a':2,'b':2}]"`
```python
---------------------------------------------------------------------------
_InactiveRpcError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/google/api_core/grpc_helpers.py in error_remapped_callable(*args, **kwargs)
56 try:
---> 57 return callable_(*args, **kwargs)
58 except grpc.RpcError as exc:
/opt/conda/lib/python3.7/site-packages/grpc/_channel.py in __call__(self, request, timeout, metadata, credentials, wait_for_ready, compression)
945 wait_for_ready, compression)
--> 946 return _end_unary_response_blocking(state, call, False, None)
947
/opt/conda/lib/python3.7/site-packages/grpc/_channel.py in _end_unary_response_blocking(state, call, with_call, deadline)
848 else:
--> 849 raise _InactiveRpcError(state)
850
_InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INVALID_ARGUMENT
details = "Component input parameter 'loop-item-param-2' is not defined."
debug_error_string = "UNKNOWN:Error received from peer ipv4:172.253.117.95:443 {created_time:"2023-05-11T05:31:01.908472896+00:00", grpc_status:3, grpc_message:"Component input parameter \'loop-item-param-2\' is not defined."}"
>
The above exception was the direct cause of the following exception:
InvalidArgument Traceback (most recent call last)
/tmp/ipykernel_11658/3314006309.py in <module>
26 )
27
---> 28 job.submit()
/opt/conda/lib/python3.7/site-packages/google/cloud/aiplatform/pipeline_jobs.py in submit(self, service_account, network, create_request_timeout, experiment)
386 pipeline_job=self._gca_resource,
387 pipeline_job_id=self.job_id,
--> 388 timeout=create_request_timeout,
389 )
390
/opt/conda/lib/python3.7/site-packages/google/cloud/aiplatform_v1/services/pipeline_service/client.py in create_pipeline_job(self, request, parent, pipeline_job, pipeline_job_id, retry, timeout, metadata)
1324 retry=retry,
1325 timeout=timeout,
-> 1326 metadata=metadata,
1327 )
1328
/opt/conda/lib/python3.7/site-packages/google/api_core/gapic_v1/method.py in __call__(self, timeout, retry, *args, **kwargs)
152 kwargs["metadata"] = metadata
153
--> 154 return wrapped_func(*args, **kwargs)
155
156
/opt/conda/lib/python3.7/site-packages/google/api_core/grpc_helpers.py in error_remapped_callable(*args, **kwargs)
57 return callable_(*args, **kwargs)
58 except grpc.RpcError as exc:
---> 59 raise exceptions.from_grpc_error(exc) from exc
60
61 return error_remapped_callable
InvalidArgument: 400 Component input parameter 'loop-item-param-2' is not defined.
```
---
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"closed by #9397 "
] | 2023-05-09T19:54:03 | 2023-05-15T19:53:00 | 2023-05-15T19:53:00 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
standalone
* 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. -->
2.0.0-beta.2
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
2.0.0-beta.17
### 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.
-->
Run a pipeline with createpvc or deletepvc. Click on the step and there is no information for task detail or logs.
### Expected result
<!-- What should the correct behavior be? -->
Should be able to see these info.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"I had the same issue but solved it by using `import json` and then\r\n\r\n```\r\nwith dsl.ParallelFor(\r\n items=[json.dump({\"a\": 1, \"b\": 10}), json.dump({\"a\": 2, \"b\": 20})], parallelism=1\r\n ) as item:\r\n task = add(val_a=item.a, val_b=item.b)\r\n```\r\n\r\nHope it helps,",
"@msmendoza do you mean `json.dumps`? Otherwise there is an error thrown, `TypeError: dump() missing 1 required positional argument: 'fp'`",
"hey @connor-mccarthy! any update on this?",
"No update at this moment. I can prioritize a review of a fix PR from any contributor."
] | 2023-05-09T17:23:12 | 2023-09-08T18:36:10 | null | NONE | null | ### Environment
* KFP SDK version:
kfp==2.0.0b16
* All dependencies version:
kfp==2.0.0b16
kfp-pipeline-spec==0.2.2
kfp-server-api==2.0.0b1
### Steps to reproduce
When running the code snippet below the following error is raised:
`kfp.components.types.type_utils.InconsistentTypeException: Incompatible argument passed to the input 'val_a' of component 'add': Argument type 'STRING' is incompatible with the input type 'NUMBER_INTEGER'`
```python
@dsl.component()
def add(val_a: int, val_b: int) -> int:
return val_a + val_b
@dsl.pipeline()
def model_training_pipeline() -> None:
with dsl.ParallelFor(
items=[{"a": 1, "b": 10}, {"a": 2, "b": 20}], parallelism=1
) as item:
task = add(val_a=item.a, val_b=item.b)
compiler.Compiler().compile(
pipeline_func=model_training_pipeline, package_path="/app/pipeline.yaml"
)
```
### Expected result
According to the [ParallelFor documentation](https://kubeflow-pipelines.readthedocs.io/en/2.0.0b16/source/dsl.html#kfp.dsl.ParallelFor), the code sample above should compile without errors. The `add` component should receive the values of the dictionaries as integer arguments.
### Materials and Reference
The code snippet below is a modification of the code snippet above, changing the `add` component to accept string arguments.
```python
@dsl.component()
def add(val_a: str, val_b: str) -> int:
return int(val_a) + int(val_b)
@dsl.pipeline()
def model_training_pipeline() -> None:
with dsl.ParallelFor(
items=[{"a": 1, "b": 10}, {"a": 2, "b": 20}], parallelism=1
) as item:
task = add(val_a=item.a, val_b=item.b)
compiler.Compiler().compile(
pipeline_func=model_training_pipeline, package_path="/app/pipeline.yaml"
)
```
The pipeline compiles without errors with this modification, however it fails to run in Google Vertex Pipelines. The `add` component doesn't even run and throws the following error in the UI:
`Failed to evaluate the expression with error: INVALID_ARGUMENT: Failed to parseJson from string.; Failed to evaluate the parameter_expression_selector.`
As the component's code is not even executed, it seems that the problem occurs when executing the DAG.
Here is the content of the `pipeline.yaml` that was compiled.
```yaml
# PIPELINE DEFINITION
# Name: model-training-pipeline
components:
comp-add:
executorLabel: exec-add
inputDefinitions:
parameters:
val_a:
parameterType: STRING
val_b:
parameterType: STRING
outputDefinitions:
parameters:
Output:
parameterType: NUMBER_INTEGER
comp-for-loop-2:
dag:
tasks:
add:
cachingOptions:
enableCache: true
componentRef:
name: comp-add
inputs:
parameters:
val_a:
componentInputParameter: pipelinechannel--loop-item-param-1
parameterExpressionSelector: parseJson(string_value)["a"]
val_b:
componentInputParameter: pipelinechannel--loop-item-param-1
parameterExpressionSelector: parseJson(string_value)["b"]
taskInfo:
name: add
inputDefinitions:
parameters:
pipelinechannel--loop-item-param-1:
parameterType: STRUCT
deploymentSpec:
executors:
exec-add:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- add
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.0.0-beta.16'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
python3 -m kfp.components.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef add(val_a: str, val_b: str) -> int:\n return int(val_a) +\
\ int(val_b)\n\n"
image: python:3.7
pipelineInfo:
name: model-training-pipeline
root:
dag:
tasks:
for-loop-2:
componentRef:
name: comp-for-loop-2
iteratorPolicy:
parallelismLimit: 1
parameterIterator:
itemInput: pipelinechannel--loop-item-param-1
items:
raw: '[{"a": 1, "b": 10}, {"a": 2, "b": 20}]'
taskInfo:
name: for-loop-2
schemaVersion: 2.1.0
sdkVersion: kfp-2.0.0-beta.16
```
---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-05-08T14:50:06 | 2023-08-09T07:41:30 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Onto an AWS-backed K8s cluster.
* KFP version:
1.8.1
### Steps to reproduce
Create a pipeline with more than 1000 edges or nodes.
### Expected result
There should be console indicating that graphs beyond a certain size are not supported.
### Materials and Reference
[This conditional](https://github.com/kubeflow/pipelines/blob/master/frontend/src/lib/StaticGraphParser.ts#L292-L295) prevent graphs that exceed a certain size from rendering, but there's no error message in the console indicating what the underlying problem is. This makes debugging difficult for end users and us as Kubeflow platform maintainers.
<!-- Don't delete message below to encourage users to support your issue! -->
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.",
"This is fixed."
] | 2023-05-08T10:11:33 | 2023-08-10T15:33:10 | 2023-08-10T15:33:09 | NONE | null | GetVertexModelOp in google-cloud-pipeline-components==2.0.0b3 references image with older version of google-cloud-pipeline-components and kfp v1 (latest):
https://github.com/kubeflow/pipelines/blob/google-cloud-pipeline-components-2.0.0b3/components/google-cloud/google_cloud_pipeline_components/experimental/model/get_model/component.py#L49
### Environment
* KFP version:
kfp==2.0.0b16
* All dependencies version:
kfp==2.0.0b16
kfp-pipeline-spec==0.2.2
kfp-server-api==2.0.0b1
google-cloud-pipeline-components==2.0.0b3
### Steps to reproduce
```python
from kfp import dsl
from google_cloud_pipeline_components.experimental.model import GetVertexModelOp
@dsl.pipeline(name="pipeline")
def a_pipeline():
get_model_op = GetVertexModelOp(model_name="my-resource-name")
```
### Expected result
Pipeline runs and GetVertexModelOp returns a reference to model.
### Actual result
From Vertex pipelines logs:
"/opt/conda/bin/python3: Error while finding module specification for 'google_cloud_pipeline_components.container.experimental.model.get_model.get_model' (ModuleNotFoundError: No module named 'google_cloud_pipeline_components.container.experimental.model"
The problem seems to be that gcr.io/ml-pipeline/google-cloud-pipeline-components:latest has kfp v1 where the directory structure is different than in v2 which google-cloud-pipeline-components==2.0.0b3 uses. | {
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"On kfp sdk 2.0.0b15, when you initialize the client, there should be a warning message says `This client only works with Kubeflow Pipeline v2.0.0-beta.1`. Recommend to upgrade both deployment and sdk to our latest release 2.0.0-rc.1 if you want to try out v2 features.",
"I have the same issue even with `sdk` version `2.0.1`. Another way of running into this error:\r\n\r\n```\r\nIn [9]: import kfp.deprecated\r\n\r\nAttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'\r\n```"
] | 2023-05-04T22:15:21 | 2023-07-26T14:48:00 | null | CONTRIBUTOR | null | ### Environment
* KFP version: `2.0.0-alpha.5`
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: `2.0.0b15`
<!-- 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: N/A
<!-- 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.
-->
The following import fails, and this is currently how many examples are setup in the master branch.
```
import kfp.deprecated.components as components
```
```
...
site-packages/kfp/deprecated/_client.py", line 396, in Client
def get_kfp_healthz(self) -> kfp_server_api.ApiGetHealthzResponse:
AttributeError: module 'kfp_server_api' has no attribute 'ApiGetHealthzResponse'. Did you mean: 'V2beta1GetHealthzResponse'?
```
### Expected result
<!-- What should the correct behavior be? -->
Expect the import `import kfp.deprecated.components as components` to work without errors.
### Materials and Reference
The import as been failing since b13, I would expect this import to work since users will be refericing the master branhc as away to stay up to date with Kubeflow Pipelines.
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/9326 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9326/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9326/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9326/events | https://github.com/kubeflow/pipelines/issues/9326 | 1,696,424,172 | I_kwDOB-71UM5lHWDs | 9,326 | [bug] urllib3 breaks lightweight components in Vertex AI and kubeflow pipelines with 2.0.x | {
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"Fixed in: https://github.com/kubeflow/pipelines/pull/9323",
"This issue is resolved in the latest v1 and v2 releases: [`kfp==1.8.21`](https://pypi.org/project/kfp/1.8.21/) and [`kfp==2.0.0b16`](https://pypi.org/project/kfp/2.0.0b16/).\r\n\r\n**Preferred solution**\r\nPip install and recompile your pipelines with `kfp==2.0.0-beta.16` or `kfp==1.8.21` to observe the fix. Recompilation is necessary because the KFP SDK compiler injects the KFP SDK version used for compilation into the pipeline YAML/JSON. This is the same version that will be installed and used at component runtime.\r\n\r\n**Alternative solutions**\r\nAlternatively, if you don't have the Python available for recompilation, you can search and replace\r\n\r\n`kfp==2.0.0-beta.14` (or whatever your current version is)\r\n\r\nwith\r\n\r\n`kfp==2.0.0-beta.16` in your compiled component/pipeline YAML/JSON.\r\n\r\nIf for some reason you cannot use `kfp==2.0.0-beta.16` or `kfp==1.8.21`, you can manually `pip install urllib3<2.0` in your local environment (the one used for authoring/compilation) and in your component at runtime:\r\n\r\n```python\r\n@dsl.component(packages_to_install=['urllib3<2.0'])\r\ndef identity(string: str) -> str:\r\n return string\r\n```",
"**Root Cause**\r\nPython Software Foundation `requests` released an [arguably](https://github.com/psf/requests/issues/6432) breaking change in a minor version: https://github.com/psf/requests/releases/tag/v2.30.0.\r\n\r\n**Impact**\r\n[Lightweight Python Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/lightweight-python-components/) compiled by KFP SDK versions earlier than [`kfp==1.8.21`](https://pypi.org/project/kfp/1.8.21/) and [`kfp==2.0.0b16`](https://pypi.org/project/kfp/2.0.0b16/) will fail with error:\r\n\r\n```\r\nImportError: cannot import name 'appengine' from 'requests.packages.urllib3.contrib'\r\n```\r\n\r\nThis is because `requests` is pip installed at runtime by several KFP dependencies: [requirements.in](https://github.com/kubeflow/pipelines/blob/3c9dbee2250a97353d25e9e0a2cb09ba1f7d893e/sdk/python/requirements.txt#L74).\r\n\r\nOther component types (Containerized Python Components and Container Components) are unaffected. We recommend that production workloads use these component types (namely pre-built container images).\r\n\r\n**Long Term Improvements**\r\nWe are assessing how we can reduce this risk in the future."
] | 2023-05-04T17:38:30 | 2023-05-04T23:30:14 | 2023-05-04T22:13:39 | MEMBER | null | ### Environment
kfp[all]==2.0.0b15 # Although the version seems to impact all kfp versions see issue [930](https://github.com/kubeflow/pipelines/issues/9300)
* How do you deploy Kubeflow Pipelines (KFP)?
Vertex AI Pipelines
* KFP version:
Vertex AI Pipelines
* KFP SDK version:
kfp[all]==2.0.0b15
### Steps to reproduce
I have created a repo that with the only dependency being: `kfp[all]==2.0.0b15` and run this component:
```
# %%
from kfp import dsl
@dsl.component
def print_component(str_exmpl: str) -> None:
print(str_exmpl)
@dsl.pipeline
def pipeline():
print_task = print_component(str_exmpl="Hello World!")
if __name__ == "__main__":
import kfp
print(kfp.__version__)
kfp.compiler.Compiler().compile(pipeline, "pipeline.yaml")
```
This fails:
```
Traceback (most recent call last):
File "/home/davidnet/.pyenv/versions/issue-kubeflow/lib/python3.11/site-packages/requests_toolbelt/_compat.py", line 48, in <module>
from requests.packages.urllib3.contrib import appengine as gaecontrib
ImportError: cannot import name 'appengine' from 'requests.packages.urllib3.contrib' (/home/davidnet/.pyenv/versions/issue-kubeflow/lib/python3.11/site-packages/urllib3/contrib/__init__.py)
```
This is tracked in the repo: [branch issue/compile](https://github.com/Davidnet/kubeflow_request_issue/tree/issue/compile)
Now, pinning the urllib dependency:
```
$ cat requirements.txt
kfp[all]==2.0.0b15
urllib3==1.26.15
```
Makes the pipeline compile. but then when running on vertex it fails :
```
"message": "ImportError: cannot import name 'appengine' from 'urllib3.contrib' (/usr/local/lib/python3.7/site-packages/urllib3/contrib/__init__.py)",
```
With the same component.
Now technically all my pipelines are failing and the only solution we think with our team is to pin urllib in each lightweight component.
This is tracked in this repo (both logs, and the compiled yaml can be found) in [here](https://github.com/Davidnet/kubeflow_request_issue/tree/issue/running) branch issue/running
### Expected result
Pipeline run succesfully
### Materials and reference
Resources: https://github.com/Davidnet/kubeflow_request_issue
### Labels
/area backend
/area sdk
/area components
---
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"I got the same problem and for me the workaround for now has been to declare all the custom training job ops outside of the pipeline definition, as shown here: https://cloud.google.com/vertex-ai/docs/pipelines/request-gcp-machine-resources\r\n\r\nNot the best solution (would prefer to declare them inside the pipeline) but at least it allows to define as many as you want.",
"Same issue with `google-cloud-pipeline-components 2.3.0`"
] | 2023-05-04T12:44:55 | 2023-08-30T08:49:00 | null | NONE | null | ### Environment
* KFP SDK version: 2.0.0-beta.15
<!-- 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 dependency versions:
* kfp 2.0.0b15
* kfp-pipeline-spec 0.2.2
* kfp-server-api 2.0.0b1
* google_cloud_pipeline_components 2.0.0b2
### Steps to reproduce
Here is sample code to reproduce the problem:
```python
import kfp
from kfp import dsl
from kfp.dsl import component
from google_cloud_pipeline_components.v1.custom_job import utils
@component
def some_other_component():
pass
# Create a Python component
@component
def my_python_component():
pass
# Define a pipeline that runs the custom training job
@dsl.pipeline(
name="resource-spec-request",
description="A simple pipeline that requests a Google Cloud machine resource",
pipeline_root='PIPELINE_ROOT',
)
def pipeline():
some_other_component_task = some_other_component()
# Convert the above component into a custom training job
custom_training_job = utils.create_custom_training_job_from_component(
my_python_component
)
training_job_task = custom_training_job(
project='<PROJECT>',
location='<LOCATION>'
).set_display_name('training-job-task')
```
The following error occurs:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/tmp/ipykernel_1198/1863020616.py in <module>
18 name="resource-spec-request",
19 description="A simple pipeline that requests a Google Cloud machine resource",
---> 20 pipeline_root='PIPELINE_ROOT',
21 )
22 def pipeline():
/opt/conda/lib/python3.7/site-packages/kfp/components/pipeline_context.py in pipeline(func, name, description, pipeline_root, display_name)
67 name=name,
68 description=description,
---> 69 display_name=display_name,
70 )
71
/opt/conda/lib/python3.7/site-packages/kfp/components/component_factory.py in create_graph_component_from_func(func, name, description, display_name)
617 component_spec=component_spec,
618 pipeline_func=func,
--> 619 display_name=display_name,
620 )
621
/opt/conda/lib/python3.7/site-packages/kfp/components/graph_component.py in __init__(self, component_spec, pipeline_func, display_name)
56 with pipeline_context.Pipeline(
57 self.component_spec.name) as dsl_pipeline:
---> 58 pipeline_outputs = pipeline_func(*args_list)
59
60 if not dsl_pipeline.tasks:
/tmp/ipykernel_1198/1863020616.py in pipeline()
24 # Convert the above component into a custom training job
25 custom_training_job = utils.create_custom_training_job_from_component(
---> 26 my_python_component
27 )
28 training_job_task = custom_training_job(
/opt/conda/lib/python3.7/site-packages/google_cloud_pipeline_components/v1/custom_job/utils.py in create_custom_training_job_from_component(component_spec, display_name, replica_count, machine_type, accelerator_type, accelerator_count, boot_disk_type, boot_disk_size_gb, timeout, restart_job_on_worker_restart, service_account, network, encryption_spec_key_name, tensorboard, enable_web_access, reserved_ip_ranges, nfs_mounts, base_output_directory, labels)
201
202 custom_training_job_dict = json_format.MessageToDict(
--> 203 component.custom_training_job.pipeline_spec
204 )
205
/opt/conda/lib/python3.7/site-packages/kfp/components/base_component.py in pipeline_spec(self)
107 """Returns the pipeline spec of the component."""
108 with BlockPipelineTaskRegistration():
--> 109 return self.component_spec.to_pipeline_spec()
110
111 @property
/opt/conda/lib/python3.7/site-packages/kfp/components/structures.py in to_pipeline_spec(self)
926 is_artifact_list=input_spec.is_artifact_list)
927
--> 928 task = pipeline_task.PipelineTask(self, args_dict)
929
930 # instead of constructing a pipeline with pipeline_context.Pipeline,
/opt/conda/lib/python3.7/site-packages/kfp/components/pipeline_task.py in __init__(self, component_spec, args)
135 is_artifact_list=output_spec.is_artifact_list,
136 ) for output_name, output_spec in (
--> 137 component_spec.outputs or {}).items()
138 }
139
/opt/conda/lib/python3.7/site-packages/kfp/components/pipeline_task.py in <dictcomp>(.0)
134 task_name=self._task_spec.name,
135 is_artifact_list=output_spec.is_artifact_list,
--> 136 ) for output_name, output_spec in (
137 component_spec.outputs or {}).items()
138 }
/opt/conda/lib/python3.7/site-packages/kfp/components/pipeline_channel.py in create_pipeline_channel(name, channel_type, task_name, value, is_artifact_list)
290 channel_type=channel_type,
291 task_name=task_name,
--> 292 value=value,
293 )
294 else:
/opt/conda/lib/python3.7/site-packages/kfp/components/pipeline_channel.py in __init__(self, name, channel_type, task_name, value)
219 name=name,
220 channel_type=channel_type,
--> 221 task_name=task_name,
222 )
223
/opt/conda/lib/python3.7/site-packages/kfp/components/pipeline_channel.py in __init__(self, name, channel_type, task_name)
106 print('TASKS END')
107 self.task = pipeline_context.Pipeline.get_default_pipeline().tasks[
--> 108 self.task_name]
109 else:
110 self.task = None
KeyError: 'custom-training-job'
```
The error does not happen when creating the some_other_component task after calling `create_custom_training_job_from_component` as follows:
```python
def pipeline():
# Convert the above component into a custom training job
custom_training_job = utils.create_custom_training_job_from_component(
my_python_component
)
some_other_component_task = some_other_component()
training_job_task = custom_training_job(
project='<PROJECT>',
location='<LOCATION>'
).set_display_name('training-job-task')
```
### Expected result
I would not expect the order of pipeline task definitions to have an influence on the result, especially when they are independent. If this behaviour cannot be made independent of the order, at least there should be a better error message presented to the user and this restriction should be documented.
---
Impacted by this bug? Give it a π. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9319 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9319/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9319/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9319/events | https://github.com/kubeflow/pipelines/issues/9319 | 1,695,175,302 | I_kwDOB-71UM5lClKG | 9,319 | Add a security policy | {
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] | null | [] | 2023-05-04T03:12:35 | 2023-05-05T23:29:55 | 2023-05-05T23:29:55 | NONE | null | Hello π
I run a security community that finds and fixes vulnerabilities in OSS. A researcher (@sim4n6) has found a potential issue, which I would be eager to share with you.
Could you add a `SECURITY.md` file with an e-mail address for me to send further details to? GitHub [recommends](https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository) a security policy to ensure issues are responsibly disclosed, and it would help direct researchers in the future.
Looking forward to hearing from you π
(cc @huntr-helper) | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9316 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9316/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9316/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9316/events | https://github.com/kubeflow/pipelines/issues/9316 | 1,695,105,640 | I_kwDOB-71UM5lCUJo | 9,316 | [backend] Untracked changes on master after `git clone` | {
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"I was able to reproduce but not the same error message:\r\n\r\n> git clone git@github.com:kubeflow/pipelines.git\r\n> Cloning into 'pipelines'...\r\n> remote: Enumerating objects: 100585, done.\r\n> remote: Counting objects: 100% (2910/2910), done.\r\n> remote: Compressing objects: 100% (1434/1434), done.\r\n> remote: Total 100585 (delta 1589), reused 2302 (delta 1312), pack-reused 97675\r\n> Receiving objects: 100% (100585/100585), 66.16 MiB | 16.38 MiB/s, done.\r\n> Resolving deltas: 100% (66032/66032), done.\r\n> warning: the following paths have collided (e.g. case-sensitive paths\r\n> on a case-insensitive filesystem) and only one from the same\r\n> colliding group is in the working tree:\r\n> \r\n> 'components/aws/sagemaker/Model/component.yaml'\r\n> 'components/aws/sagemaker/model/component.yaml'\r\n> 'components/aws/sagemaker/Model/README.md'\r\n> 'components/aws/sagemaker/model/README.md'\r\n\r\nculprit: https://github.com/kubeflow/pipelines/pull/9243\r\n@surajkota @ananth102 ",
"Hi @chensun, checking in, we are investigating and working on a fix\r\n",
"Hi, apologies for the inconvenience this has caused. I developed the change on an linux system and did not catch the error. Have created a PR to fix the error.",
"Fix has been merged, can you try with latest branch now?",
"Thanks for fixing! It works for me now."
] | 2023-05-04T01:51:02 | 2023-05-05T19:32:59 | 2023-05-05T19:32:59 | NONE | null | ### Environment
On MacOS.
```
β git clone https://github.com/kubeflow/pipelines.git
Cloning into 'pipelines'...
remote: Enumerating objects: 100317, done.
remote: Counting objects: 100% (2590/2590), done.
remote: Compressing objects: 100% (1191/1191), done.
remote: Total 100317 (delta 1461), reused 2211 (delta 1307), pack-reused 97727
Receiving objects: 100% (100317/100317), 66.21 MiB | 7.33 MiB/s, done.
Resolving deltas: 100% (65899/65899), done.
Checking connectivity... done.
β cd pipelines
β git status
On branch master
Your branch is up-to-date with 'origin/master'.
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git checkout -- <file>..." to discard changes in working directory)
modified: components/aws/sagemaker/Model/README.md
modified: components/aws/sagemaker/Model/component.yaml
no changes added to commit (use "git add" and/or "git commit -a")
```
This is preventing us fro, using the git ref as a base in a `kustomization.yaml` because kustomize will run a `git checkout` in the background and that fails due to untracked files.
### Steps to reproduce
Simply do a `git clone` on a clean location.
### Expected result
There should not be untracked files. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/9300 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/9300/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/9300/comments | https://api.github.com/repos/kubeflow/pipelines/issues/9300/events | https://github.com/kubeflow/pipelines/issues/9300 | 1,694,497,035 | I_kwDOB-71UM5k__kL | 9,300 | [sdk] `kfp<=1.8.20` incompatible with `urllib3>=2.0.0` | {
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"This is related to: [request issue](https://github.com/requests/toolbelt/issues/361) I had to start upgrading to 2.0.0b15",
"Actually nevermind it also happens in the 2.0.x versions see [issue](https://github.com/googleapis/python-aiplatform/issues/2167)",
"This issue is resolved in the latest v1 and v2 releases: [`kfp==1.8.21`](https://pypi.org/project/kfp/1.8.21/) and [`kfp==2.0.0b16`](https://pypi.org/project/kfp/2.0.0b16/).\r\n\r\n**Preferred solution**\r\nPip install and recompile your pipelines with `kfp==2.0.0-beta.16` or `kfp==1.8.21` to observe the fix. Recompilation is necessary because the KFP SDK compiler injects the KFP SDK version used for compilation into the pipeline YAML/JSON. This is the same version that will be installed and used at component runtime.\r\n\r\n**Alternative solutions**\r\nAlternatively, if you don't have the Python available for recompilation, you can search and replace\r\n\r\n`kfp==2.0.0-beta.14` (or whatever your current version is)\r\n\r\nwith\r\n\r\n`kfp==2.0.0-beta.16` in your compiled component/pipeline YAML/JSON.\r\n\r\nIf for some reason you cannot use `kfp==2.0.0-beta.16` or `kfp==1.8.21`, you can manually `pip install urllib3<2.0` in your local environment (the one used for authoring/compilation) and in your component at runtime:\r\n\r\n```python\r\n@dsl.component(packages_to_install=['urllib3<2.0'])\r\ndef identity(string: str) -> str:\r\n return string\r\n```"
] | 2023-05-03T17:27:05 | 2023-05-04T22:13:52 | 2023-05-04T22:13:52 | NONE | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.8.20
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
kfp 1.8.20
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
```
### Steps to reproduce
```bash
$ python3.10 -m venv venv
$ source venv/bin/activate
$ pip install --upgrade pip
$ pip install kfp==1.8.20 urllib3==2.0.0
$ python -c "import kfp"
Traceback (most recent call last):
File "~/kfp-urllib/venv/lib/python3.10/site-packages/requests_toolbelt/_compat.py", line 48, in <module>
from requests.packages.urllib3.contrib import appengine as gaecontrib
ImportError: cannot import name 'appengine' from 'requests.packages.urllib3.contrib' (~/kfp-urllib/venv/lib/python3.10/site-packages/urllib3/contrib/__init__.py)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "~/kfp-urllib/venv/lib/python3.10/site-packages/kfp/__init__.py", line 25, in <module>
from ._client import Client
File "~/kfp-urllib/venv/lib/python3.10/site-packages/kfp/_client.py", line 29, in <module>
from kfp._auth import get_auth_token
File "~/kfp-urllib/venv/lib/python3.10/site-packages/kfp/_auth.py", line 34, in <module>
import requests_toolbelt.adapters.appengine
File "~/kfp-urllib/venv/lib/python3.10/site-packages/requests_toolbelt/__init__.py", line 12, in <module>
from .adapters import SSLAdapter, SourceAddressAdapter
File "~/kfp-urllib/venv/lib/python3.10/site-packages/requests_toolbelt/adapters/__init__.py", line 12, in <module>
from .ssl import SSLAdapter
File "~/kfp-urllib/venv/lib/python3.10/site-packages/requests_toolbelt/adapters/ssl.py", line 16, in <module>
from .._compat import poolmanager
File "~/kfp-urllib/venv/lib/python3.10/site-packages/requests_toolbelt/_compat.py", line 50, in <module>
from urllib3.contrib import appengine as gaecontrib
ImportError: cannot import name 'appengine' from 'urllib3.contrib' (~/kfp-urllib/venv/lib/python3.10/site-packages/urllib3/contrib/__init__.py)
```
### Expected result
A successful import
### Materials and Reference
Remove of that `appengine` from `urllib3==2.0.0`: https://github.com/urllib3/urllib3/releases/tag/2.0.0
---
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