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"/assign @james-jwu ",
"Found a bug, in 1.7.0-rc.2 release, the builtin sample is broken.\r\nI think root cause is that, the builtin samples are built using master version of SDK. Master version of v2 compatible SDK is not usable, because the SDK is in master, but the launcher image is pinned to last released ver... | 2021-05-19T06:12:31 | 2021-08-05T01:53:18 | 2021-08-05T01:53:18 | CONTRIBUTOR | null | {
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"/assign @chensun "
] | 2021-05-19T06:10:03 | 2021-05-31T20:39:04 | 2021-05-31T20:39:04 | CONTRIBUTOR | null | Here's what I currently get when using v2 lwt components in KFP v1. This can be a common mistake when we start to build v2 only pipelines. So I think it's worth making it more readable.
```
Traceback (most recent call last):
File "/tmp/tmp.Kc1muRYxPb", line 716, in <module>
executor_main()
File "/tmp/tmp.Kc1muRYxPb", line 706, in executor_main
executor_input = json.loads(args.executor_input)
File "/usr/local/lib/python3.7/json/__init__.py", line 348, in loads
return _default_decoder.decode(s)
File "/usr/local/lib/python3.7/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/local/lib/python3.7/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
```
I'm testing this
```
import kfp
from kfp import dsl
from kfp.v2.dsl import component, InputArtifact, Artifact
gcs_download_op = kfp.components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/961b17fa6844e1d79e5d3686bb557d830d7b5a95/components/google-cloud/storage/download_blob/component.yaml'
)
@component
def echo_artifact(msg: InputArtifact(Artifact)):
"""Print an artifact."""
with open(msg.path) as f:
print(f.read())
@dsl.pipeline(
name='Exit Handler',
description=
'Downloads a message and prints it. The exit handler will run after the pipeline finishes (successfully or not).'
)
def pipeline_exit_handler(url='gs://ml-pipeline/shakespeare1.txt'):
"""A sample pipeline showing exit handler."""
exit_task = echo_artifact('exit!')
with dsl.ExitHandler(exit_task):
download_task = gcs_download_op(url)
echo_task = echo_artifact(download_task.output)
``` | {
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"/assign",
"Hi @difince, if you are looking for issues to contribute, this would be my recommendation. (It can be several PRs too)\r\n\r\nFeel free to reject based on your own interests.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further ... | 2021-05-19T06:08:05 | 2022-03-03T04:05:15 | 2022-03-03T04:05:15 | CONTRIBUTOR | null | context: https://github.com/kubeflow/pipelines/pull/4166
remaining occurrences: https://github.com/kubeflow/pipelines/search?p=2&q=containerop+path%3Asamples%2Fcore | {
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"TODO:\r\n* [ ] document sample folder structure in samples/README.md\r\n* [ ] check samples, mark those only run on v1 with comments\r\n* [ ] check samples, mark those only run on v2 with v2_ in example name, or move into v2 folder.\r\n* [ ] move some v2 samples from samples/test folder to v2 folder",
"This issu... | 2021-05-19T06:06:46 | 2022-03-03T02:05:30 | null | CONTRIBUTOR | null | Propose using the following convention to organize samples:
* all core samples are by default compatible with both v1 and v2.
* samples that only run on v2 should be added either in samples/v2/core folder, or next to a corresponding v1 sample and have a v2 suffix, e.g. exit_handler_v2.py
* core samples that do not currently run on v2, but we have plan to support so can stay in samples folder, but we should add a comment saying clarifying status and plan. | {
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"Recommend using https://github.com/kubeflow/pipelines/blob/master/test/presubmit-backend-test.sh as a reference"
] | 2021-05-19T06:04:05 | 2021-06-08T05:37:53 | 2021-06-08T05:37:53 | CONTRIBUTOR | null | https://github.com/kubeflow/pipelines/tree/master/v2
this should run:
* go mod tidy
* go test ./... (including tests that use an one-off MLMD local server) | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5684 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5684/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5684/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5684/events | https://github.com/kubeflow/pipelines/issues/5684 | 895,023,219 | MDU6SXNzdWU4OTUwMjMyMTk= | 5,684 | [SDK] better compiler error message when input type mismatch between parameter/artifact | {
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"/assign @chensun ",
"Should have been covered by https://github.com/kubeflow/pipelines/pull/5759"
] | 2021-05-19T06:00:46 | 2021-06-08T00:22:40 | 2021-06-08T00:22:40 | CONTRIBUTOR | null | https://github.com/kubeflow/pipelines/blob/f757a8842fd6d1ed3d37ab38c0707d127ea2048c/samples/core/exit_handler/exit_handler_test.py#L24-L34
download_gcs component has input type 'GCS Path', it has a cryptic error message when the input is passed a string parameter:
> File "/Users/gongyuan/kfp/pipelines/sdk/python/kfp/compiler/v2_compat.py", line 108, in update_op artifact_info = {"fileInputPath": op.input_artifact_paths[artifact_name]} KeyError: 'GCS path'
Because this can be a common mistake, I think it deserves a better error message which includes:
* clearly indicates what is going wrong here, including related information in the error message
* a link to public documentation explaining the rules of parameters vs artifacts | {
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"@Bobgy, I am a new contributor looking for my first issue. \r\nCould I work on this ? ",
"Thank you @difince ! Yes, this is suitable for first time contributor.\n\nThe desired change is to review all pipelines in samples folder, if they have @pipeline annotation, change the pipeline name to only contain lowerca... | 2021-05-19T05:59:19 | 2021-05-24T07:36:32 | 2021-05-24T07:36:32 | CONTRIBUTOR | null | Good: "my-pipeline"
Bad: "my_pipeline"
No underscores, spaces etc special characters. | {
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"I think this would be a great step forward in terms of making it possible to easy run any type of web app with Kubeflow. The example resource from above would need some additional values of course, such as resource limits and requests, etc. Another important value that needs to be added is a `type` classification,... | 2021-05-19T03:34:34 | 2022-03-03T03:05:25 | 2022-03-03T03:05:25 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Besides tensorboard, KFP viewer controller supports generic viewers.
A viewer is a long running container that exposes a webapp through a certain port. (along with required setup to expose it through ingress, e.g. virtualservice in istio)
It can help visualize outputs of a pipeline component, but it can also be used outside of KFP like https://github.com/kubeflow/pipelines/issues/5651.
There are a few different use-cases we are currently getting:
* tensorboard (supported)
* file browser https://github.com/kubeflow/pipelines/issues/5651
* captum insights https://captum.ai/docs/captum_insights
* jupyter notebooks / vscode / rstudio (if we unify with Kubeflow notebooks controller)
All of them fit into this category, that makes it seem like a generic viewer operator that only abstracts the part of setting up ingress and lifecycle control seems like a good fit. The specific configuration for each different type of service we want to expose can be configured by users of viewer CRD.
#### Strawman Proposal
A generic viewer CRD like the following:
```
apiVersion: pipelines.kubeflow.org/v1beta2
kind: Viewer
spec:
ingress:
type: istio.virtualservice # maybe we can have more type supports
containers:
- name: main
image: tensorflow:2.3
command: ['python3', '-m']
arguments: ['tensorboard', '--port', '8080', '--bind-all']
envs:
- name: AWS_SECRET
valueFrom:
- xxxx
port: 8080
```
This custom resource will be used to setup the webapp for external access with:
* deployment
* service
* virtualservice
* authorizationpolicy
The major value coming from the generic viewer operator is to unify the resources needed to make this webapp available to users securely. Also, when creating/deleting this custom resource, operator will make sure the group of resources are created/deleted/updated.
I think the major controversial things to discuss is whether the viewer should encode domain knowledge about each type of service to start up. With the number of different use-cases we have seen, sounds to me that we'd better leave those domain knowledge to a different layer of abstraction. Curious about how others think about that.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
This also helps mitigate the problem that Kubeflow community has two operators to support these features: https://github.com/kubeflow/kubeflow/issues/5921.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5680 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5680/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5680/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5680/events | https://github.com/kubeflow/pipelines/issues/5680 | 894,872,712 | MDU6SXNzdWU4OTQ4NzI3MTI= | 5,680 | v2 compatible on full Kubeflow | {
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"/assign @Bobgy ",
"I'm trying to run v2 compatible pipeline on full Kubeflow, as expected, it fails when uploading to minio storage:\r\n```\r\nF0519 07:16:43.219511 1 main.go:56] Failed to execute component: failed to upload output artifact \"output_dataset_one\" to remote storage URI \"minio://mlpipeline/... | 2021-05-19T00:03:28 | 2021-09-25T05:51:25 | 2021-09-25T05:51:25 | CONTRIBUTOR | null | UPDATE:
PRs have been written:
* https://github.com/kubeflow/pipelines/pull/5697
* https://github.com/kubeflow/pipelines/pull/5750
* https://github.com/kubeflow/gcp-blueprints/pull/284
===
This issue tracks efforts to make [v2 compatible pipelines](https://www.kubeflow.org/docs/components/pipelines/sdk/v2/) run on [full Kubeflow](https://www.kubeflow.org/docs/components/pipelines/installation/overview/#full-kubeflow-deployment).
Depends on #4649
The major differences from KFP standalone are:
* MLMD grpc service is in kubeflow namespace by default.
* MinIO service is in kubeflow namespace by default.
* In Kubeflow on GCP, the default bucket is on GCS, so it's not `mlpipeline`.
For MLMD grpc service, we are already reading a configmap called `metadata-grpc-configmap`. The configmap is already configured in full Kubeflow for each user namespace: https://github.com/kubeflow/pipelines/blob/7608fcdbb4210c153b4a97b019787c6291473b9f/manifests/kustomize/base/installs/multi-user/pipelines-profile-controller/sync.py#L52-L63
Therefore, it already works out of the box.
Because in Kubeflow on GCP, the default bucket is not `mlpipeline`, we need configurations that can set default pipeline root and credentials for each namespace, this is essentially https://github.com/kubeflow/pipelines/issues/4649 for v2 compatible pipelines.
The following comments are investigation logs towards the conclusion I summarized here. | {
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"/assign @zijianjoy ",
"@zijianjoy I want to propose a few more changes:\r\n* [ ] artifact names in ML Metadata tab is empty\r\n \r\n* [ ] Hide ML Metadata tab from pipeline node details, because useful... | 2021-05-18T14:28:43 | 2021-07-01T00:10:23 | 2021-07-01T00:10:23 | CONTRIBUTOR | null | Starting from v2 compatible mode, all the KFP semantics information should be available in MLMD.
Therefore, now we can start to render KFP UI in argo agnostic ways.
This is an umbrella issue to find out things we can improve under this theme.
Known issues:
* [x] https://github.com/kubeflow/pipelines/issues/5670
Depends on https://github.com/kubeflow/pipelines/issues/5669 | {
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"@Bobgy I am new contributor, looking for my first issue .. Could I work on this one ? \r\nAs I understand a new rest endpoint need to be added ? ",
"Hi @difince, this issue is too complex as first issue, because it also includes designing spec for the rest endpoint",
"Thank you for the response @Bobgy ",
"T... | 2021-05-18T14:25:20 | 2022-03-09T01:48:25 | null | CONTRIBUTOR | null | Umbrella issue, this tracks
1. coming up with a standard spec that describes pipeline run results for all the engines, e.g. we now have Argo, Tekton and Vertex AI pipelines.
1. implement REST API for this spec | {
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"It's possible to specify the user that the container should run under. Backend can auto-set that on all containers.",
"@Ark-kun do you mean our backend sets all containers to run as root by default?",
"@Bobgy What is the plan to support non-root containers ? it is difficult to do any PoC with V2 in an enterpri... | 2021-05-18T14:24:29 | 2022-07-02T00:37:30 | null | CONTRIBUTOR | null | Currently, because the launcher writes input artifacts to paths like:
* `/gcs/xxx`
* `/minio/xxx`
* `/s3/xxxx`
These paths are not accessible by non-Root users by default.
When using a component with non-Root image, launcher fails when preparing input/output artifacts.
Because `/gcs/xxx` is currently a contract for KFP v2 python component wrappers, we cannot change to a different path like `/tmp/gcs/xxx` etc. | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-18T14:18:30 | 2022-03-03T06:05:13 | 2022-03-03T06:05:13 | CONTRIBUTOR | null | As explained in https://github.com/kubeflow/pipelines/issues/5671, we cannot know if an artifact is used by local path in v2 python components.
So we need to figure out how we can know that first and then skip downloading unneeded artifacts. | {
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"/assign @capri-xiyue ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed ... | 2021-05-18T14:15:41 | 2022-04-17T07:27:19 | null | CONTRIBUTOR | null | For v1 components, we can rely on artifact placeholders to understand whether one artifact is used via InputUri or InputPath. Only the input artifacts consumed by InputPath need to be downloaded.
For v2 python components, we cannot distinguish at the moment. We can distinguish v2 python components by checking if the root placeholder `{{$}}` is used in the component args/commands. | {
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"/assign @zijianjoy ",
"### Current INPUT/OUTPUT tab\r\n\r\nRender Input Parameters, Input Artifacts, Output Parameters, Output Artifacts.\r\n\r\nThey are read from Workflow object.\r\n\r\n### Use MLMD\r\n\r\nBased on execution, we can find a list of events to identify artifact and input/output for this execution... | 2021-05-18T14:11:11 | 2021-06-25T04:55:53 | 2021-06-25T04:55:53 | CONTRIBUTOR | null | KFP Inputs/Outputs tab in run details page is currently very coupled to argo.
For v2 compatible pipelines, we can use information from MLMD to render the Inputs/Outputs tab in KFP semantics. | {
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"/assign @capri-xiyue ",
"b/187556737",
"One problem I found from https://github.com/kubeflow/pipelines/issues/5668, is that\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/89364315e96ca0e14106ef178336f72265a8e91a/samples/test/metrics_visualization_v2.py#L62\r\nFor now, we log a custom property called \"accur... | 2021-05-18T14:04:19 | 2021-07-26T11:57:21 | 2021-07-20T05:26:15 | CONTRIBUTOR | null | TODOs:
* [x] Design, figure out the exact list of items we need to change (size/M)
Known work items:
* [x] https://github.com/kubeflow/pipelines/issues/5788 (size/XS)
* [x] https://github.com/kubeflow/pipelines/pull/5842
* [x] #5789 (size/S)
* [x] [v2compat] remove redundant fields “pipeline_name”, “pipeline_run_id” from execution custom properties
* [x] https://github.com/kubeflow/pipelines/issues/5978
* [x] make scalar metrics distinguishable. UPDATE: figured out that we treat all properties of type float as scalar metrics in metadata (custom properties).
* [x] #5985
* [x] #5986
* [x] https://github.com/kubeflow/pipelines/issues/5803 (decided this is not necessary)
* [ ] P2 pipeline root task execution
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"/assign @zijianjoy ",
"Do we visualize HTML, Markdown, Tensorboard in the new visualization tab?",
"> Do we visualize HTML, Markdown, Tensorboard in the new visualization tab?\n\nYes, they are tracked in a different issue in P1.",
"We might need to update documentation https://www.kubeflow.o... | 2021-05-18T12:33:37 | 2023-01-14T15:15:38 | 2021-06-28T06:10:11 | CONTRIBUTOR | null | components can output metrics artifacts that are rendered in UI.
Sample pipeline: https://github.com/kubeflow/pipelines/blob/89364315e96ca0e14106ef178336f72265a8e91a/samples/test/metrics_visualization_v2.py
TODOs:
* [x] ~~add MLMD state verification of this pipeline: https://github.com/kubeflow/pipelines/blob/89364315e96ca0e14106ef178336f72265a8e91a/samples/test/metrics_visualization_v2_test.py#L21~~
* [x] scalar metric (no need to support showing scalar metrics in run list for now), wait for resolution of https://github.com/kubeflow/pipelines/issues/5669#issuecomment-845676621
* [x] ROC curve
* [x] confusion matrix
* [x] add v2 metrics visualization section in https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/
Also suggest trying to write new UI code in recent best practices and try to separate them from existing components. We want to take a gradual approach to modernize the UI codebase. | {
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"/assign @capri-xiyue ",
"Needs further TODO item break down",
"Hi @Bobgy do you have the following in mind ? https://github.com/kubeflow/pipelines/issues/5509\r\nOr will there be a native way such that deleting runs from the pripelines webinterface will also delete the artifacts from minio and make the cacher ... | 2021-05-18T12:25:56 | 2021-08-20T08:40:11 | 2021-08-20T08:40:11 | CONTRIBUTOR | null | internal tracker: b/181133870
Work Items estimate:
* [x] #5726 (size/L)
* [x] #5816 (size/M)
* [x] #5817(size/M)
* [x] #5818(size/S)
* [x] #5819 (size/M)
* [x] #5820 (size/M)
* [x] #5977
total: 4w, 1d for SDK team | {
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"/assign @chensun \r\nfor SDK\r\n/assign @zijianjoy \r\nfor UI",
"I'd really like to see `mlpipeline-ui-metadata` gone in the future.\r\nBut first there needs to be an alternative to it.\r\nSupporting \"type-based visualizations\" and \"visualizations as components\" will provide a better alternative.",
"@Ark-k... | 2021-05-18T12:24:08 | 2022-03-02T21:50:58 | 2022-03-02T21:50:58 | CONTRIBUTOR | null | support v1 visualizations:
https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/
Changes needed:
* [x] Doc: update https://kubeflow.org/docs/components/pipelines/sdk/output-viewer/ according to https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
* [x] https://github.com/kubeflow/pipelines/pull/5832 SDK: remove hacks for mlpipeline-ui-metadata artifact in v2 compatible mode -- result: we should be able to see mlpipeline-ui-metadata as a usual artifact in MLMD.
* [x] UI: also supports visualizing mlpipeline-ui-metadata artifact from MLMD | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-18T09:14:03 | 2022-03-03T04:05:38 | 2022-03-03T04:05:38 | NONE | null | ### Environment
* KFP version:
* KFP SDK version: 1.6.0.rc0
* All dependencies version:
### Steps to reproduce
Within a Kubeflow Jupyter notebook, create a v2 pipeline. It seems json file should be provided when compiling using Compiler().compile method
Upload json file to Kubeflow 1.3 pipeline UI as a new pipeline
Then, Kubeflow will return an error.
### Expected result
Should upload json pipeline file successfully
### Materials and Reference
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"Do any other pipelines run successfully?",
"I had the same issue (with a custom pipeline), this started to happen after i launched a large number of runs and i had to stop the workloads from the GKE panel because the Kubeflow Pipelines dashboard wasnt responding.\r\n\r\nRight now i cant run any kind of pipeline,... | 2021-05-18T03:15:42 | 2021-05-21T03:10:04 | 2021-05-21T03:10:04 | NONE | null | ### What steps did you take
Tried out the default kubeflow pipeline for TFX taxi prediction model in GCP marketplace Kubeflow pipeline version 1.4.1
<!-- A clear and concise description of what the bug is.-->
### What happened:
The error message for the csvexamplegen step is as follows.
This step is in Error state with this message: failed to save outputs: Error response from daemon: No such container: b715c336e667d305429003d1ce1c9b795ced595aa76b2bf252c679d2947b3643
<img width="1414" alt="Screen Shot 2021-05-18 at 1 07 09 pm" src="https://user-images.githubusercontent.com/13513068/118584703-dccb5a00-b7da-11eb-82be-9c7f3812c6f1.png">
### What did you expect to happen:
The pipeline was getting executed end to end earlier . The main container itself for the first step is getting executed successfully. But its failing at the wait container stage.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
* GCP AI pipeline V(1.4.1)
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* 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:
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### Anything else you would like to add:
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### Labels
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---
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https://api.github.com/repos/kubeflow/pipelines/issues/5659 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5659/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5659/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5659/events | https://github.com/kubeflow/pipelines/issues/5659 | 893,315,471 | MDU6SXNzdWU4OTMzMTU0NzE= | 5,659 | [sdk] BadRequestError: Request header error: there is no user identity header. | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-17T13:08:33 | 2022-03-03T04:05:36 | 2022-03-03T04:05:36 | NONE | null | ### Environment
* KFP version:
* KFP SDK version: 1.0.4
* All dependencies version:
kfp (1.4.0)
kfp-pipeline-spec (0.1.7)
kfp-server-api (1.4.1)
### Steps to reproduce
```
client = kfp.Client()
run_result = client.create_run_from_pipeline_func(ml_pipeline,
experiment_name=experiment_name,
run_name=run_name,
namespace="internship",
arguments=arguments)
```
I compiled the pipeline and I want to create a run directly from the terminal running python script.py
I was able to make a run in a jupyter notebook inside the notebook server adding an envory filter but I couldn't make it in the terminal.
But I got
```
HTTP response headers: HTTPHeaderDict({'Audit-Id': '94ed6020-ba67-4a74-9f5c-d3dee5c77cfc', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Mon, 17 May 2021 10:21:24 GMT', 'Trailer': 'Grpc-Trailer-Content-Type', 'Transfer-Encoding': 'chunked'})
HTTP response body: {"error":"Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header.","message":"Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header.","code":10,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Request header error: there is no user identity header.","error_details":"Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header."}]}
```
### Expected result
to get a link to the pipeline ui to see the run
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https://api.github.com/repos/kubeflow/pipelines/issues/5656 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5656/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5656/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5656/events | https://github.com/kubeflow/pipelines/issues/5656 | 892,980,876 | MDU6SXNzdWU4OTI5ODA4NzY= | 5,656 | [feature] consistently support object stores | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | open | false | null | [] | null | [
"Sorry for the slow response on this, but I can confirm that `go-cloud` supports IAM Roles for Service Accounts in order to access objects on S3.\r\n\r\nSupport was added with this PR: https://github.com/google/go-cloud/pull/2773\r\n\r\nI've also tested it independently. The IAM role needs to be attached to a Serv... | 2021-05-17T06:48:46 | 2022-04-26T15:17:17 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Consistently support different object stores (MinIO, S3, GCS, Azure blob, ...) in KFP, no matter it's for UI preview / visualization, metrics or in pipeline tasks.
Propose to unify using Go CDK: https://gocloud.dev/.
Changes:
1. In https://bit.ly/kfp-v2-compatible mode, KFP pipeline tasks will use Go CDK to upload/download artifacts in pipeline containers.
2. Implement support for different artifact stores in KFP API server, also make it deployable standalone in user namespaces.
3. Remove artifact API implementation in KFP UI.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
1. Reduce tech debt
KFP UI uses MinIO js client, GCS client etc to access some object stores.
KFP API server uses MinIO Go client to only access MinIO.
Argo workflow supports several types of object stores natively too (but not sure what they use).
The difference in implementation makes it hard to achieve the same level of support for different object stores across KFP features. This also brings duplicate efforts in maintaining object access.
e.g. KFP v1 metrics is only supported with MinIO, because KFP API server only supports MinIO.
2. Consistently support object store features:
e.g. IRSA for S3: https://github.com/kubeflow/pipelines/issues/3405#issuecomment-841474419
or workload identity for GCS (needed an upgrade for argo)
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
No
---
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Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"You can follow the `deploy ml-pipeline-ui to support tensorboard viewer` in https://github.com/kubeflow/pipelines/blob/master/docs/config/volume-support.md to mount this volume for tensorboard. Note that this feature is in alpha phase, so it might not be stable.",
"> You can follow the `deploy ml-pipeline-ui to ... | 2021-05-17T01:18:53 | 2021-05-21T10:33:34 | 2021-05-21T10:33:34 | NONE | null | ### What steps did you take
Hi, I create a volume named 'nfs', and amount to the training pod in the pipeline. And training code generate tensorboard dir in the amount dir. But when I go to look tensorboard view, get error:
pipeline code:
```
def train_op(volume, image, cmd):
# amount volume
nfsVolume = dsl.PipelineVolume(volume=V1Volume(name="nfs",nfs={"server":"xxx","path":"/"}))
svol = dsl.PipelineVolume(name='shm-vol', empty_dir={'medium': 'Memory'})
op = dsl.ContainerOp(
name="train",
image=image,
arguments=["/bin/sh", "-c", "%s" % (cmd)],
pvolumes={
"/output": nfsVolume,
'/dev/shm': svol,
},
output_artifact_paths={
'mlpipeline-ui-metadata': '/mlpipeline-ui-metadata.json',
},
)
op.container.set_cpu_limit("0.5")
op.container.set_gpu_limit("2")
op.container.set_memory_limit("50G")
return op
```
My training code generate tensorboard data in `/output/runs`, and the create metadata code:
```
metadata = {
'outputs' : [
{
'type': 'tensorboard',
'source': 'volume://nfs/runs',
}
]
}
with open('/mlpipeline-ui-metadata.json', 'w') as f:
print(metadata)
json.dump(metadata, f)
```
### What happened:
### What did you expect to happen:
Tensorboard view show data
### Environment:
* pipeline version: 1.0.0
Anyone has goods idea ?
---
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"u... | closed | false | null | [] | null | [] | 2021-05-16T07:28:11 | 2021-05-18T08:28:46 | 2021-05-18T08:28:46 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- 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
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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
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### Materials and Reference
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---
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"👍🏽\r\nThis will come in very handy for my users!!",
"This would also be very helpful for us. Especially when trying to figure out which PVCs can be safely cleaned up.",
"Looks great! Really handy feature and very much in line with the sort of functionality our our clients are looking for. What would need hap... | 2021-05-15T14:34:04 | 2022-03-03T06:05:09 | 2022-03-03T06:05:09 | CONTRIBUTOR | null | ### Feature Area
/area backend
### What feature would you like to see?
Add a `PVC Viewer`/`File Browser` spec to the Viewer controller.
### What is the use case or pain point?
This feature would allow people to launch a "File Browser" pod, which allows them to easily drag and drop files into a PVC or download files and folders present in a PVC. For KFP users, this can be useful if a pipeline step produces some form of data in a PVC that the user would like to easily download. The same CR would then be used to extend the Volumes Web App with a button that allows people to start a "File Browser" pod to easily upload and download file to and from their PVCs (most likely for the PVCs used with notebook servers). I currently have built a dedicated controller for this, but given that the pipelines viewer CRD is built to be extendable I thought it would make sense to add it to an existing controller. A demo of this functionality can be seen below.
### Is there a workaround currently?
There currently is no easy way for users to upload and download data to and from their PVCs, which is a large hurdle for people that have local data that they would like to use within their notebooks or pipelines.
---
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"Can you please try some other `Client` methods like list_runs?\r\nThere might be auth error.",
"I executed `client.list_runs()`, where I defined `client` object in my first message, and I got the following result:\r\n`{'experiments': None, 'next_page_token': None, 'total_size': None}`\r\n\r\nWhen I executed `cli... | 2021-05-12T20:33:19 | 2022-10-06T19:44:08 | null | NONE | null | Hi,
I've deployed Kubeflow onto an AWS EKS cluster. I tried to create and run Kubeflow pipeline using notebook UI inside Kubeflow cluster. However, no experiment gets created. I followed the example in https://www.kubeflow.org/docs/components/pipelines/sdk/build-pipeline/ to build the pipeline. Furthermore, I followed the instructions in https://www.kubeflow.org/docs/distributions/aws/pipeline/ to authenticate Kubeflow Pipeline using SDK inside cluster. Below, you can find how I did the authentication:
`authservice_session='<cookie I've obtained from notebook url which is http://localhost:8080/_/jupyter/>'
ALB_ADDRESS='<Value of Address field obtained by executing command "kubectl get ingress -n istio-system">'
HOST=ALB_ADDRESS+'/pipeline'
client = kfp.Client(host=HOST, cookies=authservice_session)`
When I executed the following command, I got an error.
`client.create_run_from_pipeline_package(
pipeline_file='pipeline.yaml',
arguments={
'url': 'https://storage.googleapis.com/ml-pipeline-playground/iris-csv-files.tar.gz'
})`
The error is:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-22-56afc4f05e24> in <module>
2 pipeline_file='pipeline.yaml',
3 arguments={
----> 4 'url': 'https://storage.googleapis.com/ml-pipeline-playground/iris-csv-files.tar.gz'
5 })
/usr/local/lib/python3.6/dist-packages/kfp/_client.py in create_run_from_pipeline_package(self, pipeline_file, arguments, run_name, experiment_name, namespace)
739 '%Y-%m-%d %H-%M-%S'))
740 experiment = self.create_experiment(name=experiment_name, namespace=namespace)
--> 741 run_info = self.run_pipeline(experiment.id, run_name, pipeline_file, arguments)
742 return RunPipelineResult(self, run_info)
743
/usr/local/lib/python3.6/dist-packages/kfp/_client.py in run_pipeline(self, experiment_id, job_name, pipeline_package_path, params, pipeline_id, version_id)
548 import IPython
549 html = ('<a href="%s/#/runs/details/%s" target="_blank" >Run details</a>.'
--> 550 % (self._get_url_prefix(), response.run.id))
551 IPython.display.display(IPython.display.HTML(html))
552 return response.run
AttributeError: 'NoneType' object has no attribute 'id'
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5638 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5638/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5638/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5638/events | https://github.com/kubeflow/pipelines/issues/5638 | 889,576,543 | MDU6SXNzdWU4ODk1NzY1NDM= | 5,638 | [frontend] File format / path of the TFX artifacts from TFDV should be updated | {
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"FYI, @1025kb ",
"Still same",
"@zijianjoy did you already fix this in your recent PR?",
"Yes the change has been applied in https://github.com/kubeflow/pipelines/pull/6061/files#diff-388538ba7580c2729c010006b01837a9dbf9a04702c185970d025ab5dbcd22ba."
] | 2021-05-12T03:32:12 | 2021-07-19T06:11:37 | 2021-07-19T06:11:37 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? - GCP Cloud AI Pipelines
* KFP version: 1.4.1
### Steps to reproduce
1. Use TFX taxi template to create a pipeline
2. Add StatisticsGen component and run the pipeline.(Step 5)
3. Click "Visualizations" of `StatisticsGen` component after the run finished successfully.
- This will result in `OSError: Invalid input path gs://jiyongjung-gke-kubeflowpipelines-default/tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/257/eval/stats_tfrecord.`.
### Expected result
Visualization of TFDV analysis.
### Materials and Reference
TFX changed the file path and format of several artifact types recently. For example, [cl for statistics artifact](https://github.com/tensorflow/tfx/commit/2a1a00a675760e6c96887aa59ba67a91e0f8a4cc). But it seems like it is not propagated to KFP. [Related code](https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/frontend/src/lib/OutputArtifactLoader.ts#L276-L327).
TFX started to record artifacts' [generated version as custom properties](https://github.com/tensorflow/tfx/blob/c78ed9678f790a55b444d8f4c57f584f3b3a4d53/tfx/types/artifact_utils.py#L36), so we might be able to use it to support both versions.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5635 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5635/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5635/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5635/events | https://github.com/kubeflow/pipelines/issues/5635 | 887,835,576 | MDU6SXNzdWU4ODc4MzU1NzY= | 5,635 | New kfp sdk release (1.5.0) | {
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"\"We're planning an SDK release this week. Probably have an RC version out mid-week at least.\"\r\nFrom: https://github.com/kubeflow/pipelines/issues/5254#issuecomment-835991598",
"Thank you! I see it's been released now"
] | 2021-05-11T16:40:01 | 2021-05-25T13:41:11 | 2021-05-25T13:41:11 | NONE | null | The new use_k8s_secret function in the kfp_server_api sdk is very useful but it's not yet released in the kfp library (https://pypi.org/project/kfp/). Is there a plan for when the next release will be? Thanks much! | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5627 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5627/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5627/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5627/events | https://github.com/kubeflow/pipelines/issues/5627 | 885,304,899 | MDU6SXNzdWU4ODUzMDQ4OTk= | 5,627 | [05/10/2021] Presubmit failure: kubeflow-pipelines-tfx-python36 | {
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"The file was deleted by https://github.com/tensorflow/tfx/pull/3700."
] | 2021-05-10T23:01:33 | 2021-05-11T06:10:42 | 2021-05-11T06:10:42 | COLLABORATOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5604/kubeflow-pipelines-tfx-python36/1391884823848554496
```
+ cd /home/prow/go/src/github.com/kubeflow/pipelines/tfx/tfx/examples/chicago_taxi_pipeline
+ python3 taxi_pipeline_kubeflow_gcp_test.py
python3: can't open file 'taxi_pipeline_kubeflow_gcp_test.py': [Errno 2] No such file or directory
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5620 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5620/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5620/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5620/events | https://github.com/kubeflow/pipelines/issues/5620 | 881,550,704 | MDU6SXNzdWU4ODE1NTA3MDQ= | 5,620 | ERROR:Exception when calling CoreV1Api->create_namespaced_pod: (404) Reason: Not Found | {
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"@Ark-kun Hello Alexey, would you like to look at this example? Thank you!",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has ... | 2021-05-09T01:01:13 | 2022-03-03T04:05:35 | 2022-03-03T04:05:35 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? In GCP AI Platform
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: : kfp-1.0.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. Successfully installed docstring-parser-0.7.3 fire-0.4.0 kfp-1.4.0 kfp-pipeline-spec-0.1.7
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.
This is throwing an error - 2021-05-07 22:16:27:ERROR:Exception when calling CoreV1Api->create_namespaced_pod: (404)
Reason: Not Found
Althought i have set to configure it in Kubetcl in GCP.
-->
### Expected result
# Target images for creating components should be added in GCP container registry.
PREPROCESS_IMG = 'gcr.io/%s/ms-coco/preprocess:latest' % PROJECT_NAME
TOKENIZE_IMG = 'gcr.io/%s/ms-coco/tokenize:latest' % PROJECT_NAME
TRAIN_IMG = 'gcr.io/%s/ms-coco/train:latest' % PROJECT_NAME
PREDICT_IMG = 'gcr.io/%s/ms-coco/predict:latest' % PROJECT_NAME
<!-- What should the correct behavior be? -->
Should update the container registry in GCP
- It is empty only the added file is not there.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
sample code - https://github.com/kubeflow/pipelines/blob/master/samples/contrib/image-captioning-gcp/Image%20Captioning%20TF%202.0.ipynb
I run it in GCP Kubeflow AI Platform notebook.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"presubmit can be implemented by using `addlicense` go CLI tool and then verify no changes have been made\r\n\r\nAlso we can recommend \"Copyright Inserter\" vscode plugin: https://marketplace.visualstudio.com/items?itemName=minherz.copyright-inserter",
"This issue has been automatically marked as stale because i... | 2021-05-08T08:58:20 | 2022-03-03T06:05:17 | 2022-03-03T06:05:17 | CONTRIBUTOR | null | After https://github.com/kubeflow/pipelines/pull/5587,
we should stick to "The Kubefow Authors" copyright header.
But people may miss the new convention,
Proposal:
1. p0 add another PR checklist to educate contributors
2. p2 add presubmit (not sure how we can actually achieve that) | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5614 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5614/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5614/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5614/events | https://github.com/kubeflow/pipelines/issues/5614 | 880,327,800 | MDU6SXNzdWU4ODAzMjc4MDA= | 5,614 | [05/07/2021] Presubmit failure: kubeflow-pipelines-tfx-python36 | {
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"Package installed:\r\n\r\nSuccessfully installed Babel-2.9.1 Flask-Babel-1.0.0 Flask-JWT-Extended-3.25.1 Flask-OpenID-1.2.5 Flask-SQLAlchemy-2.5.1 Mako-1.1.4 MarkupSafe-1.1.1 Send2Trash-1.5.0 WTForms-2.3.3 alembic-1.6.2 apache-airflow-2.0.2 apache-airflow-providers-ftp-1.1.0 apache-airflow-providers-http-1.1.1 apa... | 2021-05-08T05:44:44 | 2021-05-08T10:57:07 | 2021-05-08T10:57:07 | COLLABORATOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5594/kubeflow-pipelines-tfx-python36/1390893518523731968
```
Traceback (most recent call last):
File "kubeflow_dag_runner_test.py", line 26, in <module>
from tfx.extensions.google_cloud_big_query.example_gen import component as big_query_example_gen_component
File "/usr/local/lib/python3.6/site-packages/tfx/extensions/google_cloud_big_query/example_gen/component.py", line 26, in <module>
from tfx.extensions.google_cloud_big_query.example_gen import executor
File "/usr/local/lib/python3.6/site-packages/tfx/extensions/google_cloud_big_query/example_gen/executor.py", line 26, in <module>
from google.cloud import bigquery
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery/__init__.py", line 35, in <module>
from google.cloud.bigquery.client import Client
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery/client.py", line 59, in <module>
from google.cloud.bigquery import _pandas_helpers
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery/_pandas_helpers.py", line 44, in <module>
from google.cloud.bigquery import schema
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery/schema.py", line 19, in <module>
from google.cloud.bigquery_v2 import types
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery_v2/__init__.py", line 19, in <module>
from .types.encryption_config import EncryptionConfiguration
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery_v2/types/__init__.py", line 18, in <module>
from .encryption_config import EncryptionConfiguration
File "/usr/local/lib/python3.6/site-packages/google/cloud/bigquery_v2/types/encryption_config.py", line 24, in <module>
__protobuf__ = proto.module(
AttributeError: module 'proto' has no attribute 'module'
```
/area testing
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https://api.github.com/repos/kubeflow/pipelines/issues/5606 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5606/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5606/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5606/events | https://github.com/kubeflow/pipelines/issues/5606 | 878,868,513 | MDU6SXNzdWU4Nzg4Njg1MTM= | 5,606 | Error: persistentvolumeclaim "azure-managed-disk" not found | {
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"u... | closed | false | null | [] | null | [] | 2021-05-07T12:00:19 | 2021-05-07T12:00:38 | 2021-05-07T12:00:38 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- 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
<!--
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This may include information such as: a description of the process, code snippets, log output, or screenshots.
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### Expected result
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---
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https://api.github.com/repos/kubeflow/pipelines/issues/5605 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5605/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5605/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5605/events | https://github.com/kubeflow/pipelines/issues/5605 | 878,867,414 | MDU6SXNzdWU4Nzg4Njc0MTQ= | 5,605 | Error: persistentvolumeclaim "azure-managed-disk" not found | {
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"/assign @aronchick \r\nDo you have a good contact for this issue? This is one of the azure tutorials",
"`kubectl delete pvc azure-managed-disk --namespace kubeflow`\r\n\r\nIf you look at the PVC's YAML you'll see it is created in namespace `kubeflow` which is not the default namespace and hence you aren't actual... | 2021-05-07T11:59:36 | 2021-05-27T16:17:51 | 2021-05-27T16:17:26 | NONE | null | **Question:**
I've followed the [azure end-to-end pipeline](https://www.kubeflow.org/docs/distributions/azure/azureendtoend/) tutorial, but am getting this error when running an experiment, does anyone know what might be the problem?
I've run `kubectl apply -f pvc.yaml` but somehow I dont see the volume when I do `kubectl get pvc`...am I supposed to?
I also dont understand why I am unable to delete the PVC
PVC.yaml
pipeline.py
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https://api.github.com/repos/kubeflow/pipelines/issues/5602 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5602/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5602/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5602/events | https://github.com/kubeflow/pipelines/issues/5602 | 878,372,645 | MDU6SXNzdWU4NzgzNzI2NDU= | 5,602 | [sdk] pyinstaller ("import kfp") -> OSError: could not get source code | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-05-07T03:03:07 | 2022-03-03T04:05:20 | 2022-03-03T04:05:20 | NONE | null | ### Environment
* KFP SDK version: 1.20, 1.3.0, 1.4.0
### Steps to reproduce
```python
# file name: a.py
import kfp
print ("xxx")
```
`pyinstaller -F a.py` creates an executable `a` in ./dist/
run the executable `./dist/a`
### Expected result
```
xxx
```
But got the following error
### Issues
```python
Traceback (most recent call last):
File "a.py", line 1, in <module>
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "PyInstaller/loader/pyimod03_importers.py", line 540, in exec_module
File "kfp/__init__.py", line 21, in <module>
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "PyInstaller/loader/pyimod03_importers.py", line 540, in exec_module
File "kfp/components/__init__.py", line 15, in <module>
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "PyInstaller/loader/pyimod03_importers.py", line 540, in exec_module
File "kfp/components/_airflow_op.py", line 23, in <module>
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "PyInstaller/loader/pyimod03_importers.py", line 540, in exec_module
File "kfp/components/_python_op.py", line 31, in <module>
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "PyInstaller/loader/pyimod03_importers.py", line 540, in exec_module
File "kfp/components/_components.py", line 29, in <module>
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "PyInstaller/loader/pyimod03_importers.py", line 540, in exec_module
File "kfp/components/_data_passing.py", line 73, in <module>
File "inspect.py", line 973, in getsource
File "inspect.py", line 955, in getsourcelines
File "inspect.py", line 786, in findsource
OSError: could not get source code
[300906] Failed to execute script a
```
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https://api.github.com/repos/kubeflow/pipelines/issues/5598 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5598/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5598/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5598/events | https://github.com/kubeflow/pipelines/issues/5598 | 877,557,807 | MDU6SXNzdWU4Nzc1NTc4MDc= | 5,598 | [backend] Cache Server is unable to deploy with external database using SSL | {
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"u... | closed | false | null | [] | null | [
"Can we consider this fixed after merging https://github.com/kubeflow/pipelines/pull/5599 @maganaluis ? ",
"@NikeNano Yes, I missed tagging this issue to the PR. "
] | 2021-05-06T14:26:53 | 2021-05-17T13:40:19 | 2021-05-17T13:40:19 | CONTRIBUTOR | null | ### Environment
Using Kustomize.
* KFP version:
KFP 1.3
* KFP SDK version:
KFP 1.4
### Steps to reproduce
We are unable to deploy the Cache Server with an external databases which uses SSL. In order to setup the correct parameters, the configuration should allow for extra parameters.
### Expected result
We should be able to pass extra parameters to the Cache Server deployment:
```
...
- name: DBCONFIG_PASSWORD
valueFrom:
secretKeyRef:
name: mysql-secret
key: password
- name: DBCONFIG_EXTRAPARAMS
value: '{"tls": "true"}'
- name: NAMESPACE_TO_WATCH
valueFrom:
fieldRef:
fieldPath: metadata.namespace
args: ["--db_driver=$(DBCONFIG_DRIVER)",
"--db_host=$(DBCONFIG_HOST_NAME)",
"--db_port=$(DBCONFIG_PORT)",
"--db_name=$(DBCONFIG_DB_NAME)",
"--db_user=$(DBCONFIG_USER)",
"--db_password=$(DBCONFIG_PASSWORD)",
"--db_extra_params=$(DBCONFIG_EXTRAPARAMS)",
"--namespace_to_watch=$(NAMESPACE_TO_WATCH)",
]
imagePullPolicy: Always
...
```
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5597 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5597/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5597/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5597/events | https://github.com/kubeflow/pipelines/issues/5597 | 877,426,995 | MDU6SXNzdWU4Nzc0MjY5OTU= | 5,597 | Unable to artifact output files in aws s3 | {
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"Can someone please help on this",
"Update: My pipeline is running on namespace 'admin'. created secret file for namespace admin and kubeflow.\r\nUpdated deployment ml-pipeline-ui in kubeflow namespace to use aws-secret. Still in kubeflow UI am getting the below error:\r\n\r\nFailed to get object in bucket 'bucke... | 2021-05-06T12:07:19 | 2021-07-09T10:50:23 | 2021-07-09T10:50:23 | NONE | null | ### What steps did you take
I installed kubeflow v1.2 in AWS following official documentation -https://www.kubeflow.org/docs/distributions/aws/pipeline/
### What happened:
Created notebook server and executed kubeflow pipeline. AM able to view artifacts in UI stored in minio loation. Created secret.yaml file
apiVersion: v1
kind: Secret
metadata:
name: aws-secret
namespace: kubeflow
type: Opaque
data:
AWS_ACCESS_KEY_ID: <YOUR_BASE64_ACCESS_KEY>
AWS_SECRET_ACCESS_KEY: <YOUR_BASE64_SECRET_ACCESS>
Updated deployment ml-pipeline-ui to use AWS credential environment variables by running kubectl edit deployment ml-pipeline-ui -n kubeflow.
### What did you expect to happen:
Kubeflow to store the files in s3
### Environment:
AWS-ubuntu eks cluster
* How do you deploy Kubeflow Pipelines (KFP)? --https://www.kubeflow.org/docs/distributions/aws/pipeline/
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.2
<!-- 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:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
After applying secrets yaml am able to connect to s3 to read and write files using use_aws_secret but artifacts are not getting logged automatically in s3. Artifacts are logged in minio alone. How to log artifacts in s3
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5593 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5593/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5593/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5593/events | https://github.com/kubeflow/pipelines/issues/5593 | 876,939,987 | MDU6SXNzdWU4NzY5Mzk5ODc= | 5,593 | [feature] Choose what metrics to show on the individual experiment page | {
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"We are thinking about implementing this feature in KFP v2, because a basic requirement is that\r\n\r\nIn v2, we envision that each metric can be a separate artifact.\r\nAnd pipeline DSL can allow users to return artifacts they select.\r\n\r\nWith these two new features, it'll be possible to select pipeline level o... | 2021-05-05T23:36:33 | 2021-09-12T13:25:13 | 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?
For runs that output many metrics, the current experiment page only displays limited number of metrics.
It will be good to add a selector for users to choose what metrics to display, so they can be used for sorting and further analysis.
Example:
Run that generates many metrics
Only 2 metrics are displayed on the experiment page
### What is the use case or pain point?
To analyze runs that output many metrics and reduce the "noisy" information displayed on the run comparison page
### Is there a workaround currently?
The "compare runs" function can somehow achieve the purpose. But an overview and sorting over the "focus" metrics is better.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5590 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5590/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5590/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5590/events | https://github.com/kubeflow/pipelines/issues/5590 | 876,343,608 | MDU6SXNzdWU4NzYzNDM2MDg= | 5,590 | [backend] Pipelines 1.5.0 api-server argo version mismatch & samples cannot be compiled | {
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"Hi @mgiessing, this issue has been fixed in latest version. Can you confirm?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it h... | 2021-05-05T11:18:28 | 2022-03-03T06:05:18 | 2022-03-03T06:05:18 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Pipelines is not yet deployed, I want to build the api-server docker image
* KFP version:
1.5.0
* KFP SDK version:
1.5.0
### Steps to reproduce
Difficult to reproduce as you'd need a ppc64le system, however that problem is not related to the underlying platform as the error indicates a wrong argo version and also happened on x86-Macbook Pro. Essentially these steps have been performed
```
git clone https://github.com/kubeflow/pipelines.git
cd pipelines && git checkout 1.5.0
## changing some ppc64le specifics in the dockerfile ##
docker build -t api-server:1.5.0.ppc64le -f backend/Dockerfile .
```
I've also build the image on my x86-Macbook and get the same errors during the `dsl-compile ...` command
See:
https://github.com/kubeflow/pipelines/blob/1.5.0/backend/Dockerfile#L35
and especially this line fails:
https://github.com/kubeflow/pipelines/blob/1.5.0/backend/Dockerfile#L40
### Expected result
I'd expect the to run without issues
### Materials and Reference
#### Error output / docker build log ppc64le-linux
```
[...]
Step 27/42 : RUN set -e; < /samples/sample_config.json jq .[].file --raw-output | while read pipeline_yaml; do pipeline_py="${pipeline_yaml%.yaml}"; mv "$pipeline_py" "${pipeline_py}.tmp"; echo 'import kfp; kfp.components.default_base_image_or_builder="gcr.io/google-appengine/python:2020-03-31-141326"' | cat - "${pipeline_py}.tmp" > "$pipeline_py"; /bin/bash -c "source activate py37 && dsl-compile --py \"$pipeline_py\" --output \"$pipeline_yaml\" || python3 \"$pipeline_py\""; done
---> Running in eb21ce3bd146
/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
Traceback (most recent call last):
File "/root/miniconda3/envs/py37/bin/dsl-compile", line 33, in <module>
sys.exit(load_entry_point('kfp==1.5.0', 'console_scripts', 'dsl-compile')())
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 104, in main
not args.disable_type_check,
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 91, in compile_pyfile
_compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 54, in _compile_pipeline_function
raise ValueError('A function with @dsl.pipeline decorator is required in the py file.')
ValueError: A function with @dsl.pipeline decorator is required in the py file.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:From /samples/core/parameterized_tfx_oss/parameterized_tfx_oss.py:75: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.
Instructions for updating:
external_input is deprecated, directly pass the uri to ExampleGen.
WARNING:absl:The "input" argument to the CsvExampleGen component has been deprecated by "input_base". Please update your usage as support for this argument will be removed soon.
/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
Traceback (most recent call last):
File "/root/miniconda3/envs/py37/bin/dsl-compile", line 33, in <module>
sys.exit(load_entry_point('kfp==1.5.0', 'console_scripts', 'dsl-compile')())
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 104, in main
not args.disable_type_check,
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 91, in compile_pyfile
_compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 54, in _compile_pipeline_function
raise ValueError('A function with @dsl.pipeline decorator is required in the py file.')
ValueError: A function with @dsl.pipeline decorator is required in the py file.
/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
Traceback (most recent call last):
File "/root/miniconda3/envs/py37/bin/dsl-compile", line 33, in <module>
sys.exit(load_entry_point('kfp==1.5.0', 'console_scripts', 'dsl-compile')())
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 104, in main
not args.disable_type_check,
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 91, in compile_pyfile
_compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)
File "/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 58, in _compile_pipeline_function
raise ValueError('There are multiple pipelines: %s. Please specify --function.' % func_names)
ValueError: There are multiple pipelines: ['flipcoin_pipeline', 'flipcoin_exit_pipeline']. Please specify --function.
/root/miniconda3/envs/py37/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
Removing intermediate container eb21ce3bd146
---> 6bd1cd1530e1
Step 28/42 : FROM ppc64le/debian:stretch
[...]
```
#### Error output / docker build log x86-Macbook
```
[...]
Step 21/36 : RUN set -e; < /samples/sample_config.json jq .[].file --raw-output | while read pipeline_yaml; do pipeline_py="${pipeline_yaml%.yaml}"; mv "$pipeline_py" "${pipeline_py}.tmp"; echo 'import kfp; kfp.components.default_base_image_or_builder="gcr.io/google-appengine/python:2020-03-31-141326"' | cat - "${pipeline_py}.tmp" > "$pipeline_py"; dsl-compile --py "$pipeline_py" --output "$pipeline_yaml" || python3 "$pipeline_py"; done
---> Running in a008c231c82d
/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
2021-05-05 11:10:07.987152: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-05-05 11:10:07.987241: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
Traceback (most recent call last):
File "/usr/local/bin/dsl-compile", line 33, in <module>
sys.exit(load_entry_point('kfp==1.5.0', 'console_scripts', 'dsl-compile')())
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 104, in main
not args.disable_type_check,
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 91, in compile_pyfile
_compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 54, in _compile_pipeline_function
raise ValueError('A function with @dsl.pipeline decorator is required in the py file.')
ValueError: A function with @dsl.pipeline decorator is required in the py file.
2021-05-05 11:10:13.142424: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-05-05 11:10:13.142485: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
WARNING:absl:From /samples/core/parameterized_tfx_oss/parameterized_tfx_oss.py:75: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.
Instructions for updating:
external_input is deprecated, directly pass the uri to ExampleGen.
WARNING:absl:The "input" argument to the CsvExampleGen component has been deprecated by "input_base". Please update your usage as support for this argument will be removed soon.
/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
Traceback (most recent call last):
File "/usr/local/bin/dsl-compile", line 33, in <module>
sys.exit(load_entry_point('kfp==1.5.0', 'console_scripts', 'dsl-compile')())
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 104, in main
not args.disable_type_check,
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 91, in compile_pyfile
_compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 54, in _compile_pipeline_function
raise ValueError('A function with @dsl.pipeline decorator is required in the py file.')
ValueError: A function with @dsl.pipeline decorator is required in the py file.
/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
Traceback (most recent call last):
File "/usr/local/bin/dsl-compile", line 33, in <module>
sys.exit(load_entry_point('kfp==1.5.0', 'console_scripts', 'dsl-compile')())
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 104, in main
not args.disable_type_check,
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 91, in compile_pyfile
_compile_pipeline_function(pipeline_funcs, function_name, output_path, type_check)
File "/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/main.py", line 58, in _compile_pipeline_function
raise ValueError('There are multiple pipelines: %s. Please specify --function.' % func_names)
ValueError: There are multiple pipelines: ['flipcoin_pipeline', 'flipcoin_exit_pipeline']. Please specify --function.
/usr/local/lib/python3.7/site-packages/kfp-1.5.0-py3.7.egg/kfp/compiler/compiler.py:1117: UserWarning: Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
warnings.warn("Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.")
Removing intermediate container a008c231c82d
---> 6ab54eda2f90
Step 22/36 : FROM debian:stretch
[...]
```
---
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https://api.github.com/repos/kubeflow/pipelines/issues/5589 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5589/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5589/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5589/events | https://github.com/kubeflow/pipelines/issues/5589 | 876,327,632 | MDU6SXNzdWU4NzYzMjc2MzI= | 5,589 | SDK - Creating lightweight component inside lightweight component function leads to wrong inner component | {
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"For quick debug try:\r\n```python\r\n def inpod_pipeline_func(a: int, b: int):\r\n add_task = add(a, b)\r\n print(add_task.outputs)\r\n show_result(add_task.outputs['func_name'], add_task.outputs['result'])\r\n```",
"Thanks for your quick reply. I've made two tests with your guidance.\r\n... | 2021-05-05T10:57:40 | 2021-06-03T00:19:18 | 2021-06-03T00:19:18 | NONE | null | ### Environment
* Kubernetes: 1.18.9
* KFP version: 1.4.0
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.4.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```shell
$ pip list | grep kfp
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
```
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
I want to launch a new pipeline (not local) from one component named `create_pipeline_in_pod` of the pipeline named `outer_pipeline` . The demo codes are below.
```python
import kfp
import kfp.dsl as dsl
from kfp.components import func_to_container_op
EXP_NAME = 'demo'
RUN_NAME = 'outer_pipeline'
def create_pipeline_in_pod(data_dir: str):
import kfp
import kfp.dsl as dsl
from kfp.components import func_to_container_op
from typing import NamedTuple
print('create_pipeline_in_pod')
EXP_NAME = 'demo'
RUN_NAME = 'pipeline_in_pod'
@func_to_container_op
def add(
a: int,
b: int # todo: can't recognize return type
) -> NamedTuple('output', [('func_name', str), ('result', int)]):
from collections import namedtuple
output = namedtuple('output', ['func_name', 'result'])
return output('add_two_numbers', a + b)
@func_to_container_op
def show_result(func_name, result):
print(f'{func_name} result: {result}')
@dsl.pipeline(name=RUN_NAME)
def inpod_pipeline_func(a: int, b: int):
add_task = add(a, b)
show_result(add_task.outputs['func_name'], add_task.outputs['result'])
if __name__ == '__main__':
# compile and submit
print('begin compile in pod...')
kfp.compiler.Compiler().compile(inpod_pipeline_func,
f'{data_dir}/{RUN_NAME}.yaml')
print('finish compile in pod')
# run pipeline outside
client = kfp.Client(host='my_k8s_master_ip:8888', namespace='kubeflow')
client.create_run_from_pipeline_func(inpod_pipeline_func,
arguments={
'a': '1',
'b': '2'
},
experiment_name=EXP_NAME,
run_name=RUN_NAME)
print('submit pipeline in pod')
op = func_to_container_op(create_pipeline_in_pod, packages_to_install=['kfp'])
# Define the pipeline
@dsl.pipeline(name=RUN_NAME)
def pipeline_func(data_dir: str):
vop = dsl.VolumeOp(
name='create_pvc',
resource_name='data_volume',
size='1Gi', # if size not change, then use the same pvc
storage_class='local-path',
modes=dsl.VOLUME_MODE_RWO)
op(data_dir).add_pvolumes({data_dir: vop.volume})
if __name__ == '__main__':
print('begin compile...')
kfp.compiler.Compiler().compile(pipeline_func, f'{RUN_NAME}.yaml')
print('finish compile')
client = kfp.Client(host='my_k8s_master_ip:8888', namespace='kubeflow')
client.create_run_from_pipeline_func(pipeline_func,
arguments={'data_dir': '/mnt'},
experiment_name=EXP_NAME,
run_name=RUN_NAME)
print('submit pipeline')
```
The above code will fail when doing `kfp.compiler.Compiler().compile(inpod_pipeline_func,
f'{data_dir}/{RUN_NAME}.yaml')`.
And the error messages in the kubeflow pipeline ui are below.
```sh
create_pipeline_in_pod
begin compile in pod...
Traceback (most recent call last):
File "/tmp/tmp.aHWqREfBIL", line 50, in <module>
_outputs = create_pipeline_in_pod(**_parsed_args)
File "/tmp/tmp.aHWqREfBIL", line 32, in create_pipeline_in_pod
f'{data_dir}/{RUN_NAME}.yaml')
File "/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py", line 975, in compile
package_path=package_path)
File "/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py", line 1029, in _create_and_write_workflow
pipeline_conf)
File "/usr/local/lib/python3.7/site-packages/kfp/compiler/compiler.py", line 861, in _create_workflow
pipeline_func(*args_list, **kwargs_dict)
File "/tmp/tmp.aHWqREfBIL", line 26, in inpod_pipeline_func
show_result(add_task.outputs['func_name'], add_task.outputs['result'])
KeyError: 'func_name'
```
I've adapted three ablation tests.
#### Ablation test 1: Directly compile the code inside `create_pipeline_in_pod` function
It compiles perfectly and the kubeflow pipeline ui shows the right results.
#### Ablation test 2: Define an inner pipeline without passing small values
It also compiles perfectly and the kubeflow pipeline ui shows the right results of two runs. (Yes. The two runs are parallel below the `demo` experiment). The codes of the new `create_pipeline_in_pod` function are below.
```python
def create_pipeline_in_pod(data_dir: str):
import kfp
import kfp.dsl as dsl
from kfp.components import func_to_container_op
print('create_pipeline_in_pod')
EXP_NAME = 'demo'
RUN_NAME = 'pipeline_in_pod'
@func_to_container_op
def show_result(info):
print(info)
@dsl.pipeline(name=RUN_NAME)
def inpod_pipeline_func(info):
show_result(info)
# compile and submit
print('begin compile in pod...')
kfp.compiler.Compiler().compile(inpod_pipeline_func,
f'{data_dir}/{RUN_NAME}.yaml')
print('finish compile in pod')
client = kfp.Client(host='my_k8s_master_ip:8888', namespace='kubeflow')
client.create_run_from_pipeline_func(inpod_pipeline_func,
arguments={
'info': 'hello world',
},
experiment_name=EXP_NAME,
run_name=RUN_NAME)
print('submit pipeline in pod')
```
#### Ablation test 3: Change the `add` function name to `_add` or `add_here`
Still the same error messages as above.
### Expected result
<!-- What should the correct behavior be? -->
The pipeline defined inside the `create_pipeline_in_pod` component compiles perfectly.
Thanks a lot.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5588 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5588/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5588/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5588/events | https://github.com/kubeflow/pipelines/issues/5588 | 876,268,627 | MDU6SXNzdWU4NzYyNjg2Mjc= | 5,588 | Managed storage default sql username is not root as described | {
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"This issue gives the same errors as found here: https://stackoverflow.com/questions/66600365/gcp-ai-platform-pipelines-kfp-use-managed-storage-cloudsqlproxy-issue#",
"Thanks for reporting! This is a sub problem of https://github.com/kubeflow/pipelines/issues/5717.\r\nLet's track this issue in the more generic is... | 2021-05-05T09:44:48 | 2021-06-01T08:13:55 | 2021-06-01T08:13:55 | NONE | null | @Svendegroote91 Sorry for the late reply, reading through your description, it sounds like
> database username & password left empty to default to root user without password.
this part is wrong, you need to type `root` into database username, it's not the default when you leave it to empty.
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/issues/4973#issuecomment-768226895_
The GCP AI platform Pipelines description for managed storage also says that if username is empty then it defaults to 'root' but the default is an empty string at least when adding a password. I had to type in 'root' to make it work. | {
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"There were brief periods the node was in NotReady state:\r\n\r\n> Normal NodeNotReady 9m40s (x10 over 40h) kubelet Node gke-kfp-standalone-1-kfp-standalone-1-60baa0a0-mr50 status is now: NodeNotReady\r\n\r\nbut it recovered soon",
"EDIT: this is unrelated to this issue\r\n\r\nAnother symptom:\r... | 2021-05-05T02:47:23 | 2021-05-05T06:15:42 | 2021-05-05T06:14:46 | CONTRIBUTOR | null | There are multiple types of errors I'm observing when running tests:
1. Pod fail with CreateContainerError
> Warning Failed 103s kubelet Error: context deadline exceeded
Warning FailedSync 91s (x2 over 102s) kubelet error determining status: rpc error: code = Unknown desc = Error: No such container: a3311041ff774eb4530aa3a46c05f42af0cfb1bfedfaa8569188afff225bdd10
In pod logs, there's error message: `failed to try resolving symlinks in path "/var/log/pods/kubeflow_pipeline-with-loop-static-tsdhk-940757082_3a3f5c55-7210-45aa-9b48-cbc9396d310b/main/0.log": lstat /var/log/pods/kubeflow_pipeline-with-loop-static-tsdhk-940757082_3a3f5c55-7210-45aa-9b48-cbc9396d310b/main/0.log: no such file or directory`, but I don't know where it comes from.
when I try to find what fails by `kubectl get pod -o wide | grep CreateContainerError`
they all happen in one node `gke-kfp-standalone-1-kfp-standalone-1-60baa0a0-mr50`. | {
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"I understand the issue.\r\nI wonder how reliable `metadata_execution_id` is. Have you seen any cases where Metadata Writer failed to set it?\r\n\r\nWe're a little bit hesitant to introduce a dependency between cache server and metadata writer services.",
"> I wonder how reliable metadata_execution_id is.\r\n\r\n... | 2021-05-04T10:20:28 | 2021-06-11T00:49:43 | 2021-06-11T00:49:43 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
We deploy it via ArgoCD.
* KFP version: 1.0.4
Two components involved in issue:
* ml-pipeline/metadata-writer:1.0.4
* ml-pipeline/cache-server:1.0.4
### Steps to reproduce
This is a race condition between metadata-writer and cache-server, so not easy to re-produce, but we observe it quite frequently.
If `cache-server/watcher` gets pod completed event before `metadata_writer` can inject the `pipelines.kubeflow.org/metadata_execution_id` label, metadata link becomes missing for the cache record forever.
### Expected result
Expecting `pipelines.kubeflow.org/metadata_execution_id` label values in ExecutionOutput field of cache record to reflect the real id, instead of an empty value, so that we can continue being able to see the linked metadata in UI for re-runs of cached steps later on.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
Both components watches for pod events to take action, see:
https://github.com/kubeflow/pipelines/blob/1.0.4/backend/src/cache/server/watcher.go#L30-L91
https://github.com/kubeflow/pipelines/blob/1.0.4/backend/metadata_writer/src/metadata_writer.py#L129-L258
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5579 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5579/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5579/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5579/events | https://github.com/kubeflow/pipelines/issues/5579 | 874,306,190 | MDU6SXNzdWU4NzQzMDYxOTA= | 5,579 | In metacontroller statefulset container securitycontext block allowPrivilegeEscalation and privileged are set to true . Does the controller container really need those privileges | {
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"@orugantichetan can you raise the issue in metacontroller repo instead?"
] | 2021-05-03T07:19:41 | 2021-06-11T00:50:37 | 2021-06-11T00:50:37 | NONE | null | /kind question
/kind/question
metacontroller statefulset container securitycontext block allowPrivilegeEscalation and privileged are set to true . Does the container really need those privileges
In metacontroller repo those privileged permissions are not set [https://github.com/metacontroller/metacontroller/blob/v0.3.0/manifests/metacontroller.yaml](url)
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"We will upgrade metacontroller and investigate if there is any breaking change in this major version bump.\r\n\r\nAnother option is to remove metacontroller, and use kubeoperator or helm instead.",
"The following is possible. If you are interested in a merge request please tell me.\r\n\r\nI \r\n1. upgraded to 2.... | 2021-05-03T06:27:59 | 2021-09-14T12:36:32 | 2021-09-14T12:36:32 | NONE | null | /kind question
/kind/question
can metacontroller be upgraded to latest version from V0.3.0
from v1.2.0 metacontroller have support for distroless image .
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"/assign\r\n\r\nWill take a look. "
] | 2021-05-02T21:14:47 | 2021-05-16T07:44:45 | 2021-05-16T07:44:45 | 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: 1.4.0, 1.5.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version: n/a
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
* Compile test pipeline below (run py script)
```py
import kfp as kfp
@kfp.components.func_to_container_op
def print_func(param: str):
print(str(param))
return
@kfp.dsl.pipeline(name='pipeline')
def pipeline(param: str):
print_func(param)
return
if __name__ == '__main__':
kfp.compiler.Compiler().compile(pipeline, __file__ + ".zip")
```
* Upload to KF
* Grab the `pipeline-id` to use it in the command line sdk
* Using the command line SDK, run the following:
```sh
$ kfp run submit --pipeline-id <pipeline-id> --experiment-name 'Test = parsing' param=12345 # Succeeds
$ kfp run submit --pipeline-id <pipeline-id> --experiment-name 'Test = parsing' param=some_name=4567 # Fails
dictionary update sequence element #0 has length 3; 2 is required
```
Running the parameter directly from the KFP UI with value `some_name=4567` works fine and prints `some_name=4567`.
#### Problem Explanation
The pipeline expects an input parameter named `param` to run and it prints the value of that param. The SDK will parse pipeline parameters on the `=` sign [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/cli/run.py#L79). The value contains an `=` sign, so we are trying to update the `dict()` with `('param', 'some_name', '4567')` when the goal is to update it with `('param', 'some_name=4567')`.
#### Problem Significance
The equality sign `=` is typically used to partition datasets in distributed storage (S3, HDFS, etc) leading to paths like
```
hdfs://path/to/table/year_partition=2020/month_partition=01/other_partition=other_value/file1.parquet
hdfs://path/to/table/year_partition=2020/month_partition=02/other_partition=other_value/file1.parquet
...
```
Due to the problem discussed above, we cannot use the CLI SDK to provide parameters that contain the `=` sign. However, we _can_ use the KFP UI and pass such paramters, leading to inconsistent behavior.
<!--
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? -->
Running
```sh
$ kfp run submit --pipeline-id <pipeline-id> --experiment-name 'Test = parsing' param=some_name=4567
```
should succeed just like running the pipeline via the KFP UI with the value `some_name=4567` for the run parameter **param**.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
#### Suggested Solution
[This line](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/cli/run.py#L79) should be
```py
arg_dict = dict(arg.split('=', maxsplit=1) for arg in args)
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/assign",
"I don`t get this issue on the latest version of Kubeflow pipelines. Could which of the `XGB` examples did you use @padrian2s.",
"I use the default XGB after KF install, the one that is already published into DB. \r\n@NikeNano ",
"I vaguely remember this is already fixed in later KFP version",
"... | 2021-05-02T09:46:21 | 2022-03-03T06:05:11 | 2022-03-03T06:05:11 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
deploy manually (kustomize build)
* KFP version:
KFP 1.3
* KFP SDK version:
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
### Steps to reproduce
upload xgboost sample pipeline, which has no pipeline input parameters
{"error_message":"Error creating pipeline: Create pipeline failed: Failed to get parameters from the workflow: InvalidInputError: Failed to parse the parameter.: error unmarshaling JSON: json: cannot unmarshal array into Go value of type v1alpha1.Workflow","error_details":"Error creating pipeline: Create pipeline failed: Failed to get parameters from the workflow: InvalidInputError: Failed to parse the parameter.: error unmarshaling JSON: json: cannot unmarshal array into Go value of type v1alpha1.Workflow"}
### Expected result
TBD
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"<img width=\"1192\" alt=\"Screen Shot 2021-04-29 at 11 33 56 AM\" src=\"https://user-images.githubusercontent.com/52932582/116601525-763fd400-a8df-11eb-86aa-9be8708b2715.png\">\r\n",
"Duplicate of https://github.com/kubeflow/pipelines/issues/3226"
] | 2021-04-29T18:35:05 | 2021-05-18T14:30:44 | 2021-05-18T14:30:44 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipeline Standalone with GCP
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
Submit a pipeline which will generate artifatcs, then submit the pipeline run.
Go to the UI which shows the artifact list, click `Created At` to sort by created time. In addition, the filter also does not work.
<!-- Deploy a pipeline which will generate artifacts, submit a pipeline run
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
The sort behavior should work
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Update area:\r\n\r\n```\r\n with open('/mlpipeline-ui-metadata.json', 'w') as f:\r\n json.dump(metadata, f)\r\n```",
"Example of outputting mlpipeline-ui-metadata in component yaml component: https://github.com/kubeflow/pipelines/blob/d362cab2046b92efa5f015c6a980bc9371b4b63b/components/tensorflow/tensorboard... | 2021-04-29T07:57:41 | 2021-07-16T00:41:56 | 2021-07-16T00:41:56 | CONTRIBUTOR | null | https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/ | {
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"I've tested as similar approach using 1.6.0.rc0 here:\r\n\r\n```\r\nimport kfp\r\nimport kfp.components as comp\r\nimport kfp.v2.dsl as dsl\r\nfrom kfp.v2 import compiler\r\nfrom kfp.v2.dsl import (\r\n component,\r\n Input,\r\n Output,\r\n Artifact,\r\n Dataset,\r\n Metrics,\r\n Model\r\n)\r\... | 2021-04-28T16:53:15 | 2022-03-03T00:05:17 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Deployed Standalone on k3s
* KFP version:
1.50
* KFP SDK version:
1.5.0rc3
### Steps to reproduce
Create lightweight component using OutputArtifact, compiled with V1 compiler, using V2 Compatible, and run through the UI. The following error appears, I think in the effort to create the output artifact data library.
```bash
Traceback (most recent call last):
File "/tmp/tmp.VtSD5WzDcx", line 594, in <module>
executor_main()
File "/tmp/tmp.VtSD5WzDcx", line 588, in executor_main
function_to_execute=function_to_execute)
File "/tmp/tmp.VtSD5WzDcx", line 353, in __init__
artifacts_list[0])
File "/tmp/tmp.VtSD5WzDcx", line 366, in _make_output_artifact
return OutputArtifact(artifact_type=type(artifact), artifact=artifact)
File "/tmp/tmp.VtSD5WzDcx", line 296, in __init__
os.makedirs(self.path, exist_ok=True)
File "/usr/local/lib/python3.7/os.py", line 208, in makedirs
head, tail = path.split(name)
File "/usr/local/lib/python3.7/posixpath.py", line 107, in split
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
F0428 16:41:59.832704 37 main.go:56] Failed to successfuly execute component: exit status 1
goroutine 1 [running]:
github.com/golang/glog.stacks(0xc000c80300, 0xc000652000, 0x61, 0xb6)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:769 +0xb9
github.com/golang/glog.(*loggingT).output(0x2890520, 0xc000000003, 0xc0000fe000, 0x27d6a2a, 0x7, 0x38, 0x0)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:720 +0x3b3
github.com/golang/glog.(*loggingT).printf(0x2890520, 0x3, 0x1b2c9c8, 0x2b, 0xc000a65f58, 0x1, 0x1)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:655 +0x153
github.com/golang/glog.Fatalf(...)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:1148
main.main()
/build/cmd/launch/main.go:56 +0x357
```
### Expected result
The artifact should be created without error, so that the pipeline can run successfully.
### Materials and Reference
I am working on a sample case in response to the discussion taking place in #5453. The code at the moment is
```python
from kfp import components
from kfp.dsl.io_types import OutputArtifact, Model
from kfp.dsl import pipeline, PipelineExecutionMode
from kfp.compiler import Compiler
def train_model(model: OutputArtifact(Model)) -> str:
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from joblib import dump
import os
fake_features, fake_labels = make_classification(n_features = 5)
classifier = RandomForestClassifier(n_estimators=3)
classifier.fit(fake_features, fake_labels)
dump(classifier, os.path.join(model.path, 'model.joblib'))
return model.uri
train_model_op = components.create_component_from_func_v2(train_model, packages_to_install=['sklearn'])
kf_serving_op = components.load_component_from_file('./components/kfserving_v2.yaml')
@pipeline(
name='output_artifact_sample',
description='Demonstrate using OutputArtifact URI information in next component',
pipeline_root='minio://sample_output_artifiact_root'
)
def simple_kf_serving_pipeline():
train_step = train_model_op()
kf_serving_op(
action = 'apply',
model_name = 'output_artifact_sample',
model_uri = train_step.outputs['Output'],
framework = 'sklearn',
service_account= 'sa',
namespace='default')
def main():
Compiler(mode = PipelineExecutionMode.V2_COMPATIBLE).compile(simple_kf_serving_pipeline, 'sample_artifact_pipeline.yaml')
if __name__ == "__main__":
main()
```
---
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https://api.github.com/repos/kubeflow/pipelines/issues/5558 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5558/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5558/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5558/events | https://github.com/kubeflow/pipelines/issues/5558 | 870,072,899 | MDU6SXNzdWU4NzAwNzI4OTk= | 5,558 | [feature] Configurable KFP Artifact types while storing to Minio | {
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"Not that familiar with kfserving, but with respect to #5453.\r\n\r\nCan I assume there are 2 separate issues?\r\n\r\n- for model retrieval, kfserving only support local model or http/https model (#5453)\r\n- for remote model artifacts, it expects an unarchived model binary? (This ticket)\r\n\r\n\r\n",
"Yes indee... | 2021-04-28T15:24:38 | 2022-02-21T08:55:19 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
We are trying to marry "KF Pipelines + KF Artifacts + KF Serving" in a nice use-case where we train a (sklearn) model using KFP, store it as an output artifact to the artifact store of pipelines (minio) and then deploy that model directly from the artifact store using KFServing.
### What is the use case or pain point?
The problem we are facing is that the output artifacts are by default stored as .tgz , so even a .joblib blob model object is still wrapped into .tgz at creation time of the artifact in minio. On the other end, the default KF serving application server expects a .joblib file, not a .tgz.
There are at least two ways to tackle this issue:
(1) On the KFP side, by adding some kind of flag that would allow to dump some artifacts to minio uncompressed (i.e. directly as .joblib in our case) - something we discussed with @eterna2 already in the Kubeflow Slack, or
(2) On the KFServing side, by adding a functionality to the storage -initiazer such that it can unpack the .tgz during the inference service creation time. This suggestion was made in this [ticket](https://github.com/kubeflow/kfserving/issues/1569).
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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* How did you deploy Kubeflow Pipelines (KFP)?
Hi, we deployed Kubeflow using the [kfctl_k8s_istio.v1.2.0.yaml](https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml) file.
* KFP version:
Kubeflow Pipelines Version: 1.0.4
* KFP SDK version:
v1.2.0
### Steps to reproduce
We setup Identity Aware Proxy, all the traffic is getting routed to the istio gateway, then we're redirected to the Central Dashboard. Once we arrive there, everything works as expected. Whenever we navigate to **Pipelines --> Experiments**, we get the following error message:
`
{"error":"Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header.","message":"Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header.","code":10,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Request header error: there is no user identity header.","error_details":"Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header."}]}
`
We see this same error message only under **Pipelines --> Experiments** and under **Pipelines --> Archive**. All the other components of Kubeflow like Katib, and Juypter are working just fine. I also double checked all our header configs. We're currently passing the following under _**Kubeflow-config**_
_cluster-name
clusterDomain: cluster.local
istio-namespace: istio-system
userid-header: X-Goog-Authenticated-User-Email
userid-prefix: accounts.google.com:_
After doing some more poking around, I noticed that an Envoy Filter was created under the _**istio-system**_ namespace that was adding the _**anonymous@kubeflow.org**_ user as a header. However, if you look at the configs, it's applying the **_kubeflow-userid_** instead of the _**x-goog-authenticated-user-email**_. I modified it, and it didn't seem to impact anything.
_apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
name: add-user-filter
namespace: istio-system
spec:
filters:
- filterConfig:
inlineCode: |
function envoy_on_request(request_handle)
request_handle:headers():add("kubeflow-userid","anonymous@kubeflow.org")
end
filterName: envoy.lua
filterType: HTTP
insertPosition:
index: FIRST
listenerMatch:
listenerType: GATEWAY
workloadLabels:
app: istio-ingressgateway_
You're guidance to resolve the header issue would be much appreciated.
### Expected result
We expect the **Kubeflow Pipeline Experiments** and **Kubeflow Pipeline Archives** to load correctly for each user. Right not, it's not loading for anyone.
Thank you for your time,
Royer R Ruiz
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https://api.github.com/repos/kubeflow/pipelines/issues/5555 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5555/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5555/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5555/events | https://github.com/kubeflow/pipelines/issues/5555 | 869,309,587 | MDU6SXNzdWU4NjkzMDk1ODc= | 5,555 | [feature] Allow jobs to be scheduled on AWS Fargate | {
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"@yuhuishi-convect we might want to switch to argo v3 emissary executor: https://argoproj.github.io/argo-workflows/workflow-executors/#emissary-emissary\r\nwhich doesn't require privileged permission.\r\n",
"This can be resolved by https://github.com/kubeflow/pipelines/issues/1654, when we switch to that executoo... | 2021-04-27T22:15:47 | 2022-03-03T00:05:28 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
I am trying to run a large number of kubeflow pipeline jobs on AWS [Fargate](https://aws.amazon.com/fargate/?nc=sn&loc=0&whats-new-cards.sort-by=item.additionalFields.postDateTime&whats-new-cards.sort-order=desc&fargate-blogs.sort-by=item.additionalFields.createdDate&fargate-blogs.sort-order=desc).
The kubeflow pipeline components are deployed on AWS EKS. While the EKS has a Fargate profile that allows scheduling pods onto *virtual* nodes, Kubeflow pipeline jobs contain privileged containers that prevent them from using Fargate machine resources (https://docs.aws.amazon.com/eks/latest/userguide/fargate.html).
### What is the use case or pain point?
This feature enables more cost-efficient job scheduling since many jobs (e.g., hyperparameter tuning, scenario analysis ...) are *ephermal*, so scheduling them on a serverless machine pool such as provided by Fargate makes more sense. This avoids the need to reserve a pool of nodes upfront while supporting the *burst* type of workloads.
However, kubeflow pipeline jobs use privileged containers that are not supported by Fargate. For example, the `wait` container
```
containers:
- name: wait
image: 'gcr.io/ml-pipeline/argoexec:v2.7.5-license-compliance'
command:
- argoexec
- wait
env:
- name: ARGO_POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: ARGO_CONTAINER_RUNTIME_EXECUTOR
value: pns
resources: {}
volumeMounts:
- name: podmetadata
mountPath: /argo/podmetadata
- name: mlpipeline-minio-artifact
readOnly: true
mountPath: /argo/secret/mlpipeline-minio-artifact
- name: input-artifacts
mountPath: /mainctrfs/tmp/inputs/config/data
subPath: config
- name: input-artifacts
mountPath: /mainctrfs/tmp/inputs/data/data
subPath: convect-prepare-data-out_path
- name: pipeline-runner-token-j2fm7
readOnly: true
mountPath: /var/run/secrets/kubernetes.io/serviceaccount
terminationMessagePath: /dev/termination-log
terminationMessagePolicy: File
imagePullPolicy: IfNotPresent
securityContext:
capabilities:
add:
- SYS_PTRACE
```
needs further configurations under `securityContext`.
I am wondering if there are any workarounds or better solutions to make the jobs schedulable on serverless resource pools such as Fargate.
### Is there a workaround currently?
I do not see any solutions so far.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5553 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5553/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5553/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5553/events | https://github.com/kubeflow/pipelines/issues/5553 | 869,202,635 | MDU6SXNzdWU4NjkyMDI2MzU= | 5,553 | [backend] Local install: proxy-agent error "Could not resolve host: metadata.google.internal" | {
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"Yes, for local install, you should use\r\n```\r\nkubectl apply -k \"github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION\"\r\n```\r\n\r\nwe should update documentation in KFP standalone: https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployme... | 2021-04-27T19:42:14 | 2021-04-30T02:23:38 | 2021-04-30T02:23:38 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
```
# Install k3d kubectl
brew install k3d kubectl
k3d cluster create kubeflow
k3d kubeconfig merge ml-kubeflow --kubeconfig-switch-context
# Install Kubeflow Pipelines
export PIPELINE_VERSION=1.5.0
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
```
* KFP version:
1.5.0
* KFP SDK version:
N/A
### Steps to reproduce
1. Install using steps above on MacOS w/ latest Docker running.
2. `kubectl get pods -A`
3. `proxy-agent-<suffix>` pod shows Error status
```
kubeflow proxy-agent-<suffix> 0/1 CrashLoopBackOff 13 49m
```
4. `kubectl logs proxy-agent-<suffux> -n kubeflow` shows the following error:
```
++ curl http://metadata.google.internal/computeMetadata/v1/instance/zone -H 'Metadata-Flavor: Google'
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0curl: (6) Could not resolve host: metadata.google.internal
```
### Expected result
`proxy-agent-<suffix>` pod shows Running status
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
I've seen this happen in versions 1.4.1 and 1.5. I would guess there's something GCP specific in the `dev` env manifest but I'm not sure.
```
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
```
I don't remember this happening in 1.3 and I think the manifest was different in the 1.3 install instructions and it used the `platform-agnostic-pns` env.
```
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION"
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5548 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5548/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5548/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5548/events | https://github.com/kubeflow/pipelines/issues/5548 | 868,350,145 | MDU6SXNzdWU4NjgzNTAxNDU= | 5,548 | [sdk] mlpipelines-metrics path is wrong when using kfp.components.load_component_from_text | {
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"> /mlpipeline-metrics.json, but instead I am getting /tmp/outputs/MLPipeline_Metrics/data\r\n\r\nThis is by design. Metrics is a normal output and has same style of paths as other outputs. Also, putting files in root is problematic for multiple reasons. It's not possible to mount volumes to the root to grab the fi... | 2021-04-27T01:28:35 | 2022-03-03T03:05:15 | 2022-03-03T03:05:15 | NONE | null | Let me describe what my problem is:
- I am using load_component_from_text to define my components
- In the pipeline I am generating a mlpipeline-metrics artifact
- After compiling the pipeline I am checking the yaml file that is created
- Within the yaml file, I was expecting the path of this artifact to be **/mlpipeline-metrics.json**, but instead I am getting **/tmp/outputs/MLPipeline_Metrics/data**
- This is causing Kubeflow UI to not show any output metrics.
- I am changing the paths on the yaml file to be **/mlpipeline-metrics.json** (as expected) and everything works as expected
Before I used to do this with ContainerOp and the paths seemed to be generated properly, which makes me think that there may be something wrong with how the aths are generated when the pipeline is compiled
### Steps to reproduce
```
load_component_from_text('''
name: name_of_component
outputs:
- {name: MLPipeline Metrics, type: Metrics}
implementation:
container:
image: image_name
command: [python, -u, script_name.py]
args: [
...
--metrics_path, {outputPath: MLPipeline Metrics}]
''')
```
### Expected result
yaml file with that contains the following information
```
name: model-training
container:
args: [ ...
--metrics_path, /mlpipeline-metrics.json]
command: [python, -u, script_name.py]
image: image_name
...
...
outputs:
artifacts:
- {name: mlpipeline-metrics, path: /mlpipeline-metrics.json}
```
### Instead of the expected output I am getting the next output
```
name: model-training
container:
args: [ ...
--metrics_path, /tmp/outputs/MLPipeline_Metrics/data]
command: [python, -u, script_name.py]
image: image_name
...
...
outputs:
artifacts:
- {name: mlpipeline-metrics, path:/tmp/outputs/MLPipeline_Metrics/data}
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5547 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5547/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5547/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5547/events | https://github.com/kubeflow/pipelines/issues/5547 | 867,837,577 | MDU6SXNzdWU4Njc4Mzc1Nzc= | 5,547 | [Test Infra] v2 sample test failing with run timeout 4-26 | {
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"Strange, the temporary error goes away by itself. It might be some temporary infra issue",
"closing for now, we can reopen if we find similar issues"
] | 2021-04-26T15:36:09 | 2021-04-27T13:56:22 | 2021-04-27T13:56:22 | CONTRIBUTOR | null | Example: https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5546/kubeflow-pipelines-samples-v2/1386628232907853824
One test running: https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/45981700-5acd-4d4e-b77d-3a1780f9ab68
All PRs are failing on this.
However, even if we wait for a while and check the run status, it failed at the end.
This needs more investigation than just increasing timeout.
## Details
some symptoms I noticed:
1. kaniko container build took a really long time at "[36mINFO[0m[0347] Taking snapshot of full filesystem..." step
2. many pods fail with `CreateContainerError`, but at the time I checked, pod events has been GCed. I need to verify on a container while it is running to understand why.
## Guesses
because of 2, might be worth trying pinning Kaniko to an older version, maybe our latest one has some problems | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5545 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5545/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5545/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5545/events | https://github.com/kubeflow/pipelines/issues/5545 | 867,225,810 | MDU6SXNzdWU4NjcyMjU4MTA= | 5,545 | pipeline_conf.set_ttl_seconds_after_finished(seconds=10) NOT WORKING | {
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"I think [this issue](https://github.com/kubeflow/pipelines/issues/5234) could guide you to a solution @prasadkyp7 ",
"+1 to NikeNano's answer",
"we upgraded kubeflow to latest version but still TTL is not working. \r\nNeed an urgent help on this\r\n",
"@prasadkyp7 can you add more details?\r\nWhat config did... | 2021-04-26T03:43:39 | 2021-08-12T14:27:07 | 2021-06-11T00:54:14 | NONE | null | * How did you deploy Kubeflow Pipelines (KFP)?
**_WE are using full kubeflow deployment on AWS Cloud_**
* 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. -->
Kubeflow UI is not showing us any version
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.4.0
kfp-pipeline-spec 0.1.6
kfp-server-api 1.4.1
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1.create a pipeline code
2. set the ttl using pipeline config
pipeline_conf = kfp.dsl.PipelineConf()
x=pipeline_conf.set_ttl_seconds_after_finished(seconds=10)
3. create the pipline using
kfp.compiler.Compiler().compile(loadTrain_pipeline,'loadTrain_pipeline_2.zip',pipeline_conf=x) and upload it in UI.
4. TEST this pipeline by creating a run.
EXPECTED RESULT:
As per setting TTL to 10 seconds , the pods created must be deleted after 10 sec.
But its not deleting the pods, in my case i am creating 1000's pods and as i am not deleting the successful pods Kubeflow UI is crashing. | {
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"This is expected. Your pipeline doesn't specify any default value for param.\r\nOne option is to provide a default value when you define the pipeline function, for instance:\r\n```\r\n @kfp.dsl.pipeline(name='pipeline')\r\n def pipeline(param: int = 10):\r\n ...\r\n```\r\nAnother option, probably more common... | 2021-04-25T17:01:55 | 2021-05-18T02:54:24 | 2021-05-18T02:54:24 | NONE | null | Trying to work with KFP SDK directly with argo workflow and when compiling a pipeline with input parameter a default value is not created so the workflow is invalid.
### Environment
* KFP version: not relevant, submitting directly to argocd
* KFP SDK version: 1.5.0
### Steps to reproduce
Run the following python code and open the created file (pipe.yaml):
```python
import kfp as kfp
@kfp.components.func_to_container_op
def print_func(param: dict):
print(str(param))
@kfp.components.func_to_container_op
def list_func(param: int) -> list:
return list(range(param))
@kfp.dsl.pipeline(name='pipeline')
def pipeline(param: int):
list_func_op = list_func(param)
with kfp.dsl.ParallelFor([{'a': 1, 'b': 10}, {'a': 2, 'b': 20}]) as param:
print_func(param)
if __name__ == '__main__':
workflow_dict = kfp.compiler.Compiler()._create_workflow(pipeline)
del workflow_dict['spec']['serviceAccountName']
kfp.compiler.Compiler._write_workflow(workflow_dict, 'pipe.yaml')
```
When submitting to argo workflow the following error occures:
```
HTTP response headers: HTTPHeaderDict({'Content-Type': 'application/json', 'Trailer': 'Grpc-Trailer-Content-Type', 'Date': 'Sun, 25 Apr 2021 16:37:21 GMT', 'Transfer-Encoding': 'chunked'})
HTTP response body: {"code":2,"message":"spec.arguments.param.value is required"}
```
### Expected result
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: pipeline-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.4.0, pipelines.kubeflow.org/pipeline_compilation_time: '2021-04-25T19:37:22.235892',
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"name": "param", "type": "Integer"}],
"name": "pipeline"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.4.0}
spec:
entrypoint: pipeline
templates:
- name: for-loop-2
inputs:
parameters:
- {name: loop-item-param-1}
dag:
tasks:
- name: print-func
template: print-func
arguments:
parameters:
- {name: loop-item-param-1, value: '{{inputs.parameters.loop-item-param-1}}'}
- name: list-func
container:
args: [--param, '{{inputs.parameters.param}}', '----output-paths', /tmp/outputs/Output/data]
command:
- sh
- -ec
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |
def list_func(param):
return list(range(param))
def _serialize_json(obj) -> str:
if isinstance(obj, str):
return obj
import json
def default_serializer(obj):
if hasattr(obj, 'to_struct'):
return obj.to_struct()
else:
raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
return json.dumps(obj, default=default_serializer, sort_keys=True)
import argparse
_parser = argparse.ArgumentParser(prog='List func', description='')
_parser.add_argument("--param", dest="param", type=int, required=True, default=argparse.SUPPRESS)
_parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)
_parsed_args = vars(_parser.parse_args())
_output_files = _parsed_args.pop("_output_paths", [])
_outputs = list_func(**_parsed_args)
_outputs = [_outputs]
_output_serializers = [
_serialize_json,
]
import os
for idx, output_file in enumerate(_output_files):
try:
os.makedirs(os.path.dirname(output_file))
except OSError:
pass
with open(output_file, 'w') as f:
f.write(_output_serializers[idx](_outputs[idx]))
image: python:3.7
inputs:
parameters:
- {name: param}
outputs:
artifacts:
- {name: list-func-Output, path: /tmp/outputs/Output/data}
metadata:
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"args": ["--param", {"inputValue": "param"}, "----output-paths", {"outputPath":
"Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\nprintf \"%s\"
\"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n", "def
list_func(param):\n return list(range(param))\n\ndef _serialize_json(obj)
-> str:\n if isinstance(obj, str):\n return obj\n import json\n def
default_serializer(obj):\n if hasattr(obj, ''to_struct''):\n return
obj.to_struct()\n else:\n raise TypeError(\"Object of
type ''%s'' is not JSON serializable and does not have .to_struct() method.\"
% obj.__class__.__name__)\n return json.dumps(obj, default=default_serializer,
sort_keys=True)\n\nimport argparse\n_parser = argparse.ArgumentParser(prog=''List
func'', description='''')\n_parser.add_argument(\"--param\", dest=\"param\",
type=int, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"----output-paths\",
dest=\"_output_paths\", type=str, nargs=1)\n_parsed_args = vars(_parser.parse_args())\n_output_files
= _parsed_args.pop(\"_output_paths\", [])\n\n_outputs = list_func(**_parsed_args)\n\n_outputs
= [_outputs]\n\n_output_serializers = [\n _serialize_json,\n\n]\n\nimport
os\nfor idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except
OSError:\n pass\n with open(output_file, ''w'') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n"],
"image": "python:3.7"}}, "inputs": [{"name": "param", "type": "Integer"}],
"name": "List func", "outputs": [{"name": "Output", "type": "JsonArray"}]}',
pipelines.kubeflow.org/component_ref: '{}', pipelines.kubeflow.org/arguments.parameters: '{"param":
"{{inputs.parameters.param}}"}'}
- name: pipeline
inputs:
parameters:
- {name: param}
dag:
tasks:
- name: for-loop-2
template: for-loop-2
arguments:
parameters:
- {name: loop-item-param-1, value: '{{item}}'}
withItems:
- {a: 1, b: 10}
- {a: 2, b: 20}
- name: list-func
template: list-func
arguments:
parameters:
- {name: param, value: '{{inputs.parameters.param}}'}
- name: print-func
container:
args: [--param, '{{inputs.parameters.loop-item-param-1}}']
command:
- sh
- -ec
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |
def print_func(param):
print(str(param))
import json
import argparse
_parser = argparse.ArgumentParser(prog='Print func', description='')
_parser.add_argument("--param", dest="param", type=json.loads, required=True, default=argparse.SUPPRESS)
_parsed_args = vars(_parser.parse_args())
_outputs = print_func(**_parsed_args)
image: python:3.7
inputs:
parameters:
- {name: loop-item-param-1}
metadata:
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"args": ["--param", {"inputValue": "param"}], "command": ["sh", "-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3
-u \"$program_path\" \"$@\"\n", "def print_func(param):\n print(str(param))\n\nimport
json\nimport argparse\n_parser = argparse.ArgumentParser(prog=''Print func'',
description='''')\n_parser.add_argument(\"--param\", dest=\"param\", type=json.loads,
required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs
= print_func(**_parsed_args)\n"], "image": "python:3.7"}}, "inputs": [{"name":
"param", "type": "JsonObject"}], "name": "Print func"}', pipelines.kubeflow.org/component_ref: '{}',
pipelines.kubeflow.org/arguments.parameters: '{"param": "{{inputs.parameters.loop-item-param-1}}"}'}
arguments:
parameters:
- {name: param, value: <some default value>}
```
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5537 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5537/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5537/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5537/events | https://github.com/kubeflow/pipelines/issues/5537 | 866,589,985 | MDU6SXNzdWU4NjY1ODk5ODU= | 5,537 | [bug] Failed to retry run (Can not trigger retry from UI when the failed step has dependent steps) | {
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"/assign @capri-xiyue "
] | 2021-04-24T04:17:49 | 2021-05-26T16:01:42 | 2021-05-26T16:01:42 | NONE | null | ### What steps did you take
This issue happened after we upgraded to Kubeflow Pipeliines `1.5.0`
Minimum example to reproduce:
```python
import kfp
@kfp.components.create_component_from_func
def fail_func():
raise ValueError("failure")
@kfp.components.create_component_from_func
def dummy_func():
print("hello")
@kfp.dsl.pipeline(name="my-pipeline")
def pipeline():
task_1 = fail_func()
task_2 = dummy_func().after(task_1)
if __name__ == "__main__":
client = kfp.Client()
client.create_run_from_pipeline_func(
pipeline,
arguments={},
experiment_name="a-kfp-test",
run_name="Test manual retry",
)
```
### What happened:
After the `Fail func` fails, if click `Retry` on UI the following error message would show up:
>"error":"Retry run failed.: InternalServerError: Workflow cannot be retried with node xxx in Omitted phase: workflow cannot be retried","code":13,...
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone on EKS, also reproduced on Minikube.
* KFP version:
`1.5.0`
* KFP SDK version:
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5534 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5534/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5534/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5534/events | https://github.com/kubeflow/pipelines/issues/5534 | 865,983,030 | MDU6SXNzdWU4NjU5ODMwMzA= | 5,534 | load_pipeline_from_file | {
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"@luiu1234 What is the reason for preferring yaml over python for loading pipeline from file? We are currently heading towards the direction of supporting pipeline construction using python only. \r\n\r\n/cc @chensun ",
"Hi @zijianjoy ,\r\nreason is consistency.\r\nFor me is (a little bit) inconsistent to use use... | 2021-04-23T10:34:14 | 2022-03-03T03:05:21 | 2022-03-03T03:05:21 | NONE | null | Hi,
you can create components as a yaml, and load them with `load_component_from_file`.
Why there's not the same feature with pipelines?
Instead of using `@dsl.pipeline`, define the pipeline also with a yaml, and then just having a py script with a `load_pipeline_from_file` function. Or even better, pass `--pipeline-file` as a parameter when uploading the pipeline to kubeflow?
Note: this is not to be confused with the static yaml that is upload to gcp kubeflow. This pipeline.yaml will contain same info as pipeline.py and then will generate the static.yaml | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5533 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5533/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5533/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5533/events | https://github.com/kubeflow/pipelines/issues/5533 | 865,891,790 | MDU6SXNzdWU4NjU4OTE3OTA= | 5,533 | [backend] minio/mysql pod pending because PV provisioned to wrong zone in regional cluster | {
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"In GCP, it's recommended to use GCS & Cloud SQL, so we can avoid this problem.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it... | 2021-04-23T08:42:53 | 2022-03-03T03:05:16 | 2022-03-03T03:05:16 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
kfp standalone
* KFP version: 1.5.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
1. deploy a regional cluster
2. deploy KFP standalone following https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/
3. observe if pvs are provisioned in zones with node
4. Sometimes it isn't, e.g. I saw that minio-pvc was bound to a PV in us-central1-b, while my regional cluster only has nodes in us-central1-{a,c,f}.
5. so my minio pod was stuck pending
<!--
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
pvs should be provisioned in zones with node
<!-- What should the correct behavior be? -->
### Materials and Reference
https://kubernetes.io/docs/concepts/storage/storage-classes/#volume-binding-mode
In GCP, the default storage class has `volumeBindingMode: immediate`, so volume is provisioned immediately without taking into consideration which nodes we have.
We should better use a storage class that supports `WaitForFirstConsumer`, e.g. in GCP we can use [standard-rwo](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/gce-pd-csi-driver) which is configured as `WaitForFirstConsumer`.
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5529 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5529/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5529/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5529/events | https://github.com/kubeflow/pipelines/issues/5529 | 865,256,745 | MDU6SXNzdWU4NjUyNTY3NDU= | 5,529 | Kubeflow crashing | {
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"Hello @prasadkyp7 ,\r\n\r\nWould you like to provide more detail on the deployment environment?\r\n\r\nAre.you using Full Kubeflow deployment, or KFP standalone? What kind of cloud provider you are using?\r\n\r\nVersioning questions:\r\n1. How do you deploy Kubeflow Pipelines (KFP)?\r\n2. KFP version:\r\n3. KFP SD... | 2021-04-22T17:50:23 | 2022-03-03T04:05:33 | 2022-03-03T04:05:33 | NONE | null |
when we run many pipelines(more than 500) using KFP-API , the entire kubeflow and UI is crashing. when i look back in kubernetes its showing this error-failed to create a watcher for certificate files: inotify_init: too many open files. We are not able to track/understand this error. But this crash is happening because of running many pipelines. Any help would be greatly appreciated. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5524 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5524/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5524/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5524/events | https://github.com/kubeflow/pipelines/issues/5524 | 864,591,295 | MDU6SXNzdWU4NjQ1OTEyOTU= | 5,524 | [Test infra] 2021.4.22 -- v2 sample test failing -- API Exception 502 | {
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"Deleted the unhealthy node",
"I'm not seeing google cloud automatically bringing up a new node, so I edited in UI directly. I'm not sure what the canonical process should be.",
"after deleting the unhealthy node, it recovered",
"Closing the issue, but maybe we need to figure out next time how unhealthy nodes... | 2021-04-22T06:31:17 | 2021-04-22T07:54:21 | 2021-04-22T07:54:21 | CONTRIBUTOR | null | I found that all the pods in node `gke-kfp-standalone-1-kfp-standalone-1-60baa0a0-07jc` are either stuck at Terminating or ContainerCreating.
So I'm going to delete this node.
Test link: https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5515/kubeflow-pipelines-samples-v2/1385113976681009152 | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-04-22T03:01:37 | 2022-03-03T06:05:25 | 2022-03-03T06:05:25 | CONTRIBUTOR | null | ### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. customize tensorboard image with https://github.com/kubeflow/pipelines/pull/5515, use image tensorflow/tensorflow:2.3.2
2. start tensorboard in KFP UI.
3. click tensorboard link.
4. TB UI loads, but fails with error message `Failed to fetch runs` and redirects to root path `/`.
5. upcoming network requests to TB fails, because root path does not proxy to tensorboard
### Expected result
<!-- What should the correct behavior be? -->
TB UI should work properly.
TB url should be sth like `https://71a74112589c16e8-dot-asia-east1.pipelines.googleusercontent.com/apis/v1beta1/_proxy/viewer-0004e5ec1428299e43070b9c17d48e201c8a6f7c-service.kubeflow.svc.cluster.local:80/tensorboard/viewer-0004e5ec1428299e43070b9c17d48e201c8a6f7c/`
but found `https://71a74112589c16e8-dot-asia-east1.pipelines.googleusercontent.com/#scalars`
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
This is clearly an upstream issue, because I verified doing the same with `tensorflow:2.2.2` and `gcr.io/deeplearning-platform-release/tf2-cpu.2-3:latest` and everything was as I expected.
I also verified tensorflow/tensorflow:2.4.0 has the same problem.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5519 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5519/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5519/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5519/events | https://github.com/kubeflow/pipelines/issues/5519 | 863,892,346 | MDU6SXNzdWU4NjM4OTIzNDY= | 5,519 | [feature] GCP Cloud ML Engine Component Support Local Training | {
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"/assign @Bobgy \r\nsounds like an useful idea",
"I think that the existing component cannot really do that. It uses REST client to call the GCP API and cannot do what gcloud local is doing. So the easiest solution is to create a new component for this purpose.\r\n\r\nBut\r\n\r\nWhat do you think about using cust... | 2021-04-21T13:27:00 | 2022-03-03T04:05:19 | 2022-03-03T04:05:19 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
[GCP Components](https://github.com/kubeflow/pipelines/tree/master/components/gcp/ml_engine)
### What feature would you like to see?
Support to run [AI Platform training job locally.]( https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train)
### What is the use case or pain point?
A pain point is the pipeline testing part, which took a long time to test changes by running the pipeline in GCP, which adds additional cost when every time spinning up the new machine during the training component development phase. After the local training support feature, developers can do it fully locally and once the pipeline is ready they can upload it in GCP.
### Is there a workaround currently?
Writing training component from scratch.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5518 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5518/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5518/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5518/events | https://github.com/kubeflow/pipelines/issues/5518 | 863,883,639 | MDU6SXNzdWU4NjM4ODM2Mzk= | 5,518 | [bug] Lexical error in pipeline samples | {
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"/assign @difince ",
"Updated the text with https://github.com/kubeflow/pipelines/commit/acc2c2aaafa6c3a6b0cc90eef5d61cfd43440aef"
] | 2021-04-21T13:17:53 | 2021-04-22T21:31:15 | 2021-04-22T21:31:15 | MEMBER | null | ### What steps did you take
It is just a lexical error in pipeline sample file - [Data passing in python components - Files.py](https://github.com/kubeflow/pipelines/blob/d79071c0bef19442483abc101769a0d893e72f42/samples/tutorials/Data%20passing%20in%20python%20components/Data%20passing%20in%20python%20components%20-%20Files.py)
> Small lists, dictionaries and JSON structures are fine, but keep an eye on the size and consider switching to file-based data passing methods **taht** are more suitable for bigger data (more than several kilobytes) or binary data.
<!-- A clear and concise description of what the bug is.-->
### What did you expect to happen:
**taht** to be written correctly: that
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
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/area samples
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---
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"I'm going to experiment with uploading this Markdown content to GCS, and see if that is handled better.\r\n\r\nUPDATE: the same happens with .md files hosted in GCS. They're large but not staggeringly so (11.3MB), but still cause out-of-memory errors in the browser tab.",
"Hi @jackwhelpton, this seems like an ex... | 2021-04-20T20:13:23 | 2021-05-25T04:10:09 | 2021-05-25T04:10:09 | CONTRIBUTOR | null | ### Environment
* KFP version: 1.0.1
### Steps to reproduce
Output a markdown artifact containing a large amount of Markdown inline:
```python
metadata = {
"outputs": [
{
"storage": "inline",
"source": "<large markdown string: ~20MB>",
"type": "markdown",
}]
}
```
### Expected result
The Visualizations tab and Run output should render the Markdown.
Instead, the original markup code is visible: instead, after some time loading, the Chrome tab will crash with SBOX_FATAL_MEMORY_EXCEEDED.
<!-- Don't delete message below to encourage users to support your issue! -->
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"As a workaround, using the character ⚠️ directly works; this doesn't help with, for example, ℹ️ (`:information_source:`).\r\n\r\nThis doesn't help with the horizontal rule, either.",
"I'm expanding this ticket as I find other missing markdown features: let me know if each one should be spun off as a separate tic... | 2021-04-20T16:43:45 | 2022-03-11T05:40:26 | null | CONTRIBUTOR | null | ### Environment
* KFP version: 1.0.1
### Steps to reproduce
Output a markdown artifact containing the missing features: a horizontal rule, and an emoji, e.g. `:warning:`
```python
metadata = {
"outputs": [
{
"storage": "inline",
"source": "line1\n---\n:warning: Item had multiple genre labels",
"type": "markdown",
}]
}
```
### Expected result
The Visualizations tab and Run output should render the horizontal rule and emoji:
line1
---
⚠️ Item had multiple genre labels
Instead, the original markup code is visible:
`line1 --- :warning: Item had multiple genre labels`
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Hi @midhun1998, kubeflow 1.3 is about to release and argo workflow controller has been upgraded to 2.12.9",
"Thanks for letting me know @Bobgy . I'm really excited for Kubeflow 1.3 🎉. But we are planning to upgrade to Kubeflow 1.3 only post some analysis(Could take a while🙁) . In the Meantime, is it possible t... | 2021-04-20T15:47:16 | 2021-05-16T13:41:24 | 2021-05-16T13:41:24 | MEMBER | null | Hi,
We are looking to upgrade the workflow controller to v2.12.6 based on #4553 . The current version of Argo workflow controller installed is v2.3 which comes with Kubeflow v1.2. The main reason to upgrade is Argo v2.3 only outputs very few workflow metrics.
Will there be any incompatibilities? If no, will changing the image be enough or should we upgrade any other components? Please guide @Bobgy .(#5232 )
/kind question
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"Hello @jw-websensa , currently we recommend using minio rule to set expiration date: https://docs.min.io/docs/minio-bucket-lifecycle-guide.html. We will learn more about the usability and improve in the future.",
"Hello @zijianjoy, thank you for your response, I am aware about minio rules, but I think it will no... | 2021-04-20T08:10:00 | 2021-05-04T21:49:26 | 2021-04-30T00:57:36 | NONE | null | ### Feature Area
/area backend
/area components
### What feature would you like to see?
Pipeline run artifacts validity period.
### What is the use case or pain point?
I'm using Kubeflow with on-premise Kubernetes cluster.
As we use Rancher local path provisioner for Storage Class it is crucial to control disk usage (this class does not work with pvc limits).
I wonder if it is possible to set some kind of period of validity for each artifact to periodically and automatically delete the oldest.
### Is there a workaround currently?
I was thinking of preparing k8s crone job to do it manually, but I don't know specifically pipelines architecture and already noticed that deletion from minio is not enough (cacher still finds deleted artifacts available).
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"@Ark-kun what kind of folder structure do you propose?",
"I don't strongly feel that we need to hide tests, maybe users also want to know how they can\r\n* test their pipelines\r\n* extract results from their pipelines in an external system\r\n\r\nAlso, for user facing samples, they are already in separate folde... | 2021-04-20T02:48:18 | 2022-03-03T06:05:25 | 2022-03-03T06:05:25 | CONTRIBUTOR | null | > I think we might want to have some separation between what we show to the users and the internal test infra machinery.
> I'm not sure we should show the users with that test infra code.
_Originally posted by @Ark-kun in https://github.com/kubeflow/pipelines/pull/5473#discussion_r616266440_ | {
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"/cc @chensun @neuromage @Ark-kun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment ... | 2021-04-20T02:00:20 | 2022-03-03T06:05:35 | 2022-03-03T06:05:35 | CONTRIBUTOR | null | # Problem
We need to add an entry in [samples/test/config.yaml](https://github.com/kubeflow/pipelines/blob/master/samples/test/config.yaml), each time when we add a new sample. This can be error prone, also unnecessary, because the config.yaml file doesn't contain much information so far.
# Proposal
We can just scan all files in the samples folder with `_test.py` suffix and run them. WDUT?
Originally posted in https://github.com/kubeflow/pipelines/pull/5473#discussion_r616284827 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5507 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5507/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5507/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5507/events | https://github.com/kubeflow/pipelines/issues/5507 | 862,524,121 | MDU6SXNzdWU4NjI1MjQxMjE= | 5,507 | [kubeflow v1.3.0-rc.1] cache server certificate secret not being created | {
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"Hi @raffaelespazzoli, can you report your kubernetes version?",
"Also transferred to KFP repo",
"@Bobgy yeah, I intended to open this on the manifests repo. sorry for this., can you add the link to where you transferred it to?\r\nI'm seeing this issue with both minkube 1.17.1 and openshift 4.7 (corresponding t... | 2021-04-19T21:08:20 | 2022-03-03T06:05:36 | 2022-03-03T06:05:36 | NONE | null | I'm trying installing kubeflow v1.3.0-rc.1 with the official manifests and it seems to me that the manifests for creating the certs for the cache-server are not present.
It is still hard to say for certain for sure as the kustomize composition is still very hard to follow. That said, thanks for having removed kfctl and kfdef. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5504 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5504/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5504/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5504/events | https://github.com/kubeflow/pipelines/issues/5504 | 861,319,830 | MDU6SXNzdWU4NjEzMTk4MzA= | 5,504 | [backend] Managed Storage Option Fails on KFP | {
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"After some offline troubleshooting, found that\r\n> Artifact storage Cloud Storage bucket: With managed storage, Kubeflow Pipelines stores pipeline artifacts in a Cloud Storage bucket. Specify the name of the bucket you want Kubeflow Pipelines to store artifacts in. If the specified bucket doesn't exist, the Kubef... | 2021-04-19T13:38:12 | 2021-04-20T14:45:41 | 2021-04-20T14:45:41 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
On Google Cloud Platform via "AI Platform" > "Pipelines" > "New Pipeline"
* KFP version:
* 1.4.1
* KFP SDK version:
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.4.1
### Steps to reproduce
1) Log into console.cloud.google.com
2) Navigate to AI Platform > Kubeflow Pipelines
3) Click "New Instance" and then "Configure"
4) Choose your cluster and Namespace
5) Select "Use managed storage" and list Cloud SQL instance and Google Cloud Storage bucket
6) Click deploy
### Expected result
I expect there to be a working KFP instance that allows me to run the saple Chicago Taxi dataset pipeline. Instead I receive this error: `Error: failed to retrieve list of pipelines. Click Details for more information.`
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
@Bobgy , I messaged you in the Slack channel on this.
The GCP walkthrough for this option is available here: https://cloud.google.com/ai-platform/pipelines/docs/setting-up#full-access
#4356 #3332
---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-04-19T12:08:23 | 2022-03-03T06:05:34 | 2022-03-03T06:05:34 | CONTRIBUTOR | null | ### Environment
* KFP version: 1.1
* KFP SDK version: 1.4
* All dependencies version:
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.4.1
### Steps to reproduce
Run the following pipeline:
```
import kfp
@kfp.components.create_component_from_func
def foo() -> list:
return [1,2,3]
@kfp.components.create_component_from_func
def bar() -> list:
return [{'a': 1}, {'a': 2}, {'a': 3}]
@kfp.components.create_component_from_func
def baz(a: dict) -> list:
print(type(a))
print(a)
@kfp.dsl.pipeline(
name='temp',
description='temp'
)
def temp():
a = foo()
b = bar()
with kfp.dsl.ParallelFor(a.output) as item:
baz(item)
with kfp.dsl.ParallelFor(b.output) as item:
baz(item)
```
### Expected result
The parallel for over `b` should create 3 tasks, with each task printing `<class 'dict'>` followed by `{'a': x}`. The parallel for over `a` should result in a type error (`int` cannot be converted to `dict`).
Instead, the parallel for over `b` creates a single task `for-loop-1`, which fails to run with error message `This step is in Error state with this message: failed to resolve {{item}}`. The parallel for over `a` runs successfully and prints `<class 'int'>` followed by `x`.
This only seems to be an issue when using `create_component_from_func`; if I create the component spec using YAML it works.
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https://api.github.com/repos/kubeflow/pipelines/issues/5496 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5496/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5496/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5496/events | https://github.com/kubeflow/pipelines/issues/5496 | 859,917,466 | MDU6SXNzdWU4NTk5MTc0NjY= | 5,496 | [feature] graph_component accepting non-PipelineParam arguments | {
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"/assign @neuromage @chensun ",
"@neuromage @chensun Any progress regarding this issue?",
"@Ark-kun We want to support non-PipelineParam arguments on graph_component so that we can define the graph function with some default arguments. Do you think it makes sense to implement this feature? Thank you. ",
"@Tom... | 2021-04-16T15:25:15 | 2022-03-03T04:05:39 | 2022-03-03T04:05:39 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
`@graph_component` decorator accepting literal values, not just `PipelineParam`s. Currently, if a literal is passed, e.g. `0`, then the following error will be raised:
```
ValueError: arguments to {function_name} should be PipelineParams.
```
### What is the use case or pain point?
If a sequential recursive loop is to be implemented, it may start with a literal:
```python
@graph_component
def sequential_loop(iter_num: int, i: int = 0):
...
incr_i = ... # increase the loop counter (e.g. via python)
with dsl.Condition(incr_i < iter_num):
sequential_loop(iter_num, incr_i)
@dsl.pipeline("main")
def main(iter_num: int):
sequential_loop(iter_num) # throws error!
```
It's even worse if such loop is somewhere deeper in the call stack.
I think it can be done by using `@graph_component` to wrap the function's arguments into some type that could be treated like PipelineParam inside of an OpsGroup (i.e. not be inlined) and like a literal outside of it (so that a literal is passed to the OpsGroup's parameter).
I imagine it to work similarly to `LoopArguments`.
### Is there a workaround currently?
```python
@graph_component
def sequential_loop(iter_num: int, i: int = 0):
...
incr_i = ... # increase the loop counter (e.g. via python)
with dsl.Condition(incr_i < iter_num):
sequential_loop(iter_num, incr_i
@dsl.pipeline("main")
def main(iter_num: int, start_idx: int = 0):
sequential_loop(iter_num, start_idx)
```
---
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https://api.github.com/repos/kubeflow/pipelines/issues/5493 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5493/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5493/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5493/events | https://github.com/kubeflow/pipelines/issues/5493 | 859,415,028 | MDU6SXNzdWU4NTk0MTUwMjg= | 5,493 | default resource overhead for each individual profile | {
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"Hello @hsinhoyeh , you can disable the pipeline pods by setting the following label from your profile's namespace labels: `pipelines.kubeflow.org/enabled: false`.\r\n\r\nYou can do this for all the profiles that don't require Kubeflow Pipelines. For future roadmap, we plan to remove visualization server too. \r\n\... | 2021-04-16T03:09:15 | 2022-03-03T04:05:37 | 2022-03-03T04:05:37 | CONTRIBUTOR | null |
**Question:**
Not sure whether this question has been discussed before, but I am curious about the resource overhead for each individual profile.
In the current configuration ( in v1.2, v1.3), when creating one single profile, there are two running pods are created by default:
```
ml-pipeline-ui-artifact-c8fbcb8f9-k88bz
ml-pipeline-visualizationserver-6b78c9646-s7q4c
```
each one takes additional istio sidecar container (cost 100m cpu/ 128Mi memory).
so in total cpu requested 100m * 2 + 128m * 2 = 0.5 CPU unit, memory requested 128 *2 + 128 * 2 = 512Mi.
(even the profile has been created but no further actions were conducted.)
so let say today we have to create 100 profiles (one for each individual participant) but only 20% of them are heavy users (80-20 rule), and our system wasted 100 * 80% * 0.5 CPU unit (and 512Mi + 100 * 80% memory).
This seems to be more like a resource leaking, not the design concept. any thoughts? or documents? thanks
---
transferred from https://github.com/kubeflow/kubeflow/issues/5832 | {
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"Thank you @Mullefa for identifying this issue! Would you like to create a PR to bump up python version for the fix?",
"Thank you for identifying the issue.\r\n\r\nWe're interested to know more about your pipeline. What is the upstream component you use with the confusion matrix component?",
"This issue has bee... | 2021-04-15T14:47:42 | 2022-03-03T03:05:22 | 2022-03-03T03:05:22 | NONE | null | ### What steps did you take
I created a task in a pipeline using the [confusion matrix](https://github.com/kubeflow/pipelines/tree/ab63956f3a61d4d11b27ac26f097e1784588fed9/components/local/confusion_matrix) component.
### What happened:
The task failed with the following message:
```
Traceback (most recent call last):
File "/ml/confusion_matrix.py", line 29, in <module>
import pandas as pd
ImportError: No module named pandas
```
I believe this is because the Python dependencies are [installed for Python 3](https://github.com/kubeflow/pipelines/blob/master/components/local/base/Dockerfile), whereas the command in the component definition is `python2 ...`.
### What did you expect to happen:
For the task to complete successfully.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
GCP AI Platform Pipelines
* KFP version:
1.4.1
* KFP SDK version:
1.4.0
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
If this issue is legitimate (and I haven't misinterpreted the error), I believe the [roc component](https://github.com/kubeflow/pipelines/tree/ab63956f3a61d4d11b27ac26f097e1784588fed9/components/local/roc) will suffer from the same issue.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | closed | false | null | [] | null | [
"Hmmm, this is exactly kubeflow/metadata server: https://github.com/kubeflow/metadata/tree/master/api\r\nThey used grpc-gateway to generate a REST python client for Metadata API.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity o... | 2021-04-15T08:39:32 | 2022-03-03T06:05:27 | 2022-03-03T06:05:27 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Instead of https://github.com/grpc/grpc-web which we are currently using,
use https://github.com/grpc-ecosystem/grpc-gateway to make MLMD grpc API accessible from outside the cluster as a REST endpoint with multiple language clients including Python.
### What is the use case or pain point?
There is no python client for MLMD grpc API through grpc-web proxy!
<!-- It helps us understand the benefit of this feature for your use case. -->
grpc-web only supports javascript/typescript clients, see https://github.com/grpc/grpc-web.
I cannot see any documentation how other client languages may be supported.
On the other hand, grpc-gateway supports porting the grpc API into REST api. It also generates swagger API definitions, which we can use to generate client for all language variants, including python, our primary user language.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
We can only use `kubectl port-forward` the grpc service to localhost, then use python grpc client to connect to localhost.
However, there's no way we can use a python grpc client to access metadata api through HTTP proxy that do not support grpc (e.g. [inverse proxy](https://github.com/google/inverting-proxy) which we deploy for KFP standalone on GCP).
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5480 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5480/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5480/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5480/events | https://github.com/kubeflow/pipelines/issues/5480 | 858,437,230 | MDU6SXNzdWU4NTg0MzcyMzA= | 5,480 | [backend] go get github.com/kubeflow/pipelines got k8s.io/kubernetes@v0.17.9: invalid version: unknown revision v0.17.9 | {
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"Thank you @ZhengJuCn for filing this request! Would you like to make a PR for the go get module supportability?",
"cc @Bobgy @capri-xiyue ",
"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 contributio... | 2021-04-15T03:22:26 | 2022-05-22T05:02:31 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
install pipelines with kubeflow 1.3.0-rc.0 with kubeflow/manifest repo example manifest
* KFP version:
with pipeline docker image, pipeline version is 1.5.0-rc.2
* KFP SDK version:
not use
### Steps to reproduce
just exec 'go get github.com/kubeflow/pipelines'
then got error `go get k8s.io/kubernetes@v0.17.9: k8s.io/kubernetes@v0.17.9: invalid version: unknown revision v0.17.9`
### Expected result
get pipelines module success
### Materials and Reference
**error message:**
go: github.com/kubeflow/pipelines upgrade => v0.0.0-20210414214412-ab63956f3a61
get "k8s.io/kubernetes": found meta tag get.metaImport{Prefix:"k8s.io/kubernetes", VCS:"git", RepoRoot:"https://github.com/kubernetes/kubernetes"} at //k8s.io/kubernetes?go-get=1
go get: github.com/kubeflow/pipelines@v0.0.0-20210414214412-ab63956f3a61 requires
k8s.io/kubernetes@v0.17.9: reading k8s.io/kubernetes/go.mod at revision v0.17.9: unknown revision v0.17.9
**in kubernetes repo i found an issue:**
k8s.io/kubernetes is not intended to be used with go get
the published staging repos can be used that way (e.g. go get k8s.io/api@<version>)
If you want to depend on k8s.io/kubernetes with go modules, you would need to define require directives for matching levels of all of the published staging repositories in your go.mod file
is this error? could someone help solve this problem, thanks
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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2021-04-14 09:52:20.924348: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "taxi_pipeline_kubeflow_gcp_test.py", line 24, in <module>
from tfx.examples.chicago_taxi_pipeline import taxi_pipeline_kubeflow_gcp
File "/usr/local/lib/python3.6/site-packages/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow_gcp.py", line 38, in <module>
from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor
File "/usr/local/lib/python3.6/site-packages/tfx/extensions/google_cloud_ai_platform/pusher/executor.py", line 26, in <module>
from tfx.extensions.google_cloud_ai_platform import runner
File "/usr/local/lib/python3.6/site-packages/tfx/extensions/google_cloud_ai_platform/runner.py", line 32, in <module>
from tfx.extensions.google_cloud_ai_platform import training_clients
File "/usr/local/lib/python3.6/site-packages/tfx/extensions/google_cloud_ai_platform/training_clients.py", line 22, in <module>
from google.cloud.aiplatform import gapic
ModuleNotFoundError: No module named 'google.cloud.aiplatform' | {
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"I am experiencing the same issue when submitting a pyspark job to a dataproc cluster with this component: https://raw.githubusercontent.com/kubeflow/pipelines/1.5.0-rc.2/components/gcp/dataproc/submit_pyspark_job/component.yaml",
"I think I was able to resolve the issue by applying K8s secret to authenticate the... | 2021-04-14T17:23:12 | 2021-09-07T15:31:13 | 2021-09-07T15:31:13 | NONE | null | ### Environment
GCP AI platform
* How did you deploy Kubeflow Pipelines (KFP)?
AI platform deployment
* KFP version:
1.4.0
* KFP SDK version:
1.4.0
### Steps to reproduce
```
mlengine_train_op = comp.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/1.5.0-rc.0/components/gcp/ml_engine/train/component.yaml')
hptune_op = mlengine_train_op(
project_id=settings.project_id,
region=settings.region,
args=dict_to_args(hptune_job_ags),
job_dir=os.path.join(settings.gcs_working_dir),
master_image_uri=self.settings.training_job.master_image_uri, # using custom container to run the training
worker_image_uri="",
training_input=training_input_dict, # this include hyperameters and the SA
job_id_prefix="some_prefix",
job_id=""
)
```
### the log of this component run is:
```
INFO:googleapiclient.discovery:URL being requested: GET
https://ml.googleapis.com/...?alt=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 "/ml/kfp_component/launcher/__main__.py", line 45, in <module>
main()
File "/ml/kfp_component/launcher/__main__.py", line 42, in main
launch(args.file_or_module, args.args)
File "/ml/kfp_component/launcher/launcher.py", line 45, in launch
return fire.Fire(module, command=args, name=module.__name__)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 127, in Fire
component_trace = _Fire(component, args, context, name)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 366, in _Fire
component, remaining_args)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 542, in _CallCallable
result = fn(*varargs, **kwargs)
File "/ml/kfp_component/google/ml_engine/_train.py", line 105, in train
job_dir_output_path=job_dir_output_path)
File "/ml/kfp_component/google/ml_engine/_create_job.py", line 62, in create_job
job_dir_output_path=job_dir_output_path,
File "/ml/kfp_component/google/ml_engine/_create_job.py", line 89, in execute_and_wait
job_dir_output_path=self._job_dir_output_path,
File "/ml/kfp_component/google/ml_engine/_common_ops.py", line 93, in wait_for_job_done
job = ml_client.get_job(project_id, job_id)
File "/ml/kfp_component/google/ml_engine/_client.py", line 69, in get_job
name=job_name).execute()
File "/usr/local/lib/python3.7/site-packages/googleapiclient/_helpers.py", line 130, in positional_wrapper
return wrapped(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 846, in execute
method=str(self.method), body=self.body, headers=self.headers)
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 183, in _retry_request
raise exception
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 164, in _retry_request
resp, content = http.request(uri, method, *args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/google_auth_httplib2.py", line 209, in request
self.credentials.before_request(self._request, method, uri, request_headers)
File "/usr/local/lib/python3.7/site-packages/google/auth/credentials.py", line 133, in before_request
self.refresh(request)
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/credentials.py", line 111, in refresh
self._retrieve_info(request)
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/credentials.py", line 88, in _retrieve_info
request, service_account=self._service_account_email
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/_metadata.py", line 234, in get_service_account_info
return get(request, path, params={"recursive": "true"})
File "/usr/local/lib/python3.7/site-packages/google/auth/compute_engine/_metadata.py", line 150, in get
response = request(url=url, method="GET", headers=_METADATA_HEADERS)
File "/usr/local/lib/python3.7/site-packages/google_auth_httplib2.py", line 120, in __call__
url, method=method, body=body, headers=headers, **kwargs
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 1724, in new_request
redirections, connection_type)
File "/usr/local/lib/python3.7/site-packages/httplib2/__init__.py", line 1991, in request
cachekey,
File "/usr/local/lib/python3.7/site-packages/httplib2/__init__.py", line 1651, in _request
conn, request_uri, method, body, headers
File "/usr/local/lib/python3.7/site-packages/httplib2/__init__.py", line 1558, in _conn_request
conn.request(method, request_uri, body, headers)
File "/usr/local/lib/python3.7/http/client.py", line 1277, in request
self._send_request(method, url, body, headers, encode_chunked)
File "/usr/local/lib/python3.7/http/client.py", line 1323, in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "/usr/local/lib/python3.7/http/client.py", line 1272, in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "/usr/local/lib/python3.7/http/client.py", line 1032, in _send_output
self.send(msg)
File "/usr/local/lib/python3.7/http/client.py", line 993, in send
self.sock.sendall(data)
BrokenPipeError: [Errno 32] Broken pipe
```
### Materials and Reference
referring to [Ml_engine_train_op component ](https://github.com/kubeflow/pipelines/blob/master/components/gcp/ml_engine/train/README.md)
---
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-04-14T15:35:15 | 2022-03-03T06:05:27 | 2022-03-03T06:05:26 | MEMBER | null | After merging #4995 we realized that there is some conflicting behaviour between the `[[RunUUID]]` macro and `kfp.dsl.RUN_ID_PLACEHOLDER` (which points to `{{workflow.uid}}` (related to #3709).
I will bring here some discussion from #4995 so we can continue here.
@Bobgy says:
> To reduce backward compatibility concerns, I'd suggest the folloowing rollout plan:
>
> 1. Now KFP api server supports both `[[RunUUID]]` and `{{workflow.uid}}` as KFP run ID placeholder.
> 2. After a certain period (3months?), change kfp.dsl.RUN_ID_PLACEHOLDER to [[RunUUID]]. (Wait 3 months, because there are people who upgrade their SDK a lot more than their KFP cluster.)
> 3. After another certain period (3month?), merge #3709. (Again, the wait period is to allow old SDK users to keep using new KFP api server without problem.)
@StefanoFioravanzo says:
> > Now KFP api server supports both [[RunUUID]] and {{workflow.uid}} as KFP run ID placeholder.
>
> Indeed, and I think this is actually good. This is why:
>
>We cannot just update `kfp.dsl.RUN_ID_PLACEHOLDER` to be `[[RunUUID]]` because the current placeholder is an Argo macro, which will be replaced anywhere in the workflow, while the `[[RunUUID]]` works just for pipeline parameters.
> We should keep support for `[[RunUUID]]` in one-off runs because it is a consistent mechanism to retrieve the Run UUID in pipeline parameters. People use this in SWFs and they expect it to work in one-off runs as well (We've seen this with our customers)
> The KFP Python SDK placeholder can be used as well, it's more generic and it does overlap a little bit with `[[RunUUID]]`, but this doesn't mean we need to support just one or the other. They are two different mechanisms that happen to overlap in this particular case (run id as pipeline parameter). I do still think that merging #3709 would make things more clean, but it's an orthogonal discussion.
>
> Maybe, as a longer term plan:
>
> It's the KFP API server who replaces the argo string with the run id throughout the workflow. That's how we can get the run id using that string. We can make the API server also replace all macro (`[[ * ]]`) occurrences throughout the workflow the exact same way. Then we can phase out the argo string. | {
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"I think option 1 and 2 can probably both be useful",
"Some feature requests that is related to this https://github.com/kubeflow/pipelines/issues/1641 https://github.com/kubeflow/pipelines/issues/4714",
"Note, a prerequisite for implementing this feature is supporting passing full image to KFP viewer API. Curre... | 2021-04-14T08:59:38 | 2021-05-11T07:45:42 | 2021-05-11T07:45:42 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Allow users to customize the tensorboard image they want to use in KFP component visualizations.
https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/
There are mainly two ways I can think of to support this feature:
Option 1:
1. add an input textbox for image in Tensorboard plugin that users can directly edit
2. (optional) let the textbox remember historic entries
Option 2 - let component output decide
1. when a component emits visualization metadata, add a new field `image` they can specify tensorboard image
2. KFP UI loads the `mlpipeline-ui-metadata` and default to show the image option in component metadata
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Existing issues that can be addressed by this feature: https://github.com/kubeflow/pipelines/issues/5449
It's currently not possible to choose any tensorboard image version unless it is [hard-coded in KFP UI](https://github.com/kubeflow/pipelines/blob/2c682299202ad019bb2b2bb2ac8903c72ab245cf/frontend/src/components/viewers/Tensorboard.tsx#L216).
Users or pipeline components know the best about which tensorboard version/image type can visualize its output, not the KFP cluster operator.
### 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 👍. We prioritize fulfilling features with the most 👍.
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"@jdavis34265 I think this is a very good point!\r\nThere were some early usages of \"The Kubeflow Authors\" in pipelines repo too, see https://github.com/kubeflow/pipelines/search?p=1&q=the+kubeflow+authors. However, we are not enforcing this style consistently.\r\n\r\nI think it's reasonable to discuss this propo... | 2021-04-14T06:55:39 | 2021-06-01T09:49:05 | 2021-06-01T09:49:05 | NONE | null | I wanted to start a discussion about the open-source nature of the Kubeflow projects.
Most Kubeflow projects already follow the opens-source guidelines, but I've noticed some issues in the Kubeflow Pipelines project.
I see that the Kubeflow Pipelines has a great developer community that takes a big part in development.
Despite that I've noticed that all copyrights are being assigned to the Google LLC corporation.
Most open-source projects, including major projects by Google (TensorFlow, Kubernetes etc) assign copyright to the project contributors.
To improve the openness of the Kubeflow Pipelines project **I propose to change the license messages to: "Copyright [year] The Kubeflow Pipelines Authors"**.
I found explicit guidance for this in the Google's open-source guidelines: https://opensource.google/docs/releasing/authors/
**Update the copyright statements in your LICENSE file and all file headers to list “The [Project] Authors” rather than “Google”:
Before: Copyright 2014 Google LLC
After: Copyright 2014 The [Project Name] Authors.**
---
Some examples of Google projects adopting this approach:
TensorFlow: "Copyright 2015 The TensorFlow Authors." https://github.com/tensorflow/tensorflow/blob/master/tensorflow/__init__.py
Knative: "Copyright <year> The Knative Authors" https://knative.dev/community/contributing/repository-guidelines/
Kubernetes: "Copyright 2015 The Kubernetes Authors." https://github.com/kubernetes/kubernetes/blob/master/hack/update-vendor-licenses.sh
Go: "Copyright (c) 2009 The Go Authors." https://golang.org/LICENSE
Kubeflow:
"Copyright 2018 The Kubeflow Authors" https://github.com/kubeflow/common/blob/master/hack/boilerplate/boilerplate.go.txt https://github.com/kubeflow/katib/issues/20
"Copyright 2020 kubeflow.org." https://github.com/kubeflow/kfserving/blob/master/python/kfserving/kfserving/__init__.py
/cc @theadactyl @Bobgy @Ark-kun @chensun @SinaChavoshi @animeshsingh @elikatsis @NikeNano @Tomcli @RedbackThomson @eterna2 @zijianjoy @surajkota @Jeffwan @StefanoFioravanzo @jinchihe @DavidSpek | {
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"P.S. The error is intermittent and disappeared after re-run.",
"It seems we should add retry when calling KFP API",
"https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/26a16bda-2f69-4cc1-baf1-cde02947a64e\r\n\r\nI'm seeing this once too, among many pipelines in one test, this ... | 2021-04-13T20:10:19 | 2021-05-18T21:47:48 | 2021-05-18T21:47:47 | CONTRIBUTOR | null | https://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/bb97866a-d8ad-44a1-bd8e-c28f8df1a560
```
1.1
F0413 20:03:23.231673 1 main.go:56] Failed to successfuly execute component: rpc error: code = Aborted desc = mysql_query aborted: errno: 1213, error: Deadlock found when trying to get lock; try restarting transaction
goroutine 1 [running]:
github.com/golang/glog.stacks(0xc000088e00, 0xc00022a280, 0xe0, 0x135)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:769 +0xb9
github.com/golang/glog.(*loggingT).output(0x10ff320, 0xc000000003, 0xc000430380, 0x1082606, 0x7, 0x38, 0x0)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:720 +0x3b3
github.com/golang/glog.(*loggingT).printf(0x10ff320, 0x3, 0xc1e218, 0x2b, 0xc00063ff58, 0x1, 0x1)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:655 +0x153
github.com/golang/glog.Fatalf(...)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:1148
main.main()
/build/cmd/launch/main.go:56 +0x357
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5462 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5462/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5462/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5462/events | https://github.com/kubeflow/pipelines/issues/5462 | 856,404,147 | MDU6SXNzdWU4NTY0MDQxNDc= | 5,462 | [frontend] Pipeline generic installation does not include a virtual service | {
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"Thank you @nakfour for submitting this request!\r\n\r\n@Bobgy and I have discussed about this issue and our initial thinking is to perform following refactorings:\r\n\r\n1. For `ml-pipeline-ui` VirtualService, we need to create a folder called `ml-pipeline-ui` under https://github.com/kubeflow/pipelines/tree/maste... | 2021-04-12T22:21:53 | 2021-04-19T16:44:17 | 2021-04-19T16:44:17 | MEMBER | null | When installing "platform-agnostic" and trying to get to the pipeline UI from the Kubeflow central dashboard, we get "Sorry, /pipeline/ is not a valid page". Checking for virtualservices in namespace kubeflow, there are none for pipelines. Looking at the code, we dont see a VirtualService created : https://github.com/kubeflow/manifests/tree/master/apps/pipeline/upstream/base/installs/generic
While multi-user install does include a virtualservice: https://github.com/kubeflow/manifests/blob/master/apps/pipeline/upstream/base/installs/multi-user/virtual-service.yaml | {
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"> fileOutputs: \r\n> MLPipeline UI metadata: /mlpipeline-ui-metadata.json\r\n\r\nWe do not support `fileOutputs`. It was deprecated from the start. Please use `outputPath` placeholder instead.\r\n\r\n> args:\r\n> - {outputPath: MLPipeline UI metadata}\r\n\r\nThis is correct. Thank you.\r\n\r\nLet's d... | 2021-04-12T19:46:14 | 2021-05-19T06:33:39 | 2021-05-18T13:19:08 | MEMBER | null | ### What steps did you take
## Approach 1:
https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/
As mentioned in the above link, we tried to write mlpipeline-ui-metadata.json to `/mlpipeline-ui-metadata.json`
this throws the below error
`invalid mount config for type "bind": invalid specification: destination can't be '/`
Sample code from component yaml
```
name: Training
description: |
Training for PyTorch.
inputs:
- {name: dataset}
- {name: params, type: JsonArray}
outputs:
- {name: output}
- {name: tensorboard_root}
- {name: MLPipeline UI metadata, type: UI metadata}
implementation:
container:
image: "pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime"
.....
fileOutputs:
MLPipeline UI metadata: /mlpipeline-ui-metadata.json
```
## Approach 2:
Tried writing the metadata information to a custom path as well using outputPath keyword.
We did not face the error faced in previous approach, yet we could not find the tar file in the output artifacts.
Sample code from component yaml
```
name: Training
description: |
Training for PyTorch.
inputs:
- {name: dataset}
- {name: params, type: JsonArray}
outputs:
- {name: output}
- {name: tensorboard_root}
- {name: MLPipeline UI metadata, type: UI metadata}
implementation:
container:
image: "pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime"
.....
args:
- {outputPath: MLPipeline UI metadata}
```
### What happened:
In approach 1 the training failed with the given error and in approach 2 the metadata file is not found.
### What did you expect to happen:
Metadata information should be shown in output artifacts and the corresponding visualization should be shown.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.4.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
kfp 1.4.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.4.1
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Good catch! +1 to set a locale during test"
] | 2021-04-11T21:50:39 | 2021-04-15T15:29:13 | 2021-04-15T15:29:13 | CONTRIBUTOR | null | When I run Frontend tests on my machine I get many errors:
```
Test Suites: 9 failed, 44 passed, 53 total
Tests: 34 failed, 1038 passed, 1072 total
Snapshots: 14 failed, 307 passed, 321 total
```
I expect the tests to pass under any system locale.
Perhaps the tests can specify an explicit locale that the date functions will use.
Couple of the errors:
```
● PipelineDetails › closes side panel when close button is clicked
expect(received).toMatchSnapshot()
Snapshot name: `PipelineDetails closes side panel when close button is clicked 1`
- Snapshot
+ Received
@@ -101,11 +101,11 @@
className="summaryKey"
>
Uploaded on
</div>
<div>
- 9/5/2018, 4:03:02 AM
+ 2018-9-5 05:03:02
</div>
<div
className="summaryKey"
>
Description
654 | tree.find('SidePanel').simulate('close');
655 | expect(tree.state('selectedNodeId')).toBe('');
> 656 | expect(tree).toMatchSnapshot();
| ^
657 | });
658 |
659 | it('shows correct versions in version selector', async () => {
at Object.toMatchSnapshot (src/pages/PipelineDetails.test.tsx:656:18)
```
BTW, see how the time differs by an hour.
```
FAIL src/components/Trigger.test.tsx (30.921s)
● Trigger › cron › builds a 1-minute cron trigger with specified start date
expect(jest.fn()).toHaveBeenLastCalledWith(...expected)
Expected: {"catchup": true, "maxConcurrentRuns": "10", "trigger": {"cron_schedule": {"cron": "0 * * * * ?", "end_time": undefined, "start_time": 2018-03-23T07:53:00.000Z}}}
Received
3: {"catchup": true, "maxConcurrentRuns": "10", "trigger": {"cron_schedule": {"cron": "0 * * * * ?", "end_time": undefined, "start_time": 2018-12-21T15:53:00.000Z}}}
-> 4
@@ -3,9 +3,9 @@
"maxConcurrentRuns": "10",
"trigger": Object {
"cron_schedule": Object {
"cron": "0 * * * * ?",
"end_time": undefined,
- "start_time": 2018-03-23T07:53:00.000Z,
+ "start_time": 2018-03-23T15:53:00.000Z,
},
},
},
```
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"I like the suggestion. Having file-based inputs always helps.\r\nI think that KFServe consuming the model as file, rather than as URL is significantly easier for the pipeline authors.\r\n\r\nThe change might be non-trivial, since the KFServing component needs to copy the data to the serving pod[s].\r\n\r\nYou can ... | 2021-04-09T16:04:00 | 2022-03-03T03:05:17 | 2022-03-03T03:05:17 | NONE | null | Apologies if this is the wrong project, I am not sure if this component is developed here or over in kfserving, but should there be a version of KFServing (or maybe at way to do it with one version) such that the URI is an InputPath so a newly trained model can be served as part of a pipeline? I have searched the issues for a few days trying to see if this has been discussed before and have come up empty.
https://github.com/kubeflow/pipelines/blob/02dbcfa062ea975fcf8793b711640ed20442389e/components/kubeflow/kfserving/component.yaml#L6 | {
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"node_id": "MDU6TGFiZWwxNjgyNzE3Mzky"... | closed | false | null | [] | null | [
"cc @Bobgy @zijianjoy ",
"Thanks for the report @nakfour!\r\nI want to confirm you are doing the right thing, read KFP standalone installation guide in https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deploying-kubeflow-pipelines.\r\n\r\nCRDs are installed separated in:\r\n\r... | 2021-04-09T14:49:23 | 2021-04-19T16:44:17 | 2021-04-19T16:44:17 | MEMBER | null | As part of the effort to get the Openshift distribution ready for KF 1.3 release, we tried to install the "platform-agnostic" pipeline kustomization and it is missing the "Viewer" CRD. Also discussed here https://github.com/kubeflow/manifests/issues/1810.
PR here: https://github.com/kubeflow/manifests/blob/63c9026ee3e01ad8514882358d57d944d3a92b52/distributions/stacks/openshift/application/pipeline-agnostic/kustomization.yaml | {
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"Hi @myoshimu, you can edit the parameter and replace {{kfp-default-bucket}} with your own bucket.\r\n\r\nbut you are right, it should be replaced automatically during installation, it's strange that failed",
"/assign @zijianjoy ",
"/assign @capri-xiyue ",
"This issue has been automatically marked as stale be... | 2021-04-09T07:29:08 | 2021-12-13T19:22:55 | 2021-12-13T19:22:55 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Deployed the KFP from GCP marketplace with [this step](https://cloud.google.com/ai-platform/pipelines/docs/getting-started
).
* KFP version:
1.4.1
* KFP SDK version:
1.4.1
### Steps to reproduce
1) Deploy KFP with the step below
https://cloud.google.com/ai-platform/pipelines/docs/getting-started
2) From the Google Cloud AI Platform Pipelines, open Pipeline Dashboard.
3) Go "Pipelines"
4) Open "[Demo] TFX - Taxi tip prediction model trainer"
5) Choose "Create Run" and you can see the csvexamplegen failed.
6) The log shows following error:
```
2021-04-09 06:48:54.757999: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib
2021-04-09 06:48:54.758104: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
INFO:absl:tensorflow_ranking is not available: No module named 'tensorflow_ranking'
INFO:absl:tensorflow_text is not available: No module named 'tensorflow_text'
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:apache_beam.typehints.native_type_compatibility:Using Any for unsupported type: typing.MutableMapping[str, typing.Any]
INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with gRPC connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Adding KFP pod name parameterized-tfx-oss-lgngg-449390883 to execution
INFO:absl:Adding KFP pod name parameterized-tfx-oss-lgngg-449390883 to execution
/usr/local/lib/python3.7/dist-packages/setuptools/distutils_patch.py:26: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.
"Distutils was imported before Setuptools. This usage is discouraged "
Traceback (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/tfx/orchestration/kubeflow/container_entrypoint.py", line 360, in <module>
main()
File "/usr/local/lib/python3.7/dist-packages/tfx/orchestration/kubeflow/container_entrypoint.py", line 353, in main
execution_info = launcher.launch()
File "/usr/local/lib/python3.7/dist-packages/tfx/orchestration/launcher/base_component_launcher.py", line 198, in launch
self._exec_properties)
File "/usr/local/lib/python3.7/dist-packages/tfx/orchestration/launcher/base_component_launcher.py", line 167, in _run_driver
component_info=self._component_info)
File "/usr/local/lib/python3.7/dist-packages/tfx/dsl/components/base/base_driver.py", line 311, in pre_execution
component_info=component_info)
File "/usr/local/lib/python3.7/dist-packages/tfx/components/example_gen/driver.py", line 141, in _prepare_output_artifacts
base_driver._prepare_output_paths(example_artifact) # pylint: disable=protected-access
File "/usr/local/lib/python3.7/dist-packages/tfx/dsl/components/base/base_driver.py", line 46, in _prepare_output_paths
if fileio.exists(artifact.uri):
File "/usr/local/lib/python3.7/dist-packages/tfx/dsl/io/fileio.py", line 63, in exists
return _get_filesystem(path).exists(path)
File "/usr/local/lib/python3.7/dist-packages/tfx/dsl/io/plugins/tensorflow_gfile.py", line 54, in exists
return tf.io.gfile.exists(path)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py", line 268, in file_exists_v2
_pywrap_file_io.FileExists(compat.path_to_bytes(path))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Error executing an HTTP request: HTTP response code 400 with body '{
"error": {
"code": 400,
"message": "Invalid bucket name: '{{kfp-default-bucket}}'",
"errors": [
{
"message": "Invalid bucket name: '{{kfp-default-bucket}}'",
"domain": "global",
"reason": "invalid"
}
]
}
}
'
when reading metadata of gs://{{kfp-default-bucket}}/tfx_taxi_simple/12cd6e2d-d7a7-4c4b-a1ad-970c24cd3166/CsvExampleGen/examples/11
Runtime execution graph. Only steps that are currently running or have already completed are shown.
```
### Expected result
The sample pipeline successfully complete.
### Materials and Reference
It looks like the bucket 'tfx_taxi_simple' does not exist.
https://github.com/kubeflow/pipelines/blob/master/samples/core/parameterized_tfx_oss/parameterized_tfx_oss.py
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Hi @chauhang, thank you for the report!\r\nIt's not currently possible, but I think the best way to support this is:\r\n1. you build a tensorboard image that already installs torch-tb-profiler\r\n2. you use https://github.com/kubeflow/pipelines/issues/5471 to switch to your custom tensorboard image\r\n\r\nHow does... | 2021-04-09T04:10:29 | 2021-05-11T07:45:43 | 2021-05-11T07:45:43 | CONTRIBUTOR | null | We are working on KFP components for PyTorch and trying to figure out how to see the PyTorch Profiler Tensorboard plugin in the KFP Tensorboard viewer. It requires additional `pip install torch-tb-profiler`. For more details please [see]( https://github.com/pytorch/kineto/tree/master/tb_plugin).
What will be the steps to get this addon installed so that it starts showing up in the KFP Tensorboard viewer? | {
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"1. https://github.com/kubeflow/pipelines/blob/9116a5a162ac36a2615d4185d879dabfd0d7ad9a/backend/api/experiment.proto#L178-L182 has not been changed. \r\n\r\n2. swagger file has been changed in this PR https://github.com/kubeflow/pipelines/pull/4906/files#r573449242\r\n\r\nI think it's due to higher version generat... | 2021-04-08T10:10:33 | 2021-04-17T01:52:14 | 2021-04-17T01:52:14 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.0-rc
<!-- 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
1. clone the repo with latest version
2. cd frontend
3. npm run apis
4. git status and get modified files
5. found the definition of `ExperimentStorageState` change into `ApiExperimentStorageState` while the import statements in `frontend/src/pages/ExperimentDetails.tsx`, `frontend/src/components/ExperimentList.tsx`, etc. still `import ExperimentStorageState`, leading to import error
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
No import error, frontend and backend keep consistent.
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
cc @Jeffwan | {
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"The intermediate data storage is considered to be implementation detail.\r\nWe advice our users to make any data export explicit. This way they can easily control the location of the exported data.\r\nIf you want to put the artifact into a GCS bucket, just pass it to an `Upload to GCS` component.\r\nSee this pipel... | 2021-04-07T14:49:07 | 2022-04-18T17:27:34 | 2022-04-18T17:27:34 | NONE | null | ### Feature Area
<area>sdk</area>
### What feature would you like to see?
SDK to download component output artifact after run
### What is the use case or pain point?
Output artifact can only be downloaded via UI, but not via SDK. Automation is difficult.
### Is there a workaround currently?
Don't know how to do it now.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Can you provide a sample for us to verify the problem?",
"In addition, can you also deploy latest KFP to see whether it gets fixed?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
... | 2021-04-07T09:58:09 | 2021-12-13T19:23:13 | 2021-12-13T19:23:13 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
kfctl and KubeFlow v1.1.0 istio dex
* KFP version:
v1.0.0
* KFP SDK version:
v1.1.2
### Steps to reproduce
Latest version of TFX (0.29.0rc0) introduced an IR of the pipeline definitions. For larger pipelines (>20 nodes) these are quite large (>5Mb), which exceeds the default gRPC limit and the UI gives the following error message:
`{"error":"grpc: received message larger than max (5429435 vs. 4194304)","message":"grpc: received message larger than max (5429435 vs. 4194304)","code":8}`
I update the backend limit in the deployment to be twice the default size:
-name: DBCONFIG_GROUPCONCATMAXLEN
value: "8388608"
under upstream/base/pipeline/ml-pipeline-apiserver-deployment.yaml and I can exec into the pod and verify that the value is indeed set.
Despite this, the UI still raised and error with the default size of 4194304.
I removed and redeployed all pipeline components as well as the mysql DB but this didn't change anything.
To be on the safe side I exec:ed into the api-server and ran:
sed -i 's/4194304/8388608/g' config.json
which updated config.json and viper should then repopulate this field but still no difference.
Following a comment in this issue kubeflow/pipelines#2310, I also tested running:
set GLOBAL group_concat_max_len=8388608;
but this had no effect either.
### Expected result
Changing the gRPC message limit in the DB config should also take effect in the UI.
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Note, updating MLMD is fairly complex, we need to update all the 7 locations listed in https://github.com/kubeflow/pipelines/blob/1128631cd5b4335080fb70ca35f864dbedb8b2cf/manifests/kustomize/base/metadata/base/metadata-grpc-deployment.yaml#L19-L24\r\n\r\nSome of them are not well documented, so I'd suggest delayin... | 2021-04-07T09:47:06 | 2023-08-07T18:46:16 | null | CONTRIBUTOR | null | Currently pipelines uses gcr.io/tfx-oss-public/ml_metadata_store_server version `0.25.1` but `0.29.0` is the latest version. The image tag in [metadata-grpc-deployment.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/base/metadata/base/metadata-grpc-deployment.yaml) needs updating.
To automate this process, all image tags should be listed in the `images:` section of the `kustiomization.yaml` files ([here](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/base/metadata/base/kustomization.yaml) for this specific case). Once that is done, Renovate can be setup to create PRs updating the image tags of the manifests.
@Bobgy Would you like me to go through the manifests and set the images in the `kustomization.yaml` files and add the necessary Renovate config for kustomize?
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"According to @chensun , the `staging_gcs_path` is a required field in sdk code. \r\nThis `kfp.compile.build_python_component` was deprecated before but was revived recently. We may want to deprecate it again sometime in the future.",
"Could you try using `kfp.components.create_component_from_func` instead? You c... | 2021-04-07T08:30:31 | 2021-04-13T11:43:57 | 2021-04-13T11:43:57 | NONE | null | Hi! Is it possible to use `kfp.compile.build_python_component` without using google cloud storage (for the parameter `staging_gcs_path`)?
I'm trying to run the [pipeline SDK tutorial ](https://v0-7.kubeflow.org/docs/pipelines/sdk/sdk-overview/) locally and this step
```
OUTPUT_DIR = 'local_tmp_dir/
my_op = compiler.build_python_component(
component_func=my_python_func,
staging_gcs_path=OUTPUT_DIR,
target_image=TARGET_IMAGE)
```
outputs
`ValueError: Error: local_tmp_dir/ should be a GCS path.`
Thank you! :) | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5436 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5436/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5436/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5436/events | https://github.com/kubeflow/pipelines/issues/5436 | 851,814,320 | MDU6SXNzdWU4NTE4MTQzMjA= | 5,436 | Using a Google Service Accounts on AI Platform Pipeline Deployment to access Google Secret Manager | {
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} | [] | closed | false | null | [] | null | [] | 2021-04-06T21:12:34 | 2021-04-08T17:32:18 | 2021-04-08T17:32:18 | NONE | null | Based on this [documentation](https://www.kubeflow.org/docs/gke/pipelines/authentication-pipelines/), it looks like we cannot use any other Google Service Accounts when we deploy Kubeflow Pipelines through AI-platform pipelines other than the Compute Engine default service account:
> For AI Platform Pipelines, Compute Engine default service account is the only supported option.
Is this still true?
On the pipelines UI, it looks like we're able to provide a Kubernetes Service Account:
I've tried binding my Kubernertes Service Account to a Google Service account following the [workload identity process](https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity), but it doesn't seem to be working. So I might be missing the point of having this option if it will always use the Compute Engine default service account.
That being said, is it possible then for a pipeline deployed on AI Platform Pipelines to access google secrets manager? It looks like the Compute Engine default service account does not have the right permissions to access secrets from there. | {
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