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"/assign @capri-xiyue ",
"PR merged https://github.com/kubeflow/pipelines/pull/6569\r\nTO DO \r\n- [ ] Update v2 caching doc ",
"reassigned it to @chensun",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for... | 2021-07-27T14:10:00 | 2023-01-18T08:35:06 | 2023-01-18T08:35:06 | CONTRIBUTOR | null | - [ ] ~#8696~ | {
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"Is this an issue for tracking the implementation of replacements for things such as `{{workflow.status}}`? \r\n\r\nIf so:\r\n\r\n`{{$.pipeline_task_name}}` and `{{$.pipeline_task_uuid}}` don't seem to work on Vertex AI at the moment (not sure about a self hosted KFP). Is this expected? I've also tried various plac... | 2021-07-27T14:00:48 | 2023-01-18T09:43:28 | null | CONTRIBUTOR | null | null | {
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"P0 work items done"
] | 2021-07-27T14:00:24 | 2021-08-24T13:32:26 | 2021-08-24T13:32:25 | CONTRIBUTOR | null | Support v2 placeholders in KFP v2.
Tasks
* [x] #6151
* [x] #6152
* [x] #6153
* [x] #6154
* [ ] #6155
* [x] https://github.com/kubeflow/pipelines/pull/6353 | {
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"Done"
] | 2021-07-27T14:00:08 | 2021-08-02T14:19:40 | 2021-08-02T12:10:42 | CONTRIBUTOR | null | Explanation: backend compiler compiles from [KFP pipeline spec](https://github.com/kubeflow/pipelines/blob/master/api/v2alpha1/pipeline_spec.proto) to argo workflow (potentially, other implementations may compile it to other execution spec).
Design: [bit.ly/kfp-v2](https://bit.ly/kfp-v2)
This issue tracks implementing
Tasks:
* [x] https://github.com/kubeflow/pipelines/pull/6111
* [x] https://github.com/kubeflow/pipelines/pull/6139
* [x] https://github.com/kubeflow/pipelines/pull/6144
* [x] #6147
* [x] https://github.com/kubeflow/pipelines/pull/6140 | {
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"I'll directly track this in #6110"
] | 2021-07-27T14:00:03 | 2021-07-28T10:53:01 | 2021-07-28T10:53:00 | CONTRIBUTOR | null | null | {
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"The AIPlatformClient in KFP SDK will be deprecated and we would recommend starting to use `google-cloud-aiplatform`. \r\n\r\nCurrently, the `create_run_from_job_spec` is completed moved over and `create_schedule_from_job_spec` is in-progress. We are working on a code sample of usage in `google-cloud-aiplatform` (V... | 2021-07-27T13:05:29 | 2022-03-15T16:18:49 | 2021-07-28T07:41:14 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
I'm using kfp SDK to use Vertex pipeline of GCP by `AIPlatformClient`. So I want to propose several pull requests to the functions like `create_run_from_job_spec` and `create_schedule_from_job_spec` for fixing bugs and adjust specifications. But I found that `AIPlatformClient` will be deprecated in v1.9 and will be moved to `google-cloud-aiplatform` package.
I looked into the document and was not able to find the features that is compatible with `AIPlatformClient.create_schedule_from_job_spec`.
So I want to ask that is it okay to keep making changes to the AIPlatformClient SDK. If it's okay and will the pull requests will be merged, I will try to make pull requests for the feature request.
But if it's better to move to `google-cloud-aiplatform`, I want a guide to move current workflow to the new SDK.
I understand that it's still work in progress and difficult to handle, but I appreciate if someone could tell me the direction.
@ji-yaqi I'm not sure you're the right person but seems that you're contributing to the google SDK, so let me mention you here for notice.
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-07-26T17:04:58 | 2022-03-03T02:05:15 | null | COLLABORATOR | null |
Currently we shows stacktrace on Input/Output banner if the artifact is `aiplatform://xxx`. We should skip the preview and downloadable link generation. Maybe show a banner to explain that this is unavailable for Google Cloud specific resource prefix. | {
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"followers_url": "https://a... | null | [
"I've also just tested the issues with 1.7.0-rc2.\r\n\r\nI've tried kfp 1.4.0 and 1.6.6.",
"Downgrading the metadata images seems to have solved this for me:\r\n\r\n```\r\nimages:\r\n - name: gcr.io/ml-pipeline/metadata-envoy\r\n newTag: 1.4.0\r\n - name: gcr.io/tfx-oss-public/ml_metadata_store_server\r\n ... | 2021-07-26T10:37:03 | 2022-09-30T03:29:40 | null | CONTRIBUTOR | null | /kind bug
I upgraded Kubeflow from 1.4.0 to 1.7.0-rc1 with the platnform-agnostic manifests.
While I now see correct visualizations of statistics from runs that happened before upgrading to 1.7.0-rc1, new runs only display the markdown details.
The TFX pipelines I submit are exactly the same. On the new runs the ML Metadata tab of the components prints:
"Corresponding ML Metadata not found."
Furthermore I don't see any new executions on the executions page despite running many pipelines since upgrading.
I don't see anything special in the logs of the TFX pods except:
```
WARNING:absl:metadata_connection_config is not provided by IR.
```
But that was present before upgrading to 1.7.0-rc1.
The only errors I see in the metadata-grpc-deployment pod is:
```
name: "sp-lstm-rh6xt"
Internal: mysql_query failed: errno: 1062, error: Duplicate entry '48-sp-lstm-rh6xt' for key 'type_id'
Cannot create node for type_id: 48 name: "sp-lstm-rh6xt"
```
Which I also think is normal?
Basically I don't think executions and artifacts are getting written to the DB for some reason in 1.7.0-rc1. Not sure how to debug this. This causes the visualizations to not show up as far as I can see.
Metadata in the TFX pipelines is configured via the get_default_kubeflow_metadata_config tfx.orchestration.kubeflow function.
Environment:
Kubeflow version: 1.4.0 -> 1.7.0-rc1
kfctl version: Not used. Using tfx.orchestration.kubeflow to submit pipelines.
Kubernetes platform: Upstream kubeadm: k8s v1.20.5
Kubernetes version: (use kubectl version):
OS (e.g. from /etc/os-release): Centos 8
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/6134 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6134/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6134/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6134/events | https://github.com/kubeflow/pipelines/issues/6134 | 952,460,879 | MDU6SXNzdWU5NTI0NjA4Nzk= | 6,134 | [sdk] v2 compiler - duplicate component definitions when used twice | {
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"/assign @chensun @neuromage ",
"This is a known issues, and it was a regression we introduced when workaround the limitation that IR doesn't support optional inputs.\r\nI left a TODO in the code:\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/42e13ebc41c6d318eebe797047ccd0d83641c0d8/sdk/python/kfp/dsl/_compon... | 2021-07-26T03:25:11 | 2022-03-03T02:05:14 | null | CONTRIBUTOR | null | ### Environment
* KFP version: 1.7.0-rc.2
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: master
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
Compile this pipeline in v2 mode:
```
from kfp.v2 import dsl
from kfp.v2 import components
@components.create_component_from_func
def hello_world(text: str):
print(text)
return text
@dsl.pipeline(name='component-used-twice')
def pipeline_component_used_twice(text: str = 'hi there'):
task1 = hello_world(text)
task2 = hello_world(text)
```
### Expected result
<!-- What should the correct behavior be? -->
In generated pipeline spec JSON, hello_world component should be generated only once.
But I got `comp-hello-world` and `comp-hello-world-2`, and `exec-hello-world`, `exec-hello-world-2`.
### Materials and Reference
Here's the generated JSON pipeline spec:
```
{
"pipelineSpec": {
"components": {
"comp-hello-world": {
"executorLabel": "exec-hello-world",
"inputDefinitions": {
"parameters": {
"text": {
"type": "STRING"
}
}
}
},
"comp-hello-world-2": {
"executorLabel": "exec-hello-world-2",
"inputDefinitions": {
"parameters": {
"text": {
"type": "STRING"
}
}
}
}
},
"deploymentSpec": {
"executors": {
"exec-hello-world": {
"container": {
"args": [
"--text",
"{{$.inputs.parameters['text']}}"
],
"command": [
"sh",
"-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
"def hello_world(text):\n print(text)\n return text\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Hello world', description='')\n_parser.add_argument(\"--text\", dest=\"text\", type=str, required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs = hello_world(**_parsed_args)\n"
],
"image": "python:3.7"
}
},
"exec-hello-world-2": {
"container": {
"args": [
"--text",
"{{$.inputs.parameters['text']}}"
],
"command": [
"sh",
"-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
"def hello_world(text):\n print(text)\n return text\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Hello world', description='')\n_parser.add_argument(\"--text\", dest=\"text\", type=str, required=True, default=argparse.SUPPRESS)\n_parsed_args = vars(_parser.parse_args())\n\n_outputs = hello_world(**_parsed_args)\n"
],
"image": "python:3.7"
}
}
}
},
"pipelineInfo": {
"name": "component-used-twice"
},
"root": {
"dag": {
"tasks": {
"hello-world": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-hello-world"
},
"inputs": {
"parameters": {
"text": {
"componentInputParameter": "text"
}
}
},
"taskInfo": {
"name": "hello-world"
}
},
"hello-world-2": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-hello-world-2"
},
"inputs": {
"parameters": {
"text": {
"componentInputParameter": "text"
}
}
},
"taskInfo": {
"name": "hello-world-2"
}
}
}
},
"inputDefinitions": {
"parameters": {
"text": {
"type": "STRING"
}
}
}
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.6.6"
},
"runtimeConfig": {
"parameters": {
"text": {
"stringValue": "hi there"
}
}
}
}
```
<!-- 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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"Closing because v2 compatible is no longer supported, we are working on the complete KFP v2.\n\n/cc @chensun \nWe used to have this issue for v2 compatible, a lot of them are still unsolved for v2 engine mode as well."
] | 2021-07-26T03:14:40 | 2023-01-18T08:21:42 | 2022-01-20T12:22:00 | CONTRIBUTOR | null | This issue is a living tracker for known caveats & breaking changes of v2 compatible mode to facilitate visibility:
The information is updated as KFP 1.7.0
Breaking changes:
* https://www.kubeflow.org/docs/components/pipelines/sdk/v2/v2-compatibility/#building-pipelines-using-the-kubeflow-pipelines-sdk-v2
* `command` must be specified in [Kubeflow Pipelines component specification](https://www.kubeflow.org/docs/components/pipelines/reference/component-spec/).
* https://github.com/kubeflow/pipelines/issues/5834
* https://github.com/kubeflow/pipelines/issues/5711
* v2 compatible mode will require KFP backend 1.7.0+, but v1 mode does not have any prerequisites on KFP backend version.
Known caveats:
* [ ] Pipelines do not run on [the full Kubeflow] distributions with Multi-User Kubeflow Pipelines. [#5680]
* [x] https://github.com/kubeflow/pipelines/issues/5831
* [ ] https://github.com/kubeflow/pipelines/issues/5673
* [x] https://github.com/kubeflow/pipelines/issues/6132
* [ ] https://github.com/kubeflow/pipelines/issues/5835
* [ ] v2 compatible mode and v1 mode have different perf / scalability characteristics, which we haven't verified in large scale.
* [ ] Because there aren't enough usage, it's very likely there are more changes we haven't found/thought of.
* [ ] When consuming an input artifact by URI, its file content is still downloaded to the container regardless. [#5671]
* [ ] https://github.com/kubeflow/pipelines/issues/6429
Fixed caveats:
* [x] https://github.com/kubeflow/pipelines/issues/6293
* [x] Pipelines using [v1 mlpipeline-ui-metadata visualization](/docs/components/pipelines/sdk/output-viewer/) do not compile. [#5666]
* [x] UI integration is implemented.
* [x] Caching is work-in-progress. [#5667]
[the full Kubeflow]: /docs/components/pipelines/installation/overview/#full-kubeflow
[#5680]: https://github.com/kubeflow/pipelines/issues/5680
[#5666]: https://github.com/kubeflow/pipelines/issues/5666
[#5671]: https://github.com/kubeflow/pipelines/issues/5671
[#5667]: https://github.com/kubeflow/pipelines/issues/5667 | {
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"Hello, on our side this is one of the most important feature for v2. \r\nBeing able to run multiple pipelines and then easily compare the metrics of each pipeline in a single view is key in our workflow. \r\nWe loved that v1 offered this and we cannot wait for it to be available on v2. \r\nCheers!",
"This iss... | 2021-07-26T02:54:20 | 2023-01-18T08:21:42 | 2023-01-18T08:21:42 | CONTRIBUTOR | null | KFP v1 supports metrics feature https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/.
V2 compatible mode does not yet support it, because supporting this feature is relatively hard. I'd like some feedback how important this feature is for you. Please thumbs up on the issue and probably explain your use-case to help us triage.
## Idea
to support this feature, we need to update persistence agent, let it also parse MLMD data and extract the metrics artifacts and persist them in KFP DB. | {
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"/assign @IronPan ",
"/assign @SinaChavoshi ",
"P0, because this breaks all postsubmit builds, thus blocking us to release",
"Pasting error messages:\r\n\r\n```\r\nStep 4/9 : RUN python3 -m pip install -r requirements.txt --quiet --no-cache-dir && rm -f requirements.txt\r\n ---> Running in ac5c4843fab... | 2021-07-24T03:08:00 | 2021-07-27T00:51:45 | 2021-07-27T00:51:45 | CONTRIBUTOR | null | The first failure happened at https://github.com/kubeflow/pipelines/runs/3137853377, but the failure itself seems unrelated to the PR, so I think it's caused by some external dependency change. | {
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"POC is available at this branch\r\n\r\nhttps://github.com/zijianjoy/pipelines/tree/dag-poc"
] | 2021-07-23T21:10:00 | 2021-08-01T06:33:11 | 2021-08-01T06:33:11 | COLLABORATOR | null | null | {
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"Hi @ReggieCarey!\r\nCan you give output of `kubectl logs ml-pipeline-9b68d49cb-x67mp ml-pipeline-api-server --previous`? that should include logs from last failed container.",
"For metadata-writer, what is status of `metadata-grpc-server`? metadata-writer fails to connect to the store",
"Thanks Yuan Gong,\r\n\... | 2021-07-23T16:05:49 | 2023-08-17T11:28:08 | null | NONE | null | ### What steps did you take
I deployed Kubeflow 1.3 by using the manifests approach.
I then repaired an issue with dex running on K8s v1.21
### What happened:
The installation succeeded. All processes started up except the two. Both Metadata writer and ml-pipeline crash constantly and are restarted. ML-Pipeline always reports 1 of 2 running.
Metadata-writer sometimes appears to be fully running then fails.
No other kubeflow pods are having problems like this - even the mysql pod seems stable.
I can only assume the failure of the metadata writer is due to a continued failure in ml-pipeline api-server.
The pod keeps getting terminated by something with a reason code of 137.
See last image provided for details on the cycle time.
### What did you expect to happen:
I expect that the pipeline tools install and operate normally. This has been a consistent problem going back to KF 1.1 with no adequate resolution
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
I use the kubeflow 1.3 manifests deployment approach
* 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. -->
This install is via the kubeflow 1.3.
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
NOT APPLICABLE
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
kubectl logs ml-pipeline-9b68d49cb-x67mp ml-pipeline-api-server
```
I0723 15:20:24.692579 8 client_manager.go:154] Initializing client manager
I0723 15:20:24.692646 8 config.go:57] Config DBConfig.ExtraParams not specified, skipping
```
kubectl describe pod ml-pipeline-9b68d49cb-x67mp
```
Name: ml-pipeline-9b68d49cb-x67mp
Namespace: kubeflow
Priority: 0
Node: cpu-compute-09/10.164.208.67
Start Time: Tue, 20 Jul 2021 21:27:57 -0400
Labels: app=ml-pipeline
app.kubernetes.io/component=ml-pipeline
app.kubernetes.io/name=kubeflow-pipelines
application-crd-id=kubeflow-pipelines
istio.io/rev=default
pod-template-hash=9b68d49cb
security.istio.io/tlsMode=istio
service.istio.io/canonical-name=kubeflow-pipelines
service.istio.io/canonical-revision=latest
Annotations: cluster-autoscaler.kubernetes.io/safe-to-evict: true
kubectl.kubernetes.io/default-logs-container: ml-pipeline-api-server
prometheus.io/path: /stats/prometheus
prometheus.io/port: 15020
prometheus.io/scrape: true
sidecar.istio.io/status:
{"initContainers":["istio-init"],"containers":["istio-proxy"],"volumes":["istio-envoy","istio-data","istio-podinfo","istio-token","istiod-...
Status: Running
IP: 10.0.4.21
IPs:
IP: 10.0.4.21
Controlled By: ReplicaSet/ml-pipeline-9b68d49cb
Init Containers:
istio-init:
Container ID: docker://db62120288183c6d962e0bfb60db7780fa7bb8c9e231bc9f48976a10c1b29587
Image: docker.io/istio/proxyv2:1.9.0
Image ID: docker-pullable://istio/proxyv2@sha256:286b821197d7a9233d1d889119f090cd9a9394468d3a312f66ea24f6e16b2294
Port: <none>
Host Port: <none>
Args:
istio-iptables
-p
15001
-z
15006
-u
1337
-m
REDIRECT
-i
*
-x
-b
*
-d
15090,15021,15020
State: Terminated
Reason: Completed
Exit Code: 0
Started: Tue, 20 Jul 2021 21:28:19 -0400
Finished: Tue, 20 Jul 2021 21:28:19 -0400
Ready: True
Restart Count: 0
Limits:
cpu: 2
memory: 1Gi
Requests:
cpu: 10m
memory: 40Mi
Environment: <none>
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-8csrh (ro)
Containers:
ml-pipeline-api-server:
Container ID: docker://0a4b0d31179f67cc38ddb5ebb8eb31b32344c80fe9e4789ef20c073b02c5335b
Image: gcr.io/ml-pipeline/api-server:1.5.0
Image ID: docker-pullable://gcr.io/ml-pipeline/api-server@sha256:0d90705712e201ca7102336e4bd6ff794e7f76facdac2c6e82134294706d78ca
Ports: 8888/TCP, 8887/TCP
Host Ports: 0/TCP, 0/TCP
State: Waiting
Reason: CrashLoopBackOff
Last State: Terminated
Reason: Error
Exit Code: 137
Started: Fri, 23 Jul 2021 11:13:49 -0400
Finished: Fri, 23 Jul 2021 11:14:34 -0400
Ready: False
Restart Count: 1117
Requests:
cpu: 250m
memory: 500Mi
Liveness: exec [wget -q -S -O - http://localhost:8888/apis/v1beta1/healthz] delay=3s timeout=2s period=5s #success=1 #failure=3
Readiness: exec [wget -q -S -O - http://localhost:8888/apis/v1beta1/healthz] delay=3s timeout=2s period=5s #success=1 #failure=3
Environment Variables from:
pipeline-api-server-config-dc9hkg52h6 ConfigMap Optional: false
Environment:
KUBEFLOW_USERID_HEADER: kubeflow-userid
KUBEFLOW_USERID_PREFIX:
AUTO_UPDATE_PIPELINE_DEFAULT_VERSION: <set to the key 'autoUpdatePipelineDefaultVersion' of config map 'pipeline-install-config'> Optional: false
POD_NAMESPACE: kubeflow (v1:metadata.namespace)
OBJECTSTORECONFIG_SECURE: false
OBJECTSTORECONFIG_BUCKETNAME: <set to the key 'bucketName' of config map 'pipeline-install-config'> Optional: false
DBCONFIG_USER: <set to the key 'username' in secret 'mysql-secret'> Optional: false
DBCONFIG_PASSWORD: <set to the key 'password' in secret 'mysql-secret'> Optional: false
DBCONFIG_DBNAME: <set to the key 'pipelineDb' of config map 'pipeline-install-config'> Optional: false
DBCONFIG_HOST: <set to the key 'dbHost' of config map 'pipeline-install-config'> Optional: false
DBCONFIG_PORT: <set to the key 'dbPort' of config map 'pipeline-install-config'> Optional: false
OBJECTSTORECONFIG_ACCESSKEY: <set to the key 'accesskey' in secret 'mlpipeline-minio-artifact'> Optional: false
OBJECTSTORECONFIG_SECRETACCESSKEY: <set to the key 'secretkey' in secret 'mlpipeline-minio-artifact'> Optional: false
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-8csrh (ro)
istio-proxy:
Container ID: docker://6cd34842733729c0743c0ce153a6b15614da748e72a2352616cdf6d10eb9a997
Image: docker.io/istio/proxyv2:1.9.0
Image ID: docker-pullable://istio/proxyv2@sha256:286b821197d7a9233d1d889119f090cd9a9394468d3a312f66ea24f6e16b2294
Port: 15090/TCP
Host Port: 0/TCP
Args:
proxy
sidecar
--domain
$(POD_NAMESPACE).svc.cluster.local
--serviceCluster
ml-pipeline.$(POD_NAMESPACE)
--proxyLogLevel=warning
--proxyComponentLogLevel=misc:error
--log_output_level=default:info
--concurrency
2
State: Running
Started: Tue, 20 Jul 2021 21:28:36 -0400
Ready: True
Restart Count: 0
Limits:
cpu: 2
memory: 1Gi
Requests:
cpu: 10m
memory: 40Mi
Readiness: http-get http://:15021/healthz/ready delay=1s timeout=3s period=2s #success=1 #failure=30
Environment:
JWT_POLICY: third-party-jwt
PILOT_CERT_PROVIDER: istiod
CA_ADDR: istiod.istio-system.svc:15012
POD_NAME: ml-pipeline-9b68d49cb-x67mp (v1:metadata.name)
POD_NAMESPACE: kubeflow (v1:metadata.namespace)
INSTANCE_IP: (v1:status.podIP)
SERVICE_ACCOUNT: (v1:spec.serviceAccountName)
HOST_IP: (v1:status.hostIP)
CANONICAL_SERVICE: (v1:metadata.labels['service.istio.io/canonical-name'])
CANONICAL_REVISION: (v1:metadata.labels['service.istio.io/canonical-revision'])
PROXY_CONFIG: {}
ISTIO_META_POD_PORTS: [
{"name":"http","containerPort":8888,"protocol":"TCP"}
,{"name":"grpc","containerPort":8887,"protocol":"TCP"}
]
ISTIO_META_APP_CONTAINERS: ml-pipeline-api-server
ISTIO_META_CLUSTER_ID: Kubernetes
ISTIO_META_INTERCEPTION_MODE: REDIRECT
ISTIO_METAJSON_ANNOTATIONS: {"cluster-autoscaler.kubernetes.io/safe-to-evict":"true"}
ISTIO_META_WORKLOAD_NAME: ml-pipeline
ISTIO_META_OWNER: kubernetes://apis/apps/v1/namespaces/kubeflow/deployments/ml-pipeline
ISTIO_META_MESH_ID: cluster.local
TRUST_DOMAIN: cluster.local
Mounts:
/etc/istio/pod from istio-podinfo (rw)
/etc/istio/proxy from istio-envoy (rw)
/var/lib/istio/data from istio-data (rw)
/var/run/secrets/istio from istiod-ca-cert (rw)
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-8csrh (ro)
/var/run/secrets/tokens from istio-token (rw)
Conditions:
Type Status
Initialized True
Ready False
ContainersReady False
PodScheduled True
Volumes:
istio-envoy:
Type: EmptyDir (a temporary directory that shares a pod's lifetime)
Medium: Memory
SizeLimit: <unset>
istio-data:
Type: EmptyDir (a temporary directory that shares a pod's lifetime)
Medium:
SizeLimit: <unset>
istio-podinfo:
Type: DownwardAPI (a volume populated by information about the pod)
Items:
metadata.labels -> labels
metadata.annotations -> annotations
limits.cpu -> cpu-limit
requests.cpu -> cpu-request
istio-token:
Type: Projected (a volume that contains injected data from multiple sources)
TokenExpirationSeconds: 43200
istiod-ca-cert:
Type: ConfigMap (a volume populated by a ConfigMap)
Name: istio-ca-root-cert
Optional: false
kube-api-access-8csrh:
Type: Projected (a volume that contains injected data from multiple sources)
TokenExpirationSeconds: 3607
ConfigMapName: kube-root-ca.crt
ConfigMapOptional: <nil>
DownwardAPI: true
QoS Class: Burstable
Node-Selectors: <none>
Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning BackOff 12m (x13622 over 2d13h) kubelet Back-off restarting failed container
Warning Unhealthy 8m2s (x9931 over 2d13h) kubelet Readiness probe failed:
Normal Pulled 2m58s (x1116 over 2d13h) kubelet Container image "gcr.io/ml-pipeline/api-server:1.5.0" already present on machine
```
## Metadata-Writer Logs:
```
Failed to access the Metadata store. Exception: "upstream connect error or disconnect/reset before headers. reset reason: connection failure"
Failed to access the Metadata store. Exception: "upstream connect error or disconnect/reset before headers. reset reason: connection failure"
Failed to access the Metadata store. Exception: "upstream connect error or disconnect/reset before headers. reset reason: connection failure"
Failed to access the Metadata store. Exception: "upstream connect error or disconnect/reset before headers. reset reason: connection failure"
Traceback (most recent call last):
File "/kfp/metadata_writer/metadata_writer.py", line 63, in <module>
mlmd_store = connect_to_mlmd()
File "/kfp/metadata_writer/metadata_helpers.py", line 62, in connect_to_mlmd
raise RuntimeError('Could not connect to the Metadata store.')
RuntimeError: Could not connect to the Metadata store.
```
### 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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"IIRC, Kubeflow v1.3 enables multi-user isolation by default. @Bobgy, is that right?\r\n\r\nIn that case, `list_experiments()` requires passing a namespace: https://github.com/kubeflow/pipelines/blob/24c551d3fc0b955f48af1206db5c80ec011db3dd/sdk/python/kfp/_client.py#L452-L461",
"@chensun I think that is correct y... | 2021-07-23T06:03:56 | 2021-10-15T04:56:57 | 2021-07-30T14:37:31 | NONE | null | I'm following the example in docs (https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/) but am getting an error when trying to access the experiments or runs (when trying to list and/or create them)
I've port-forwarded ml-pipeline-ui:
`kubectl port-forward svc/ml-pipeline-ui 3000:80 --namespace kubeflow`
Trying to list the experiments results in an error:
```
import kfp
client = kfp.Client(host='http://localhost:3000')
print(client.list_experiments())
```
The error I get from client.list_experiments():
> ApiException: (500) Reason: Internal Server Error HTTP response
> headers: HTTPHeaderDict({'X-Powered-By': 'Express', 'content-type':
> 'application/json', 'trailer': 'Grpc-Trailer-Content-Type', 'date':
> 'Thu, 22 Jul 2021 21:09:12 GMT', 'x-envoy-upstream-service-time': '7',
> 'server': 'envoy', 'connection': 'close', 'transfer-encoding':
> 'chunked'}) HTTP response body: {"error":"Internal error:
> Unauthenticated: Request header error: there is no user identity
> header.: Request header error: there is no user identity
> header.\nFailed to authorize with API resource
> references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed
> to authorize with API resource
> references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357","code":13,"message":"Internal
> error: Unauthenticated: Request header error: there is no user
> identity header.: Request header error: there is no user identity
> header.\nFailed to authorize with API resource
> references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed
> to authorize with API resource
> references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal
> error: Unauthenticated: Request header error: there is no user
> identity header.: Request header error: there is no user identity
> header.\nFailed to authorize with API resource
> references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed
> to authorize with API resource
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> error: Unauthenticated: Request header error: there is no user
> identity header.: Request header error: there is no user identity
> header.\nFailed to authorize with API resource
> references\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\nmain.apiServerInterceptor\n\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\n\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\ngoogle.golang.org/grpc.(*Server).handleStream\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\n\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1357\nFailed
> to authorize with API resource
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However, listing the pipelines works:
```
import kfp
client = kfp.Client(host='http://localhost:3000')
print(client.list_pipelines())
```
I have verified that the port-forwarding worked - I am able to access the UI at http://localhost:3000/ and can see the pipelines. I am working with Kubeflow v1.3. I have also tried adding the additional namespace argument as suggested for multi-user (https://www.kubeflow.org/docs/components/pipelines/multi-user/#when-using-the-sdk). Any ideas on where I am going wrong? | {
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"Is this a duplicate of https://github.com/kubeflow/pipelines/issues/3763?\r\n@liguodongIOT ",
"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",
"你好,我是李国冬，我已收到你的邮件，我会尽快给您回复。"
] | 2021-07-23T02:49:51 | 2022-03-03T00:05:32 | null | NONE | null | When I use kfp1.5 to deploy to k8s.
I execute run, this pipeline ui has not been able to update the status.
---
And I check that the pod has finished running
I restarted k8s and kfp, the status is normal。
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"html_url": "https://github.com/chensun",
"followers_url": "https://api.githu... | null | [
"Taking HTML visualization as an example, the usage on V2 will look like:\r\n\r\n```\r\n@component \r\ndef write_html(html_artifact: Output[HTML]):\r\n html_content = '<!DOCTYPE html><html><body><h1>Hello world</h1></body></html>'\r\n with open(html_artifact.path, 'w') as f:\r\n f.write(html_con... | 2021-07-22T22:50:35 | 2022-05-20T15:16:06 | null | CONTRIBUTOR | null | Using v2 SDK and Vertex Pipelines environment, is it possible to create a reusable component (i.e. manually write a `component.yaml` file) that consumes and/or generates the new Artifact types such as Model, Metrics, Dataset, etc.?
My understanding of these Artifact types is that they are a value/path/reference along with associated metadata. When passing or consuming these in a non-Python-based component, I can only reference or generate an Artifact's path and nothing else it seems.
For example, in the v1 SDK, it was possible to generate metrics that could be visualized by just by [dumping a JSON object](https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/#export-the-metrics-dictionary) to the given output path. This allowed the possibility of using non-Python-based components to generate metrics and other metadata.
Is such a thing possible in v2/Vertex Pipelines? If not, is it on the roadmap or is the recommendation to port all components to lightweight Python components?
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https://api.github.com/repos/kubeflow/pipelines/issues/6114 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6114/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6114/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6114/events | https://github.com/kubeflow/pipelines/issues/6114 | 950,861,735 | MDU6SXNzdWU5NTA4NjE3MzU= | 6,114 | Cache is not reused when component output was previously consumed as file, but is now consumed as value | {
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"Hello. Thank you for finding this issue.\r\n\r\nDue to bandwidth, we do not currently prioritize issue where the cache is NOT reused. (The issues where the cache is reused incorrectly have high priority though.)\r\n\r\nThis issue is difficult to solve given the architecture of the KFP compiler. Even the file passi... | 2021-07-22T16:53:14 | 2021-07-30T01:03:09 | 2021-07-30T00:53:35 | CONTRIBUTOR | null | ### What steps did you take
Start with a pipeline where a component does not pass its output (outputPath) to a subsequent component.
Add a new pipeline where an additional component passes its output (outputPath) to a subsequent component that takes the output as an input (inputValue).
In this event the subsequent pipeline does not use the cached component because the output Parameters now includes the valueFrom parameter.
### What happened:
Cache Key was different resulting in pipeline not using cached component output.
### What did you expect to happen:
The pipeline should still use the cached component output.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow 1.2
* KFP version:
1.0.4 (also tested 1.6.0 looks to still be an issue there as well).
* KFP SDK version:
1.6.3
### Anything else you would like to add:
Workflow 1
--------------------
```
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
annotations:
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
pipelines.kubeflow.org/pipeline_compilation_time: 2021-07-22T11:05:01.675812
pipelines.kubeflow.org/pipeline_spec: '{"description": "Runs the dask preprocessing
of the Misty dataset and then trains a model.", "inputs": [{"default": "s3://athn-ai-ds-mltp-misty-dev/datasets/enron",
"name": "s3path", "optional": true, "type": "String"}], "name": "End to End
of Misty Pipeline"}'
pipelines.kubeflow.org/run_name: Run of misty.dsmltp-359.endtoend (8f03e)
creationTimestamp: "2021-07-22T15:05:25Z"
generateName: end-to-end-of-misty-pipeline-
generation: 4
labels:
pipeline/runid: 2c3189c6-ade6-48b5-932f-1f2c9cacdf62
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
workflows.argoproj.io/phase: Running
name: end-to-end-of-misty-pipeline-h5hsq
namespace: tkalbach
resourceVersion: "2254518"
selfLink: /apis/argoproj.io/v1alpha1/namespaces/tkalbach/workflows/end-to-end-of-misty-pipeline-h5hsq
uid: 0cc2f56f-3aaa-4326-99f9-9ebc3adcff17
spec:
arguments:
parameters:
- name: s3path
value: s3://athn-ai-ds-mltp-misty-dev/datasets/enron
entrypoint: end-to-end-of-misty-pipeline
serviceAccountName: default-editor
templates:
- dag:
tasks:
- arguments:
parameters:
- name: s3path
value: '{{inputs.parameters.s3path}}'
name: preprocess
template: preprocess
- arguments:
parameters:
- name: preprocess-TFRecords
value: '{{tasks.preprocess.outputs.parameters.preprocess-TFRecords}}'
dependencies:
- preprocess
name: train
template: train
inputs:
parameters:
- name: s3path
metadata:
annotations:
sidecar.istio.io/inject: "false"
labels:
pipelines.kubeflow.org/cache_enabled: "true"
name: end-to-end-of-misty-pipeline
outputs: {}
- container:
args:
- --raw-data
- '{{inputs.parameters.s3path}}'
- --tfrecords
- /tmp/outputs/TFRecords/data
- --labels
- /tmp/outputs/Labels/data
- --vocab
- /tmp/outputs/Vocab/data
- --dask-image
- 473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56
- --workers
- "5"
command:
- run_preprocess
image: 473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56
name: ""
resources: {}
inputs:
parameters:
- name: s3path
metadata:
annotations:
pipelines.kubeflow.org/arguments.parameters: '{"Raw Data": "{{inputs.parameters.s3path}}",
"Workers": "5"}'
pipelines.kubeflow.org/component_ref: '{"digest": "bbb3a746e8bd1c3e71d6fdbc64d9c54df47ecbbdaead27bc982a1f6f6c6030b6"}'
pipelines.kubeflow.org/component_spec: '{"description": "Run the preprocessing
of Enron dataset.", "implementation": {"container": {"args": ["--raw-data",
{"inputValue": "Raw Data"}, "--tfrecords", {"outputPath": "TFRecords"},
"--labels", {"outputPath": "Labels"}, "--vocab", {"outputPath": "Vocab"},
"--dask-image", "473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56",
"--workers", {"inputValue": "Workers"}], "command": ["run_preprocess"],
"image": "473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56"}},
"inputs": [{"description": "Path where raw data is stored.", "name": "Raw
Data", "type": "String"}, {"description": "The number of workers to use
for this run.", "name": "Workers", "type": "Integer"}], "name": "Preprocess",
"outputs": [{"description": "The TFRecord files.", "name": "TFRecords",
"type": "String"}, {"description": "The indexed label file.", "name": "Labels",
"type": "String"}, {"description": "The vocab JSON file.", "name": "Vocab",
"type": "String"}]}'
pipelines.kubeflow.org/task_display_name: Preprocess Dataset.
sidecar.istio.io/inject: "false"
labels:
clean: "true"
pipelines.kubeflow.org/cache_enabled: "true"
pipelines.kubeflow.org/enable_caching: "true"
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
pipelines.kubeflow.org/pipeline-sdk-type: kfp
name: preprocess
nodeSelector:
instanceType: m5.xlarge
procurement: ondemand
tenant: misty
outputs:
artifacts:
- name: preprocess-Labels
path: /tmp/outputs/Labels/data
- name: preprocess-TFRecords
path: /tmp/outputs/TFRecords/data
- name: preprocess-Vocab
path: /tmp/outputs/Vocab/data
parameters:
- name: preprocess-TFRecords
valueFrom:
path: /tmp/outputs/TFRecords/data
tolerations:
- effect: NoSchedule
key: tenant
operator: Equal
value: misty
- container:
args:
- --input
- '{{inputs.parameters.preprocess-TFRecords}}'
- --epochs
- "1"
- --learning-rate
- "0.001"
- --model-dir
- /tmp/outputs/Model_dir/data
- --tensorboard-dir
- /tmp/outputs/Tensorboard_dir/data
- --port
- "8080"
command:
- run_train
image: 473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/train:c662f3f
name: ""
ports:
- containerPort: 8080
hostPort: 8080
resources:
limits:
nvidia.com/gpu: "1"
inputs:
parameters:
- name: preprocess-TFRecords
metadata:
annotations:
pipelines.kubeflow.org/arguments.parameters: '{"Dataset": "{{inputs.parameters.preprocess-TFRecords}}",
"Epochs": "1", "Learning Rate": "0.001", "Port": "8080"}'
pipelines.kubeflow.org/component_ref: '{"digest": "1e57e72eb3cb918ff2519cd4e0e828b72521453ad98b397e58866b16b395018d"}'
pipelines.kubeflow.org/component_spec: '{"description": "Run the training
of Enron Model.", "implementation": {"container": {"args": ["--input", {"inputValue":
"Dataset"}, "--epochs", {"inputValue": "Epochs"}, "--learning-rate", {"inputValue":
"Learning Rate"}, "--model-dir", {"outputPath": "Model dir"}, "--tensorboard-dir",
{"outputPath": "Tensorboard dir"}, "--port", {"inputValue": "Port"}], "command":
["run_train"], "image": "473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/train:c662f3f"}},
"inputs": [{"description": "Path where raw data is stored.", "name": "Dataset",
"type": "String"}, {"description": "Number of epochs to train for.", "name":
"Epochs", "type": "Integer"}, {"description": "Learning rate to use while
training.", "name": "Learning Rate", "type": "Float"}, {"description": "The
port to start tensorboard on.", "name": "Port", "type": "Integer"}], "name":
"Train", "outputs": [{"description": "Path to save the model.", "name":
"Model dir", "type": "String"}, {"description": "Path to save the tensorboard
logs to.", "name": "Tensorboard dir", "type": "String"}]}'
pipelines.kubeflow.org/task_display_name: Train Model.
sidecar.istio.io/inject: "false"
labels:
clean: "true"
pipelines.kubeflow.org/cache_enabled: "true"
pipelines.kubeflow.org/enable_caching: "true"
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
pipelines.kubeflow.org/pipeline-sdk-type: kfp
name: train
nodeSelector:
instanceType: p2.xlarge
procurement: ondemand
tenant: misty
outputs:
artifacts:
- name: train-Model-dir
path: /tmp/outputs/Model_dir/data
- name: train-Tensorboard-dir
path: /tmp/outputs/Tensorboard_dir/data
tolerations:
- effect: NoSchedule
key: tenant
operator: Equal
value: misty
- effect: NoSchedule
key: processing
operator: Equal
value: gpu
status:
finishedAt: null
nodes:
end-to-end-of-misty-pipeline-h5hsq:
children:
- end-to-end-of-misty-pipeline-h5hsq-2090586888
displayName: end-to-end-of-misty-pipeline-h5hsq
finishedAt: null
id: end-to-end-of-misty-pipeline-h5hsq
inputs:
parameters:
- name: s3path
value: s3://athn-ai-ds-mltp-misty-dev/datasets/enron
name: end-to-end-of-misty-pipeline-h5hsq
phase: Running
startedAt: "2021-07-22T15:05:25Z"
templateName: end-to-end-of-misty-pipeline
type: DAG
end-to-end-of-misty-pipeline-h5hsq-2090586888:
boundaryID: end-to-end-of-misty-pipeline-h5hsq
displayName: preprocess
finishedAt: null
id: end-to-end-of-misty-pipeline-h5hsq-2090586888
inputs:
parameters:
- name: s3path
value: s3://athn-ai-ds-mltp-misty-dev/datasets/enron
name: end-to-end-of-misty-pipeline-h5hsq.preprocess
phase: Running
startedAt: "2021-07-22T15:05:25Z"
templateName: preprocess
type: Pod
phase: Running
startedAt: "2021-07-22T15:05:25Z"
```
Workflow 2
-----------------
```
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
annotations:
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
pipelines.kubeflow.org/pipeline_compilation_time: 2021-07-22T11:05:01.675812
pipelines.kubeflow.org/pipeline_spec: '{"description": "Runs the dask preprocessing
of the Misty dataset and then trains a model.", "inputs": [{"default": "s3://athn-ai-ds-mltp-misty-dev/datasets/enron",
"name": "s3path", "optional": true, "type": "String"}], "name": "End to End
of Misty Pipeline"}'
pipelines.kubeflow.org/run_name: Run of misty.dsmltp-359.endtoend (8f03e)
creationTimestamp: "2021-07-22T15:05:25Z"
generateName: end-to-end-of-misty-pipeline-
generation: 4
labels:
pipeline/runid: 2c3189c6-ade6-48b5-932f-1f2c9cacdf62
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
workflows.argoproj.io/phase: Running
name: end-to-end-of-misty-pipeline-h5hsq
namespace: tkalbach
resourceVersion: "2254518"
selfLink: /apis/argoproj.io/v1alpha1/namespaces/tkalbach/workflows/end-to-end-of-misty-pipeline-h5hsq
uid: 0cc2f56f-3aaa-4326-99f9-9ebc3adcff17
spec:
arguments:
parameters:
- name: s3path
value: s3://athn-ai-ds-mltp-misty-dev/datasets/enron
entrypoint: end-to-end-of-misty-pipeline
serviceAccountName: default-editor
templates:
- dag:
tasks:
- arguments:
parameters:
- name: s3path
value: '{{inputs.parameters.s3path}}'
name: preprocess
template: preprocess
- arguments:
parameters:
- name: preprocess-TFRecords
value: '{{tasks.preprocess.outputs.parameters.preprocess-TFRecords}}'
dependencies:
- preprocess
name: train
template: train
inputs:
parameters:
- name: s3path
metadata:
annotations:
sidecar.istio.io/inject: "false"
labels:
pipelines.kubeflow.org/cache_enabled: "true"
name: end-to-end-of-misty-pipeline
outputs: {}
- container:
args:
- --raw-data
- '{{inputs.parameters.s3path}}'
- --tfrecords
- /tmp/outputs/TFRecords/data
- --labels
- /tmp/outputs/Labels/data
- --vocab
- /tmp/outputs/Vocab/data
- --dask-image
- 473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56
- --workers
- "5"
command:
- run_preprocess
image: 473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56
name: ""
resources: {}
inputs:
parameters:
- name: s3path
metadata:
annotations:
pipelines.kubeflow.org/arguments.parameters: '{"Raw Data": "{{inputs.parameters.s3path}}",
"Workers": "5"}'
pipelines.kubeflow.org/component_ref: '{"digest": "bbb3a746e8bd1c3e71d6fdbc64d9c54df47ecbbdaead27bc982a1f6f6c6030b6"}'
pipelines.kubeflow.org/component_spec: '{"description": "Run the preprocessing
of Enron dataset.", "implementation": {"container": {"args": ["--raw-data",
{"inputValue": "Raw Data"}, "--tfrecords", {"outputPath": "TFRecords"},
"--labels", {"outputPath": "Labels"}, "--vocab", {"outputPath": "Vocab"},
"--dask-image", "473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56",
"--workers", {"inputValue": "Workers"}], "command": ["run_preprocess"],
"image": "473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/dask_preprocess:591db56"}},
"inputs": [{"description": "Path where raw data is stored.", "name": "Raw
Data", "type": "String"}, {"description": "The number of workers to use
for this run.", "name": "Workers", "type": "Integer"}], "name": "Preprocess",
"outputs": [{"description": "The TFRecord files.", "name": "TFRecords",
"type": "String"}, {"description": "The indexed label file.", "name": "Labels",
"type": "String"}, {"description": "The vocab JSON file.", "name": "Vocab",
"type": "String"}]}'
pipelines.kubeflow.org/task_display_name: Preprocess Dataset.
sidecar.istio.io/inject: "false"
labels:
clean: "true"
pipelines.kubeflow.org/cache_enabled: "true"
pipelines.kubeflow.org/enable_caching: "true"
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
pipelines.kubeflow.org/pipeline-sdk-type: kfp
name: preprocess
nodeSelector:
instanceType: m5.xlarge
procurement: ondemand
tenant: misty
outputs:
artifacts:
- name: preprocess-Labels
path: /tmp/outputs/Labels/data
- name: preprocess-TFRecords
path: /tmp/outputs/TFRecords/data
- name: preprocess-Vocab
path: /tmp/outputs/Vocab/data
parameters:
- name: preprocess-TFRecords
valueFrom:
path: /tmp/outputs/TFRecords/data
tolerations:
- effect: NoSchedule
key: tenant
operator: Equal
value: misty
- container:
args:
- --input
- '{{inputs.parameters.preprocess-TFRecords}}'
- --epochs
- "1"
- --learning-rate
- "0.001"
- --model-dir
- /tmp/outputs/Model_dir/data
- --tensorboard-dir
- /tmp/outputs/Tensorboard_dir/data
- --port
- "8080"
command:
- run_train
image: 473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/train:c662f3f
name: ""
ports:
- containerPort: 8080
hostPort: 8080
resources:
limits:
nvidia.com/gpu: "1"
inputs:
parameters:
- name: preprocess-TFRecords
metadata:
annotations:
pipelines.kubeflow.org/arguments.parameters: '{"Dataset": "{{inputs.parameters.preprocess-TFRecords}}",
"Epochs": "1", "Learning Rate": "0.001", "Port": "8080"}'
pipelines.kubeflow.org/component_ref: '{"digest": "1e57e72eb3cb918ff2519cd4e0e828b72521453ad98b397e58866b16b395018d"}'
pipelines.kubeflow.org/component_spec: '{"description": "Run the training
of Enron Model.", "implementation": {"container": {"args": ["--input", {"inputValue":
"Dataset"}, "--epochs", {"inputValue": "Epochs"}, "--learning-rate", {"inputValue":
"Learning Rate"}, "--model-dir", {"outputPath": "Model dir"}, "--tensorboard-dir",
{"outputPath": "Tensorboard dir"}, "--port", {"inputValue": "Port"}], "command":
["run_train"], "image": "473391520281.dkr.ecr.us-east-1.amazonaws.com/misty/dsmltp-359/train:c662f3f"}},
"inputs": [{"description": "Path where raw data is stored.", "name": "Dataset",
"type": "String"}, {"description": "Number of epochs to train for.", "name":
"Epochs", "type": "Integer"}, {"description": "Learning rate to use while
training.", "name": "Learning Rate", "type": "Float"}, {"description": "The
port to start tensorboard on.", "name": "Port", "type": "Integer"}], "name":
"Train", "outputs": [{"description": "Path to save the model.", "name":
"Model dir", "type": "String"}, {"description": "Path to save the tensorboard
logs to.", "name": "Tensorboard dir", "type": "String"}]}'
pipelines.kubeflow.org/task_display_name: Train Model.
sidecar.istio.io/inject: "false"
labels:
clean: "true"
pipelines.kubeflow.org/cache_enabled: "true"
pipelines.kubeflow.org/enable_caching: "true"
pipelines.kubeflow.org/kfp_sdk_version: 1.6.5
pipelines.kubeflow.org/pipeline-sdk-type: kfp
name: train
nodeSelector:
instanceType: p2.xlarge
procurement: ondemand
tenant: misty
outputs:
artifacts:
- name: train-Model-dir
path: /tmp/outputs/Model_dir/data
- name: train-Tensorboard-dir
path: /tmp/outputs/Tensorboard_dir/data
tolerations:
- effect: NoSchedule
key: tenant
operator: Equal
value: misty
- effect: NoSchedule
key: processing
operator: Equal
value: gpu
status:
finishedAt: null
nodes:
end-to-end-of-misty-pipeline-h5hsq:
children:
- end-to-end-of-misty-pipeline-h5hsq-2090586888
displayName: end-to-end-of-misty-pipeline-h5hsq
finishedAt: null
id: end-to-end-of-misty-pipeline-h5hsq
inputs:
parameters:
- name: s3path
value: s3://athn-ai-ds-mltp-misty-dev/datasets/enron
name: end-to-end-of-misty-pipeline-h5hsq
phase: Running
startedAt: "2021-07-22T15:05:25Z"
templateName: end-to-end-of-misty-pipeline
type: DAG
end-to-end-of-misty-pipeline-h5hsq-2090586888:
boundaryID: end-to-end-of-misty-pipeline-h5hsq
displayName: preprocess
finishedAt: null
id: end-to-end-of-misty-pipeline-h5hsq-2090586888
inputs:
parameters:
- name: s3path
value: s3://athn-ai-ds-mltp-misty-dev/datasets/enron
name: end-to-end-of-misty-pipeline-h5hsq.preprocess
phase: Running
startedAt: "2021-07-22T15:05:25Z"
templateName: preprocess
type: Pod
phase: Running
startedAt: "2021-07-22T15:05:25Z"
```
### Labels
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<!-- /area frontend -->
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<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"That's strange. Do you have any other code between `client.set_user_namespace('my-namespace')` and `client.get_user_namespace()`?\r\n\r\nIf you look at the code, they just set/read a dictionary entry using the same key. (The dictionary can be dump to or load from a local file).\r\n\r\nhttps://github.com/kubeflow/p... | 2021-07-22T15:15:04 | 2021-07-23T13:40:36 | 2021-07-23T13:40:36 | NONE | null | ### Environment
* KFP version: 1.6.0
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.6.3
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
**All dependencies version:**
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
* kfp-pipeline-spec 0.1.8
* kfp-server-api 1.6.0
### 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.
-->
Creating a client using standard constructor and calling the method `get_user_namespace` is actually returning the user for the session and not the namespace.
```
client = kfp.Client()
client.set_user_namespace('my-namespace')
client.get_user_namespace() != 'my-namespace'
```
### Expected result
<!-- What should the correct behavior be? -->
`get_user_namespace()` method should return the namespace set for the current authenticated session.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
I am authenticating with custom script which is using cookie based session `kfp.Client(host=kfp_host_url, cookies=cookie)` and the username is added in Dex as a static member.
Namespace is different than the username (in my case the user is `ci@company.com` and the namespace is `ci`).
This method `set_user_namespace` doesen't seem to be changing session state and somehow `get_user_namespace` is hardwired to return the user of the session, _maybe some other bug is responsible_, trying to wrap my head around the source of this.
Anybody else has encountered this 🐝 havior?
@Ark-kun any ideas?
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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https://api.github.com/repos/kubeflow/pipelines/issues/6112 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6112/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6112/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6112/events | https://github.com/kubeflow/pipelines/issues/6112 | 950,775,279 | MDU6SXNzdWU5NTA3NzUyNzk= | 6,112 | [bug] Kubeflow building component tutorial get error when compiling with kfp.v2.compiler | {
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"followers_url": "https://api.githu... | null | [] | 2021-07-22T15:12:23 | 2021-08-17T14:57:43 | 2021-08-17T14:57:43 | NONE | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
kfp = 1.6.4
I just try to test using my custom component in Vertex AI pipelines.
I use the steps as this [link](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/) to build my container and upload to google container registry. Then use the following code to build my pipeline
`
def my_pipeline():
get_lines_step = create_step_get_lines(
# Input name "Input 1" is converted to pythonic parameter name "input_1"
input_1='one\ntwo\nthree\nfour\nfive\nsix\nseven\neight\nnine\nten',
parameter_1='5',
)
`
and compile the pipeline
`
from kfp.v2 import compiler
if __name__ == "__main__":
pipeline_func = my_pipeline
pipeline_path = 'workflow_get_lines.py' + '.json'
compiler.Compiler().compile(
pipeline_func=pipeline_func, package_path=pipeline_path
)
`
Then I can not compile the pipeline. Using kfp.compiler does not work too, as Vertex AI code
`
from kfp.v2.google.client import AIPlatformClient # noqa: F811
api_client = AIPlatformClient(
project_id=PROJECT_ID,
region=REGION,
)
`
`
response = api_client.create_run_from_job_spec(
pipeline_path,
pipeline_root=PIPELINE_ROOT,
)
`
does not support it
I can not also find any Vertex AI tutorial that builds a component with a script and a Dockerfile. The only way tutorial shows is building a component from a python function with @component. I think using only functions to build components is difficult to build a complicate component.
### What happened:
I get the following error:
......
/opt/conda/lib/python3.7/site-packages/kfp/dsl/_component_bridge.py in _input_artifact_path_placeholder(input_key)
377 raise TypeError('Input "{}" with type "{}" cannot be paired with '
378 'InputPathPlaceholder.'.format(
--> 379 input_key, inputs_dict[input_key].type))
380 else:
381 return "{{{{$.inputs.artifacts['{}'].path}}}}".format(input_key)
TypeError: Input "Input 1" with type "String" cannot be paired with InputPathPlaceholder.
### What did you expect to happen:
compiled
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
I try to use google Vertex AI python SDK to deploy the pipeline to Vertex AI pipelines
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of
user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp = 1.6.4
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6110 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6110/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6110/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6110/events | https://github.com/kubeflow/pipelines/issues/6110 | 950,635,054 | MDU6SXNzdWU5NTA2MzUwNTQ= | 6,110 | KFP v2 tracker | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-07-22T13:03:58 | 2023-08-15T17:03:58 | null | CONTRIBUTOR | null | Design: [bit.ly/kfp-v2](https://bit.ly/kfp-v2).
Project: https://github.com/kubeflow/pipelines/projects/9
Tracking by major release milestones:
* [x] KFP 2.0 Alpha
* [x] #8697
* [x] #8698
* [x] #8802
* [x] #8815
* [x] #8818
* [ ] #8858
* v2 API integration
* [x] #8703
* [x] #8706
* [ ] #8705
* Feature: Volume
* [x] #8707
* [x] #8758
* [x] #8708
* [x] #8715
* Feature: Secret
* [x] #8707
* [x] #8758
* [x] #8709
* Feature: User log
* [x] #8714
* Feature: Optional inputs & default value
* [x] #8716
* Feature: Environment variable
* [x] #8704
* Feature: Accelerator config
* [x] #7043
* Feature: resource requests
* [x] #7047
* Feature: nodeSelector
* [x] #8959
* Non-feature: Documentation
* [ ] #8713
* Non-feature: Testing & Release
* [ ] #6168
* [ ] #6184
* Non-feature: Backend data model
* [x] #8736
* Bugs:
* [x] #8724
* [x] #8725
* Security
* [x] #9136
* [ ] KFP 2.0 Post-GA
* Non-feature: Telemetry
* [ ] #8712
* Feature: Status-IR and MLMD calls
* [x] #8960
* [x] #8961
* [ ] #8964
* [ ] #8965
* [ ] #8966
* [ ] #8710
* [x] #8711
* [ ] #8721
* [ ] #6155
* [ ] #8717
* [ ] #8718
* [ ] #8719
* [ ] #8720
* [ ] #8833
* [ ] TODO(chesu): ...
--------
Tracking by feature/area:
* [ ] v2 orchestration engine
* [x] #6149
* [x] #6150
* [x] #6156
* [x] #6157
* [x] #6163
* [ ] v2 e2e testing
* [x] #6167
* [ ] #6168
* [ ] misc
* [x] #6354
* [x] #6183
* [ ] #6184
* [x] https://github.com/kubeflow/pipelines/issues/6185
* [ ] #7058
* [ ] #6198
* [x] #6199
* [x] #7104
* [x] #8832
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"Sure, I can take this.",
"I'm scheduling to 1.8 release, do you want it earlier?",
"Closing this feature request, as we no longer want to move forward with https://github.com/kubeflow/pipelines/pull/6080."
] | 2021-07-22T08:21:42 | 2021-07-26T22:37:14 | 2021-07-26T22:37:14 | COLLABORATOR | null | Follow up on https://github.com/kubeflow/pipelines/pull/6080
To make legacy data passing work in v2 compatible mode, we need to support `{{{{$.inputs.artifacts['{}'].value}}}}` placeholder in v2 launcher. | {
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"Kubernetes containers running on the usually remote compute nodes are not expected to get access to any local files.\r\n\r\nPlease read the Data-passing tutorial for [command-line programs](https://github.com/Ark-kun/kfp_samples/blob/ae1a5b6/2019-10%20Kubeflow%20summit/106%20-%20Creating%20components%20from%20comm... | 2021-07-22T00:31:47 | 2021-07-23T09:30:33 | 2021-07-23T09:30:33 | NONE | null | ### Environment
* KFP version:
1.6.5
* KFP SDK version:
1.6.0
* All dependencies version:
kfp 1.6.5
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
### Steps to reproduce
Running Kubeflow on a K3s cluster on my machine. I built a simple python function that takes as input:
- Input folder path that contains some files to be transformed
- Output folder that will contain the transformed files
- A parameter to the transformation itself.
The inputs are passed to the python code by using the argparse library:
parser.add_argument('--input-path', type=str, required=True)
parser.add_argument('--output-path', type=str, required=True)
parser.add_argument('--classes', nargs='+', required=True)
Wrapped the python code in a container, tested it with mounted volumes and it works as expected. Wrote the .yaml file to build the pipeline component using the inputPath placeholder for both the output and input path. My interface looks like this:
```
inputs:
- {name: input_path, type: string}
- {name: output_path, type: string}
- {name: classes, type: list}
outputs:
- {name: output_path, type: string, description: 'Output path to save both images and Yolo converted annotations.'}
```
My command and args sections looks like this:
```
command: [python3, ./src/pascal_2_yolo_converter.py]
args: [
--input-path, {inputPath: input_path},
--output-path, {outputPath: output_path},
--classes, {inputValue: classes},
]
```
I'm trying to make the pipeline (composed ins this example by only this simple component) read the files from a local folder, transform them and write the transformed files on another local folder.
When uploading the pipeline to kubeflow pipelines UI and filling the input fields, I do not get the expected results (the creation of the output folder with the transformed files). By modifying the code to print those inputs I can see in the logs that the 'classes' parameter is correctly managed, but the input and output paths are printed as '/tmp/inputs/input_path/data' and '/tmp/outputs/output_path/data' respectively.
I expected that using local files with kubeflow would be tricky because the applications are containerized, but I don't understand what's going on with the input/output.
Am I doing something wrong? Is there a better way to work with files locally on a local K8s cluster?
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Hi @Nagarajj, thank you for drafting this detailed proposal!\r\n\r\n> Specified as a container which runs after publisher actions (Python Container rather than Go Binary).\r\n\r\nAre you OK with the perf implication? Because a container will have to be a separate Pod, and the overhead of scheduling & starting a Po... | 2021-07-21T15:53:05 | 2022-03-11T05:57:45 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area components -->
### What feature would you like to see?
IR Spec specifies Lifecycle [Hooks](https://github.com/kubeflow/pipelines/blob/c89ed71cf6339cdcdd957d4dca4b1f32c10db9c9/api/v2alpha1/pipeline_spec.proto#L546-L566) (pre-cache) which could be used to override the caching logic in the driver. A similar hook (post_publish) would be useful which can be fired after the publisher, to help capture the entire lineage metadata of output values/artifacts, input values/artifacts, pipeline/task status etc.
It would be ideal if the hook could be
1. Specified as a container which runs after publisher actions (Python Container rather than Go Binary).
2. Synchronous execution for reliability reasons (Synchronous over asynchronous).
### What is the use case or pain point?
Having these hooks will help integrate with the existing external metadata management systems. These external metadata management systems are already in use within the company for governance/compliance/audit reasons along with metadata management.
### Is there a workaround currently?
It is not possible to achieve this with the current IR spec.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Thank you for the report! Let me try to reproduce",
"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"
] | 2021-07-21T15:33:14 | 2022-03-03T00:05:33 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Using KF 1.3 steps
while ! kustomize build example | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done
* KFP version:
Pipeline UI is running gcr.io/ml-pipeline/frontend:1.5.0
### Steps to reproduce
1. Upload pipeline
2. Choose upload by URL
3. Provide a public HTTP URL to pipeline file
### Expected result
Pipeline should be uploaded. I am able to upload pipeline using local file. By URL should work the same way.
### Materials and Reference
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Facing same issue. Any update on this issue?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"We still face the issue. Can I help in someway to fix the issue?"
] | 2021-07-21T13:50:50 | 2022-10-14T17:35:47 | null | NONE | null | ### What happened:
Im running a pipeline with an exithandler. This setup work well when the pipeline crashes or completes, but on termination the exit handler fails with this error message: `This step is in Failed state with this message: Step exceeded its deadline`
### What did you expect to happen:
I expected the exit handler to perform the same way it would when a pipeline crashes.
### Environment:
KF 1.5
Ubuntu 18
* How do you deploy Kubeflow Pipelines (KFP)?
KFP standalone on an ubuntu server.
* KFP version:
KFP 1.5
* KFP SDK version:
1.6.2
### 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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"/assign @zijianjoy ",
"manifests presubmit test is already failing with kpt v1: https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5926/kubeflow-pipelines-manifests/1417793964035018752",
"```\r\n+ kpt cfg tree /home/prow/go/src/github.com/kubeflow/pipelines/manifests/kustomize/hack/.... | 2021-07-21T11:50:37 | 2022-03-11T05:38:21 | 2022-03-11T05:38:20 | CONTRIBUTOR | null | 349.0.0 (2021-07-20)
Breaking Changes
* **(Kpt)** kpt updated to v1.0.0-beta.1. This version has several
breaking changes from previous versions. Documentation is available at
<https://kpt.dev> and migration instructions can be found at
<https://kpt.dev/installation/migration>. Old versions are available at
<https://github.com/GoogleContainerTools/kpt>. | {
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"1. I think you can list the endpoint via kubectl `kubectl describe configmap inverse-proxy-config -n kubeflow | grep googleusercontent.com`, you probably need some script to query for all k8s context you have. Reference: https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deployi... | 2021-07-21T05:50:24 | 2022-03-03T00:05:24 | null | CONTRIBUTOR | null | Suppose I have several instances of KFP installed from Google Cloud Marketplace.
I also have Google Cloud token with `cloud-platform` scopes. (Similar to the one given by `gcloud auth print-access-token`). I can call any Google Cloud API using that token. But how can I call KFP API?
1) Can I programmatically list the Hosted KFP instances (as I see in the Google Cloud AI Platform UX) and get their inverse proxy URLs?
2) Given a KFP inverse proxy URL and token, how can I can any KFP API? I've tried to pass the token as `Authentication: Bearer: <token>`, but that did not quite work for me.
I've read some KFP documentation which mentioned creating service accounts and client IDs, but I did not see a solution that would work with just a token.
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https://api.github.com/repos/kubeflow/pipelines/issues/6093 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6093/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6093/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6093/events | https://github.com/kubeflow/pipelines/issues/6093 | 949,112,778 | MDU6SXNzdWU5NDkxMTI3Nzg= | 6,093 | [bug] pipeline parameters in google_cloud_pipeline_components.aiplatform.TabularCreateDatasetOp | {
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"/assign @IronPan @SinaChavoshi ",
"Good find, seems like we are splitting on \"=\" instead of splitting on the first instance of '=' and taking the remaining argument as the input query string. Marking as P0 as directly query is broken with this issue. "
] | 2021-07-20T21:28:35 | 2021-08-12T21:59:54 | 2021-08-12T21:59:54 | NONE | null | ### What steps did you take
Create a simple pipeline that tries to create a tabular dataset from a bigquery table.
```
@dsl.pipeline(
name='train-churn',
pipeline_root='gs://blah/test1'
)
def pipeline(project_id: str, dataset_id: str, table_id: str):
query = dedent(f"""
<query elided>
""")
query_done = bigquery_query_op(
query=query,
project_id=project_id,
dataset_id=dataset_id,
table_id=table_id,
dataset_location='us'
)
# todo bq op unfortunately returns nothing. so we compose our own coordinates for dataset creation
bq_source_rendered = f'bq://{str(project_id)}.{str(dataset_id)}.{str(table_id)}'
dataset = gcc.aiplatform.TabularDatasetCreateOp(
display_name='test_name_1',
bq_source=bq_source_rendered
)
dataset.after(query_done)
<.... rest of pipeline .... >
```
### What happened:
gcp error log:
```
"__main__", mod_spec)
File "/opt/python3.7/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 284, in <module>
main()
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/remote_runner.py", line 268, in main
key, value = arg[2:].split("=")
ValueError: too many values to unpack (expected 2)
```
AI platform runner does not parse and unpack the parameters correctly, fails on dataset creation.
### What did you expect to happen:
Pipeline should complete.
### Environment:
KFP v2 using vertex. (locally compiled pipeline)
* KFP SDK version:
```
kfp 1.6.5
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
```
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area testing -->
<!-- /area samples -->
/area components
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/assign",
"You are always more than welcome to make a PR @sfujiwara if you find things like this. ",
"@NikeNano Thanks. I made PR #6098. Could you please review it?"
] | 2021-07-20T15:06:41 | 2021-07-22T07:21:25 | 2021-07-22T07:21:25 | CONTRIBUTOR | null | ### Environment
* KFP version: -
* KFP SDK version: 1.6.5
* All dependencies version:
* kfp-pipeline-spec: 0.1.8
* kfp-server-api: 1.6.0
### Steps to reproduce
The type hint of `create_run_from_job_spec` says `str`, but it actually returns `dict`.
https://github.com/kubeflow/pipelines/blob/74d27e7e7ec88c62154a3dc5fb19cb27fc2922e6/sdk/python/kfp/v2/google/client/client.py#L258-L268
```python
from kfp.v2 import compiler, dsl
from kfp.v2.google.client import AIPlatformClient
compiler.Compiler().compile(pipeline_func=pipeline_func, package_path=pipeline_json)
api_client = AIPlatformClient(project_id=project_id, region="us-central1")
# `response` is actually `dict`, but the type hint says `str`.
response = api_client.create_run_from_job_spec(...)
```
### Expected result
- The return types of `create_run_from_job_spec` should be `dict`
- The return types of `_submit_job` (called in `create_run_from_job_spec`) should be `dict`
https://github.com/kubeflow/pipelines/blob/74d27e7e7ec88c62154a3dc5fb19cb27fc2922e6/sdk/python/kfp/v2/google/client/client.py#L207-L211
### 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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"@Hmr-ramzi What is the distribution you use to deploy your Kubeflow cluster? https://www.kubeflow.org/docs/started/installing-kubeflow/#install-a-packaged-kubeflow-distribution",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity o... | 2021-07-20T12:49:06 | 2022-03-03T02:05:18 | null | NONE | null | ### What steps did you take
Deloy kubeflow 1.3 using kustomize. Create a kubeflow user. Create a default experiment. Create a run with default example conditional-excution-pipeline
### What happened:
A pod was generated for the pipeline in the user namespace but remaining on state container Creating and failing with error:
Warning FailedMount 80s (x46 over 168m) kubelet Unable to attach or mount volumes: unmounted volumes=[docker-sock], unattached volumes=[podmeta │
│ ata docker-sock mlpipeline-minio-artifact default-editor-token-2jchv[]: timed out waiting for the condition
### What did you expect to happen:
The pipeline was expected to start normally
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
using kustomize
* KFP version:
kubeflow 1.3 from the manifests repo
* KFP SDK version:
kubeflow 1.3 from the manifests repo
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
/area backend
---
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"/cc @Bobgy ",
"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"
] | 2021-07-20T00:38:40 | 2022-03-03T02:05:24 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
I used option 2 in standalone deployment
* KFP version: Deployed from master branch
* KFP SDK version: 1.6.4
### Steps to reproduce
1. Create a new GKE cluster (version 1.19.9-gke.1900)
a. When creating the cluster, under `Security` options, enable `Workload Identity`
b. Ensure you have a service account for using workload identity (e.g. `workload-identity@<PROJECT-ID>.iam.gserviceaccount.com`)
c. Ensure this account is bound to KSA `[kubeflow/pipeline-runner]`. Example:
```
gcloud iam service-accounts add-iam-policy-binding --role roles/iam.workloadIdentityUser --member "serviceAccount:<PROJECT-ID>.svc.id.goog[kubeflow/pipeline-runner]" workload-identity@<PROJECT-ID>.iam.gserviceaccount.com
```
2. Deploy KFP using option 2 as described [here](https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize)
3. Run a v2-compatible pipeline with `pipeline_root` set to a GCS directory in which the workload identity service account above has write permissions.
### Expected result
Pipeline completes successfully.
### What happened instead
Pipeline failed, with launcher complaining about lacking permissions.
### Fix
Enable GKE metadata server on the default node-pool using [these instructions](https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity#option_2_node_pool_modification), e.g.
```
gcloud container node-pools update default-pool --cluster=<CLUSTER-NAME> --region=<REGION> --workload-metadata=GKE_METADATA
```
<!-- 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/6087 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6087/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6087/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6087/events | https://github.com/kubeflow/pipelines/issues/6087 | 947,976,371 | MDU6SXNzdWU5NDc5NzYzNzE= | 6,087 | [feature] Add a delete_schedule and update_schedule functions to AIPlatformClient | {
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"cc @sasha-gitg @SinaChavoshi @ji-yaqi ",
"Close this since the client is moved to Vertex SDK. "
] | 2021-07-19T19:55:16 | 2022-02-03T23:06:12 | 2022-02-03T23:06:12 | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
I would like to be able to delete and update existing schedules created via `create_schedule_from_job_spec`.
### What is the use case or pain point?
Pipelines are updated over time, and need to be redeployed. `create_schedule_from_job_spec` manages long-term resources in the form of cloud functions and schedules that will need to be updated as part of a redeployment event. At the moment, I must manage these resources manually, which makes it easier to implement everything myself rather than using `AIPlatformClient`.
### Is there a workaround currently?
Circumvent AIPlatformClient. Create and update schedule / functions / pubsub manually.
---
<!-- 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/6086 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6086/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6086/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6086/events | https://github.com/kubeflow/pipelines/issues/6086 | 947,973,609 | MDU6SXNzdWU5NDc5NzM2MDk= | 6,086 | [feature] Optionally disable API enablement when calling AIPlatformClient.create_schedule_from_job_spec | {
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"Hi Axel, do you mean to skip checking for permission for APIs, and check those permissions at the time of API usage? ",
"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"
] | 2021-07-19T19:50:57 | 2022-03-03T02:05:19 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
I would like to manipulate a flag in the function parameters of `AIPlatform.create_schedule_from_job_spec` in order to disable the logic which automatically tries to enable the necessary APIs. An acceptable alternative would be to expose a function at a lower level of abstraction that decouples API enablement from pipeline scheduling.
### What is the use case or pain point?
This function call currently fails when run by service accounts in a Cloud Build context or similar due to insufficient permissions. Furthermore, I have been unable to identify the required role necessary to allow a service account to enable APIs. It would be useful to include a link to this role in the error when encountering this failure mode. I have already enabled all of the requisite APIs, and can verify functionality when run from a user account.
### Is there a workaround currently?
Currently I enable admin permissions for my service account (in a dev environment). This is not ideal.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6085 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6085/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6085/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6085/events | https://github.com/kubeflow/pipelines/issues/6085 | 947,966,870 | MDU6SXNzdWU5NDc5NjY4NzA= | 6,085 | [feature] Parameter parity in AIPlatformClient functions | {
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"cc @sasha-gitg @SinaChavoshi ",
"Assigning to @chensun for comments/ triage. ",
"This AIPlatformClient would be deprecated soon. Thanks for the FR, we have it in our options to add into Vertex SDK. ",
"@ji-yaqi any updates on this?",
"Hi @axelmagn, we will deprecate AIplatformClient in v2.0.0. Meanwhile, ... | 2021-07-19T19:40:33 | 2022-03-02T10:06:43 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
`AIPlatformClient.create_run_from_job_spec` accepts parameters such as `labels` and `network` that are not accepted by `AIPlatformClient.create_schedule_from_job_spec`. I think that any parameter I can specify in `create_run` should be specifiable in `create_schedule`.
### What is the use case or pain point?
I would like to be able to specify `labels` for my scheduled jobs in order to enforce an organization regime on my pipeline jobs.
### Is there a workaround currently?
I can implement this myself using Cloud Scheduler and Cloud Functions.
---
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Reference: https://www.webtips.dev/feature-flags-in-react | {
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"On Firefox it is showing another error message:\r\n\r\n<img width=\"1440\" alt=\"Screen Shot 2021-07-17 at 09 09 51\" src=\"https://user-images.githubusercontent.com/1009397/126036414-8e763826-6a7b-4901-8ed2-e712bef5a44d.png\">\r\n"
] | 2021-07-16T15:49:35 | 2021-07-19T03:44:18 | 2021-07-19T03:44:18 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Following the Kubeflow v1.2 documentation for GCP: https://v1-2-branch.kubeflow.org/docs/distributions/gke/
* KFP version: 1.0.4
### Steps to reproduce
- Access the `Experiments` page and click on the `+ Create run` button.
- Select `Run Type` -> `Recurring`.
- Set `Run Trigger` -> `Trigger type` to `Cron`.
- It will show a link to the cron expression format:
- Click the `here` link and it shows the following result:
### Expected result
I would expect to properly load [this doc page](https://pkg.go.dev/github.com/robfig/cron?utm_source=godoc#hdr-CRON_Expression_Format).
### Materials and Reference
I'm not a frontend developer so I'm not sure what is happening but I believe it is not redirecting/rendering the page properly, maybe because of the Shadow DOM!?
Also, a suggestion would be to include a `target="_blank"` to the `a` tag.
---
<!-- 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/6067 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6067/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6067/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6067/events | https://github.com/kubeflow/pipelines/issues/6067 | 946,248,926 | MDU6SXNzdWU5NDYyNDg5MjY= | 6,067 | Istio injection always disabled | {
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"Can it be resolved by adding istio injection label to true as https://github.com/kubeflow/manifests/pull/1153?\r\n\r\ncc @Bobgy to confirm the use case.",
"@karlschriek which argo executor supports Istio sudecars?\n\nFrom what I learned from https://argoproj.github.io/argo-workflows/sidecar-injection/. It seems ... | 2021-07-16T12:29:06 | 2021-08-19T13:23:54 | 2021-08-19T13:23:54 | NONE | null | While trying to put together a Kubeflow distribution that includes as many as possible elements inside the Istio mesh, we found that for Kubeflow Pipelines steps the istio sidecar injection is always overriden to "false".
(This would for example make it impossible for a pipelines step to make calls to a KFServing model in the same namespace which has an istio sidecar enabled).
This seems to be hardcoded here:
https://github.com/kubeflow/pipelines/blob/fff62e1521e89dd31c87eec03073b8f8627d73a3/backend/src/apiserver/resource/resource_manager.go#L384
I presume that was done because the Pipelines owner are very sure that KFP would not work with injection enabled. As far as I know, newer versions of Argo Workflows should now support it.
Could we turn this into an ENV var?
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"I think the problem happens in the line\r\n```\r\n for (let tab in SidePanelTab) {\r\n```\r\n\r\nIn typescript, an enum object also contains look up map for the string -> enum conversion, so 'MANIFEST', 'INPUT_OUTPUT', each enum key is also iterated here.",
"That is a good catch, thank you @Bobgy !\r\n\r\nI c... | 2021-07-16T07:09:16 | 2021-07-19T04:57:16 | 2021-07-19T04:57:16 | CONTRIBUTOR | null | /assign @zijianjoy
there is an error message for each enum
```
Unable to find corresponding tab name for ML_METADATA
Unable to find corresponding tab name for POD
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this will help improve local testing speed
https://github.com/kubeflow/pipelines/blob/master/v2/test/components/kaniko.yaml | {
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"The package from `pip install google_cloud_pipeline_components` does not include YAML files. But there should be YAML file in the SDK. E.g., https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/experimental/gcp_launcher/component.yaml\r\n",
"/assign @SinaChav... | 2021-07-16T00:57:01 | 2021-08-03T17:47:46 | 2021-08-03T17:47:46 | NONE | null | ### Steps to reproduce
Follow the instructions at https://github.com/kubeflow/pipelines/tree/master/components/google-cloud
e.g.,
```
source_root=$(pwd)
git clone https://github.com/kubeflow/pipelines.git
cd pipelines/components/google-cloud
python setup.py bdist_wheel clean
WHEEL_FILE=$(find "$source_root/pipelines/components/google-cloud/dist/" -name "google_cloud_pipeline_components*.whl")
pip3 install --upgrade $WHEEL_FILE
```
The resulting wheel file does not include the yaml files.
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"/assign @chensun ",
"For this problem, I think I know the reason. But it comes to the other question, how do I retrieve the pipelineParam value?",
"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 contr... | 2021-07-15T20:00:13 | 2022-03-03T02:05:24 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
* KFP version: 1.0.0
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.0.0
### Steps to reproduce
I used the kubeflow python sdk `train.apply(mount_pvc('claim-name', 'pipeline', '/mnt/pipeline'))`
and I have an argument of a if statement
if something: train.apply(mount_pvc('claim-name', 'pipeline', '/mnt/pipeline'))
And no matter the what is the value for something, the volume will be attached in the run.
However, when I deleted everything (the whole line) it turns out that the volume will not be attached.
I am not sure why? Is it because the implementation doing some kind of pre-scan of the api used and
will create the component before?
### Expected result
It will mount and not mount based on the condition.
### Materials and Reference
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---
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https://api.github.com/repos/kubeflow/pipelines/issues/6053 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6053/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6053/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6053/events | https://github.com/kubeflow/pipelines/issues/6053 | 945,284,719 | MDU6SXNzdWU5NDUyODQ3MTk= | 6,053 | [backend] visualization server image size increased from 1.3G to 6.4G | {
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"@zijianjoy Why did you downgrade to TFX 0.26?",
"@vaskozl Sorry for missing to include the context in PR description, I tried to upgrade TFX to the latest. However, the dependency graph doesn't resolve because of the requirement conflict on `grpcio`. We will have to wait for https://github.com/tensorflow/tensorf... | 2021-07-15T11:31:41 | 2021-07-20T18:03:22 | 2021-07-16T03:16:38 | CONTRIBUTOR | null | Look at existing visualization server image sizes: gcr.io/ml-pipeline/visualization-server
Compare that with newly built size: gcr.io/ml-pipeline-test/visualization-server@sha256:fb3768db11f2a0bc6f3a94e7191d32eaa2bdaf53a6ca38d2d03de0f6f47c5ede
I think the change is caused by using tfx image as base: https://github.com/kubeflow/pipelines/commit/290d201cdf0baae27f3e79ad6add7d81af2249c8. Maybe we should not do that.
## Side effect
Release cloud build task is now timing out on pulling the visualization server image, just because it's too large https://console.cloud.google.com/cloud-build/builds;region=global/f92a0259-dd8c-4cd4-a7d9-43c6569f06b5;step=30?project=ml-pipeline-test
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/6049 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6049/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6049/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6049/events | https://github.com/kubeflow/pipelines/issues/6049 | 945,076,924 | MDU6SXNzdWU5NDUwNzY5MjQ= | 6,049 | [frontend] Metrics not getting displayed in kubeflow UI after changing the storage to s3 | {
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"This is a current limitation. Welcome contributions!",
"Related code:\r\n* persistence agent calls read artifact API in backend to read metrics artifacts: https://github.com/kubeflow/pipelines/blob/fff62e1521e89dd31c87eec03073b8f8627d73a3/backend/src/agent/persistence/worker/metrics_reporter.go#L149\r\n* read ar... | 2021-07-15T07:15:55 | 2022-03-03T02:05:26 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?https://www.kubeflow.org/docs/distributions/aws/deploy/install-kubeflow/
kubeflow 1.2 on aws
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.0.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->The artifacts were getting stored in minio initially. Changed the storage to s3. After that am unable to see the metrics in run output
### Expected result Metrics getting displayed on run output
<!-- What should the correct behavior be? -->
### Materials and Reference https://www.kubeflow.org/docs/distributions/aws/
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/6048 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6048/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6048/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6048/events | https://github.com/kubeflow/pipelines/issues/6048 | 944,941,746 | MDU6SXNzdWU5NDQ5NDE3NDY= | 6,048 | [feature] Return a list of runs' metadata including run parameters sorted by run details | {
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"Hi @dutingda, you can follow this example for filtering (same feature exists for runs too): https://www.kubeflow.org/docs/components/pipelines/tutorials/sdk-examples/#example-2-listing-pipelines-with-a-filter.\r\n\r\nPlease let us know what's missing from documentation and feature",
"> Hi @dutingda, you can foll... | 2021-07-15T02:34:24 | 2022-03-02T21:05:17 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
I just need help about the query the above result, is there a way to do that?
I need to query the list of runs that is filtered by the start time and get the metadata
of the run parameters for all the runs. But not sure about which SDK I need to use
and how it should be used since the documentation does not provide enough details
of one possible API I can used (`list_runs`)
### What is the use case or pain point?
For the need of cleaning up the least recently used cache of the data I used stored in the PVC
### Is there a workaround currently?
Not sure
<!-- Without this feature, how do you accomplish your task today? -->
---
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"Hello @jalola , this is related to Argo workflow issue, would you like to create an issue there to resolve this problem? https://github.com/argoproj/argo-workflows",
"Thanks @zijianjoy \r\nThere is an issue related: https://github.com/argoproj/argo-workflows/issues/6201\r\n",
"This issue has been automatically... | 2021-07-14T02:41:17 | 2022-03-03T02:05:21 | null | NONE | null | ### What steps did you take
1. Create a pipeline
2. Make a component with some bug so it will crash later in the pipeline
3. Run the pipeline until the component fail
4. Wait for the pod to be deleted
### What happened:
5. Check the Logs tab of that pod, it shows "Failed to retrieve pod logs."
6. Check the Input/Output of that pod, no main logs was saved
### What did you expect to happen:
I think 5 is expected because it get the logs directly from the Pod so when the Pod is deleted, the logs cannot be retrieved
But for 6, I think it should have the main-logs (main.log) so we can know what is wrong with the code
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow manifest v1.3 bu kustomize
https://github.com/kubeflow/manifests/tree/v1.3-branch
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
build version dev_local
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
1.6.3
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
I notice that,
If the pipeline failed because of a bug or manually terminate (error message: "This step is in Failed state with this message: failed with exit code 1" or "This step is in Failed state with this message: failed with exit code 137"), I cannot see the main-logs
If The component failed with error message "This step is in Failed state with this message: Step exceeded its deadline", the main-logs is there after the pod deleted
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
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"u... | closed | false | null | [] | null | [
"Nevermind, found it:\r\nhttps://github.com/kubeflow/pipelines/blob/ec9021f5b04521d394fb3a95903b86b478bf6550/v2/test/README.md"
] | 2021-07-13T07:13:02 | 2021-07-13T08:59:55 | 2021-07-13T08:59:55 | NONE | null | ### What steps did you take
1. Deploy kubeflow pipelines (using kind) on local cluster according to: https://www.kubeflow.org/docs/components/pipelines/installation/localcluster-deployment/
2. Build a sample pipeline with v2 SDK according to: https://www.kubeflow.org/docs/components/pipelines/sdk/v2/build-pipeline/
3. Upload generated pipeline.yaml file to kubeflow pipelines frontend, use default values (pre-filled)
4. Create run from newly built pipeline, paste `https://storage.googleapis.com/ml-pipeline-playground/iris-csv-files.tar.gz` as URL parameter
### What happened:
The pipeline fails at the first step with the following log information:
```bash
33 main.go:55] Failed to execute component: unable to get pipeline with PipelineName "my-pipeline" PipelineRunID "my-pipeline-xbv4z": Failed PutParentContexts(parent_contexts:{child_id:11 parent_id:9}): rpc error: code = Unimplemented desc =
```
### What did you expect to happen:
The pipeline runs through 🤷♂️
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Local cluster: https://www.kubeflow.org/docs/components/pipelines/installation/localcluster-deployment/
* KFP version:
1.6.0
* KFP SDK version:
kfp 1.6.4
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
### Anything else you would like to add:
I followed the tutorials strictly, no own code whatsoever. It also fails with the same error when I manually change the pipeline name to 'my-pipeline' like it is named in the code.
### Labels
<!-- /area frontend -->
/area backend
/area sdk
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6020 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6020/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6020/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6020/events | https://github.com/kubeflow/pipelines/issues/6020 | 942,204,503 | MDU6SXNzdWU5NDIyMDQ1MDM= | 6,020 | [sdk] Some keys in json used in ParallelFor break compilation | {
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"/assign @chensun",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-07-12T15:54:54 | 2022-03-03T02:05:29 | null | CONTRIBUTOR | null | ### Environment
* KFP SDK version: 1.6.3
* All dependencies version:
* kfp 1.6.3
* kfp-pipeline-spec 0.1.8
* kfp-server-api 1.4.1
### Steps to reproduce
Create a pipeline where `dsl.ParallelFor` either:
* accepts a list literal with the first item being a dictionary with `"name"` key
* accesses its item's `.name` field
* both (behaves like the first one)
In the first and third case, the compilation fails with a bizarre errors. In the second case, it simply produces wrong yaml.
Example of the first case:
```python
from kfp import dsl
from kfp.components import load_component_from_text
from kfp_tekton.tekton import CEL_ConditionOp
PrintOp = load_component_from_text("""
name: print
inputs:
- name: msg
outputs:
- name: stdout
implementation:
container:
image: alpine:3.6
command:
- concat:
- "echo "
- { inputValue: msg }
""")
@dsl.pipeline("decomposition test: non-dict")
def decomposition_test_00():
string_list_literal = ["foo", "bar"]
with dsl.ParallelFor(string_list_literal) as item:
PrintOp(f"{item}")
@dsl.pipeline("decomposition test: dict without 'name' key")
def decomposition_test_01():
dict_ok_list_literal = [
{
"_name": "foo",
"_value": 42,
},
{
"_name": "bar",
"_value": 3141592
}
]
with dsl.ParallelFor(dict_ok_list_literal) as item:
name = item._name
value = item._value
PrintOp(f"{name}={value}")
@dsl.pipeline("decomposition test: dict with 'name' key")
def decomposition_test_02():
dict_err_list_literal = [
{
"name": "foo",
"value": 42,
},
{
"name": "bar",
"value": 3141592
}
]
with dsl.ParallelFor(dict_err_list_literal) as item:
name = item.name
value = item.value
PrintOp(f"{name}={value}")
decomposition_tests = [
decomposition_test_00,
decomposition_test_01,
decomposition_test_02,
]
if __name__ == '__main__':
from kfp.compiler import Compiler
for i, decomposition_test in enumerate(decomposition_tests):
print(decomposition_test._component_human_name)
Compiler().compile(decomposition_test, __file__.replace('.py', f'-{i}.argo.yaml'))
```
The compilation breaks in two distinct ways, I'm not sure what determines which is triggered, as they seem to interleave when I call the script multiple times.
First one:
```
$ python3 decompose.py
decomposition test: non-dict
decomposition test: dict with 'name' key
Traceback (most recent call last):
File "(...)/decompose.py", line 71, in <module>
Compiler().compile(decomposition_test, __file__.replace('.py', f'-{i}.argo.yaml'))
File "(...)/site-packages/kfp/compiler/compiler.py", line 1053, in compile
self._create_and_write_workflow(
File "(...)/site-packages/kfp/compiler/compiler.py", line 1106, in _create_and_write_workflow
workflow = self._create_workflow(
File "(...)/site-packages/kfp/compiler/compiler.py", line 946, in _create_workflow
workflow = self._create_pipeline_workflow(
File "(...)/site-packages/kfp/compiler/compiler.py", line 713, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "(...)/site-packages/kfp/compiler/compiler.py", line 653, in _create_dag_templates
inputs, outputs = self._get_inputs_outputs(
File "(...)/site-packages/kfp/compiler/compiler.py", line 303, in _get_inputs_outputs
if loop_group.loop_args.name in param.name:
TypeError: 'in <string>' requires string as left operand, not LoopArgumentVariable
```
Second one:
```
$ python3 decompose.py
decomposition test: non-dict
decomposition test: dict with 'name' key
Traceback (most recent call last):
File "(...)/decompose.py", line 71, in <module>
Compiler().compile(decomposition_test, __file__.replace('.py', f'-{i}.argo.yaml'))
File "(...)/site-packages/kfp/compiler/compiler.py", line 1053, in compile
self._create_and_write_workflow(
File "(...)/site-packages/kfp/compiler/compiler.py", line 1106, in _create_and_write_workflow
workflow = self._create_workflow(
File "(...)/site-packages/kfp/compiler/compiler.py", line 896, in _create_workflow
pipeline_func(*args_list, **kwargs_dict)
File "(...)/decompose.py", line 56, in decomposition_test_02
with dsl.ParallelFor(dict_err_list_literal) as item:
File "(...)/site-packages/kfp/dsl/_ops_group.py", line 226, in __init__
loop_args = _for_loop.LoopArguments(
File "(...)/site-packages/kfp/dsl/_for_loop.py", line 76, in __init__
LoopArgumentVariable(
File "(...)/site-packages/kfp/dsl/_for_loop.py", line 159, in __init__
super().__init__(
File "(...)/site-packages/kfp/dsl/_pipeline_param.py", line 171, in __init__
raise ValueError(
ValueError: Only letters, numbers, spaces, "_", and "-" are allowed in name.
Must begin with a letter. Got name: {{pipelineparam:op=;name=loop-item-param-2-subvar-name}}-subvar-value
```
If the json is passed as a parameter instead of a literal:
```python
from kfp import dsl
from kfp.components import load_component_from_text
from kfp_tekton.tekton import CEL_ConditionOp
PrintOp = load_component_from_text("""
name: print
inputs:
- name: msg
outputs:
- name: stdout
implementation:
container:
image: alpine:3.6
command:
- concat:
- "echo "
- { inputValue: msg }
""")
@dsl.pipeline("decomposition test: non-dict")
def decomposition_test_00(
string_list_literal = ["foo", "bar"]
):
with dsl.ParallelFor(string_list_literal) as item:
PrintOp(f"{item}")
@dsl.pipeline("decomposition test: dict without 'name' key")
def decomposition_test_01(
dict_ok_list_literal = [
{
"_name": "foo",
"_value": 42,
},
{
"_name": "bar",
"_value": 3141592
}
]
):
with dsl.ParallelFor(dict_ok_list_literal) as item:
name = item._name
value = item._value
PrintOp(f"{name}={value}")
@dsl.pipeline("decomposition test: dict with 'name' key")
def decomposition_test_02(
dict_err_list_literal = [
{
"name": "foo",
"value": 42,
},
{
"name": "bar",
"value": 3141592
}
]
):
with dsl.ParallelFor(dict_err_list_literal) as item:
name = item.name
value = item.value
PrintOp(f"{name}={value}")
decomposition_tests = [
decomposition_test_00,
decomposition_test_01,
decomposition_test_02,
]
if __name__ == '__main__':
from kfp.compiler import Compiler
for i, decomposition_test in enumerate(decomposition_tests):
print(decomposition_test._component_human_name)
Compiler().compile(decomposition_test, __file__.replace('.py', f'-{i}.argo.yaml'))
```
then all three compile, but there is a vital difference between the second and third pipeline:
Second pipeline (no `"name"` key used):
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: decomposition-test-dict-without-name-key-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline_compilation_time: '2021-07-12T15:38:57.369128',
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "[{\"_name\": \"foo\",
\"_value\": 42}, {\"_name\": \"bar\", \"_value\": 3141592}]", "name": "dict_ok_list_literal",
"optional": true}], "name": "decomposition test: dict without ''name'' key"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}
spec:
entrypoint: decomposition-test-dict-without-name-key
templates:
- name: decomposition-test-dict-without-name-key
dag:
tasks:
- name: for-loop-1
template: for-loop-1
arguments:
parameters:
- {name: dict_ok_list_literal-loop-item-subvar-_name, value: '{{item._name}}'}
- {name: dict_ok_list_literal-loop-item-subvar-_value, value: '{{item._value}}'}
withParam: '{{workflow.parameters.dict_ok_list_literal}}'
- name: for-loop-1
inputs:
parameters:
- {name: dict_ok_list_literal-loop-item-subvar-_name}
- {name: dict_ok_list_literal-loop-item-subvar-_value}
dag:
tasks:
- name: print
template: print
arguments:
parameters:
- {name: dict_ok_list_literal-loop-item-subvar-_name, value: '{{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_name}}'}
- {name: dict_ok_list_literal-loop-item-subvar-_value, value: '{{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_value}}'}
- name: print
container:
args: []
command: ['echo {{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_name}}={{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_value}}']
image: alpine:3.6
inputs:
parameters:
- {name: dict_ok_list_literal-loop-item-subvar-_name}
- {name: dict_ok_list_literal-loop-item-subvar-_value}
metadata:
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"command": [{"concat": ["echo ", {"inputValue": "msg"}]}], "image": "alpine:3.6"}},
"inputs": [{"name": "msg"}], "name": "print", "outputs": [{"name": "stdout"}]}',
pipelines.kubeflow.org/component_ref: '{"digest": "759d33433106734a4f8d748310b0ec7fc618e428176f270b6e33b782526870c8"}',
pipelines.kubeflow.org/arguments.parameters: '{"msg": "{{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_name}}={{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_value}}"}'}
arguments:
parameters:
- {name: dict_ok_list_literal, value: '[{"_name": "foo", "_value": 42}, {"_name":
"bar", "_value": 3141592}]'}
serviceAccountName: pipeline-runner
```
Third pipeline (`"name"` used):
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: decomposition-test-dict-with-name-key-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline_compilation_time: '2021-07-12T15:38:57.381484',
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "[{\"name\": \"foo\",
\"value\": 42}, {\"name\": \"bar\", \"value\": 3141592}]", "name": "dict_err_list_literal",
"optional": true}], "name": "decomposition test: dict with ''name'' key"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}
spec:
entrypoint: decomposition-test-dict-with-name-key
templates:
- name: decomposition-test-dict-with-name-key
dag:
tasks:
- {name: for-loop-1, template: for-loop-1, withParam: '{{workflow.parameters.dict_err_list_literal}}'}
- name: for-loop-1
dag:
tasks:
- {name: print, template: print}
- name: print
container:
args: []
command: [echo dict_err_list_literal-loop-item=None]
image: alpine:3.6
metadata:
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"command": [{"concat": ["echo ", {"inputValue": "msg"}]}], "image": "alpine:3.6"}},
"inputs": [{"name": "msg"}], "name": "print", "outputs": [{"name": "stdout"}]}',
pipelines.kubeflow.org/component_ref: '{"digest": "759d33433106734a4f8d748310b0ec7fc618e428176f270b6e33b782526870c8"}',
pipelines.kubeflow.org/arguments.parameters: '{"msg": "dict_err_list_literal-loop-item=None"}'}
arguments:
parameters:
- {name: dict_err_list_literal, value: '[{"name": "foo", "value": 42}, {"name":
"bar", "value": 3141592}]'}
serviceAccountName: pipeline-runner
```
The key difference being:
```
< command: ['echo {{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_name}}={{inputs.parameters.dict_ok_list_literal-loop-item-subvar-_value}}']
---
> command: [echo dict_err_list_literal-loop-item=None]
```
`dict_err_list_literal-loop-item=None` is nothing like the `"name"` field of the item.
### Expected result
The compilation either working just fine and producing results which are analogous.
### Materials and Reference
While the second case was very puzzling for me, the second error message of the first case certainly looks like stringified PipelineParam of a sub-var, i.e. `LoopArguments`. So I took a look at its code and [here is what I found](https://github.com/kubeflow/pipelines/blob/a0fd14b276bc05fb1ddcd3681621e9f44a23bb2a/sdk/python/kfp/dsl/_for_loop.py#L59-L76):
```python
if isinstance(items, list) and isinstance(items[0], dict):
subvar_names = set(items[0].keys())
for item in items:
if not set(item.keys()) == subvar_names:
raise ValueError(
'If you input a list of dicts then all dicts should have the same keys. '
'Got: {}.'.format(items))
# then this block creates loop_args.variable_a and loop_args.variable_b
for subvar_name in subvar_names:
if not self._subvar_name_is_legal(subvar_name):
raise ValueError(
"Tried to create subvariable named {} but that's not a legal Python variable "
'name.'.format(subvar_name))
setattr(
self, subvar_name,
LoopArgumentVariable(
self.name, subvar_name, loop_args_op_name=self.op_name))
```
i.e. if a literal if used, an attribute gets assigned to the class.
But later on in the class, custom attribute accessor [is defined anyway](https://github.com/kubeflow/pipelines/blob/a0fd14b276bc05fb1ddcd3681621e9f44a23bb2a/sdk/python/kfp/dsl/_for_loop.py#L91-L96):
```python
def __getattr__(self, item):
# this is being overridden so that we can access subvariables of the
# LoopArguments (i.e.: item.a) without knowing the subvariable names ahead
# of time
self.referenced_subvar_names.append(item)
return LoopArgumentVariable(self.name, item, loop_args_op_name=self.op_name)
```
So when I removed the first fragment entirely, I immediately got rid of the error and the first case produced yaml similar to the second case, i.e. containing `command: [echo loop-item-param-2=None]`. That explains the difference between literal and non-literal versions. But why both contain any error in general?
Because `LoopArguments` is a subclass of `PipelineParam`, I checked [its code](https://github.com/kubeflow/pipelines/blob/a0fd14b276bc05fb1ddcd3681621e9f44a23bb2a/sdk/python/kfp/dsl/_pipeline_param.py#L140-L183):
```python
class PipelineParam(object):
"""(...)
Args:
name: name of the pipeline parameter.
(...)
value: The actual value of the PipelineParam. If provided, the PipelineParam
is "resolved" immediately. For now, we support string only.
(...)
"""
def __init__(self,
name: str,
op_name: Optional[str] = None,
value: Optional[str] = None,
param_type: Optional[Union[str, Dict]] = None,
pattern: Optional[str] = None):
valid_name_regex = r'^[A-Za-z][A-Za-z0-9\s_-]*$'
if not re.match(valid_name_regex, name):
raise ValueError(
'Only letters, numbers, spaces, "_", and "-" are allowed in name. '
'Must begin with a letter. Got name: {}'.format(name))
if op_name and value:
raise ValueError('op_name and value cannot be both set.')
self.name = name
# ensure value is None even if empty string or empty list
# so that serialization and unserialization remain consistent
# (i.e. None => '' => None)
self.op_name = op_name if op_name else None
self.value = value
```
Which explains `dict_err_list_literal-loop-item=None` --- it's the `LoopArgument`'s `name` field, `=`, and the default value of the `value` field --- `None`.
Now, that's inconsistent with how the user can access any other field in the list of json objects/dicts. What's worse: adding a new field in PipelineParam can potentially break the now-working-fine user pipelines.
I expect few users used `name` and `value` of `LoopArguments` expecting the ones from `PipelineParam` --- in fact, I don't think its fields are useful to users in general, as they describe the internal compiler info, like what's the name (most of the time it's similar to the name of variable, so why would user need that?), its default value (it's written directly in the signature, so why would user need that?) or the task that produced it (PipelineParams are produced either in pipeline-or-graph signatures, `dsl.ParallelFor(...) as item` or `.output`/`.outputs[...]` --- why would user need that?). Besides, it seems to me that it's much easier to explain to the user that ALL of the fields accessed in `LoopArguments` are simply it's keys. Therefore, I assume it's ok to simply change it so that during the pipeline building, all fields of `LoopArguments` are treated as json fields, and it only turns back to the behaviour of the regular `PipelineParam` inside of the `Compiler`.
## Proposed solution (early POC)
For that, I created a POC with the following changes:
* remove the above-mentioned setting attributes in `LoopArguments` --- as already shown, it's not really needed for the syntax to work fine (alternatively, one may add a check which only sets the attribute if it's not there yet)
* change attribute access in PipelineParam's magical methods methods to `object.__getattribute__` so that e.g. `__str__` (which used a lot) works just like before no matter where it's used
* introduce a global variable `loop_arguments_decomposing`, defaulting to `True`
* temporarily set that variable to `False` in `LoopArgument`'s constructor (otherwise, `object.__getattribute__` would need to be used again, which is more effort), and placing it inside of try-catch or some context manager so that exceptions won't affect setting it back
* change `__getattr__` method into a `__getattribute__` method with the following implementation (very similar to the previous one, except for checking for `loop_arguments_decomposing` and `object.__getattribute__` (could use `super()` too):
```python
def __getattribute__(self, item):
global loop_arguments_decomposing
if not loop_arguments_decomposing:
return object.__getattribute__(self, item)
referenced_subvar_names = object.__getattribute__(self, 'referenced_subvar_names')
name = object.__getattribute__(self, 'name')
op_name = object.__getattribute__(self, 'op_name')
referenced_subvar_names.append(item)
return LoopArgumentVariable(name, item, loop_args_op_name=op_name)
```
* temporarily set the `loop_arguments_decomposing` to `False` in `_create_workflow` in compiler.py, right after the `pipeline_func` call, and placing it inside of try-catch or some context manager so that exceptions won't affect setting it back
* (optional) add `__getitem__` to `LoopArguments` to be able to access keys that aren't valid Python identifiers, e.g. containing `-` --- it should work fine with the `loop_arguments_decomposing`, as it's a magical method
When I did all of that, I got both cases (with literals and without literals) working just fine:
With literals:
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: decomposition-test-dict-with-name-key-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline_compilation_time: '2021-07-12T17:27:43.321192',
pipelines.kubeflow.org/pipeline_spec: '{"name": "decomposition test: dict with
''name'' key"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}
spec:
entrypoint: decomposition-test-dict-with-name-key
templates:
- name: decomposition-test-dict-with-name-key
dag:
tasks:
- name: for-loop-3
template: for-loop-3
arguments:
parameters:
- {name: loop-item-param-2-subvar-name, value: '{{item.name}}'}
- {name: loop-item-param-2-subvar-value, value: '{{item.value}}'}
withItems:
- {name: foo, value: 42}
- {name: bar, value: 3141592}
- name: for-loop-3
inputs:
parameters:
- {name: loop-item-param-2-subvar-name}
- {name: loop-item-param-2-subvar-value}
dag:
tasks:
- name: print
template: print
arguments:
parameters:
- {name: loop-item-param-2-subvar-name, value: '{{inputs.parameters.loop-item-param-2-subvar-name}}'}
- {name: loop-item-param-2-subvar-value, value: '{{inputs.parameters.loop-item-param-2-subvar-value}}'}
- name: print
container:
args: []
command: ['echo {{inputs.parameters.loop-item-param-2-subvar-name}}={{inputs.parameters.loop-item-param-2-subvar-value}}']
image: alpine:3.6
inputs:
parameters:
- {name: loop-item-param-2-subvar-name}
- {name: loop-item-param-2-subvar-value}
metadata:
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"command": [{"concat": ["echo ", {"inputValue": "msg"}]}], "image": "alpine:3.6"}},
"inputs": [{"name": "msg"}], "name": "print", "outputs": [{"name": "stdout"}]}',
pipelines.kubeflow.org/component_ref: '{"digest": "759d33433106734a4f8d748310b0ec7fc618e428176f270b6e33b782526870c8"}',
pipelines.kubeflow.org/arguments.parameters: '{"msg": "{{inputs.parameters.loop-item-param-2-subvar-name}}={{inputs.parameters.loop-item-param-2-subvar-value}}"}'}
arguments:
parameters: []
serviceAccountName: pipeline-runner
```
With pipeline parameter:
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: decomposition-test-dict-with-name-key-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline_compilation_time: '2021-07-12T17:27:52.110929',
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"default": "[{\"name\": \"foo\",
\"value\": 42}, {\"name\": \"bar\", \"value\": 3141592}]", "name": "dict_err_list_literal",
"optional": true}], "name": "decomposition test: dict with ''name'' key"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}
spec:
entrypoint: decomposition-test-dict-with-name-key
templates:
- name: decomposition-test-dict-with-name-key
dag:
tasks:
- name: for-loop-1
template: for-loop-1
arguments:
parameters:
- {name: dict_err_list_literal-loop-item-subvar-name, value: '{{item.name}}'}
- {name: dict_err_list_literal-loop-item-subvar-value, value: '{{item.value}}'}
withParam: '{{workflow.parameters.dict_err_list_literal}}'
- name: for-loop-1
inputs:
parameters:
- {name: dict_err_list_literal-loop-item-subvar-name}
- {name: dict_err_list_literal-loop-item-subvar-value}
dag:
tasks:
- name: print
template: print
arguments:
parameters:
- {name: dict_err_list_literal-loop-item-subvar-name, value: '{{inputs.parameters.dict_err_list_literal-loop-item-subvar-name}}'}
- {name: dict_err_list_literal-loop-item-subvar-value, value: '{{inputs.parameters.dict_err_list_literal-loop-item-subvar-value}}'}
- name: print
container:
args: []
command: ['echo {{inputs.parameters.dict_err_list_literal-loop-item-subvar-name}}={{inputs.parameters.dict_err_list_literal-loop-item-subvar-value}}']
image: alpine:3.6
inputs:
parameters:
- {name: dict_err_list_literal-loop-item-subvar-name}
- {name: dict_err_list_literal-loop-item-subvar-value}
metadata:
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}
annotations: {pipelines.kubeflow.org/component_spec: '{"implementation": {"container":
{"command": [{"concat": ["echo ", {"inputValue": "msg"}]}], "image": "alpine:3.6"}},
"inputs": [{"name": "msg"}], "name": "print", "outputs": [{"name": "stdout"}]}',
pipelines.kubeflow.org/component_ref: '{"digest": "759d33433106734a4f8d748310b0ec7fc618e428176f270b6e33b782526870c8"}',
pipelines.kubeflow.org/arguments.parameters: '{"msg": "{{inputs.parameters.dict_err_list_literal-loop-item-subvar-name}}={{inputs.parameters.dict_err_list_literal-loop-item-subvar-value}}"}'}
arguments:
parameters:
- {name: dict_err_list_literal, value: '[{"name": "foo", "value": 42}, {"name":
"bar", "value": 3141592}]'}
serviceAccountName: pipeline-runner
```
It seems to me that this solution is quite simple and very general, as one can add as many fields to the `PipelineParam` as they like as no non-magical methods are expected to be available to the user (the only one I can think of is `ignore_type`, but I don't think it's really used with pipeline items? we could add it as an exception in `__getattribute__`, but I'd rather avoid that, as it has the same problems as `"name"` and `"value"`, just a little less probable to be used, maybe --- it seems easier to explain to the user that they need to use a function `ignore_type` instead of a method `.ignore_type()`, rather than explain why they can't have a field called `"ignore_type"` in their json...) and all magical methods are implemented using `object.__getattribute__` instead of the usual dot access --- which isn't much, considering it's just two classes with a little amount of methods, most of which are only used in the compiler where the dot access is fine.
What do you think? Does that solution sound ok?
I will post a PR later on, after I polish the code a little bit.
---
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/6019 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6019/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6019/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6019/events | https://github.com/kubeflow/pipelines/issues/6019 | 942,154,790 | MDU6SXNzdWU5NDIxNTQ3OTA= | 6,019 | [feature] Ability to Specify Kaniko Job Name | {
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... | open | false | null | [] | null | [
"/cc @chensun \r\nI think we welcome contributions if you would like to.",
"@Bobgy If this feature is still needed and no one has worked on it yet, can I pick it up as a first time issue?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further... | 2021-07-12T15:01:07 | 2022-03-02T10:06:27 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
The ability to specify the name of the `kaniko` Job generated by kfp.containers.ContainerBuilder
### What is the use case or pain point?
For internal users of Twitter, we want to track the state of their image building jobs to display in different mediums such as a Web Dashboard or CLI command. Since the name is currently randomly generated, the best way of fetching the name is to use the `kubectl` CLI to grab the name of the most recent job. This has a small chance of error if multiple users are running jobs under the same namespace so it would be ideal to specify the name or store the name as an accessible property.
Happy to work on a pull request if this seams reasonable.
### Is there a workaround currently?
Subclass kfp.containers.ContainerBuilder and override a private API.
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"/cc @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-07-12T13:31:56 | 2022-03-03T02:05:28 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
The ability to pass through any Kaniko supported argument to kfp.containers.ContainerBuilder.build to build the Pod spec with.
### What is the use case or pain point?
For internal users of Twitter, we would like the ability for them to specify any supported Kaniko argument to kfp.containers.ContainerBuilder.build to build their images.
Happy to work on a pull request if this seems like a reasonable feature improvement.
### Is there a workaround currently?
Not really besides modding the class in our repo and requiring users to use that instead.
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"cc @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"We have completed the last item: switch to Emissary executor by default: https://www.kubeflow.org/docs/components/pip... | 2021-07-12T03:34:20 | 2022-06-04T06:59:59 | 2022-06-04T06:57:00 | CONTRIBUTOR | null | TODOs:
* [x] https://github.com/kubeflow/pipelines/issues/7148
* [x] #6014
* [x] #6987
* [x] change emissary executor to default
* [x] MLMD upgrade to v1.5.0
* [x] Argo upgrade to v3.2.3 | {
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"I think we need to plan this for the next release.",
"Hi KFP team, my team uses GKE and we got an email from GCP today: \"[Action Required] Prepare for GKE 1.22 Open source upstream beta API Removal\". The email includes:\r\n\r\n> As of Kubernetes 1.22, API clients will no longer be able to use previously deprec... | 2021-07-12T03:31:51 | 2022-01-08T21:55:21 | 2022-01-08T21:55:21 | CONTRIBUTOR | null | Deprecated APIs in k8s 1.22: https://kubernetes.io/docs/reference/using-api/deprecation-guide/#v1-22
TODOs:
* [x] move CRDs to v1 version: https://github.com/kubeflow/pipelines/search?q=apiextensions.k8s.io%2Fv1beta1&type= #6679
* [x] move cluster role and cluster role binding to v1 version, got warning message:
```
Warning: rbac.authorization.k8s.io/v1beta1 ClusterRole is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 ClusterRole
```
* [x] Upgrade third-party dependencies to a version with v1 crd, including application controller and metacontroller | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-07-11T03:44:06 | 2022-03-03T02:05:34 | null | CONTRIBUTOR | null | ## Problem
When developing KFP, we have ad hoc scripts for building images and deploying them to the cluster. It's painful to maintain all the scripts, document & educate people to use them.
Besides that, we lack a way to deploy from head with one single command, which is often useful for testing.
## Proposal
Adopt https://skaffold.dev/docs/, whenever we want to develop and test, just run:
```
skaffold dev
``` | {
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"u... | open | false | null | [] | null | [
"Does your cache work other than cases of recurring run ?\r\n\r\nI have seen this issue occurring in the past. Where in cache did not work at all. Try restarting the deployment called `cache-server`.\r\n\r\n```\r\nkubectl rollout restart deploy/cache-server\r\n```",
"> Does your cache work other than cases of rec... | 2021-07-09T08:31:22 | 2022-03-03T00:05:30 | null | NONE | null | ### What steps did you take
- Create a pipeline with two steps and data passing
- Compile it
- Create a recurring run via KFP UI and schedule a periodic run every minutes/hours/days (doesn't change anything)
### What happened:
- Second run does not use cache from the first run
### What did you expect to happen:
- Second run should use cache from the first run
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines standalone on GCP
* KFP version:
KFP 1.6.0
* KFP SDK version:
KFP SDK 1.6.4
### Anything else you would like to add:
Cache is working fine when I run the pipeline manually.
Pipeline : https://pastebin.com/bUguyB7X
Pipeline YAML : https://pastebin.com/RrktipPC
Pod YAML (first step) : https://pastebin.com/t18AWtvX
Pod YAML (second step) : https://pastebin.com/S7pCCXzY
---
<!-- 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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"@munagekar Welcome contributing to create a RecurringRun CRD for this feature!",
"/assign\r\nI will take a look at this. ",
"To get some more background, what is the difference that you expect from this `CRD` compare to the [scheduledworkflow](https://github.com/kubeflow/pipelines/tree/master/backend/src/crd#r... | 2021-07-09T05:19:47 | 2023-08-10T01:43:29 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area backend
### What feature would you like to see?
A CRD for recurring run. To make it possible to delete recurring runs with kubectl. Currently it is possible to delete and create recurring runs only with sdk or kubeflow gui, there is no state for recurring runs on the cluster.
Kubernetes has CronJobs, similarly pipeline runs are tracked with [workflow](https://argoproj.github.io/argo-workflows/fields/#workflow), I would like a similar CRD for recurring run.
Argo has [cron-worklfow](https://argoproj.github.io/argo-workflows/fields/#cronworkflow) and [cron-workflow status](https://argoproj.github.io/argo-workflows/fields/#cronworkflowstatus) could these be used instead ?
### What is the use case or pain point?
It is easier for dev-ops to track recurring-run with kubectl and see the status.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Use the sdk or gui.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Hello @harshsaini , for now you can create two components, one for launching the Katib jobs, another for waiting for this external job to finish. \r\n\r\nFor the performance issue, we need to implement a control plane concept to introduce a watcher for external job completion. This is currently blocked for the int... | 2021-07-08T17:28:11 | 2022-03-03T02:05:35 | 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?
Kubeflow Pipelines does not support a way to intermittently poll for completion of longer running tasks on an external system. Request: Create an op that allows for intermittent polling or execution to determine if an external task has finished.
### What is the use case or pain point?
The use case is around model training. KFP provides HPO execution option that launches a job via [Katib](https://github.com/kubeflow/pipelines/blob/master/components/kubeflow/katib-launcher/component.yaml), however, it [busy waits](https://github.com/kubeflow/pipelines/blob/b57bbeeb982744556ab79a132de5d5fd0c7c0c82/components/kubeflow/katib-launcher/src/launch_experiment.py#L70) for completion of the job.
This is problematic for a few reasons:
- waste of compute resources and cost since such jobs can take days or weeks to complete depending on the type of model and task.
- KFP hosted on transient/preemptible environments can lose track of the experiment if the pod is evicted or killed.
- it is hard for end users to determine max busy wait time via the [current launcher](https://github.com/kubeflow/pipelines/blob/b57bbeeb982744556ab79a132de5d5fd0c7c0c82/components/kubeflow/katib-launcher/component.yaml#L7)
### Is there a workaround currently?
Currently, we do not use pipelines to launch Katib jobs but have our own wrappers to manage this execution. Another option is to use the provided launcher but we have noticed that we lose track of the experiment if the node hosting the launcher gets preemptied.
---
<!-- 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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"Closing as v2 compatible mode is no longer supported."
] | 2021-07-07T14:55:52 | 2023-01-18T08:21:49 | 2023-01-18T08:21:48 | CONTRIBUTOR | null | Samples:
* [ ] Consume artifact by URI sample (use tfjob etc)
* [ ] Support for components that can produce/consume MLMD metadata
* [ ] Visualize v2 metrics -- components can output metrics artifacts that are rendered in UI. [Sample pipeline](https://github.com/kubeflow/pipelines/blob/307e91aaae5e9c71dde1fddaffa10ffd751a40e8/samples/test/metrics_visualization_v2.py#L103) (UI not working)
* [ ] Pipeline root documentation
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"the issue is too small to track"
] | 2021-07-07T02:18:41 | 2021-07-15T14:16:24 | 2021-07-15T14:16:24 | CONTRIBUTOR | null | These fields are already present in pipeline/pipeline run contexts (size/XS) | {
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"There are several possible solutions to this.\r\n\r\nFirst thing to understand is how the `create_component_from_func` function works. This function realizes the \"Lightweight python components\" feature.\r\nAll KFP components are `ComponentSpec` files (`component.yaml`). Many people write them manually and that's... | 2021-07-07T00:16:22 | 2022-03-02T15:05:16 | null | NONE | null | I have a deployment like the following:
```
root_dir
├── root.py
├── same.yaml
├── same_step_0.py
├── same_step_1.py
├── same_step_2.py
└── utils
├── __init__.py
├── file_one.py
└── file_two.py
```
The `root.py` file is the standard kubeflow root file, and then all the steps have functions in them that are handled with `create_component_from_func` - like this:
```
same_step_0_op = create_component_from_func(
func=same_step_0.generated_main,
base_image="library/python:3.9-slim-buster",
packages_to_install=[
"dill",
"requests",
"numpy==1.19.5",
"requests",
],
)
```
The file same_step_0.py references a function in utils.file_one - but when building the package and uploading, it's not being included. It does appear (AFAICT) that the functionality to copy all dependent files is there - https://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/sdk/python/kfp/containers/_component_builder.py#L222 - is there a special flag I need to set?
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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https://api.github.com/repos/kubeflow/pipelines/issues/5980 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5980/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5980/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5980/events | https://github.com/kubeflow/pipelines/issues/5980 | 937,727,745 | MDU6SXNzdWU5Mzc3Mjc3NDU= | 5,980 | [bug(sdk)] serializer does not work for complex type hint. | {
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"The typing hints using `typing` did not work, which is fixed in the PR here.\r\nhttps://github.com/kubeflow/pipelines/pull/5979",
"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"
] | 2021-07-06T10:02:21 | 2022-03-03T03:04:41 | null | CONTRIBUTOR | null | ### What steps did you take
```python
from typing import Dict, List
@component
def get_parallel_offsets(
rows_count: int,
parallel_num: int,
) -> List[Dict[str, int]]:
"""
Get the offset of each parallel (from the number of rows and the number of parallels in the table.)
Parameters
----------
rows_count: int
number of bigquery table's rows
parallel_num: int
number of parallels
"""
from collections import namedtuple
import math
import json
if rows_count % parallel_num == 0:
offset_step = limit = int(rows_count / parallel_num)
else:
offset_step = limit = math.ceil(rows_count / parallel_num)
# NOTE: When using `json.dump`, if a number with a large number of digits is included, the number will be converted to Scientific Notation format, so convert it to a string type once.
offsets = [
{"index": index, "offset": offset, "upper_bounds": offset+limit}
for index, offset in enumerate(range(0, rows_count, offset_step))
]
return offsets
```
When a type is specified for a dict like the one above, or a type hint is added to a list element, the return value is serialized as string type. This behavior may result in incorrect deserialization. This happens because we are only looking at one type name.
ref. https://github.com/kubeflow/pipelines/blob/df1ab4db5e72e2ddb6f098343a3faf51599087d1/sdk/python/kfp/components/_data_passing.py#L109-L118
I want it to be serialized by pattern matching using regex(`List\[.+\]`) etc.
### What happened:
Returns a value like `List[Dict[A,B]]` and fails to deserialize when trying to read it with other functions.
### What did you expect to happen:
I want even complex type hints to be serialized correctly.
### Environment:
kfp master branch version
### Labels
<!-- /area sdk -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Discussed with @zijianjoy offline,\r\nCurrently with v2 compatible mode, \r\n\r\n- cached status icon\r\n- a link to cached execution\r\nThe above tasks can be achieved now.\r\n\r\nFor the following tasks, currently we can't fetch original execution with current logic in launcher.\r\nWe may need to store the orig... | 2021-07-06T03:05:46 | 2021-09-17T06:22:36 | 2021-09-14T16:49:32 | CONTRIBUTOR | null | For cached tasks, the following are helpful on UI:
- [x] * cached status icon
- [x] * a link to cached execution
- [x] * (p1) a link to original execution this cache is taken from
- [x] * (p2) the original execution should have a link to the original run (deleting the original run will invalidate cache)
- [x] * (p2) the run details page should ideally support using url hash to select a node, so that we can generate a URL that directly selects a node in a run details page | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5967 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5967/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5967/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5967/events | https://github.com/kubeflow/pipelines/issues/5967 | 936,111,870 | MDU6SXNzdWU5MzYxMTE4NzA= | 5,967 | [sdk] Block using v2 `@component` decorator in v1 mode. | {
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"followers_url": "https://api.githu... | null | [
"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"
] | 2021-07-02T23:13:19 | 2022-03-03T04:05:13 | 2022-03-03T04:05:13 | COLLABORATOR | null | The new decorator is meant for v2 and v2_compatible mode only. We should throw an explicit error if users use this decorator but compile in v1 mode. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5965 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5965/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5965/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5965/events | https://github.com/kubeflow/pipelines/issues/5965 | 936,066,531 | MDU6SXNzdWU5MzYwNjY1MzE= | 5,965 | [bug] Post submit integration tests failing - argoproj/argo repo not found | {
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"This should be fixed by [kubeflow/pipelines #5966](https://github.com/kubeflow/pipelines/pull/5966). I added [kubeflow/pipelines #5969](https://github.com/kubeflow/pipelines/pull/5969) to clean up other instances of **argoproj/argo**\r\n\r\n"
] | 2021-07-02T21:06:02 | 2021-07-09T00:48:18 | 2021-07-09T00:48:18 | MEMBER | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
### What happened:
The following error occurs while building the Dockerfile at `test/sample-test/Dockerfile`:
```
gzip: argo-linux-amd64.gz: not in gzip format
The command '/bin/sh -c curl -sLO https://github.com/argoproj/argo/releases/download/${ARGO_VERSION}/argo-linux-amd64.gz && gunzip argo-linux-amd64.gz && chmod +x argo-linux-amd64 && mv ./argo-linux-amd64 /usr/local/bin/argo' returned a non-zero code: 1
```
### What did you expect to happen:
`argo-linux-amd64.gz` should have been downloaded and the container should have been built.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
Argo appears to have removed the argoproj/argo repository, or renamed it to argoproj/argo-workflows. Requests for argoproj/argo urls now redirect to argoproj/argocon21.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
/area testing
<!-- /area samples -->
<!-- /area components -->
---
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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/5962 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5962/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5962/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5962/events | https://github.com/kubeflow/pipelines/issues/5962 | 935,199,277 | MDU6SXNzdWU5MzUxOTkyNzc= | 5,962 | [backend] Failed to retry run: Workflow cannot be retried with node in Omitted phase | {
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"u... | closed | false | null | [] | null | [
"As https://github.com/kubeflow/pipelines/issues/5878#issuecomment-863651456 mentioned, you need to wait for next KFP release 1.7.0. ",
"I think we can close now",
"> As [#5878 (comment)](https://github.com/kubeflow/pipelines/issues/5878#issuecomment-863651456) mentioned, you need to wait for next KFP release 1... | 2021-07-01T20:42:08 | 2021-07-06T14:57:34 | 2021-07-05T06:32:27 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipeline Standalone via kustomize manifests on GKE
* KFP version:
1.6.0
* KFP SDK version:
```
kfp 1.6.3
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
```
### Steps to reproduce
1. Run a pipeline with a component depending upon another failing component
2. Retry the run
```jsonc
// Failed to retry run: 5b79857a-be4f-4876-98ae-105845e892ee with error:
{
"error": "Retry run failed.: InternalServerError: Workflow cannot be retried with node nlp-pipeline-mcclt-4094140295 in Omitted phase: workflow cannot be retried",
"code": 13,
"message": "Retry run failed.: InternalServerError: Workflow cannot be retried with node nlp-pipeline-mcclt-4094140295 in Omitted phase: workflow cannot be retried",
"details": [{
"@type": "type.googleapis.com/api.Error",
"error_message": "Internal Server Error",
"error_details": "Retry run failed.: InternalServerError: Workflow cannot be retried with node nlp-pipeline-mcclt-4094140295 in Omitted phase: workflow cannot be retried"
}]
}
```
### Expected result
The pipeline is retreid.
### Materials and Reference
I noticed this bug was fixed in 1.5.1 already but hasn't been fixed in 1.6.x.
I tired to get some info on when a fix would land in a 1.6 release but have not seen any activity there since the issue was closed.
https://github.com/kubeflow/pipelines/issues/5878
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/5955 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5955/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5955/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5955/events | https://github.com/kubeflow/pipelines/issues/5955 | 933,914,995 | MDU6SXNzdWU5MzM5MTQ5OTU= | 5,955 | Wait Container is Stuck and so is the Argo Workflow Controller for Large datasets only. | {
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"We are using argo version 2.x. The latest argo version is 3.x\r\nCan you also create a issue in argo side so that argo side can confirm whether this is a argo issue that could also happen in version 3.x or it is a argo issue that only happens before version 3.x?",
"@capri-xiyue. Thanks for the quick reply. I cha... | 2021-06-30T17:00:50 | 2022-03-03T02:05:31 | null | NONE | null | ### What steps did you take
I have a machine learning pipeline that runs several machine learning algorithms in parallel as components (pods in kubernetes)
When I run the pipeline on relatively small datasets (200,000 rows), the pipeline takes some time but finishes executing (3+ hours) with all components finished.
When I run the pipeline on a large dataset ([UCI poker dataset](https://archive.ics.uci.edu/ml/datasets/Poker%2BHand) : 1,000,000 rows), some components do not finish executing.
Logs of wait container in the pod end with:
```
time="2021-06-30T14:12:58Z" level=info msg="Waiting on main container"
time="2021-06-30T14:13:16Z" level=info msg="main container started with container ID: ba40e83f3a08789e921b340f3f8d9f1b04f1ff299b5777e9c4551840b1a13f82"
time="2021-06-30T14:13:16Z" level=info msg="Starting annotations monitor"
time="2021-06-30T14:13:16Z" level=info msg="docker wait ba40e83f3a08789e921b340f3f8d9f1b04f1ff299b5777e9c4551840b1a13f82"
time="2021-06-30T14:13:16Z" level=info msg="Starting deadline monitor"
time="2021-06-30T14:17:58Z" level=info msg="Alloc=3679 TotalAlloc=11473 Sys=70080 NumGC=6 Goroutines=12"
time="2021-06-30T14:22:58Z" level=info msg="Alloc=3681 TotalAlloc=11484 Sys=70080 NumGC=8 Goroutines=12"
time="2021-06-30T14:27:58Z" level=info msg="Alloc=3679 TotalAlloc=11494 Sys=70080 NumGC=11 Goroutines=12"
time="2021-06-30T14:32:58Z" level=info msg="Alloc=3681 TotalAlloc=11504 Sys=70080 NumGC=13 Goroutines=12"
time="2021-06-30T14:37:58Z" level=info msg="Alloc=3679 TotalAlloc=11514 Sys=70080 NumGC=16 Goroutines=12"
time="2021-06-30T14:42:59Z" level=info msg="Alloc=3681 TotalAlloc=11524 Sys=70080 NumGC=18 Goroutines=12"
time="2021-06-30T14:47:58Z" level=info msg="Alloc=3679 TotalAlloc=11535 Sys=70080 NumGC=21 Goroutines=12"
```
Logs of workflow controller end with:
```
time="2021-06-30T16:29:19Z" level=info msg="Alloc=4714 TotalAlloc=92880 Sys=70080 NumGC=154 Goroutines=101"
W0630 16:34:14.567224 1 reflector.go:302] pkg/mod/k8s.io/client-go@v0.0.0-20191225075139-73fd2ddc9180/tools/cache/reflector.go:98: watch of *v1.ConfigMap ended with: too old resource version: 126291 (129732)
time="2021-06-30T16:34:19Z" level=info msg="Alloc=4801 TotalAlloc=93109 Sys=70080 NumGC=157 Goroutines=101"
time="2021-06-30T16:39:19Z" level=info msg="Alloc=4770 TotalAlloc=93249 Sys=70080 NumGC=159 Goroutines=101"
```
According to [this post](https://github.com/argoproj/argo-workflows/issues/6152), I should update the argo workflow image. The
image is:
`gcr.io/cloud-marketplace/google-cloud-ai-platform/kubeflow-pipelines/argoexecutor:1.4.1`
So, I update the workflow-controller to use the image:
`argoproj/argoexec:latest`
After this also the workflow container ends with the following logs:
```
time="2021-03-11T20:06:43.616Z" level=info msg="Get leases 200"
time="2021-03-11T20:06:43.621Z" level=info msg="Update leases 200"
time="2021-03-11T20:06:48.630Z" level=info msg="Get leases 200"
time="2021-03-11T20:06:48.650Z" level=info msg="Update leases 200"
```
According to [this ](https://github.com/argoproj/argo-workflows/issues/5371)issue, there should be `serviceAccountName: argo`
in the workflow-controller deployment file. My deployment file already has that. But it still does not work.
The looks like an argo issue and I am reporting a bug here because I think it maybe due to kubeflow not using the right argo workflow version.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Python SDK
* KFP version:
1.4.1
* KFP SDK version:
kfp 1.6.3
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
### Anything else you would like to add:
Since the dataset is big, I have specified cpu and memory requests and limits. The usage is always less than the limits.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area backend
/area sdk
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"@Bobgy is there any reason for not having \"v\" as a prefix atm? ",
"It was the convention from the start when we didn't consider this use-case, but I think you raised a good point for adding \"v\" prefix.\r\n\r\nWelcome some discussion on what's the best way forward, do we create two tags for each release in a ... | 2021-06-30T14:53:57 | 2022-03-11T21:28:18 | null | NONE | null | When trying to use the go client in github.com/kubeflow/pipelines/backend/api/go_client, I find that `go mod` can't fetch the corresponding tags in this github repo, because all tags defined are missing the 'v' prefix and are considered non-canonical tag formats.
E.g., tag '1.5.1' should be 'v1.5.1'. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5951 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5951/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5951/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5951/events | https://github.com/kubeflow/pipelines/issues/5951 | 933,360,961 | MDU6SXNzdWU5MzMzNjA5NjE= | 5,951 | [bug] Using set_gpu_limit with bad resource manage | {
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"Can you help provide your definition of pipeline? ",
"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... | 2021-06-30T06:44:21 | 2022-03-03T04:05:10 | 2022-03-03T04:05:10 | NONE | null | ### What steps did you take
Add set_gpu_limit(1) to my pipeline, then create run. But I can only create 2 runs.
In fact, my k8s have 4 nodes, and each node has 4 gpu, like that:
### What happened:
When I create third run, I got the error like this:
Then I remove set_gpu_limit(1) from my pipeline, and I can create so many runs, like this:
### Environment:
* pipeline version: 1.0.0
So anyone has goods idea ?
---
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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/5948 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5948/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5948/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5948/events | https://github.com/kubeflow/pipelines/issues/5948 | 933,251,885 | MDU6SXNzdWU5MzMyNTE4ODU= | 5,948 | [bug] <run Tutorial failed> | {
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"new info: could not chroot into main for artifact collection",
"1. Can you help uploading the screenshot of error msg again?(You didn't upload it successfully)\r\n2. Which KFP version are you using?\r\n3. Which storage(minio, aws or gcp XXX)provider do you choose for artifact storage?",
"Hello @hellobiek , I ... | 2021-06-30T02:39:34 | 2021-11-10T03:34:00 | 2021-07-03T18:14:25 | NONE | null | when I run this tutiol：Run of [Tutorial] Data passing in python components
I met the error msg as follows: This step is in Error state with this message: failed to save outputs: key unsupported: cannot get key for artifact location, because it is invalid
![Uploading 截屏2021-06-30 上午10.38.40.png…]()
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https://api.github.com/repos/kubeflow/pipelines/issues/5944 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5944/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5944/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5944/events | https://github.com/kubeflow/pipelines/issues/5944 | 932,759,134 | MDU6SXNzdWU5MzI3NTkxMzQ= | 5,944 | [frontend] incorrect DAG with argo v3.1.0 | {
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"/assign @zijianjoy ",
"A quick walkthrough of the workflow changes in https://github.com/argoproj/argo-workflows/blame/v3.1.0/ui/src/models/workflows.ts:\r\n\r\nThere are 3 areas which have changed since 2.12.9:\r\n\r\n1. `ContainerNode` interface: https://github.com/argoproj/argo-workflows/blame/v3.1.0/ui/src/m... | 2021-06-29T14:32:05 | 2021-07-03T12:52:49 | 2021-07-03T12:52:49 | 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: 1.7.0-alpha.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. run v2 sample test
2. note that, two steps are running at the same time, but there's a dependency edge between them:
### Expected result
<!-- What should the correct behavior be? -->
the two build image steps should have no dependency to each other
### 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/5943 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5943/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5943/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5943/events | https://github.com/kubeflow/pipelines/issues/5943 | 932,530,011 | MDU6SXNzdWU5MzI1MzAwMTE= | 5,943 | [bug] pipelines flaky on GKE after upgrading to argo v3.1+ docker executor | {
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"Downgraded to KFP 1.6.0 and verified the same problems, they seem to be related to my GKE cluster than argo/kfp.",
"I'm seeing this again on test infra:\r\nhttps://4e18c21c9d33d20f-dot-datalab-vm-staging.googleusercontent.com/#/runs/details/d249c419-a9a7-4653-8212-856f87ad1e91\r\nhttps://4e18c21c9d33d20f-dot-dat... | 2021-06-29T11:33:27 | 2021-08-04T13:55:42 | 2021-08-04T13:55:42 | CONTRIBUTOR | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
pipelines flaky after upgrading to argo v3.1.0, when using argo docker executor
1. install kfp 1.7.0-alpha.1
2. run v2 sample tests in the local cluster
### What happened:
Some pipelines fail randomly with several types of error:
`This step is in Error state with this message: Error (exit code 1): path /tmp/outputs/metrics/data does not exist in archive /tmp/argo/outputs/artifacts/output-named-tuple-metrics.tgz`
`This step is in Error state with this message: Error (exit code 1): Error: No such container:path: 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data tar: This does not look like a tar archive tar: Exiting with failure status due to previous errors`
https://1bbe723ceaf1ede1-dot-asia-east1.pipelines.googleusercontent.com/#/runs/details/da866240-98f4-42d1-86e1-2c6b17a7b944
`This step is in Error state with this message: Error (exit code 1): failed to wait for main container to complete: timed out waiting for the condition: Error response from daemon: No such container: 2723a1dcd93b23f100a96bd82480b8917b8f805827d41e1f70f515655cb1d9e1`
https://1bbe723ceaf1ede1-dot-asia-east1.pipelines.googleusercontent.com/#/runs/details/1f5a60a1-885a-4de5-8222-d906e92130bc
Both type of errors have very similar logs in argo wait container:
```
time="2021-06-29T11:14:29.706Z" level=info msg="Starting annotations monitor"
time="2021-06-29T11:14:29.706Z" level=info msg="Starting deadline monitor"
time="2021-06-29T11:14:29.707Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:29.790Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Created {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:29.790Z" level=info msg="mapped container name \"wait\" to container ID \"1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431\" (created at 2021-06-29 11:14:17 +0000 UTC, status Created)"
time="2021-06-29T11:14:29.790Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:30.791Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:30.862Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:30.862Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:31.862Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:31.990Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:31.990Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:32.990Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:33.067Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:33.067Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:34.067Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:34.158Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:34.158Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:35.159Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:35.286Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:35.286Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:36.286Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:36.359Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:36.359Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:37.360Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:37.535Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:37.535Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:38.535Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:38.598Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:38.598Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:39.598Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:39.661Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:39.661Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:40.661Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:40.782Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:40.782Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:41.782Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:41.877Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:41.877Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:41.877Z" level=info msg="mapped container name \"main\" to container ID \"32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5\" (created at 2021-06-29 11:14:32 +0000 UTC, status Created)"
time="2021-06-29T11:14:42.710Z" level=info msg="docker wait 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5"
time="2021-06-29T11:14:42.768Z" level=error msg="`docker wait 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5` failed: Error response from daemon: No such container: 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5\n"
time="2021-06-29T11:14:42.877Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:42.935Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:42.935Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:43.935Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:44.012Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:44.012Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:44.071Z" level=info msg="docker wait 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5"
time="2021-06-29T11:14:44.071Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:44.265Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:44.265Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:44.302Z" level=info msg="Main container completed"
time="2021-06-29T11:14:44.302Z" level=info msg="No Script output reference in workflow. Capturing script output ignored"
time="2021-06-29T11:14:44.302Z" level=info msg="Saving logs"
time="2021-06-29T11:14:44.303Z" level=info msg="[docker logs 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5]"
time="2021-06-29T11:14:45.013Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:45.112Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:45.112Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:45.268Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:45.423Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:45.423Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:46.113Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:46.167Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:46.168Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:46.423Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:46.589Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Created {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:46.589Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:46.878Z" level=info msg="S3 Save path: /tmp/argo/outputs/logs/main.log, key: artifacts/add-pipeline-fz5cn/2021/06/29/add-pipeline-fz5cn-2565795640/main.log"
time="2021-06-29T11:14:46.878Z" level=info msg="Creating minio client minio-service.kubeflow:9000 using static credentials"
time="2021-06-29T11:14:46.879Z" level=info msg="Saving from /tmp/argo/outputs/logs/main.log to s3 (endpoint: minio-service.kubeflow:9000, bucket: mlpipeline, key: artifacts/add-pipeline-fz5cn/2021/06/29/add-pipeline-fz5cn-2565795640/main.log)"
time="2021-06-29T11:14:47.168Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:47.259Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/logs/main.log
time="2021-06-29T11:14:47.259Z" level=info msg="Successfully saved file: /tmp/argo/outputs/logs/main.log"
time="2021-06-29T11:14:47.259Z" level=info msg="Saving output parameters"
time="2021-06-29T11:14:47.259Z" level=info msg="Saving path output parameter: add-2-sum"
time="2021-06-29T11:14:47.259Z" level=info msg="Copying /tmp/outputs/sum/data from base image layer"
time="2021-06-29T11:14:47.259Z" level=info msg="sh -c docker cp -a 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data - | tar -ax -O"
time="2021-06-29T11:14:47.284Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Up {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:47.284Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:47.413Z" level=error msg="`sh -c docker cp -a 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data - | tar -ax -O` failed: Error: No such container:path: 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data\ntar: This does not look like a tar archive\ntar: Exiting with failure status due to previous errors\n"
time="2021-06-29T11:14:47.413Z" level=error msg="executor error: Error: No such container:path: 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data\ntar: This does not look like a tar archive\ntar: Exiting with failure status due to previous errors\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.InternalError\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:60\ngithub.com/argoproj/argo-workflows/v3/workflow/common.RunCommand\n\t/go/src/github.com/argoproj/argo-workflows/workflow/common/util.go:238\ngithub.com/argoproj/argo-workflows/v3/workflow/common.RunShellCommand\n\t/go/src/github.com/argoproj/argo-workflows/workflow/common/util.go:255\ngithub.com/argoproj/argo-workflows/v3/workflow/executor/docker.(*DockerExecutor).GetFileContents\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/docker/docker.go:58\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).SaveParameters\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:492\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:57\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.NewWaitCommand.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:18\ngithub.com/spf13/cobra.(*Command).execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:846\ngithub.com/spf13/cobra.(*Command).ExecuteC\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:950\ngithub.com/spf13/cobra.(*Command).Execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:887\nmain.main\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/main.go:15\nruntime.main\n\t/usr/local/go/src/runtime/proc.go:204\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1374"
time="2021-06-29T11:14:47.413Z" level=info msg="Saving output artifacts"
time="2021-06-29T11:14:47.414Z" level=info msg="Staging artifact: add-2-sum"
time="2021-06-29T11:14:47.414Z" level=info msg="Copying /tmp/outputs/sum/data from container base image layer to /tmp/argo/outputs/artifacts/add-2-sum.tgz"
time="2021-06-29T11:14:47.414Z" level=info msg="Archiving main:/tmp/outputs/sum/data to /tmp/argo/outputs/artifacts/add-2-sum.tgz"
time="2021-06-29T11:14:47.414Z" level=info msg="sh -c docker cp -a 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data - | gzip > /tmp/argo/outputs/artifacts/add-2-sum.tgz"
time="2021-06-29T11:14:47.589Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:47.680Z" level=warning msg="path /tmp/outputs/sum/data does not exist in archive /tmp/argo/outputs/artifacts/add-2-sum.tgz"
time="2021-06-29T11:14:47.680Z" level=error msg="executor error: path /tmp/outputs/sum/data does not exist in archive /tmp/argo/outputs/artifacts/add-2-sum.tgz\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:55\ngithub.com/argoproj/argo-workflows/v3/workflow/executor/docker.(*DockerExecutor).CopyFile\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/docker/docker.go:88\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).stageArchiveFile\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:402\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).saveArtifact\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:290\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).SaveArtifacts\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:276\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:62\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.NewWaitCommand.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:18\ngithub.com/spf13/cobra.(*Command).execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:846\ngithub.com/spf13/cobra.(*Command).ExecuteC\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:950\ngithub.com/spf13/cobra.(*Command).Execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:887\nmain.main\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/main.go:15\nruntime.main\n\t/usr/local/go/src/runtime/proc.go:204\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1374"
time="2021-06-29T11:14:47.681Z" level=info msg="Annotating pod with output"
time="2021-06-29T11:14:47.694Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Up {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:47.694Z" level=info msg="ignoring container \"kfp-launcher\" created at 2021-06-29 11:13:20 +0000 UTC, too long before process started"
time="2021-06-29T11:14:47.739Z" level=info msg="Patch pods 200"
time="2021-06-29T11:14:47.898Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow --filter=label=io.kubernetes.pod.name=add-pipeline-fz5cn-2565795640"
time="2021-06-29T11:14:48.025Z" level=info msg="listed containers" containers="map[kfp-launcher:{7a39cf1866758e4c53dd138c38f90b2a683ae0059a3ecc2ea0b439a28dbb38c1 Exited {0 63760562000 <nil>}} main:{32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5 Up {0 63760562072 <nil>}} wait:{1531d46bf944fcdb3ac8ea356e0536bb2a01ba9791e9007699f6d195401fc431 Up {0 63760562057 <nil>}}]"
time="2021-06-29T11:14:48.025Z" level=info msg="Killing sidecars [\"kfp-launcher\"]"
time="2021-06-29T11:14:48.032Z" level=info msg="Get pods 200"
time="2021-06-29T11:14:48.034Z" level=info msg="zero container IDs, assuming all containers have exited successfully"
time="2021-06-29T11:14:48.034Z" level=info msg="Alloc=10350 TotalAlloc=17307 Sys=73297 NumGC=5 Goroutines=13"
time="2021-06-29T11:14:48.173Z" level=fatal msg="Error: No such container:path: 32b49d8ac659f4e77ec768bd22ca38cfa97abd2006a185a4cce5c7d4a4f418f5:/tmp/outputs/sum/data\ntar: This does not look like a tar archive\ntar: Exiting with failure status due to previous errors\ngithub.com/argoproj/argo-workflows/v3/errors.New\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:49\ngithub.com/argoproj/argo-workflows/v3/errors.InternalError\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:60\ngithub.com/argoproj/argo-workflows/v3/workflow/common.RunCommand\n\t/go/src/github.com/argoproj/argo-workflows/workflow/common/util.go:238\ngithub.com/argoproj/argo-workflows/v3/workflow/common.RunShellCommand\n\t/go/src/github.com/argoproj/argo-workflows/workflow/common/util.go:255\ngithub.com/argoproj/argo-workflows/v3/workflow/executor/docker.(*DockerExecutor).GetFileContents\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/docker/docker.go:58\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).SaveParameters\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:492\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:57\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.NewWaitCommand.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:18\ngithub.com/spf13/cobra.(*Command).execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:846\ngithub.com/spf13/cobra.(*Command).ExecuteC\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:950\ngithub.com/spf13/cobra.(*Command).Execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.0.0/command.go:887\nmain.main\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/main.go:15\nruntime.main\n\t/usr/local/go/src/runtime/proc.go:204\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1374"
```
I did some investigation, `/tmp/outputs/metrics/data` is path in the container where output artifacts are expected to be emitted. The archive path is an implementation detail of docker executor, so we can ignore it here. To verify, we may need to confirm whether inside the container the artifact was correctly generated.
### What did you expect to happen:
Pipelines run successfully
### 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/5942 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5942/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5942/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5942/events | https://github.com/kubeflow/pipelines/issues/5942 | 932,478,646 | MDU6SXNzdWU5MzI0Nzg2NDY= | 5,942 | [pH] add PR title conventional format check | {
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"Some mistakes I see often:\r\n* `feat(scope) title` (miss `:`)\r\n* `feat(scope):title` (miss space after `:`)\r\n* `feat (scope): title` (extra space between feat and (scope))\r\n\r\nthey can be identified by the bot that checks PR title as semantic format."
] | 2021-06-29T10:38:58 | 2021-06-30T07:02:36 | 2021-06-30T07:02:36 | CONTRIBUTOR | null | I just learned that https://github.com/zeke/semantic-pull-requests can check PR title format.
It looks convenient to set up.
This issue is the last item of https://github.com/kubeflow/pipelines/issues/3920#issuecomment-645280671 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5941 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5941/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5941/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5941/events | https://github.com/kubeflow/pipelines/issues/5941 | 932,392,708 | MDU6SXNzdWU5MzIzOTI3MDg= | 5,941 | [backend & dsl] use argo workflows v1 API | {
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"I made a mistake, the change was that argo upgraded to CRD version v1.\r\nBut argo CRDs are still of version v1alpha1."
] | 2021-06-29T09:03:05 | 2021-06-29T11:08:01 | 2021-06-29T11:08:01 | CONTRIBUTOR | null | After upgrading to argo workflows v3.1.0, the workflow CRD has upgraded to v1.
We should upgrade our backend API usages and SDK compilation targets to v1. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5940 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5940/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5940/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5940/events | https://github.com/kubeflow/pipelines/issues/5940 | 932,382,227 | MDU6SXNzdWU5MzIzODIyMjc= | 5,940 | [test infra] api server has frequent "Failed to update run" error logs | {
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"name": "lifecycle/stale",
"color": "bbbbbb",
"default": false,
"description": "The issue / pull request is stale, any activities remove this label.... | open | false | null | [] | null | [
"There is no visible problems in test infra, but reducing noisy error message is still vastly better when trying to debug and find the actual error message.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for y... | 2021-06-29T08:51:16 | 2022-03-02T15:05:32 | null | CONTRIBUTOR | null | > /go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/go_client._ReportService_ReportWorkflow_Handler
InternalServerError: Failed to update run a7a0c80b-e863-45de-ad4b-a8320c64b425. Row not found
Not sure why this happens, need to confirm whether there are any leaked workflows. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5937 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5937/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5937/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5937/events | https://github.com/kubeflow/pipelines/issues/5937 | 932,142,684 | MDU6SXNzdWU5MzIxNDI2ODQ= | 5,937 | [bug] condition sample stuck in running state with argo v3.1.0 | {
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"I tried to delete cache db and run the condition pipeline again without cache, but it is stuck in running state again.\r\n\r\n\r\nConclusion: we can rule out caching from possible reasons for this.",
"``... | 2021-06-29T02:21:49 | 2021-07-03T12:52:49 | 2021-07-03T12:52:49 | CONTRIBUTOR | null | ### What steps did you take
1. run v2 sample test
### What happened:
1. it times out waiting for condition sample to finish
2. the sample runs and gets cached as usual
3. but the workflow itself is always stuck in running state
### What did you expect to happen:
the sample should finish as normal
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? KFP standalone 1.6.0-alpha.1
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: master
<!-- 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.-->
Here's status of the argo workflow, it's also stuck in running. So this is not a problem with persistence agent.
```
status:
artifactRepositoryRef:
default: true
conditions:
- status: "False"
type: PodRunning
finishedAt: null
nodes:
single-condition-pipeline-xb2hw:
children:
- single-condition-pipeline-xb2hw-690456091
displayName: single-condition-pipeline-xb2hw
finishedAt: null
id: single-condition-pipeline-xb2hw
inputs:
parameters:
- name: force_flip_result
value: heads
- name: text
value: condition test
name: single-condition-pipeline-xb2hw
phase: Running
progress: 4/4
startedAt: "2021-06-28T13:58:12Z"
templateName: single-condition-pipeline
templateScope: local/single-condition-pipeline-xb2hw
type: DAG
single-condition-pipeline-xb2hw-151083147:
boundaryID: single-condition-pipeline-xb2hw
children:
- single-condition-pipeline-xb2hw-329071385
- single-condition-pipeline-xb2hw-1404454342
displayName: condition-1
finishedAt: "2021-06-28T13:59:30Z"
id: single-condition-pipeline-xb2hw-151083147
inputs:
parameters:
- name: text
value: condition test
name: single-condition-pipeline-xb2hw.condition-1
outboundNodes:
- single-condition-pipeline-xb2hw-1421231961
- single-condition-pipeline-xb2hw-1404454342
phase: Succeeded
progress: 3/3
resourcesDuration:
cpu: 0
memory: 0
startedAt: "2021-06-28T13:58:50Z"
templateName: condition-1
templateScope: local/single-condition-pipeline-xb2hw
type: DAG
single-condition-pipeline-xb2hw-329071385:
boundaryID: single-condition-pipeline-xb2hw-151083147
children:
- single-condition-pipeline-xb2hw-1421231961
displayName: flip-coin-2
finishedAt: "2021-06-28T13:58:56Z"
id: single-condition-pipeline-xb2hw-329071385
name: single-condition-pipeline-xb2hw.condition-1.flip-coin-2
outputs:
artifacts:
- name: flip-coin-2-Output
path: /tmp/outputs/Output/data
s3:
key: artifacts/single-condition-pipeline-982vv/2021/06/28/single-condition-pipeline-982vv-3001282121/flip-coin-2-Output.tgz
- name: main-logs
s3:
key: artifacts/single-condition-pipeline-982vv/2021/06/28/single-condition-pipeline-982vv-3001282121/main.log
exitCode: "0"
parameters:
- name: flip-coin-2-Output
value: heads
valueFrom:
path: /tmp/outputs/Output/data
phase: Succeeded
progress: 1/1
resourcesDuration:
cpu: 0
memory: 0
startedAt: "2021-06-28T13:58:50Z"
templateName: flip-coin-2
templateScope: local/single-condition-pipeline-xb2hw
type: Pod
single-condition-pipeline-xb2hw-690456091:
boundaryID: single-condition-pipeline-xb2hw
children:
- single-condition-pipeline-xb2hw-151083147
displayName: flip-coin
finishedAt: "2021-06-28T13:58:41Z"
hostNodeName: gke-kfp-std-default-pool-1c1207aa-2eyx
id: single-condition-pipeline-xb2hw-690456091
inputs:
parameters:
- name: force_flip_result
value: heads
name: single-condition-pipeline-xb2hw.flip-coin
outputs:
artifacts:
- name: flip-coin-Output
path: /tmp/outputs/Output/data
s3:
key: artifacts/single-condition-pipeline-xb2hw/2021/06/28/single-condition-pipeline-xb2hw-690456091/flip-coin-Output.tgz
- name: main-logs
s3:
key: artifacts/single-condition-pipeline-xb2hw/2021/06/28/single-condition-pipeline-xb2hw-690456091/main.log
exitCode: "0"
parameters:
- name: flip-coin-Output
value: heads
valueFrom:
path: /tmp/outputs/Output/data
phase: Succeeded
progress: 1/1
resourcesDuration:
cpu: 7
memory: 2
startedAt: "2021-06-28T13:58:12Z"
templateName: flip-coin
templateScope: local/single-condition-pipeline-xb2hw
type: Pod
single-condition-pipeline-xb2hw-1404454342:
boundaryID: single-condition-pipeline-xb2hw-151083147
displayName: print-msg-3
finishedAt: "2021-06-28T13:59:02Z"
hostNodeName: gke-kfp-std-default-pool-1c1207aa-2eyx
id: single-condition-pipeline-xb2hw-1404454342
inputs:
parameters:
- name: text
value: condition test
name: single-condition-pipeline-xb2hw.condition-1.print-msg-3
outputs:
artifacts:
- name: main-logs
s3:
key: artifacts/single-condition-pipeline-982vv/2021/06/28/single-condition-pipeline-982vv-3511891414/main.log
exitCode: "0"
phase: Succeeded
progress: 1/1
resourcesDuration:
cpu: 0
memory: 0
startedAt: "2021-06-28T13:58:50Z"
templateName: print-msg-3
templateScope: local/single-condition-pipeline-xb2hw
type: Pod
single-condition-pipeline-xb2hw-1421231961:
boundaryID: single-condition-pipeline-xb2hw-151083147
displayName: print-msg-2
finishedAt: "2021-06-28T13:59:09Z"
id: single-condition-pipeline-xb2hw-1421231961
inputs:
parameters:
- name: flip-coin-2-Output
value: heads
name: single-condition-pipeline-xb2hw.condition-1.print-msg-2
outputs:
artifacts:
- name: main-logs
s3:
key: artifacts/single-condition-pipeline-982vv/2021/06/28/single-condition-pipeline-982vv-3528669033/main.log
exitCode: "0"
phase: Succeeded
progress: 1/1
resourcesDuration:
cpu: 0
memory: 0
startedAt: "2021-06-28T13:59:00Z"
templateName: print-msg-2
templateScope: local/single-condition-pipeline-xb2hw
type: Pod
single-condition-pipeline-xb2hw-3316531579:
boundaryID: single-condition-pipeline-xb2hw
displayName: print-msg
finishedAt: "2021-06-28T13:59:04Z"
hostNodeName: gke-kfp-std-default-pool-1c1207aa-2eyx
id: single-condition-pipeline-xb2hw-3316531579
inputs:
parameters:
- name: flip-coin-Output
value: heads
name: single-condition-pipeline-xb2hw.print-msg
outputs:
artifacts:
- name: main-logs
s3:
key: artifacts/single-condition-pipeline-982vv/2021/06/28/single-condition-pipeline-982vv-1648329963/main.log
exitCode: "0"
phase: Succeeded
progress: 1/1
resourcesDuration:
cpu: 0
memory: 0
startedAt: "2021-06-28T13:58:50Z"
templateName: print-msg
templateScope: local/single-condition-pipeline-xb2hw
type: Pod
phase: Running
progress: 5/5
resourcesDuration:
cpu: 7
memory: 2
startedAt: "2021-06-28T13:58:12Z"
```
### 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/5933 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5933/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5933/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5933/events | https://github.com/kubeflow/pipelines/issues/5933 | 931,921,553 | MDU6SXNzdWU5MzE5MjE1NTM= | 5,933 | [feature] Support for authenticating kfp_server_api api_client | {
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"u... | closed | false | null | [] | null | [
"Just wanna clarify, can you use `kfp client` to do the authentication? What's the use case of using `kfp_server_api` instead of kfp client?",
"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 contributi... | 2021-06-28T19:49:00 | 2022-03-03T03:05:39 | 2022-03-03T03:05:39 | MEMBER | null | ### Feature Area
/area sdk
### What feature would you like to see?
A function to get auth token for configuring `kfp_server_api` api_client. I currently didn't find any function in the `kfp_server_api` class similar to the `kfp` Client class for authorization, but it turns out that retrieving the bearer token is the same. So, would it be better to add the same functionality in the `kfp_server_api` api_client as well?
### What is the use case or pain point?
Be able to disable/delete recurring jobs.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
```python
LOCAL_KFP_CREDENTIAL = os.path.expanduser('~/.config/kfp/credentials.json')
OAUTH_TOKEN_URI = 'https://www.googleapis.com/oauth2/v4/token'
def get_auth_token(client_id):
"""Gets auth token from user account."""
if os.path.exists(LOCAL_KFP_CREDENTIAL):
# fetch IAP auth token using the locally stored credentials.
with open(LOCAL_KFP_CREDENTIAL, 'r') as f:
credentials = json.load(f)
if client_id in credentials:
payload = {
"client_id": credentials[client_id]['other_client_id'],
"client_secret": credentials[client_id]['other_client_secret'],
"refresh_token": credentials[client_id]['refresh_token'],
"grant_type": "refresh_token",
"audience": client_id,
}
res = requests.post(OAUTH_TOKEN_URI, data=payload)
res.raise_for_status()
return str(json.loads(res.text)[u"id_token"])
else:
print("Exception getting auth token!")
return token
token = get_auth_token(client_id=os.environ['KF_SERVER_CLIENT_ID'])
# Configure API key authorization: Bearer
config = kfp_server_api.Configuration(
host = os.environ['HOST'],
api_key = {
'authorization': f'Bearer {token}'
}
)
# Enter a context with an instance of the API client
with kfp_server_api.ApiClient(config) as api_client:
# Create an instance of the API class
job_client = kfp_server_api.JobServiceApi(api_client=api_client)
# disable the existing recurring run
try:
api_response = job_client.disable_job(id=recurring_runs_id)
pprint(api_response)
except ApiException as e:
print(f"Exception occured while disabling recurring run {recurring_runs_id}.")
```
---
<!-- 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/5932 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5932/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5932/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5932/events | https://github.com/kubeflow/pipelines/issues/5932 | 931,759,104 | MDU6SXNzdWU5MzE3NTkxMDQ= | 5,932 | [Sample] Create visualization samples | {
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"Consider the possibility of adding yaml sample for visualization. ",
"https://github.com/kubeflow/pipelines/pull/5947#issuecomment-871704880"
] | 2021-06-28T16:44:14 | 2021-07-19T21:33:30 | 2021-07-19T18:32:15 | COLLABORATOR | null | Visualization doc:
https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#available-output-viewers
Create pipeline examples with sample data in v1 format, so we can test them after the following issues are unblocked:
- https://github.com/kubeflow/pipelines/issues/5831
- https://github.com/kubeflow/pipelines/issues/5830
This is to prepare for validation of https://github.com/kubeflow/pipelines/issues/5666 changes.
List of visualization types is:
- [x] Confusion matrix
- [x] Markdown
- [x] ROC curve
- [x] Table
- [x] TensorBoard
- [x] Web app
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https://api.github.com/repos/kubeflow/pipelines/issues/5931 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5931/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5931/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5931/events | https://github.com/kubeflow/pipelines/issues/5931 | 931,682,324 | MDU6SXNzdWU5MzE2ODIzMjQ= | 5,931 | [bug] kubeflow 1.2.0 (kfctl_aws.v1.2.0.yaml) install failed to build LoadBalancer configuration(ALB) on AWS (eks cluster). | {
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"@amalendur: The label(s) `kind/bug.` cannot be applied, because the repository doesn't have them.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5931):\n\n>/kind bug.\r\n>\r\n>### What steps did you take\r\n>I have deployed a EKS cluster and on top of that cluster I am trying to... | 2021-06-28T15:14:45 | 2021-06-29T08:13:01 | 2021-06-29T08:13:01 | NONE | null | /kind bug.
### What steps did you take
I have deployed a EKS cluster and on top of that cluster I am trying to deploy "Kubeflow" through GitLab pipeline.
In the pipeline script I have set the Kube-context before execute the Kubeflow config-file(kfctl_aws.v1.2.0.yaml).
I have executed the steps in the following order;
1. Deploy EKS cluster with Nodegrop (deploy through terraform)
2. Download the Kubeflow config-file from "https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_aws.v1.2.0.yaml"
3. Update the config-file with;
```
apiVersion: kfdef.apps.kubeflow.org/v1
kind: KfDef
metadata:
namespace: kubeflow
spec:
applications:
- kustomizeConfig:
repoRef:
name: manifests
path: namespaces/base
name: namespaces
- kustomizeConfig:
repoRef:
name: manifests
path: application/v3
name: application
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/istio-1-3-1-stack
name: istio-stack
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cluster-local-gateway-1-3-1
name: cluster-local-gateway
- kustomizeConfig:
repoRef:
name: manifests
path: istio/istio/base
name: istio
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cert-manager-crds
name: cert-manager-crds
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cert-manager-kube-system-resources
name: cert-manager-kube-system-resources
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cert-manager
name: cert-manager
- kustomizeConfig:
repoRef:
name: manifests
path: metacontroller/base
name: metacontroller
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/oidc-authservice
name: oidc-authservice
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/dex-auth
name: dex
- kustomizeConfig:
repoRef:
name: manifests
path: admission-webhook/bootstrap/overlays/application
name: bootstrap
- kustomizeConfig:
repoRef:
name: manifests
path: spark/spark-operator/overlays/application
name: spark-operator
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws
name: kubeflow-apps
- kustomizeConfig:
repoRef:
name: manifests
path: aws/istio-ingress/base_v3
name: istio-ingress
- kustomizeConfig:
repoRef:
name: manifests
path: knative/installs/generic
name: knative
- kustomizeConfig:
repoRef:
name: manifests
path: kfserving/installs/generic
name: kfserving
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/spartakus
name: spartakus
plugins:
- kind: KfAwsPlugin
metadata:
name: aws
spec:
auth:
basicAuth:
password: <password>
username: admin
region: <aws_default_region>
#enablePodIamPolicy: true
roles:
- <eks_node_role>
repos:
- name: manifests
uri: https://github.com/kubeflow/manifests/archive/v1.2.0.tar.gz
version: v1.2-branch
```
```
<aws_region> : AWS Region, where the cluster has been created.
<eks_node_role> : Name of the IAM role for the nodegroup.
<password> : Admin password
```
4. Set the Kube-context;
`aws eks --region ${AWS_REGION} update-kubeconfig --name ${CLUSTER_NAME}`
5. Set the environment;
```
wget https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0-0-gbc038f9_linux.tar.gz --no-check-certificate && tar -xvzf kfctl_v1.2.0-0-gbc038f9_linux.tar.gz && chmod +x kfctl && cp ./kfctl /usr/local/bin/kfctl
curl -o aws-iam-authenticator https://amazon-eks.s3.us-west-2.amazonaws.com/1.19.6/2021-01-05/bin/linux/amd64/aws-iam-authenticator && chmod +x ./aws-iam-authenticator && cp ./aws-iam-authenticator /usr/local/bin/aws-iam-authenticator
export AWS_CLUSTER_NAME=${CLUSTER_NAME}
export KF_NAME=${AWS_CLUSTER_NAME}
KF_DIR = /opt/ar-usv-poc-plt
kfctl build -f kfctl_aws.v1.2.0.yaml -V
kfctl apply -f kfctl_aws.v1.2.0.yaml -V
```
### What happened:
Getting the following error:
`E0628 14:56:25.659557 1 controller.go:217] kubebuilder/controller "msg"="Reconciler error" "error"="failed to build LoadBalancer configuration due to retrieval of subnets failed to resolve 2 qualified subnets. Subnets must contain the kubernetes.io/cluster/\u003ccluster name\u003e tag with a value of shared or owned and the kubernetes.io/role/elb tag signifying it should be used for ALBs Additionally, there must be at least 2 subnets with unique availability zones as required by ALBs. Either tag subnets to meet this requirement or use the subnets annotation on the ingress resource to explicitly call out what subnets to use for ALB creation. The subnets that did resolve were []" "controller"="alb-ingress-controller" "request"={"Namespace":"istio-system","Name":"istio-ingress"}`
### What did you expect to happen:
load-balancer should be configure, up & running.
### Environment:
Platform.
### Additional information:
```
subnets tagged with kubernetes.io/cluster/clusterName : shared & kubernetes.io/role/elb : 1
where, clusterName : <Name of the Cluster>
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https://api.github.com/repos/kubeflow/pipelines/issues/5930 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5930/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5930/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5930/events | https://github.com/kubeflow/pipelines/issues/5930 | 931,582,821 | MDU6SXNzdWU5MzE1ODI4MjE= | 5,930 | [frontend] artifact preview & visualization breaks with argo v3.1+ | {
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"/assign @zijianjoy ",
"Pasting an example full argo workflow:\r\n\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n annotations:\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.6.4\r\n pipelines.kubeflow.org/pipeline_compilation_time: 2021-06-29T11:08:44.423700\r\n pipelin... | 2021-06-28T13:37:29 | 2021-07-15T08:44:51 | 2021-07-15T08:44:51 | 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: 1.7.0-alpha.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
artifacts should show preview, visualizations should show up
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
Root cause: argo removed some information in workflow status.
Previously, artifacts contain an object of information, but now artifacts only contain their keys.
See workflows.argoproj.io/outputs annotation
> workflows.argoproj.io/outputs: >-
{"artifacts":[{"name":"main-logs","s3":{"key":"artifacts/file-passing-pipelines-xz8xs/2021/06/28/file-passing-pipelines-xz8xs-3422213888/main.log"}}]}
---
<!-- 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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"Will create under bug."
] | 2021-06-28T13:26:14 | 2021-06-28T15:05:32 | 2021-06-28T15:05:13 | NONE | null | /kind bug.
I have deployed a EKS cluster and on top of that cluster I am trying to deploy "Kubeflow" through GitLab pipeline.
In the pipeline script I have set the Kube-context before execute the Kubeflow config-file(kfctl_aws.v1.2.0.yaml).
I have executed the steps in the following order;
1. Deploy EKS cluster with Nodegrop (deploy through terraform)
2. Download the Kubeflow config-file from "https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_aws.v1.2.0.yaml"
3. Update the config-file with
```
apiVersion: kfdef.apps.kubeflow.org/v1
kind: KfDef
metadata:
namespace: kubeflow
spec:
applications:
- kustomizeConfig:
repoRef:
name: manifests
path: namespaces/base
name: namespaces
- kustomizeConfig:
repoRef:
name: manifests
path: application/v3
name: application
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/istio-1-3-1-stack
name: istio-stack
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cluster-local-gateway-1-3-1
name: cluster-local-gateway
- kustomizeConfig:
repoRef:
name: manifests
path: istio/istio/base
name: istio
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cert-manager-crds
name: cert-manager-crds
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cert-manager-kube-system-resources
name: cert-manager-kube-system-resources
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/cert-manager
name: cert-manager
- kustomizeConfig:
repoRef:
name: manifests
path: metacontroller/base
name: metacontroller
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/oidc-authservice
name: oidc-authservice
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/dex-auth
name: dex
- kustomizeConfig:
repoRef:
name: manifests
path: admission-webhook/bootstrap/overlays/application
name: bootstrap
- kustomizeConfig:
repoRef:
name: manifests
path: spark/spark-operator/overlays/application
name: spark-operator
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws
name: kubeflow-apps
- kustomizeConfig:
repoRef:
name: manifests
path: aws/istio-ingress/base_v3
name: istio-ingress
- kustomizeConfig:
repoRef:
name: manifests
path: knative/installs/generic
name: knative
- kustomizeConfig:
repoRef:
name: manifests
path: kfserving/installs/generic
name: kfserving
- kustomizeConfig:
repoRef:
name: manifests
path: stacks/aws/application/spartakus
name: spartakus
plugins:
- kind: KfAwsPlugin
metadata:
name: aws
spec:
auth:
basicAuth:
password: <password>
username: admin
region: <aws_default_region>
#enablePodIamPolicy: true
roles:
- <eks_node_role>
repos:
- name: manifests
uri: https://github.com/kubeflow/manifests/archive/v1.2.0.tar.gz
version: v1.2-branch
```
```
<aws_region> : AWS Region, where the cluster has been created.
<eks_node_role> : Name of the IAM role for the nodegroup.
<password> : Admin password
```
4. Set the Kube-context;
`aws eks --region ${AWS_REGION} update-kubeconfig --name ${CLUSTER_NAME}`
5. Set the environment;
```
wget https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0-0-gbc038f9_linux.tar.gz --no-check-certificate && tar -xvzf kfctl_v1.2.0-0-gbc038f9_linux.tar.gz && chmod +x kfctl && cp ./kfctl /usr/local/bin/kfctl
curl -o aws-iam-authenticator https://amazon-eks.s3.us-west-2.amazonaws.com/1.19.6/2021-01-05/bin/linux/amd64/aws-iam-authenticator && chmod +x ./aws-iam-authenticator && cp ./aws-iam-authenticator /usr/local/bin/aws-iam-authenticator
export AWS_CLUSTER_NAME=${CLUSTER_NAME}
export KF_NAME=${AWS_CLUSTER_NAME}
KF_DIR = /opt/ar-usv-poc-plt
kfctl build -f kfctl_aws.v1.2.0.yaml -V
kfctl apply -f kfctl_aws.v1.2.0.yaml -V
```
Getting the following error:
`E0628 14:56:25.659557 1 controller.go:217] kubebuilder/controller "msg"="Reconciler error" "error"="failed to build LoadBalancer configuration due to retrieval of subnets failed to resolve 2 qualified subnets. Subnets must contain the kubernetes.io/cluster/\u003ccluster name\u003e tag with a value of shared or owned and the kubernetes.io/role/elb tag signifying it should be used for ALBs Additionally, there must be at least 2 subnets with unique availability zones as required by ALBs. Either tag subnets to meet this requirement or use the subnets annotation on the ingress resource to explicitly call out what subnets to use for ALB creation. The subnets that did resolve were []" "controller"="alb-ingress-controller" "request"={"Namespace":"istio-system","Name":"istio-ingress"}`
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https://api.github.com/repos/kubeflow/pipelines/issues/5925 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5925/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5925/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5925/events | https://github.com/kubeflow/pipelines/issues/5925 | 931,326,837 | MDU6SXNzdWU5MzEzMjY4Mzc= | 5,925 | [feature] Multi-User Pipeline Definition Isolation | {
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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",
"Same problem here, we followed the [manifests](https://github.com/kubeflow/manifests#kubeflow-pipelines) and deployed pipeline with mul... | 2021-06-28T08:45:54 | 2023-02-20T15:51:01 | null | NONE | null | Hey guys,
According to the [official documentation](https://www.kubeflow.org/docs/components/pipelines/multi-user/#current-limitations), Pipeline definitions are not isolated currently.
**What feature would you like to see?**
I would like to see Pipeline isolation similar to what we have in Notebooks/Runs/Experiments.
**What is the use case or pain point?**
The way Pipelines are exposed currently allows the deletion of Pipelines owned by other users.
**Is there a workaround currently?**
Not that I am aware of.
Are there intentions to add this to future versions?
Thanks
<!-- 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/5924 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5924/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5924/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5924/events | https://github.com/kubeflow/pipelines/issues/5924 | 931,044,084 | MDU6SXNzdWU5MzEwNDQwODQ= | 5,924 | [backend] cache-server cannot mount webhook-tls-certs | {
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"Not solving the issue, but failed to reproduce with minikube:\r\n\r\n```\r\ndocker system prune --all \r\nminikube start --cpus 6 --memory 12288 --disk-size=120g --extra-config=apiserver.service-account-issuer=api --extra-config=apiserver.service-account-signing-key-file=/var/lib/minikube/certs/apiserver.key --ext... | 2021-06-27T23:35:18 | 2022-03-03T00:05:11 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
[Deployed locally on a kind cluster](https://www.kubeflow.org/docs/components/pipelines/installation/localcluster-deployment/#uninstalling-kubeflow-pipelines)
kind version: `kind v0.9.0 go1.15.2 linux/amd64`
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.2.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.2.0
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
```
export PIPELINE_VERSION=1.2.0
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION"
```
The `cache-server` pod gets stuck in the `ContainerCreating` stage. Logs
```
Name: cache-server-b68649ff-dl89c
Namespace: kubeflow
Priority: 0
Node: kind-control-plane/172.18.0.2
Start Time: Sun, 27 Jun 2021 16:22:24 -0700
Labels: app=cache-server
application-crd-id=kubeflow-pipelines
pod-template-hash=b68649ff
Annotations: <none>
Status: Pending
IP:
IPs: <none>
Controlled By: ReplicaSet/cache-server-b68649ff
Containers:
server:
Container ID:
Image: gcr.io/ml-pipeline/cache-server:1.3.0
Image ID:
Port: 8443/TCP
Host Port: 0/TCP
Args:
--db_driver=$(DBCONFIG_DRIVER)
--db_host=$(DBCONFIG_HOST_NAME)
--db_port=$(DBCONFIG_PORT)
--db_name=$(DBCONFIG_DB_NAME)
--db_user=$(DBCONFIG_USER)
--db_password=$(DBCONFIG_PASSWORD)
--namespace_to_watch=$(NAMESPACE_TO_WATCH)
State: Waiting
Reason: ContainerCreating
Ready: False
Restart Count: 0
Environment:
DBCONFIG_DRIVER: mysql
DBCONFIG_DB_NAME: <set to the key 'cacheDb' of config map 'pipeline-install-config-m2k6bmc5m7'> Optional: false
DBCONFIG_HOST_NAME: <set to the key 'dbHost' of config map 'pipeline-install-config-m2k6bmc5m7'> Optional: false
DBCONFIG_PORT: <set to the key 'dbPort' of config map 'pipeline-install-config-m2k6bmc5m7'> Optional: false
DBCONFIG_USER: <set to the key 'username' in secret 'mysql-secret-fd5gktm75t'> Optional: false
DBCONFIG_PASSWORD: <set to the key 'password' in secret 'mysql-secret-fd5gktm75t'> Optional: false
NAMESPACE_TO_WATCH: kubeflow (v1:metadata.namespace)
Mounts:
/etc/webhook/certs from webhook-tls-certs (ro)
/var/run/secrets/kubernetes.io/serviceaccount from kubeflow-pipelines-cache-token-bpbng (ro)
Conditions:
Type Status
Initialized True
Ready False
ContainersReady False
PodScheduled True
Volumes:
webhook-tls-certs:
Type: Secret (a volume populated by a Secret)
SecretName: webhook-server-tls
Optional: false
kubeflow-pipelines-cache-token-bpbng:
Type: Secret (a volume populated by a Secret)
SecretName: kubeflow-pipelines-cache-token-bpbng
Optional: false
QoS Class: BestEffort
Node-Selectors: <none>
Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 7m16s default-scheduler Successfully assigned kubeflow/cache-server-b68649ff-dl89c to kind-control-plane
Warning FailedMount 65s (x11 over 7m16s) kubelet MountVolume.SetUp failed for volume "webhook-tls-certs" : secret "webhook-server-tls" not found
Warning FailedMount 43s (x3 over 5m14s) kubelet Unable to attach or mount volumes: unmounted volumes=[webhook-tls-certs], unattached volumes=[webhook-tls-certs kubeflow-pipelines-cache-token-bpbng]: timed out waiting for the condition
```
Looks like it was not able to find `webhook-tls-certs`
### Expected result
All pods shall be in running status
<!-- What should the correct behavior be? -->
### Materials and Reference
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---
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-06-26T08:21:36 | 2022-03-03T04:05:11 | 2022-03-03T04:05:11 | NONE | null | ### Environment
* KFP version:
1.4.1
* KFP SDK version:
1.4.0
* All dependencies version:
kfp 1.4.0
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
### Steps to reproduce
1. Write a sample pipeline containing a component that fails on purpose and an exit handler. The parameter passed to the exit handler contains the run_id.
```python
import kfp
from kfp import dsl, components
@components.create_component_from_func
def echo_msg(msg: str):
"""Echo a message by parameter."""
print(msg)
@components.create_component_from_func
def raise_error():
"""Raises an error on purpose."""
raise AssertionError("Data was unexpected.")
@dsl.pipeline(
name='exit-handler-test',
description="Testing kubeflow's exit handler."
)
def pipeline_exit_handler(echoed_message: str = 'hello world'):
"""A sample pipeline showing exit handler."""
exit_task = echo_msg(kfp.dsl.RUN_ID_PLACEHOLDER)
with dsl.ExitHandler(exit_task):
raise_error()
if __name__ == '__main__':
kfp.compiler.Compiler().compile(pipeline_exit_handler, __file__ + '.yaml')
```
2. Upload the pipeline to Kubeflow (in my case, GCP's AI Pipelines).
3. Create an experiment, then a run using the pipeline.
4. After the run completes (and fails accordingly), copy the run_id from the exit handler.
5. Obtain run info:
```python
import kfp
kfp_client = kfp.Client(host="https://some-ai-pipelines-url.pipelines.googleusercontent.com/")
run_info = kfp_client.runs.get_run("<run-id-copied-from-exit-handler>")
assert run_info.run.status == 'Failed' # the run status is correct
print(run_info.run.error)
```
### Expected result
I would expect `run_info.run.error` to contain some sort of "AssertionError Data was unexpected" message but it doesn't, it's just None.
In this case, the exit handler is compiled from a function but, for the sake of code re-use, the code in step 5 would probably become an image. I'm not sure if the behaviour is the same in that case.
I'm trying to find a way to extract the reason why a pipeline failed and post it on slack.
- if the runs are recurring, i imagine it _can_ be done from the exit handler
- if the runs are one-offs and are being started by something like Airflow, Airflow should be able to get the run info and the error just as well.
### Materials and Reference
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---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/5920 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5920/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5920/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5920/events | https://github.com/kubeflow/pipelines/issues/5920 | 930,631,422 | MDU6SXNzdWU5MzA2MzE0MjI= | 5,920 | Enable sorting by status in the UI | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/lifecycle frozen",
"We can use `filter` to obtain runs by specifying states: https://github.com/kubeflow/pipelines/blob/master/backe... | 2021-06-26T06:52:17 | 2021-10-12T14:40:19 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
More of a productivity improvement. 🚀
/area frontend
### What feature would you like to see?
Enable sorting by status for runs under the Experiment and Runs section of UI which would filter all Running pipelines to the top.
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Currently, the Kubeflow pipeline experiment UI only sorts based on start time. But sometimes when there are a lot of pipelines running in the same experiment the running ones tend to be below completed ones which makes it difficult to search for a running pipeline especially when recurring runs have been configured. This feature will enable users to find and track pipelines quicker.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
No workarounds were found. At present, we manually search for the running ones.
<!-- 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/5917 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5917/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5917/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5917/events | https://github.com/kubeflow/pipelines/issues/5917 | 929,799,663 | MDU6SXNzdWU5Mjk3OTk2NjM= | 5,917 | Pass pandas dataframe from one component to other without writing and reading | {
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"I think argo does not support panda frame. As a workaround, can the user store panda dataframe in an artifact? @Bobgy Can you confirm?",
"Thanks @capri-xiyue for the heads up!\n\nIIUC, @motilalmeher is already writing the data to a storage as an artifact.\n\nFor clarification, the major reason data have to be i... | 2021-06-25T03:44:07 | 2022-03-03T04:05:12 | 2022-03-03T04:05:12 | NONE | null | I am currently working with a large dataset and in my pipeline there are many component. I want to make the process fast. Currently I am passing the data from one component to ther by storing it some storage and passing the path so that I can read the data from other component. Is there any way to pass the Pandas dataframe like passing strings. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5915 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5915/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5915/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5915/events | https://github.com/kubeflow/pipelines/issues/5915 | 929,334,289 | MDU6SXNzdWU5MjkzMzQyODk= | 5,915 | [bug] `from google_cloud_pipeline_components import aiplatform` results in TypeError: 'NoneType' object is not iterable | {
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"/assign @sasha-gitg @SinaChavoshi ",
"I managed to work around this by using python 3.9.6. No idea why that would affect this but it appears to help.",
"This issue was fixed by https://github.com/googleapis/python-aiplatform/pull/494 "
] | 2021-06-24T15:13:30 | 2021-08-03T17:52:59 | 2021-08-03T17:52:59 | NONE | null | When I try to import `aiplatform` from `google_cloud_pipeline_components`, I get a `TypeError`, and the import fails.
### What steps did you take
To reproduce the issue:
1. Create a new virtual environment (Python version 3.6.9):
`python3 -m virtualenv gcp_aip`
2. Activate the environment:
`source gcp_aip/bin/activate`
3. Install per instructions:
`pip install -U google-cloud-pipeline-components`
4. Start a new Python session and try to import aiplatform:
`python3 -c 'from google_cloud_pipeline_components import aiplatform'`
### What happened:
I get a `TypeError`, specifically the following:
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/ubuntu/.env/gcp_aip/lib/python3.6/site-packages/google_cloud_pipeline_components/aiplatform/__init__.py", line 68, in <module>
aiplatform_sdk.ImageDataset.export_data,
File "/home/ubuntu/.env/gcp_aip/lib/python3.6/site-packages/google_cloud_pipeline_components/aiplatform/utils.py", line 465, in convert_method_to_component
output_type
TypeError: 'NoneType' object is not iterable
```
### What did you expect to happen:
I expected the import to succeed.
### Environment:
Ubuntu 18.04 / Python 3.6.9.
I suspect this may have something to do with the version of Python. The error above happens with Python 3.6.9. I tried the same process with Python 3.8.5, and I did not get the above error.
### Labels
/area components
---
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Hi @raghavendramanandi, parameters are by design public.\r\nYou should use secret solutions for this, e.g. Kubernetes secret.",
"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... | 2021-06-24T09:44:54 | 2022-03-03T03:05:32 | 2022-03-03T03:05:32 | NONE | null | While running the pipeline, we can specify parameters. Is there a way to mask or show **** for those parameter values. Just like how a password field would look like. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5912 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5912/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5912/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5912/events | https://github.com/kubeflow/pipelines/issues/5912 | 928,824,724 | MDU6SXNzdWU5Mjg4MjQ3MjQ= | 5,912 | [sdk compiler] Compile is successed, but VertexPipelines validation is failed | {
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"```\r\n \"pipelineInfo\": {\r\n \"name\": \"test\"\r\n },\r\n \"root\": {\r\n \"dag\": {\r\n \"tasks\": {\r\n \"for-loop-1\": {\r\n \"componentRef\": {\r\n \"name\": \"comp-for-loop-1\"\r\n },\r\n \"dependentTasks\": [\r\n ... | 2021-06-24T04:03:35 | 2022-03-03T02:05:37 | null | CONTRIBUTOR | null | ### Environment
* KFP version: GCP VertexAi Pipelines
* KFP SDK version: build sdk on master branch
### Steps to reproduce
When I try to loop through the following python function to get the output, the validation of Vertex Pipelines fails.
```python
import kfp.components as comp
from kfp.v2.dsl import component
@component
def print_op(message: str):
"""Prints a message."""
print(message)
def get_parallel_offsets(
rows_count: int,
parallel_num: int,
) -> NamedTuple(
"Offsets",
[("offsets", "Offsets")],
):
"""
Get the offset of each parallel (from the number of rows and the number of parallels in the table.)
Parameters
----------
rows_count: int
number of bigquery table's rows
parallel_num: int
number of parallels
"""
from collections import namedtuple
import math
import json
if rows_count % parallel_num == 0:
offset_step = limit = int(rows_count / parallel_num)
else:
offset_step = limit = math.ceil(rows_count / parallel_num)
# NOTE: When using `json.dump`, if a number with a large number of digits is included, the number will be converted to Scientific Notation format, so convert it to a string type once.
offsets = [
{"index": str(index), "offset": str(offset), "upper_bounds": str(offset+limit)}
for index, offset in enumerate(range(0, rows_count, offset_step))
]
output = namedtuple("Offsets", ["offsets"])
return output(json.dumps(offsets))
@dsl.pipeline(
name="test",
)
def test(
parallel_num: int=10,
) -> None:
get_parallel_offsets_op = comp.create_component_from_func(
func=get_parallel_offsets, base_image="python:alpine"
)
get_parallel_offsets_task = get_parallel_offsets_op(
#get_count_rows_task.output, parallel_num
3000, parallel_num
)
with dsl.ParallelFor(
get_parallel_offsets_task.outputs["offsets"]
) as offset:
print_op(offset.offset)
print_op(offset.index)
```
error message
```
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/kfp-1.6.3-py3.7.egg/kfp/v2/google/client/client.py", line 344, in create_run_from_job_spec
job_id=job_id,
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/kfp-1.6.3-py3.7.egg/kfp/v2/google/client/client.py", line 228, in _submit_job
response = request.execute()
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/googleapiclient/_helpers.py", line 134, in positional_wrapper
return wrapped(*args, **kwargs)
File "/Users/takuya.hirata/.pyenv/versions/3.7.1rc1/lib/python3.7/site-packages/googleapiclient/http.py", line 915, in execute
raise HttpError(resp, content, uri=self.uri)
googleapiclient.errors.HttpError: <HttpError 400 when requesting https://asia-east1-aiplatform.googleapis.com/v1beta1/projects/zozo-mlops-dev/locations/asia-east1/pipelineJobs?pipelineJobId=test-20210624125423&alt=json returned "Iterator items parameter 'pipelineparam--get-parallel-offsets-offsets' cannot be found from the inputs of the task 'for-loop-1'.". Details: "Iterator items parameter 'pipelineparam--get-parallel-offsets-offsets' cannot be found from the inputs of the task 'for-loop-1'.">
```
<details>
<summary>compiled json</summary>
```json
{
"pipelineSpec": {
"components": {
"comp-for-loop-1": {
"dag": {
"tasks": {
"print-op": {
"componentRef": {
"name": "comp-print-op"
},
"inputs": {
"parameters": {
"message": {
"componentInputParameter": "pipelineparam--get-parallel-offsets-offsets-loop-item",
"parameterExpressionSelector": "parseJson(string_value)[\"upper_bounds\"]"
}
}
},
"taskInfo": {
"name": "print-op"
}
}
}
},
"inputDefinitions": {
"artifacts": {
"pipelineparam--get-parallel-offsets-offsets": {
"artifactType": {
"schemaTitle": "system.Artifact"
}
}
},
"parameters": {
"pipelineparam--get-parallel-offsets-offsets-loop-item": {
"type": "STRING"
}
}
}
},
"comp-get-parallel-offsets": {
"executorLabel": "exec-get-parallel-offsets",
"inputDefinitions": {
"parameters": {
"parallel_num": {
"type": "INT"
},
"rows_count": {
"type": "INT"
}
}
},
"outputDefinitions": {
"artifacts": {
"offsets": {
"artifactType": {
"schemaTitle": "system.Artifact"
}
}
}
}
},
"comp-print-op": {
"executorLabel": "exec-print-op",
"inputDefinitions": {
"parameters": {
"message": {
"type": "STRING"
}
}
}
}
},
"deploymentSpec": {
"executors": {
"exec-get-parallel-offsets": {
"container": {
"args": [
"--rows-count",
"{{$.inputs.parameters['rows_count']}}",
"--parallel-num",
"{{$.inputs.parameters['parallel_num']}}",
"----output-paths",
"{{$.outputs.artifacts['offsets'].path}}"
],
"command": [
"sh",
"-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
"def get_parallel_offsets(\n rows_count,\n parallel_num,\n):\n \"\"\"\n Get the offset of each parallel (from the number of rows and the number of parallels in the table.)\n\n Parameters\n ----------\n rows_count: int\n number of bigquery table's rows\n parallel_num: int\n number of parallels\n \"\"\"\n from collections import namedtuple\n import math\n import json\n\n if rows_count % parallel_num == 0:\n offset_step = limit = int(rows_count / parallel_num)\n else:\n offset_step = limit = math.ceil(rows_count / parallel_num)\n\n # NOTE: When using `json.dump`, if a number with a large number of digits is included, the number will be converted to Scientific Notation format, so convert it to a string type once.\n offsets = [\n {\"index\": str(index), \"offset\": str(offset), \"upper_bounds\": str(offset+limit)}\n for index, offset in enumerate(range(0, rows_count, offset_step))\n ]\n output = namedtuple(\"Offsets\", [\"offsets\"])\n return output(json.dumps(offsets))\n\nimport argparse\n_parser = argparse.ArgumentParser(prog='Get parallel offsets', description='Get the offset of each parallel (from the number of rows and the number of parallels in the table.)')\n_parser.add_argument(\"--rows-count\", dest=\"rows_count\", type=int, required=True, default=argparse.SUPPRESS)\n_parser.add_argument(\"--parallel-num\", dest=\"parallel_num\", 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 = get_parallel_offsets(**_parsed_args)\n\n_output_serializers = [\n str,\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:alpine"
}
},
"exec-print-op": {
"container": {
"args": [
"--executor_input",
"{{$}}",
"--function_to_execute",
"print_op",
"--message-output-path",
"{{$.inputs.parameters['message']}}"
],
"command": [
"sh",
"-ec",
"program_path=$(mktemp)\nprintf \"%s\" \"$0\" > \"$program_path\"\npython3 -u \"$program_path\" \"$@\"\n",
"\nimport json\nimport inspect\nfrom typing import *\n\n# Copyright 2021 The Kubeflow Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Classes for input/output types in KFP SDK.\n\nThese are only compatible with v2 Pipelines.\n\"\"\"\n\nimport os\nfrom typing import Dict, Generic, List, Optional, Type, TypeVar, Union\n\n\n_GCS_LOCAL_MOUNT_PREFIX = '/gcs/'\n_MINIO_LOCAL_MOUNT_PREFIX = '/minio/'\n_S3_LOCAL_MOUNT_PREFIX = '/s3/'\n\n\nclass Artifact(object):\n \"\"\"Generic Artifact class.\n\n This class is meant to represent the metadata around an input or output\n machine-learning Artifact. Artifacts have URIs, which can either be a location\n on disk (or Cloud storage) or some other resource identifier such as\n an API resource name.\n\n Artifacts carry a `metadata` field, which is a dictionary for storing\n metadata related to this artifact.\n \"\"\"\n TYPE_NAME = 'system.Artifact'\n\n def __init__(self,\n name: Optional[str] = None,\n uri: Optional[str] = None,\n metadata: Optional[Dict] = None):\n \"\"\"Initializes the Artifact with the given name, URI and metadata.\"\"\"\n self.uri = uri or ''\n self.name = name or ''\n self.metadata = metadata or {}\n\n @property\n def path(self):\n return self._get_path()\n\n @path.setter\n def path(self, path):\n self._set_path(path)\n\n def _get_path(self) -> Optional[str]:\n if self.uri.startswith('gs://'):\n return _GCS_LOCAL_MOUNT_PREFIX + self.uri[len('gs://'):]\n elif self.uri.startswith('minio://'):\n return _MINIO_LOCAL_MOUNT_PREFIX + self.uri[len('minio://'):]\n elif self.uri.startswith('s3://'):\n return _S3_LOCAL_MOUNT_PREFIX + self.uri[len('s3://'):]\n return None\n\n def _set_path(self, path):\n if path.startswith(_GCS_LOCAL_MOUNT_PREFIX):\n path = 'gs://' + path[len(_GCS_LOCAL_MOUNT_PREFIX):]\n elif path.startswith(_MINIO_LOCAL_MOUNT_PREFIX):\n path = 'minio://' + path[len(_MINIO_LOCAL_MOUNT_PREFIX):]\n elif path.startswith(_S3_LOCAL_MOUNT_PREFIX):\n path = 's3://' + path[len(_S3_LOCAL_MOUNT_PREFIX):]\n self.uri = path\n\n\nclass Model(Artifact):\n \"\"\"An artifact representing an ML Model.\"\"\"\n TYPE_NAME = 'system.Model'\n\n def __init__(self,\n name: Optional[str] = None,\n uri: Optional[str] = None,\n metadata: Optional[Dict] = None):\n super().__init__(uri=uri, name=name, metadata=metadata)\n\n @property\n def framework(self) -> str:\n return self._get_framework()\n\n def _get_framework(self) -> str:\n return self.metadata.get('framework', '')\n\n @framework.setter\n def framework(self, framework: str):\n self._set_framework(framework)\n\n def _set_framework(self, framework: str):\n self.metadata['framework'] = framework\n\n\nclass Dataset(Artifact):\n \"\"\"An artifact representing an ML Dataset.\"\"\"\n TYPE_NAME = 'system.Dataset'\n\n def __init__(self,\n name: Optional[str] = None,\n uri: Optional[str] = None,\n metadata: Optional[Dict] = None):\n super().__init__(uri=uri, name=name, metadata=metadata)\n\n\nclass Metrics(Artifact):\n \"\"\"Represent a simple base Artifact type to store key-value scalar metrics.\"\"\"\n TYPE_NAME = 'system.Metrics'\n\n def __init__(self,\n name: Optional[str] = None,\n uri: Optional[str] = None,\n metadata: Optional[Dict] = None):\n super().__init__(uri=uri, name=name, metadata=metadata)\n\n def log_metric(self, metric: str, value: float):\n \"\"\"Sets a custom scalar metric.\n\n Args:\n metric: Metric key\n value: Value of the metric.\n \"\"\"\n self.metadata[metric] = value\n\n\nclass ClassificationMetrics(Artifact):\n \"\"\"Represents Artifact class to store Classification Metrics.\"\"\"\n TYPE_NAME = 'system.ClassificationMetrics'\n\n def __init__(self,\n name: Optional[str] = None,\n uri: Optional[str] = None,\n metadata: Optional[Dict] = None):\n super().__init__(uri=uri, name=name, metadata=metadata)\n\n def log_roc_data_point(self, fpr: float, tpr: float, threshold: float):\n \"\"\"Logs a single data point in the ROC Curve.\n\n Args:\n fpr: False positive rate value of the data point.\n tpr: True positive rate value of the data point.\n threshold: Threshold value for the data point.\n \"\"\"\n\n roc_reading = {\n 'confidenceThreshold': threshold,\n 'recall': tpr,\n 'falsePositiveRate': fpr\n }\n if 'confidenceMetrics' not in self.metadata.keys():\n self.metadata['confidenceMetrics'] = []\n\n self.metadata['confidenceMetrics'].append(roc_reading)\n\n def log_roc_curve(self, fpr: List[float], tpr: List[float],\n threshold: List[float]):\n \"\"\"Logs an ROC curve.\n\n The list length of fpr, tpr and threshold must be the same.\n\n Args:\n fpr: List of false positive rate values.\n tpr: List of true positive rate values.\n threshold: List of threshold values.\n \"\"\"\n if len(fpr) != len(tpr) or len(fpr) != len(threshold) or len(tpr) != len(\n threshold):\n raise ValueError('Length of fpr, tpr and threshold must be the same. '\n 'Got lengths {}, {} and {} respectively.'.format(\n len(fpr), len(tpr), len(threshold)))\n\n for i in range(len(fpr)):\n self.log_roc_data_point(fpr=fpr[i], tpr=tpr[i], threshold=threshold[i])\n\n def set_confusion_matrix_categories(self, categories: List[str]):\n \"\"\"Stores confusion matrix categories.\n\n Args:\n categories: List of strings specifying the categories.\n \"\"\"\n\n self._categories = []\n annotation_specs = []\n for category in categories:\n annotation_spec = {'displayName': category}\n self._categories.append(category)\n annotation_specs.append(annotation_spec)\n\n self._matrix = []\n for row in range(len(self._categories)):\n self._matrix.append({'row': [0] * len(self._categories)})\n\n self._confusion_matrix = {}\n self._confusion_matrix['annotationSpecs'] = annotation_specs\n self._confusion_matrix['rows'] = self._matrix\n self.metadata['confusionMatrix'] = self._confusion_matrix\n\n def log_confusion_matrix_row(self, row_category: str, row: List[float]):\n \"\"\"Logs a confusion matrix row.\n\n Args:\n row_category: Category to which the row belongs.\n row: List of integers specifying the values for the row.\n\n Raises:\n ValueError: If row_category is not in the list of categories\n set in set_categories call.\n \"\"\"\n if row_category not in self._categories:\n raise ValueError('Invalid category: {} passed. Expected one of: {}'.\\\n format(row_category, self._categories))\n\n if len(row) != len(self._categories):\n raise ValueError('Invalid row. Expected size: {} got: {}'.\\\n format(len(self._categories), len(row)))\n\n self._matrix[self._categories.index(row_category)] = {'row': row}\n self.metadata['confusionMatrix'] = self._confusion_matrix\n\n def log_confusion_matrix_cell(self, row_category: str, col_category: str,\n value: int):\n \"\"\"Logs a cell in the confusion matrix.\n\n Args:\n row_category: String representing the name of the row category.\n col_category: String representing the name of the column category.\n value: Int value of the cell.\n\n Raises:\n ValueError: If row_category or col_category is not in the list of\n categories set in set_categories.\n \"\"\"\n if row_category not in self._categories:\n raise ValueError('Invalid category: {} passed. Expected one of: {}'.\\\n format(row_category, self._categories))\n\n if col_category not in self._categories:\n raise ValueError('Invalid category: {} passed. Expected one of: {}'.\\\n format(row_category, self._categories))\n\n self._matrix[self._categories.index(row_category)]['row'][\n self._categories.index(col_category)] = value\n self.metadata['confusionMatrix'] = self._confusion_matrix\n\n def log_confusion_matrix(self, categories: List[str],\n matrix: List[List[int]]):\n \"\"\"Logs a confusion matrix.\n\n Args:\n categories: List of the category names.\n matrix: Complete confusion matrix.\n\n Raises:\n ValueError: Length of categories does not match number of rows or columns.\n \"\"\"\n self.set_confusion_matrix_categories(categories)\n\n if len(matrix) != len(categories):\n raise ValueError('Invalid matrix: {} passed for categories: {}'.\\\n format(matrix, categories))\n\n for index in range(len(categories)):\n if len(matrix[index]) != len(categories):\n raise ValueError('Invalid matrix: {} passed for categories: {}'.\\\n format(matrix, categories))\n\n self.log_confusion_matrix_row(categories[index], matrix[index])\n\n self.metadata['confusionMatrix'] = self._confusion_matrix\n\n\nclass SlicedClassificationMetrics(Artifact):\n \"\"\"Metrics class representing Sliced Classification Metrics.\n\n Similar to ClassificationMetrics clients using this class are expected to use\n log methods of the class to log metrics with the difference being each log\n method takes a slice to associate the ClassificationMetrics.\n\n \"\"\"\n\n TYPE_NAME = 'system.SlicedClassificationMetrics'\n\n def __init__(self,\n name: Optional[str] = None,\n uri: Optional[str] = None,\n metadata: Optional[Dict] = None):\n super().__init__(uri=uri, name=name, metadata=metadata)\n\n def _upsert_classification_metrics_for_slice(self, slice: str):\n \"\"\"Upserts the classification metrics instance for a slice.\"\"\"\n if slice not in self._sliced_metrics:\n self._sliced_metrics[slice] = ClassificationMetrics()\n\n def _update_metadata(self, slice: str):\n \"\"\"Updates metadata to adhere to the metrics schema.\"\"\"\n self.metadata = {}\n self.metadata['evaluationSlices'] = []\n for slice in self._sliced_metrics.keys():\n slice_metrics = {\n 'slice': slice,\n 'sliceClassificationMetrics': self._sliced_metrics[slice].metadata\n }\n self.metadata['evaluationSlices'].append(slice_metrics)\n\n def log_roc_reading(self, slice: str, threshold: float, tpr: float,\n fpr: float):\n \"\"\"Logs a single data point in the ROC Curve of a slice.\n\n Args:\n slice: String representing slice label.\n threshold: Thresold value for the data point.\n tpr: True positive rate value of the data point.\n fpr: False positive rate value of the data point.\n \"\"\"\n\n self._upsert_classification_metrics_for_slice(slice)\n self._sliced_metrics[slice].log_roc_reading(threshold, tpr, fpr)\n self._update_metadata(slice)\n\n def load_roc_readings(self, slice: str, readings: List[List[float]]):\n \"\"\"Supports bulk loading ROC Curve readings for a slice.\n\n Args:\n slice: String representing slice label.\n readings: A 2-D list providing ROC Curve data points.\n The expected order of the data points is: threshold,\n true_positive_rate, false_positive_rate.\n \"\"\"\n self._upsert_classification_metrics_for_slice(slice)\n self._sliced_metrics[slice].load_roc_readings(readings)\n self._update_metadata(slice)\n\n def set_confusion_matrix_categories(self, slice: str, categories: List[str]):\n \"\"\"Stores confusion matrix categories for a slice..\n\n Categories are stored in the internal metrics_utils.ConfusionMatrix\n instance of the slice.\n\n Args:\n slice: String representing slice label.\n categories: List of strings specifying the categories.\n \"\"\"\n self._upsert_classification_metrics_for_slice(slice)\n self._sliced_metrics[slice].set_confusion_matrix_categories(categories)\n self._update_metadata(slice)\n\n def log_confusion_matrix_row(self, slice: str, row_category: str,\n row: List[int]):\n \"\"\"Logs a confusion matrix row for a slice.\n\n Row is updated on the internal metrics_utils.ConfusionMatrix\n instance of the slice.\n\n Args:\n slice: String representing slice label.\n row_category: Category to which the row belongs.\n row: List of integers specifying the values for the row.\n \"\"\"\n self._upsert_classification_metrics_for_slice(slice)\n self._sliced_metrics[slice].log_confusion_matrix_row(row_category, row)\n self._update_metadata(slice)\n\n def log_confusion_matrix_cell(self, slice: str, row_category: str,\n col_category: str, value: int):\n \"\"\"Logs a confusion matrix cell for a slice..\n\n Cell is updated on the internal metrics_utils.ConfusionMatrix\n instance of the slice.\n\n Args:\n slice: String representing slice label.\n row_category: String representing the name of the row category.\n col_category: String representing the name of the column category.\n value: Int value of the cell.\n \"\"\"\n self._upsert_classification_metrics_for_slice(slice)\n self._sliced_metrics[slice].log_confusion_matrix_cell(\n row_category, col_category, value)\n self._update_metadata(slice)\n\n def load_confusion_matrix(self, slice: str, categories: List[str],\n matrix: List[List[int]]):\n \"\"\"Supports bulk loading the whole confusion matrix for a slice.\n\n Args:\n slice: String representing slice label.\n categories: List of the category names.\n matrix: Complete confusion matrix.\n \"\"\"\n self._upsert_classification_metrics_for_slice(slice)\n self._sliced_metrics[slice].log_confusion_matrix_cell(categories, matrix)\n self._update_metadata(slice)\n\n\nT = TypeVar('T')\n\n\nclass InputAnnotation():\n \"\"\"Marker type for input artifacts.\"\"\"\n pass\n\n\n\nclass OutputAnnotation():\n \"\"\"Marker type for output artifacts.\"\"\"\n pass\n\n\n# TODO: Use typing.Annotated instead of this hack.\n# With typing.Annotated (Python 3.9+ or typing_extensions package), the\n# following would look like:\n# Input = typing.Annotated[T, InputAnnotation]\n# Output = typing.Annotated[T, OutputAnnotation]\n\n\n# Input represents an Input artifact of type T.\nInput = Union[T, InputAnnotation]\n\n# Output represents an Output artifact of type T.\nOutput = Union[T, OutputAnnotation]\n\n\ndef is_artifact_annotation(typ) -> bool:\n if hasattr(typ, '_subs_tree'): # Python 3.6\n subs_tree = typ._subs_tree()\n return len(subs_tree) == 3 and subs_tree[0] == Union and subs_tree[2] in [InputAnnotation, OutputAnnotation]\n\n if not hasattr(typ, '__origin__'):\n return False\n\n\n if typ.__origin__ != Union and type(typ.__origin__) != type(Union):\n return False\n\n\n if not hasattr(typ, '__args__') or len(typ.__args__) != 2:\n return False\n\n if typ.__args__[1] not in [InputAnnotation, OutputAnnotation]:\n return False\n\n return True\n\ndef is_input_artifact(typ) -> bool:\n \"\"\"Returns True if typ is of type Input[T].\"\"\"\n if not is_artifact_annotation(typ):\n return False\n\n if hasattr(typ, '_subs_tree'): # Python 3.6\n subs_tree = typ._subs_tree()\n return len(subs_tree) == 3 and subs_tree[2] == InputAnnotation\n\n return typ.__args__[1] == InputAnnotation\n\ndef is_output_artifact(typ) -> bool:\n \"\"\"Returns True if typ is of type Output[T].\"\"\"\n if not is_artifact_annotation(typ):\n return False\n\n if hasattr(typ, '_subs_tree'): # Python 3.6\n subs_tree = typ._subs_tree()\n return len(subs_tree) == 3 and subs_tree[2] == OutputAnnotation\n\n return typ.__args__[1] == OutputAnnotation\n\ndef get_io_artifact_class(typ):\n if not is_artifact_annotation(typ):\n return None\n if typ == Input or typ == Output:\n return None\n\n if hasattr(typ, '_subs_tree'): # Python 3.6\n subs_tree = typ._subs_tree()\n if len(subs_tree) != 3:\n return None\n return subs_tree[1]\n\n return typ.__args__[0]\n\ndef get_io_artifact_annotation(typ):\n if not is_artifact_annotation(typ):\n return None\n\n if hasattr(typ, '_subs_tree'): # Python 3.6\n subs_tree = typ._subs_tree()\n if len(subs_tree) != 3:\n return None\n return subs_tree[2]\n\n return typ.__args__[1]\n\n\n\n_SCHEMA_TITLE_TO_TYPE: Dict[str, Artifact] = {\n x.TYPE_NAME: x\n for x in [Artifact, Model, Dataset, Metrics, ClassificationMetrics]\n}\n\n\ndef create_runtime_artifact(runtime_artifact: Dict) -> Artifact:\n \"\"\"Creates an Artifact instance from the specified RuntimeArtifact.\n\n Args:\n runtime_artifact: Dictionary representing JSON-encoded RuntimeArtifact.\n \"\"\"\n schema_title = runtime_artifact.get('type', {}).get('schemaTitle', '')\n\n artifact_type = _SCHEMA_TITLE_TO_TYPE.get(schema_title)\n if not artifact_type:\n artifact_type = Artifact\n return artifact_type(\n uri=runtime_artifact.get('uri', ''),\n name=runtime_artifact.get('name', ''),\n metadata=runtime_artifact.get('metadata', {}),\n )\n\nclass InputPath:\n '''When creating component from function, :class:`.InputPath` should be used as function parameter annotation to tell the system to pass the *data file path* to the function instead of passing the actual data.'''\n def __init__(self, type=None):\n self.type = type\n\nclass OutputPath:\n '''When creating component from function, :class:`.OutputPath` should be used as function parameter annotation to tell the system that the function wants to output data by writing it into a file with the given path instead of returning the data from the function.'''\n def __init__(self, type=None):\n self.type = type\n\nclass Executor():\n \"\"\"Executor executes v2-based Python function components.\"\"\"\n\n def __init__(self, executor_input: Dict, function_to_execute: Callable):\n self._func = function_to_execute\n self._input = executor_input\n self._input_artifacts: Dict[str, Artifact] = {}\n self._output_artifacts: Dict[str, Artifact] = {}\n\n for name, artifacts in self._input.get('inputs', {}).get('artifacts',\n {}).items():\n artifacts_list = artifacts.get('artifacts')\n if artifacts_list:\n self._input_artifacts[name] = self._make_input_artifact(\n artifacts_list[0])\n\n for name, artifacts in self._input.get('outputs', {}).get('artifacts',\n {}).items():\n artifacts_list = artifacts.get('artifacts')\n if artifacts_list:\n self._output_artifacts[name] = self._make_output_artifact(\n artifacts_list[0])\n\n self._return_annotation = inspect.signature(self._func).return_annotation\n self._executor_output = {}\n\n @classmethod\n def _make_input_artifact(cls, runtime_artifact: Dict):\n return create_runtime_artifact(runtime_artifact)\n\n @classmethod\n def _make_output_artifact(cls, runtime_artifact: Dict):\n import os\n artifact = create_runtime_artifact(runtime_artifact)\n os.makedirs(os.path.dirname(artifact.path), exist_ok=True)\n return artifact\n\n def _get_input_artifact(self, name: str):\n return self._input_artifacts.get(name)\n\n def _get_output_artifact(self, name: str):\n return self._output_artifacts.get(name)\n\n def _get_input_parameter_value(self, parameter_name: str, parameter_type: Any):\n parameter = self._input.get('inputs', {}).get('parameters',\n {}).get(parameter_name, None)\n if parameter is None:\n return None\n\n if parameter.get('stringValue'):\n if parameter_type == str:\n return parameter['stringValue']\n elif parameter_type == bool:\n # Use `.lower()` so it can also handle 'True' and 'False' (resulted from\n # `str(True)` and `str(False)`, respectively.\n return json.loads(parameter['stringValue'].lower())\n else:\n return json.loads(parameter['stringValue'])\n elif parameter.get('intValue'):\n return int(parameter['intValue'])\n elif parameter.get('doubleValue'):\n return float(parameter['doubleValue'])\n\n def _get_output_parameter_path(self, parameter_name: str):\n parameter_name = self._maybe_strip_path_suffix(parameter_name)\n parameter = self._input.get('outputs',\n {}).get('parameters',\n {}).get(parameter_name, None)\n if parameter is None:\n return None\n\n import os\n path = parameter.get('outputFile', None)\n if path:\n os.makedirs(os.path.dirname(path), exist_ok=True)\n return path\n\n def _get_output_artifact_path(self, artifact_name: str):\n artifact_name = self._maybe_strip_path_suffix(artifact_name)\n output_artifact = self._output_artifacts.get(artifact_name)\n if not output_artifact:\n raise ValueError(\n 'Failed to get output artifact path for artifact name {}'.format(\n artifact_name))\n return output_artifact.path\n\n def _get_input_artifact_path(self, artifact_name: str):\n artifact_name = self._maybe_strip_path_suffix(artifact_name)\n input_artifact = self._input_artifacts.get(artifact_name)\n if not input_artifact:\n raise ValueError(\n 'Failed to get input artifact path for artifact name {}'.format(\n artifact_name))\n return input_artifact.path\n\n def _write_output_parameter_value(self, name: str,\n value: Union[str, int, float, bool, dict,\n list, Dict, List]):\n if type(value) == str:\n output = {'stringValue': value}\n elif type(value) == int:\n output = {'intValue': value}\n elif type(value) == float:\n output = {'doubleValue': value}\n else:\n # For bool, list, dict, List, Dict, json serialize the value.\n output = {'stringValue': json.dumps(value)}\n\n if not self._executor_output.get('parameters'):\n self._executor_output['parameters'] = {}\n\n self._executor_output['parameters'][name] = output\n\n def _write_output_artifact_payload(self, name: str, value: Any):\n path = self._get_output_artifact_path(name)\n with open(path, 'w') as f:\n f.write(str(value))\n\n # TODO: extract to a util\n @classmethod\n def _get_short_type_name(cls, type_name: str) -> str:\n \"\"\"Extracts the short form type name.\n\n This method is used for looking up serializer for a given type.\n\n For example:\n typing.List -> List\n typing.List[int] -> List\n typing.Dict[str, str] -> Dict\n List -> List\n str -> str\n\n Args:\n type_name: The original type name.\n\n Returns:\n The short form type name or the original name if pattern doesn't match.\n \"\"\"\n import re\n match = re.match('(typing\\.)?(?P<type>\\w+)(?:\\[.+\\])?', type_name)\n if match:\n return match.group('type')\n else:\n return type_name\n\n # TODO: merge with type_utils.is_parameter_type\n @classmethod\n def _is_parameter(cls, annotation: Any) -> bool:\n if type(annotation) == type:\n return annotation in [str, int, float, bool, dict, list]\n\n # Annotation could be, for instance `typing.Dict[str, str]`, etc.\n return cls._get_short_type_name(str(annotation)) in ['Dict', 'List']\n\n @classmethod\n def _is_artifact(cls, annotation: Any) -> bool:\n if type(annotation) == type:\n return issubclass(annotation, Artifact)\n return False\n\n @classmethod\n def _is_named_tuple(cls, annotation: Any) -> bool:\n if type(annotation) == type:\n return issubclass(annotation, tuple) and hasattr(\n annotation, '_fields') and hasattr(annotation, '__annotations__')\n return False\n\n def _handle_single_return_value(self, output_name: str, annotation_type: Any,\n return_value: Any):\n if self._is_parameter(annotation_type):\n if type(return_value) != annotation_type:\n raise ValueError(\n 'Function `{}` returned value of type {}; want type {}'.format(\n self._func.__name__, type(return_value), annotation_type))\n self._write_output_parameter_value(output_name, return_value)\n elif self._is_artifact(annotation_type):\n self._write_output_artifact_payload(output_name, return_value)\n else:\n raise RuntimeError(\n 'Unknown return type: {}. Must be one of `str`, `int`, `float`, or a'\n ' subclass of `Artifact`'.format(annotation_type))\n\n def _write_executor_output(self, func_output: Optional[Any] = None):\n if self._output_artifacts:\n self._executor_output['artifacts'] = {}\n\n for name, artifact in self._output_artifacts.items():\n runtime_artifact = {\n 'name': artifact.name,\n 'uri': artifact.uri,\n 'metadata': artifact.metadata,\n }\n artifacts_list = {'artifacts': [runtime_artifact]}\n\n self._executor_output['artifacts'][name] = artifacts_list\n\n if func_output is not None:\n if self._is_parameter(self._return_annotation) or self._is_artifact(\n self._return_annotation):\n # Note: single output is named `Output` in component.yaml.\n self._handle_single_return_value('Output', self._return_annotation,\n func_output)\n elif self._is_named_tuple(self._return_annotation):\n if len(self._return_annotation._fields) != len(func_output):\n raise RuntimeError(\n 'Expected {} return values from function `{}`, got {}'.format(\n len(self._return_annotation._fields), self._func.__name__,\n len(func_output)))\n for i in range(len(self._return_annotation._fields)):\n field = self._return_annotation._fields[i]\n field_type = self._return_annotation.__annotations__[field]\n if type(func_output) == tuple:\n field_value = func_output[i]\n else:\n field_value = getattr(func_output, field)\n self._handle_single_return_value(field, field_type, field_value)\n else:\n raise RuntimeError(\n 'Unknown return type: {}. Must be one of `str`, `int`, `float`, a'\n ' subclass of `Artifact`, or a NamedTuple collection of these types.'\n .format(self._return_annotation))\n\n import os\n os.makedirs(\n os.path.dirname(self._input['outputs']['outputFile']), exist_ok=True)\n with open(self._input['outputs']['outputFile'], 'w') as f:\n f.write(json.dumps(self._executor_output))\n\n def _maybe_strip_path_suffix(self, name) -> str:\n if name.endswith('_path'):\n name = name[0:-len('_path')]\n if name.endswith('_file'):\n name = name[0:-len('_file')]\n return name\n\n def execute(self):\n annotations = inspect.getfullargspec(self._func).annotations\n\n # Function arguments.\n func_kwargs = {}\n\n for k, v in annotations.items():\n if k == 'return':\n continue\n\n if self._is_parameter(v):\n func_kwargs[k] = self._get_input_parameter_value(k, v)\n\n if is_artifact_annotation(v):\n if is_input_artifact(v):\n func_kwargs[k] = self._get_input_artifact(k)\n if is_output_artifact(v):\n func_kwargs[k] = self._get_output_artifact(k)\n\n elif isinstance(v, OutputPath):\n if self._is_parameter(v.type):\n func_kwargs[k] = self._get_output_parameter_path(k)\n else:\n func_kwargs[k] = self._get_output_artifact_path(k)\n elif isinstance(v, InputPath):\n func_kwargs[k] = self._get_input_artifact_path(k)\n\n result = self._func(**func_kwargs)\n self._write_executor_output(result)\n\n\ndef print_op(message: str):\n \"\"\"Prints a message.\"\"\"\n print(message)\n\n\ndef executor_main():\n import argparse\n import json\n\n parser = argparse.ArgumentParser(description='Process some integers.')\n parser.add_argument('--executor_input', type=str)\n parser.add_argument('--function_to_execute', type=str)\n\n args, _ = parser.parse_known_args()\n executor_input = json.loads(args.executor_input)\n function_to_execute = globals()[args.function_to_execute]\n\n executor = Executor(executor_input=executor_input,\n function_to_execute=function_to_execute)\n\n executor.execute()\n\n\nif __name__ == '__main__':\n executor_main()\n"
],
"image": "python:3.7"
}
}
}
},
"pipelineInfo": {
"name": "test"
},
"root": {
"dag": {
"tasks": {
"for-loop-1": {
"componentRef": {
"name": "comp-for-loop-1"
},
"dependentTasks": [
"get-parallel-offsets"
],
"inputs": {
"artifacts": {
"pipelineparam--get-parallel-offsets-offsets": {
"taskOutputArtifact": {
"outputArtifactKey": "offsets",
"producerTask": "get-parallel-offsets"
}
}
}
},
"parameterIterator": {
"itemInput": "pipelineparam--get-parallel-offsets-offsets-loop-item",
"items": {
"inputParameter": "pipelineparam--get-parallel-offsets-offsets"
}
},
"taskInfo": {
"name": "for-loop-1"
}
},
"get-parallel-offsets": {
"componentRef": {
"name": "comp-get-parallel-offsets"
},
"inputs": {
"parameters": {
"parallel_num": {
"componentInputParameter": "parallel_num"
},
"rows_count": {
"runtimeValue": {
"constantValue": {
"intValue": "3000"
}
}
}
}
},
"taskInfo": {
"name": "get-parallel-offsets"
}
}
}
},
"inputDefinitions": {
"parameters": {
"parallel_num": {
"type": "INT"
}
}
}
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.6.4"
},
"runtimeConfig": {
"parameters": {
"parallel_num": {
"intValue": "10"
}
}
}
}
```
</details>
### Expected result
Successful validation of vertex pipelines.
### 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/5910 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5910/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5910/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5910/events | https://github.com/kubeflow/pipelines/issues/5910 | 928,247,917 | MDU6SXNzdWU5MjgyNDc5MTc= | 5,910 | add if __name__ == "__main__" guard for sdk to support multi-processing in components | {
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"/assign @chensun ",
"Can you please describe your scenario?\r\nThe command-line code generated by `create_component_from_func` is only intended to be run as a program.\r\n\r\nAm I understanding you correctly - multiprocessing cannot be used inside python lightweight component since it needs to be able to import ... | 2021-06-23T13:16:26 | 2022-03-03T02:05:24 | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
Add an `if __name__ == "__main__"` guard before calling function wrapped by "create_component_from_func"
### What is the use case or pain point?
An if __main__ guard is needed for python multiprocessing code to run properly.
### Is there a workaround currently?
I have not found a good workaround. It might be possible to determine whether I am in a forked call based on the depth of the call queue in some cases.
---
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"Hi @shalankig . Could you share your code to help us debug even further? From the above log, I think you are referring to a kfp experiment that doesn't exist or you might have missed to pass the namespace.",
"Hi @shalankig, did you specify namespace for your call? In KFP multi-user mode, namespace is required fo... | 2021-06-23T04:53:27 | 2022-03-03T03:05:33 | 2022-03-03T03:05:33 | NONE | null | I have successfully deployed Kubeflow on a private GKE Cluster using https://github.com/kubeflow/manifests#install-with-a-single-command
GKE Version: 1.17.17-gke.9100
Kubeflow Version: 1.3
Workload Identity Enabled: Yes
Kfp Version: 1.5.0
Error while creating a kubeflow pipeline:
`Traceback (most recent call last): File "/path/to/pipeline.py" line 1073, in run_pipeline experiment_id = client.get_experiment(experiment_name=experiment_name).id File "/usr/local /lib/python3.7/dist-packages/kfp/client.py", line 359 in get_experiment list_experiments_response = self.list_experiments(page_size=100, page_token=next_page_token, namespace=namespace).............ApiException(http_resp=r) kfp_server_spi.exceptions.ApiException: (400) `
which further leads to the below error logs:
`HTTP: response body: {"error":"Validate experiment failed: Invalid input error: Invalid resource references for experiment. Expect one namespace type with owner relationship. Got []","code":3, "message":"Validate experiment failed: Invalid input error: Invalid resource references for experiment. Expect one namespace type with owner relationship. Got []", "details":[{"@type":"type.googleapis.com/api.Error","error_message":"Invalid resource references for experiment. Expect one namespace type with owner relationship. Got []","error_details":"Validate experiment failed: Invalid input error: Invalid resource references for experiment. Expect one namespace type with owner relationship. Got []"` | {
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"/cc @aronchick \r\nwho should be the best contact for Azure distribution?",
"I am also interested in this issue as I am also in the same boat, but installed on AWS. I don't think this issue has anything to do with Azure specifically. The best that I can find from the documentation about this \"single-user mode\"... | 2021-06-23T04:35:00 | 2022-03-03T00:05:10 | null | NONE | null | ### What steps did you take
I deployed Kubeflow to Azure Kubernetes Service (AKS) with OIDC Authentication as described in the [documentation](https://www.kubeflow.org/docs/distributions/azure/authentication-oidc).
I instantiated a `kfp.Client()` like so:
```python
kubeflow_client = TektonClient(
host=f'{KUBEFLOW_PUBLIC_ENDPOINT_URL}/pipeline',
cookies=f'authservice_session={SESSION_COOKIE}',
ssl_ca_cert="cacert.pem",
namespace="laura-dang"
)
```
Then I called this function next:
```python
kubeflow_client.create_run_from_pipeline_func(
pipeline_function,
experiment_name=experiment_name,
arguments={'bucket_name': bucket_name,
'experiment_id': experiment_id,
'python_version': python_version,
'model_info': model_info},
namespace="laura-dang"
)
```
### What happened:
This error message outputted after running `kubeflow_client.create_run...` as described above:
```
---------------------------------------------------------------------------
ApiException Traceback (most recent call last)
<ipython-input-13-11f63c93dd6d> in <module>
12 python_version=notebook_config.python_version,
13 model_info=json.dumps(model_info),
---> 14 namespace=notebook_config.namespace)
<ipython-input-12-98a442d3e373> in run_pipeline(input_train_path, input_test_path, pipeline, experiment_name, python_version, model_info, namespace)
53 'python_version': python_version,
54 'model_info': model_info},
---> 55 namespace=namespace)
56 print("done")
~/.local/lib/python3.6/site-packages/kfp_tekton/_client.py in create_run_from_pipeline_func(self, pipeline_func, arguments, run_name, experiment_name, pipeline_conf, namespace)
59 TektonCompiler().compile(pipeline_func, pipeline_package_path, pipeline_conf=pipeline_conf)
60 return self.create_run_from_pipeline_package(pipeline_package_path, arguments,
---> 61 run_name, experiment_name, namespace)
62 finally:
63 os.remove(pipeline_package_path)
~/.local/lib/python3.6/site-packages/kfp/_client.py in create_run_from_pipeline_package(self, pipeline_file, arguments, run_name, experiment_name, namespace)
738 datetime.datetime.now().strftime(
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)
~/.local/lib/python3.6/site-packages/kfp/_client.py in create_experiment(self, name, description, namespace)
343 experiment = None
344 try:
--> 345 experiment = self.get_experiment(experiment_name=name, namespace=namespace)
346 except ValueError as error:
347 # Ignore error if the experiment does not exist.
~/.local/lib/python3.6/site-packages/kfp/_client.py in get_experiment(self, experiment_id, experiment_name, namespace)
447 next_page_token = ''
448 while next_page_token is not None:
--> 449 list_experiments_response = self.list_experiments(page_size=100, page_token=next_page_token, namespace=namespace)
450 next_page_token = list_experiments_response.next_page_token
451 for experiment in list_experiments_response.experiments or []:
~/.local/lib/python3.6/site-packages/kfp/_client.py in list_experiments(self, page_token, page_size, sort_by, namespace)
419 sort_by=sort_by,
420 resource_reference_key_type=kfp_server_api.models.api_resource_type.ApiResourceType.NAMESPACE,
--> 421 resource_reference_key_id=namespace)
422 return response
423
~/.local/lib/python3.6/site-packages/kfp_server_api/api/experiment_service_api.py in list_experiment(self, **kwargs)
565 """
566 kwargs['_return_http_data_only'] = True
--> 567 return self.list_experiment_with_http_info(**kwargs) # noqa: E501
568
569 def list_experiment_with_http_info(self, **kwargs): # noqa: E501
~/.local/lib/python3.6/site-packages/kfp_server_api/api/experiment_service_api.py in list_experiment_with_http_info(self, **kwargs)
680 _preload_content=local_var_params.get('_preload_content', True),
681 _request_timeout=local_var_params.get('_request_timeout'),
--> 682 collection_formats=collection_formats)
683
684 def unarchive_experiment(self, id, **kwargs): # noqa: E501
~/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py in call_api(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host)
367 response_type, auth_settings,
368 _return_http_data_only, collection_formats,
--> 369 _preload_content, _request_timeout, _host)
370
371 return self.pool.apply_async(self.__call_api, (resource_path,
~/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py in __call_api(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host)
186 except ApiException as e:
187 e.body = e.body.decode('utf-8') if six.PY3 else e.body
--> 188 raise e
189
190 content_type = response_data.getheader('content-type')
~/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py in __call_api(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host)
183 post_params=post_params, body=body,
184 _preload_content=_preload_content,
--> 185 _request_timeout=_request_timeout)
186 except ApiException as e:
187 e.body = e.body.decode('utf-8') if six.PY3 else e.body
~/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py in request(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout)
391 _preload_content=_preload_content,
392 _request_timeout=_request_timeout,
--> 393 headers=headers)
394 elif method == "HEAD":
395 return self.rest_client.HEAD(url,
~/.local/lib/python3.6/site-packages/kfp_server_api/rest.py in GET(self, url, headers, query_params, _preload_content, _request_timeout)
232 _preload_content=_preload_content,
233 _request_timeout=_request_timeout,
--> 234 query_params=query_params)
235
236 def HEAD(self, url, headers=None, query_params=None, _preload_content=True,
~/.local/lib/python3.6/site-packages/kfp_server_api/rest.py in request(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout)
222
223 if not 200 <= r.status <= 299:
--> 224 raise ApiException(http_resp=r)
225
226 return r
ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'x-powered-by': 'Express', 'content-type': 'application/json', 'trailer': 'Grpc-Trailer-Content-Type', 'date': 'Wed, 23 Jun 2021 04:13:24 GMT', 'x-envoy-upstream-service-time': '9', 'server': 'istio-envoy', 'transfer-encoding': 'chunked'})
HTTP response body: {"error":"Invalid input error: In single-user mode, ListExperiment cannot filter by namespace.","message":"Invalid input error: In single-user mode, ListExperiment cannot filter by namespace.","code":3,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"In single-user mode, ListExperiment cannot filter by namespace.","error_details":"Invalid input error: In single-user mode, ListExperiment cannot filter by namespace."}]}
```
### What did you expect to happen:
I expected the experiment to run as normal in **multi-user mode**, not single-user mode since I deployed with OIDC Authentication and I was able to login and make a notebook server in my own namespace (`laura-dang`).
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
On Azure with OIDC Authentication as mentioned above
* KFP version:
1.04
* KFP SDK version:
```
kfp 1.4.0
kfp-pipeline-spec 0.1.6
kfp-server-api 1.4.1
kfp-tekton 0.7.0
```
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/5904 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5904/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5904/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5904/events | https://github.com/kubeflow/pipelines/issues/5904 | 927,167,937 | MDU6SXNzdWU5MjcxNjc5Mzc= | 5,904 | [feature] Dataflow flex template component | {
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"node_id": "MDU6TGFiZWwxMjg5NTg... | closed | false | null | [] | null | [
"After creating the issue and attempting to open a PR to fix it I saw someone else already added this feature."
] | 2021-06-22T12:14:37 | 2021-06-22T12:22:23 | 2021-06-22T12:22:23 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
/area components
### What feature would you like to see?
In addition to launching Dataflow jobs from Python and classic templates it would be useful to add an additional component for launching Dataflow flex templates.
### What is the use case or pain point?
Dataflow flex templates have some advantages over classic templates. Most importantly they allow for dynamically changing the pipeline execution graph at runtime by mitigating the need for ValueProvider arguments.
### Is there a workaround currently?
There is currently no support for Dataflow flex templates within Kubeflow Pipelines.
---
<!-- 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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