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https://api.github.com/repos/kubeflow/pipelines/issues/5427 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5427/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5427/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5427/events | https://github.com/kubeflow/pipelines/issues/5427 | 850,290,167 | MDU6SXNzdWU4NTAyOTAxNjc= | 5,427 | [feature] Support other gcp project's gcr images to be pulled by service account with workload identity | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | closed | false | null | [] | null | [
"A GKE nodepool has a VM service account. This is used to pull images. You need to grant storage permission to this SA. ",
"Thank you for quick response!\r\n\r\nSo, workload identity cannot affect service account to pull images? I also bound the google service account that has enough permissions to some Kubeflow... | 2021-04-05T11:04:11 | 2021-04-09T00:55:54 | 2021-04-09T00:55:54 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Current workload identity supports multiple project for calling normal APIs, but we cannot pull images on other project's container registry even if the google service account has enough storage permissions (ErrImagePull occurs).
I don't know who can handle better (GCP's backend or Kubeflow Pipelines), but I confirmed that we can pull images after calling `gcloud configure-docker`.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
With this supported, we can easily call other GCP project's API on Kubeflow Pipelines easily.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
We can use normal kubernetes secret for imagePullSecret.
---
<!-- 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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"Hi @shrinath-suresh, can you explain the difference between --db and --logdir, are there any references?\n\nKFP doesn't currently support changing the argument, but we can make it configurable.\n\nWhat kind of configuration experience do you prefer? Do all s3 uris need to change the argument?\n\n\nOr we can add an... | 2021-04-04T10:26:33 | 2021-04-16T10:10:01 | 2021-04-16T06:59:38 | CONTRIBUTOR | null | ### What steps did you take
To view the output in tensorboard we have created the `mlpipeline-ui-metadata.json` file with following content (Followed the instructions from - https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#tensorboard )
```
{"outputs": [{"type": "tensorboard", "source": "s3://<S3_path>"}]}
```
to view the training output in tensorboard.
### What happened:
In the visualizations tab, `Start Tensorboard` button is appearing as expected.
Once `Start Tensorboard` is clicked, `Open Tensorboard` is appearing as expected.
But when the `Start Tensorboard` is clicked, an empty dashboard is shown.
### What did you expect to happen:
Tensorboard should be opened as below
### Environment:
<!-- Please fill in those that seem relevant. -->
* KFP version: 1.4
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 0.5.2
### Anything else you would like to add:
To test the above behavious in local machine, we tried running
```
tensorboard --logdir s3://<S3_path>
```
Tensorboard is showing an empty dashboard.
However, the dashboard is working fine with the db argument as below
```
tensorboard --db s3://<S3_path>
```
From the https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#tensorboard i couldnt figure out a way to change from `logdir` to `db`. Or is there any other way to achieve this in kubeflow `mlpipeline-ui-metadata.json` ?
### 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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"From PR https://github.com/kubeflow/pipelines/pull/5424",
"Do you know what this error comes from? These images should be on google container registry as I understand, are there any rate limiting? ",
"Let me elaborate, the above error occurs when we are building KFP docker images, we need to pull some common i... | 2021-04-03T14:52:23 | 2022-04-18T17:27:41 | 2022-04-18T17:27:41 | CONTRIBUTOR | null | See https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5424/kubeflow-pipeline-e2e-test/1378351440040300544#1:build-log.txt%3A1308
> Step #1 - "scheduledworkflow": Get https://registry-1.docker.io/v2/: net/http: request canceled (Client.Timeout exceeded while awaiting headers)
The same error message happens randomly during different image build process. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5423 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5423/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5423/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5423/events | https://github.com/kubeflow/pipelines/issues/5423 | 849,656,849 | MDU6SXNzdWU4NDk2NTY4NDk= | 5,423 | [bug] api server panics on workflow parameter without value | {
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"/assign",
"The failing line was recently changed when upgrading argo in https://github.com/kubeflow/pipelines/pull/5041, it seems we need to check nil pointer there.\r\nhttps://github.com/kubeflow/pipelines/blob/14b447a07c975a21b7b525d49f6676058f020cd8/backend/src/common/util/workflow.go#L54\r\n/cc @NikeNano ",
... | 2021-04-03T13:20:03 | 2021-04-04T01:27:22 | 2021-04-04T01:27:22 | CONTRIBUTOR | null | ### What steps did you take
<!-- A clear and concise description of what the bug is.-->
1. create a run with a parameter with no values
### What happened:
KFP api server panics with the following stacktrace:
```
I0403 13:01:11.747485 7 interceptor.go:29] /api.RunService/CreateRun handler starting
panic: runtime error: invalid memory address or nil pointer dereference
[signal SIGSEGV: segmentation violation code=0x1 addr=0x0 pc=0x15e7999]
goroutine 2100 [running]:
github.com/kubeflow/pipelines/backend/src/common/util.(*Workflow).OverrideParameters(0xc0000c2e28, 0xc000ebf4a0)
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/workflow.go:54 +0x2f9
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreateRun(0xc000326ea0, 0xc0001e8fc0, 0x0, 0x0, 0xc00034c1a0)
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:362 +0x2ca
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*RunServer).CreateRun(0xc0000aa6f0, 0x1fb0360, 0xc000ebf0e0, 0xc000ebf110, 0xc0000aa6f0, 0x2d58e08, 0xc0001793b0)
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/run_server.go:135 +0xa6
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler.func1(0x1fb0360, 0xc000ebf0e0, 0x1b97b40, 0xc000ebf110, 0xc000641ac8, 0x1, 0x1, 0x7f9af189b008)
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:1501 +0x86
main.apiServerInterceptor(0x1fb0360, 0xc000ebf0e0, 0x1b97b40, 0xc000ebf110, 0xc000edb500, 0xc000edb520, 0xc000e78b58, 0x495b68, 0x1b87600, 0xc000ebf0e0)
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30 +0xf7
github.com/kubeflow/pipelines/backend/api/go_client._RunService_CreateRun_Handler(0x1bb5100, 0xc0000aa6f0, 0x1fb0360, 0xc000ebf0e0, 0xc0007c69c0, 0x1d96158, 0x1fb0360, 0xc000ebf0e0, 0xc001278000, 0x2ddac)
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/run.pb.go:1503 +0x14b
google.golang.org/grpc.(*Server).processUnaryRPC(0xc00059e000, 0x1fd4720, 0xc000206c00, 0xc000416f00, 0xc00055e150, 0x2fa68a0, 0x0, 0x0, 0x0)
/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210 +0x4fd
google.golang.org/grpc.(*Server).handleStream(0xc00059e000, 0x1fd4720, 0xc000206c00, 0xc000416f00, 0x0)
/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533 +0xd57
google.golang.org/grpc.(*Server).serveStreams.func1.2(0xc000290720, 0xc00059e000, 0x1fd4720, 0xc000206c00, 0xc000416f00)
/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871 +0xbb
created by google.golang.org/grpc.(*Server).serveStreams.func1
/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:869 +0x204
```
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.5.0-rc.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. -->
### 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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"/kind question",
"cc @Udiknedormin ",
"@Tomcli Per my understanding:\r\n\r\n* running an op after condition group should run whenever the predicate was passed or not, as I want run it after the whole DAG\r\n* running an op after some op in the group should only run if the predicate was passed, as I only want t... | 2021-04-03T01:24:39 | 2022-04-18T17:27:33 | 2022-04-18T17:27:33 | MEMBER | null | We have few questions on how the condition is constructed in Argo. e.g. using the below condition example
https://github.com/kubeflow/pipelines/blob/master/samples/core/condition/condition.py
When we try to define a containerOp that run after dsl.condition or containerOp inside the condition block, it creates the same result when compiles to Argo. We are wondering is it the expected DSL behavior where the task dependencies should point to the Argo condition task in both cases?
```python
@dsl.pipeline(
name='Conditional execution pipeline',
description='Shows how to use dsl.Condition().'
)
def flipcoin_pipeline():
flip = flip_coin_op()
with dsl.Condition(flip.output == 'heads') as cond_a:
random_num_head = random_num_op(0, 9)
random_num_head2 = random_num_op(0, 9)
with dsl.Condition(flip.output == 'tails') as cond_b:
random_num_tail = random_num_op(10, 19)
random_num_tail2 = random_num_op(10, 19)
# print_output and print_output2 produce the same dependencies
print_output = print_op('after cond_a cond_b').after(cond_a).after(cond_b)
print_output2 = print_op('after random_num_head random_num_tail').after(random_num_head).after(random_num_tail)
```
In Argo dag:
```
dag:
tasks:
- name: condition-1
template: condition-1
when: '"{{tasks.flip-coin.outputs.parameters.flip-coin-output}}" == "heads"'
dependencies: [flip-coin]
- name: condition-2
template: condition-2
when: '"{{tasks.flip-coin.outputs.parameters.flip-coin-output}}" == "tails"'
dependencies: [flip-coin]
- {name: flip-coin, template: flip-coin}
- name: print
template: print
dependencies: [condition-1, condition-2]
- name: print-2
template: print-2
dependencies: [condition-1, condition-2]
```
Thanks.
related docs: https://docs.google.com/document/d/1QPWKoeiPFDcI1JWH-nMe7x_xIJekL11bhu7Fp3fNT24/edit# | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5415 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5415/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5415/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5415/events | https://github.com/kubeflow/pipelines/issues/5415 | 848,775,716 | MDU6SXNzdWU4NDg3NzU3MTY= | 5,415 | manifests: Istio AuthorizationPolicy is wrong for multi-user | {
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"/assign @yanniszark "
] | 2021-04-01T20:43:04 | 2021-04-02T09:43:20 | 2021-04-02T09:43:20 | CONTRIBUTOR | null | ### What steps did you take
Deploy KFP multi-user.
### What happened:
The Istio AuthorizationPolicy resources are invalid.
After fixing them, some policies were missing:
- Allow `metadata-grpc-server` traffic in MetadataDB MySQL.
- Allow all traffic to the Metadata GRPC Server.
I will PR the amended AuthorizationPolicy asap. | {
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"Thank you!\nIt's a gateway to allow using grpcweb from KFP UI.",
"Do we also need to apply the same change to `metadata-grpc-deployment` as well?\r\n\r\nMy deployment looks like:\r\n\r\n"
] | 2021-04-01T19:12:17 | 2021-04-02T09:43:20 | 2021-04-02T09:43:20 | CONTRIBUTOR | null | ### What steps did you take
Deploy multi-user pipelines manifests. The `metadata-envoy-deployment` is injected with a sidecar and crashes.
The reason is that `metadata-envoy-deployment` runs an Envoy Pod and it seems 2 Envoys don't play well together.
Thus, we need to disable injection for this Deployment.
@Bobgy if possible, can you give some context how `metadata-envoy-deployment` is used?
I will open a PR for adding a sidecar injection annotation for `metadata-envoy-deployment`. | {
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"I will be opening a PR with the aforementioned fix asap\r\n/assign @yanniszark "
] | 2021-04-01T19:07:31 | 2021-04-02T08:56:20 | 2021-04-02T08:56:20 | CONTRIBUTOR | null | The metadata writer was failing with a strange error:
```
Start watching Kubernetes Pods created by Argo
Traceback (most recent call last):
File "/kfp/metadata_writer/metadata_writer.py", line 145, in <module>
_request_timeout=2000, # Sometimes HTTP GET gets stuck
File "/usr/local/lib/python3.7/site-packages/kubernetes/watch/watch.py", line 142, in stream
resp = func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/apis/core_v1_api.py", line 12372, in list_namespaced_pod
(data) = self.list_namespaced_pod_with_http_info(namespace, **kwargs)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/apis/core_v1_api.py", line 12472, in list_namespaced_pod_with_http_info
collection_formats=collection_formats)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 344, in call_api
_return_http_data_only, collection_formats, _preload_content, _request_timeout)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 178, in __call_api
_request_timeout=_request_timeout)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 365, in request
headers=headers)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/rest.py", line 231, in GET
query_params=query_params)
File "/usr/local/lib/python3.7/site-packages/kubernetes/client/rest.py", line 222, in request
raise ApiException(http_resp=r)
kubernetes.client.rest.ApiException: (403)
Reason: Forbidden
HTTP response headers: HTTPHeaderDict({'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'X-Content-Type-Options': 'nosniff', 'Date': 'Thu, 01 Apr 2021 19:01:38 GMT', 'Content-Length': '347'})
HTTP response body: b'{"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"namespaces \\"pods\\" is forbidden: User \\"system:serviceaccount:kubeflow:kubeflow-pipelines-metadata-writer\\" cannot get resource \\"namespaces\\" in API group \\"\\" in the namespace \\"pods\\"","reason":"Forbidden","details":{"name":"pods","kind":"namespaces"},"code":403}\n'
```
This is the line that failed in the code:
```python
for event in k8s_watch.stream(
k8s_api.list_namespaced_pod,
namespace=namespace_to_watch,
label_selector=ARGO_WORKFLOW_LABEL_KEY,
timeout_seconds=1800, # Sometimes watch gets stuck
_request_timeout=2000, # Sometimes HTTP GET gets stuck
):
```
If the `namespace_to_watch` variable is empty (which is for multi-user installations), then the K8s library makes a request to `/api/v1/namespaces//pods`. Here is the excerpt from the library, which @elikatsis found:
```python
def list_namespaced_pod(self, namespace, **kwargs): # noqa: E501
"""list_namespaced_pod # noqa: E501
...
"""
kwargs['_return_http_data_only'] = True
return self.list_namespaced_pod_with_http_info(namespace, **kwargs) # noqa: E501
def list_namespaced_pod_with_http_info(self, namespace, **kwargs): # noqa: E501
"""list_namespaced_pod # noqa: E501
...
"""
...
return self.api_client.call_api(
'/api/v1/namespaces/{namespace}/pods', 'GET', # <------- namespace="": /api/v1/namespaces/pods
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1PodList', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
```
Fixed by changing the API call to `list_pod_for_all_namespaces` in case the `namespace_to_watch` variable is empty. All the kudos go to @elikatsis for this one. | {
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"@DavidSpek @Bobgy it seems like someone wrote a new image that does not runasnonroot\r\nhttps://github.com/kubeflow/manifests/pull/1756\r\n\r\nCan you update to a newer version of gcr.io/ml-pipeline/application-crd-controller:1.0-beta-non-cluster-role ? It seems to be from here https://github.com/kubernetes-sigs/a... | 2021-04-01T11:33:00 | 2021-10-15T13:30:19 | 2021-10-15T13:30:19 | MEMBER | null | ### Environment
```
export PIPELINE_VERSION=1.5.0-rc.1
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
kubectl scale deployment/cache-deployer-deployment --replicas 0 -n kubeflow
kubectl scale deployment/cache-server --replicas 0 -n kubeflow
```
### Steps to reproduce
```
[julius@fedora kubeflow]$ kubectl -n kubeflow get pods,replicasets
NAME READY STATUS RESTARTS AGE
pod/controller-manager-6d7b565545-zrhrb 0/1 CrashLoopBackOff 8 18m
pod/metadata-envoy-deployment-797b6886b7-r8478 1/1 Running 0 20m
pod/metadata-grpc-deployment-76b64ffc4f-h99sf 1/1 Running 2 20m
pod/metadata-writer-579f577c59-9xvrc 1/1 Running 0 20m
pod/minio-5b65df66c9-wsjms 1/1 Running 0 20m
pod/ml-pipeline-647d5c6c46-8qrd4 1/1 Running 1 20m
pod/ml-pipeline-persistenceagent-75fb75b66c-rr4g2 1/1 Running 0 20m
pod/ml-pipeline-scheduledworkflow-7cf474cd6d-qtm9x 1/1 Running 0 20m
pod/ml-pipeline-ui-7f97cdb4cd-96z5c 1/1 Running 0 20m
pod/ml-pipeline-viewer-crd-5f66b89768-httx2 1/1 Running 0 20m
pod/ml-pipeline-visualizationserver-656d556bdc-trmcb 1/1 Running 0 20m
pod/mysql-f7b9b7dd4-mwb49 1/1 Running 0 20m
pod/sum-pipeline-x64k4-133507279 0/2 Completed 0 10m
pod/sum-pipeline-x64k4-1823999632 0/2 Completed 0 10m
pod/sum-pipeline-x64k4-2485853679 0/2 Completed 0 9m36s
pod/sum-pipeline-x64k4-2652093435 0/2 Completed 0 11m
pod/workflow-controller-5f9c8ff668-w7vgw 1/1 Running 0 4m15s
...
[julius@fedora kubeflow]$ kubectl -n kubeflow logs pod/controller-manager-6d7b565545-zrhrb
logs are hidden because volume is too excessive
/bin/sh: 2: /root/manager: Permission denied
```
I modified the deployment to get at the logs
---
<!-- 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/5404 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5404/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5404/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5404/events | https://github.com/kubeflow/pipelines/issues/5404 | 847,503,479 | MDU6SXNzdWU4NDc1MDM0Nzk= | 5,404 | [feature] Condition combinators for dsl.Condition | {
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"/area backend",
"related issue: https://github.com/kubeflow/pipelines/issues/482",
"/assign @neuromage @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",
"This issue ha... | 2021-03-31T23:08:11 | 2023-09-13T15:03:35 | 2022-04-18T17:27:39 | CONTRIBUTOR | null | ### Feature Area
/area backend
### What feature would you like to see?
Right now, `dsl.Condition` only allows to use a single comparison:
```python
with dsl.Condition(param1 == value1):
...
```
Because `ConditionOperator` doesn't define any custom `__bool__` which would produce a warning, the following conditions silently work in unexpected ways:
```python
with dsl.Condition(param1 == value1 and param2 == value2): # yields param2 == value2
...
with dsl.Condition(param1 == value1 or param2 == value2): # yields param1 == value1
...
```
Because `and` and `or` Python cannot be overloaded, the only thing one can do to prevent those misleading behaviour is to add a warning to `__bool__` method of `ConditionOperator` (which would require turning it into an `attr` class instead of a `namedtuple`).
The same kind of a problem does occur in e.g. [pandas](https://stackoverflow.com/questions/21415661/logical-operators-for-boolean-indexing-in-pandas), and cannot easily solved in Python. However, `&` (via `__and__`) and a custom method (e.g. `and_then`) are fine to define on `ConditionOperator`:
```python
with dsl.Condition((param1 == value1) | (param2 == value2)):
...
with dsl.Condition((param1 == value1).or_else(param2 == value2)):
...
```
As seen above, brackets are needed, due to operator precedence (no overwritable binary operator has lower precedence than `==`). Arguably, it's still well readable and also enables complex conditions.
The generated yaml in such a case would include:
```
"$tasks.some-task.results.param1" == "value1" || "$tasks.some-task.results.param2" == "value2"
```
For more complex scenarios, braces would need to be used:
```python
with dsl.Condition( ((param1 == value1) | (param2 == value2)) & (param3 == value3) ):
...
```
```
("$tasks.some-task.results.param1" == "value1" || "$tasks.some-task.results.param2" == "value2") &&
"$tasks.some-task.results.param3" == "value3"
```
For readability, if multiple operators of the same kind are used, those should not produce braces:
```python
with dsl.Condition( (param1 == value1) | (param2 == value2) | (param3 == value3) ):
...
```
```
"$tasks.some-task.results.param1" == "value1" || "$tasks.some-task.results.param2" == "value2" ||
"$tasks.some-task.results.param3" == "value3"
```
Note: considering how Argo is using govaluate, handling and/or shouldn't be a problem --- `__and__` (`&`) would compile to `&&` and `__or__` (`|`) would compile to `||`. The `ConditionOperator` would be treated as a binary tree with non-`ConditionOperator` values at its leaves.
### What is the use case or pain point?
This feature allows users to create much more complex conditions, combining multiple simple predicates. Typical use case is error handling.
Conjunction of conditions can be used e.g. if some handling (like logging) depends both on some status from component and the logging level defined in a pipeline parameter:
```python
@dsl.pipeline(name="test error handling")
def test_error_handling(log_errors_of_level: int = 3):
op_lvl_2 = dsl.ContainerOp(...)
with dsl.Condition( (op_lvl_2.errCode != "") & (log_errors_of_level <= 2) ):
LogErrorOp(op_lvl_2.errCode)
```
Alternative of predicates can be used to execute error-handling code if any of parallel actions failed (or produced some undesired result):
```python
@dsl.pipeline(name="test error handling")
def test_error_handling(log_errors_of_level: int = 3):
op1 = dsl.ContainerOp(...)
op2 = dsl.ContainerOp(...)
op3 = dsl.ContainerOp(...)
with dsl.Condition( (op1.errCode != "") | (op2.errCode != "") | (op3.errCode != "") ):
LogErrorOp(op1.errCode, op2.errCode, op3.errCode)
```
### Is there a workaround currently?
While one can emulate "and" using hierarchy of conditions, although with poor readability both in DSL and generated yaml:
```python
with dsl.Condition(param1 == value1):
with dsl.Condition(param2 == value2):
...
```
...the same cannot be done with "or".
---
<!-- 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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"/assign @yanniszark "
] | 2021-03-31T18:37:18 | 2021-04-01T16:16:53 | 2021-04-01T01:43:19 | CONTRIBUTOR | null | ### What steps did you take
```sh
kustomize build pipelines/manifests/kustomize/env/platform-agnostic-multi-user
```
### What happened:
The generated ClusterRoleBinding has the namespace set as `argo`, even though kustomize sets it as `kubeflow`.
Here is the generated YAML:
```yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
labels:
application-crd-id: kubeflow-pipelines
name: argo-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: argo-cluster-role
subjects:
- kind: ServiceAccount
name: argo
namespace: argo
```
### What did you expect to happen:
Kustomize should have replaced the namespace with `kubeflow`:
```yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
labels:
application-crd-id: kubeflow-pipelines
name: argo-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: argo-cluster-role
subjects:
- kind: ServiceAccount
name: argo
namespace: kubeflow
```
The reason that didn't happen is because the ClusterRoleBinding is defined as:
```yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: argo-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: argo-cluster-role
subjects:
- kind: ServiceAccount
name: argo
namespace: argo
```
but the ServiceAccount is defined as:
```yaml
apiVersion: v1
kind: ServiceAccount
metadata:
name: argo
```
because of that, kustomize doesn't register the ServiceAccount reference and doesn't substitute the namespace.
We tried to include a patch that removes the namespace line from the `ClusterRoleBinding` and it worked, so we will PR that. | {
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"This is one of the issues we plan to solve together in bit.ly/kfp-v2. Putting it in IR-based KFP project",
"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",
"still waiting on a bug fi... | 2021-03-31T17:47:02 | 2022-03-02T15:05:19 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? we used [`kfctl`](https://github.com/kubeflow/manifests/blob/master/distributions/kfdef/kfctl_istio_dex.v1.2.0.yaml)
* KFP version: `v1.2.0`
* KFP SDK version:
```
kfp==1.4.0
kfp-pipeline-spec==0.1.6
kfp-server-api==1.4.1
```
### Steps to reproduce
this is not an exhaustive experiment by any means but our metrics stopped showing up in the run table & "run output" tab when we write more than 5 metrics to `/mlpipeline-metrics.json` and we are not sure if this is a bug or a restriction from kubeflow
can have up to 5 metrics:
when 6 metrics (or more) were sent, none showed up:
* didn't show up in table
* didn't show up in "compare runs" (the whole metrics section is empty)
### Expected result
* all metrics should be visible when "compare runs"
* it would be great to have more than 2 visible columns in the table too
### Materials and Reference
i did some googling and it seems some people used
```
file_outputs={
"mlpipeline-metrics": "/mlpipeline-metrics.json",
"mlpipeline-full-metrics": "/mlpipeline-full-metrics.json",
"mlpipeline-ui-metadata": "/mlpipeline-ui-metadata.json",
}
```
in their pipeline component. i cant find `mlpipeline-full-metrics` with `ag` in the pipelines repo though. not sure if this is what i should be doing when sending too many metrics
i couldn't really find where to look in this repo for this issue. would love any pointers
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"/assign @yanniszark ",
"metacontroller is also used in GCP distribution, but since KFP needs it, I agree we can put it here."
] | 2021-03-31T17:26:46 | 2021-04-01T01:43:19 | 2021-04-01T01:43:19 | CONTRIBUTOR | null | ### What steps did you take
The multi-user pipelines installation includes the metacontroller component.
However, it doesn't include it under `third-party`, like for other apps.
Since metacontroller is something that KFP uses, we should move it to the KFP repo under third-party and not have it under `contrib` in kubeflow/manifests. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5399 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5399/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5399/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5399/events | https://github.com/kubeflow/pipelines/issues/5399 | 846,970,631 | MDU6SXNzdWU4NDY5NzA2MzE= | 5,399 | [bug] manifests: Platform-agnostic multi-user envs should set the kubeflow header | {
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"/assign @yanniszark "
] | 2021-03-31T17:00:55 | 2021-04-01T01:43:19 | 2021-04-01T01:43:19 | CONTRIBUTOR | null | Platform-agnostic multi-user envs should set the kubeflow header to a default value (`kubeflow-userid`), so that they are self-contained and deployable without any extra configuration. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5398 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5398/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5398/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5398/events | https://github.com/kubeflow/pipelines/issues/5398 | 846,957,837 | MDU6SXNzdWU4NDY5NTc4Mzc= | 5,398 | [bug] manifests: Metadata VirtualService is missing | {
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"/assign @yanniszark "
] | 2021-03-31T16:50:06 | 2021-04-01T01:43:19 | 2021-04-01T01:43:19 | CONTRIBUTOR | null | ### What steps did you take
Install KFP from multi-user manifests.
### What happened:
The KFP Metadata UI is missing erroring out because it is missing a VirtualService. We tested with the following VirtualService:
```yaml
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: metadata-grpc
namespace: kubeflow
spec:
gateways:
- kubeflow-gateway
hosts:
- '*'
http:
- match:
- uri:
prefix: /ml_metadata
rewrite:
uri: /ml_metadata
route:
- destination:
host: ml-pipeline-ui.kubeflow.svc.cluster.local
port:
number: 80
```
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https://api.github.com/repos/kubeflow/pipelines/issues/5394 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5394/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5394/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5394/events | https://github.com/kubeflow/pipelines/issues/5394 | 845,441,219 | MDU6SXNzdWU4NDU0NDEyMTk= | 5,394 | [feature] Use Tensorboard within Kubeflow Pipelines UI with AWS Sagemaker training component | {
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"/assign @kubeflow/aws ",
"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",
"/area components/aws/sagemaker",
"This issue has been automatically marked as stale because it has not ha... | 2021-03-30T23:15:08 | 2022-08-08T19:28:23 | null | NONE | null | ### Feature Area
/area components
### What feature would you like to see?
Option to use start/use tensorboard from the Kubeflow Pipelines UI when using AWS Sagemaker training component. At the moment, that doesn't seem to be available in the sagemaker training component.
The `mlpipeline-ui-metadata.json` file output is not exposed in sagemaker training component and the code to [dump metrics visualization metadata](https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#tensorboard) needs to be added to the docker image (code not available) for the sagemaker components.
### What is the use case or pain point?
Makes it easier to open tensorboard and monitor training job metrics in the same Kubeflow UI without manual commands or going to AWS console.
### Is there a workaround currently?
Not that I know of. Maybe use initContainer to dump the metadata file or share volume storage with another step that dumps the metadata file, but more of a hack?
---
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/5381 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5381/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5381/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5381/events | https://github.com/kubeflow/pipelines/issues/5381 | 842,392,953 | MDU6SXNzdWU4NDIzOTI5NTM= | 5,381 | [feature] CLI command to run a pipeline from python files | {
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"This would be nice. We based our KFP repo on [this example](https://github.com/ksalama/kubeflow-examples/tree/master/kfp-cloudbuild), which is referred to in this [GCP article](https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture)... | 2021-03-27T02:13:46 | 2022-03-03T03:05:38 | 2022-03-03T03:05:38 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
A kfp CLI command to directly run python pipelines.
For example:
```bash
kfp run sample_pipeline.py --host <host-name> --params=key1=value1,key2=value2,...
```
This command directly compiles a python pipeline and submits it to KFP instance with specified hostname.
Expectation of `sample_pipeline.py` should be the same as `dsl-compile` command.
Or what can be even better --
Set up KFP instance connection configuration including credentials:
```bash
vim ~/.kfp/config.json # this stores all KFP related configuration in a local config file
# or
export KF_PIPELINES_ENDPOINT=<kfp-host>
```
Run samples you want:
```bash
# all host and credentials information are loaded from either a config file or env vars
# so these command do not need to repeat them
kfp run sample_pipeline.py --params=key1=value1,key2=value2,...
```
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
I have two options for doing the same thing:
* write python script wrappers that calls `create_run_from_pipeline_func`
* use `dsl-compile` and then `kfp upload` and then `kfp run`
Both options involve some unnecessary steps, I think it's beneficial providing a CLI command to make this common command easier, because
* this command can be very helpful during pipeline development
* people do not need to write `__main__` function for every pipeline, and they do not need to support arguments like host, parameters. All of these interface can be standardized in kfp run CLI.
* it's convenient for building a KFP component that runs KFP pipelines or hooking up a KFP pipeline in a CI step
---
<!-- 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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"We currently do not provide a way to re-join conditional branches.\r\n\r\nThis leads to some code duplication:\r\nhttps://github.com/kubeflow/pipelines/blob/a80421191db917322ff312626409526b0a76aa68/samples/core/continue_training_from_prod/continue_training_from_prod.py\r\n\r\nOne workaround might be to have the tr... | 2021-03-26T11:33:15 | 2022-04-18T17:27:35 | 2022-04-18T17:27:35 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Currently it does not seem to be possible to get the output of a conditional step, like so:
```
with dsl.Condition(base_model == "", name="Skip-Pretraining"):
pretraining_step = pretraining_op(
image=image,
)
outputs = pretraining_op.outputs["output"]
```
### What is the use case or pain point?
I have a pipeline with optional pretraining for transfer learning, and in further steps of my pipeline, I use either the pretrained model as a base model, which path is stored in the outputs of the step, or a different model mode in case there was no pretraining. The further steps need to know which base model to use, depending on whether there was pretraining or not.
### Is there a workaround currently?
Now I check if the pretrained model file exists in the mounted volume, but it's not ideal.
---
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",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-03-25T09:58:37 | 2022-04-18T17:27:36 | 2022-04-18T17:27:36 | 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?
From Issue #2337 I learned about the 'concat' operation which I needed when writing the Kubeflow Component Specification for my pipeline.
I am not sure where to find an exhaustive/relatively inclusive list of such operations for use, such as conditionals, comparators (string_var == ""), or of all the types of a defined input/output in 'inputs' and 'outputs', eg - {name: variable, type: String, description:"Description"} .
Where would one look for these?
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Difficult to find what are all the Specifications that Kubeflow Pipelines provide, and use them.
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
Google search to try to find Kubeflow specification that matches use case.
<!-- 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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"![Uploading Screen Shot 2021-03-24 at 2.21.49 PM.png…]()\r\n",
"Pending reproduction",
"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 be... | 2021-03-24T21:33:49 | 2022-04-18T17:27:37 | 2022-04-18T17:27:37 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.4.0 （In fact, it also happened in 1.0.x and 1.3.x 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. -->
### Steps to reproduce
It may not easy to reproduce the bug.
Sometimes the "choose file" button doesn't work when trying to upload pipeline, the screen won't pop up which allows the user to choose the file to upload. After I restarted the chrome, the "choose file" button started to work.
### Expected result
After clicking "choose file" button when trying to upload pipeline, a screen pops up which allows the user to choose the file.
### 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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"@Bobgy: The label(s) `area/deployment` cannot be applied, because the repository doesn't have them.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5368):\n\n>### What steps did you take\r\n>\r\n>```bash\r\n>kpt pkg get https://github.com/kubeflow/pipelines.git/manifests/kustomiz... | 2021-03-24T13:49:39 | 2021-10-25T06:15:07 | 2021-10-25T06:15:07 | CONTRIBUTOR | null | ### What steps did you take
```bash
kpt pkg get https://github.com/kubeflow/pipelines.git/manifests/kustomize@1.5.0-rc.0 .
```
<!-- A clear and concise description of what the bug is.-->
### What happened:
> $ kpt pkg get https://github.com/kubeflow/pipelines.git/manifests/kustomize@1.5.0-rc.0 kfp-standalone-1/kustomize/upstream
fetching package /manifests/kustomize from https://github.com/kubeflow/pipelines to kfp-standalone-1/kustomize/upstream
error: /home/ext_gongyuan_google_com/github/kf/testing/test-infra/kfp/kfp-standalone-1/kustomize/upstream/third-party/argo/upstream/manifests/namespace-install/overlays/argo-server-deployment.yaml: wrong Node Kind for expected: MappingNode was SequenceNode: value: {- op: add
path: /spec/template/spec/containers/0/args/-
value: --namespaced}
### What did you expect to happen:
There should be no error message when fetching using kpt.
### Environment:
kpt version
0.37.1
### 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 -->
/area deployment
---
<!-- 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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"Thank you for the report!\r\nContributions welcomed, the code path related is https://github.com/kubeflow/pipelines/blob/eb558423ecdca969f2dea55e0d1b0197fafbcb13/frontend/server/handlers/artifacts.ts#L200\r\nand development guide: https://github.com/kubeflow/pipelines/tree/master/frontend",
"/cc @zijianjoy ",
... | 2021-03-24T10:34:28 | 2021-07-09T17:59:27 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? GCP Marketplace
* KFP version: 1.4.1
### Steps to reproduce
1. Upload a relatively large (e.g. ~5G) file to a GCS bucket
2. Create a pipeline run using the code snippet below
3. Open the run in the KFP UI
4. Click on the "Download from GCS" component
5. Continuously open and close the "Input/Output" tab (e.g. by clicking on the "x" of the side pane, then on the component, then on the "x" again, etc.; or by clicking on "Visualizations", then "Input/Output", then "Visualizations", etc.)
6. You should see the resource usage of the `ml-pipeline-ui` pod skyrocket and eventually the pod will be restarted
Code snippet to build and run a pipeline:
```python
import kfp
from kfp.compiler import compiler
LARGE_FILE_URI = "gs://..."
KFP_HOST = "https://..."
@kfp.dsl.pipeline(
name="UI Crashing Pipeline",
description="Pipeline to help reproduce the KFP UI crash",
)
def pipeline():
download_op = kfp.components.load_component_from_url(
"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/google-cloud/storage/download_blob/component.yaml"
)
_ = download_op(gcs_path=LARGE_FILE_URI,)
if __name__ == "__main__":
compiler.Compiler().compile(pipeline, "pipeline.zip")
client = kfp.Client(KFP_HOST)
experiment = client.create_experiment(name="example")
run = client.run_pipeline(experiment.id, "ui-crashing-pipeline", "pipeline.zip")
```
Some metrics graphs of `ml-pipeline-ui` extracted from GKE (the bumps correspond to the times that I was clicking around as specified in step 5):
### Expected result
The `ml-pipeline-ui` doesn't start using insane amounts of resources and doesn't crash.
### Materials and Reference
As far as I've investigated, clicking on the "Inputs/Outputs" tab sends a request to the backend to get 255 bytes of each file to use as a preview. However, the actual pod seems to download the whole file, which causes the network and CPU spikes I've shown above.
I'm not familiar enough with the implementation, but probably a thing to look into is whether it's possible to prevent the pod from downloading the whole file. Another option could probably be to use some sort of cache for the preview.
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/lifecycle frozen",
"Some challenges for implementing wrapping ---\r\n\r\nBecause we used react-virtualized to make log viewer work w... | 2021-03-24T10:18:55 | 2021-07-29T00:41:19 | null | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
KFP UI log viewer should wrap when lines are long
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
When debugging pipelines, I often find log lines are too long so I couldn't easily browse its full content.
Because it's typically a lot harder to scroll left and right than up and down.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Scroll horizontally to see the log
---
<!-- 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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"followers_url": "https://a... | null | [] | 2021-03-22T20:52:00 | 2021-03-29T15:23:50 | 2021-03-29T15:23:50 | COLLABORATOR | null | We have upgraded backend to use newer Argo version `2.12.9`:https://github.com/kubeflow/pipelines/pull/5266. Based on https://github.com/kubeflow/pipelines/pull/5339#discussion_r598079077, we need to make sure we are also updating other contents in `/kubeflow/pipelines/frontend/third_party/argo-ui/argo_template.ts` to match https://github.com/argoproj/argo-workflows/blob/80b5ab9b8e35b4dba71396062abe32918cd76ddd/ui/src/models/workflows.ts#L863-L873. | {
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Looks like the error is caused by bazel version:
```
ERROR: /root/.cache/bazel/_bazel_root/a58ac343beec6d39a39bba9ecc91209d/external/org_tensorflow/tensorflow/version_check.bzl:47:13: Traceback (most recent call last):
File "/tmp/pip-req-build-ziym2n3h/WORKSPACE", line 66
check_bazel_version_at_least(<1 more arguments>)
File "/root/.cache/bazel/_bazel_root/a58ac343beec6d39a39bba9ecc91209d/external/org_tensorflow/tensorflow/version_check.bzl", line 47, in check_bazel_version_at_least
fail(<1 more arguments>)
Current Bazel version is 3.4.1, expected at least 3.7.2
ERROR: error loading package 'external': Package 'external' contains errors
INFO: Elapsed time: 12.728s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)
```
We'll try update bazel here: https://github.com/kubeflow/pipelines/blob/053d62e80f99a34f66b5d4035d425aed8df6c8a9/test/presubmit-tests-tfx.sh#L36 | {
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"Can you provide more info such as sample pipeline code that prompts the error and the version of KFServing you have installed?",
"```\r\nimport kfp.dsl as dsl\r\nimport kfp.compiler as compiler\r\nfrom kfp import components\r\n\r\nkfserving_op = components.load_component_from_url(\r\n \"https://raw.githubuser... | 2021-03-22T15:37:11 | 2021-04-08T01:10:02 | 2021-04-08T01:10:02 | NONE | null | ```
raceback (most recent call last):
File "kfservingdeployer.py", line 414, in <module>
main()
File "kfservingdeployer.py", line 381, in main
max_replicas=max_replicas
File "kfservingdeployer.py", line 235, in perform_action
watch=True, timeout_seconds=watch_timeout)
File "kfservingdeployer.py", line 168, in submit_api_request
outputs = custom_obj_api.create_namespaced_custom_object(*args, isvc)
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/api/custom_objects_api.py", line 225, in create_namespaced_custom_object
return self.create_namespaced_custom_object_with_http_info(group, version, namespace, plural, body, **kwargs) # noqa: E501
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/api/custom_objects_api.py", line 358, in create_namespaced_custom_object_with_http_info
collection_formats=collection_formats)
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/api_client.py", line 353, in call_api
_preload_content, _request_timeout, _host)
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/api_client.py", line 184, in __call_api
_request_timeout=_request_timeout)
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/api_client.py", line 397, in request
body=body)
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/rest.py", line 280, in POST
body=body)
File "/usr/local/lib/python3.6/site-packages/kubernetes/client/rest.py", line 233, in request
raise ApiException(http_resp=r)
kubernetes.client.exceptions.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Audit-Id': '6fb21efd-452a-4332-94ce-11cd581663b4', 'Cache-Control': 'no-cache, private', 'Content-Type': 'text/plain; charset=utf-8', 'X-Content-Type-Options': 'nosniff', 'Date': 'Mon, 22 Mar 2021 15:25:48 GMT', 'Content-Length': '19'})
HTTP response body: 404 page not found
``` | {
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"Thank you for reporting! I can validate that this issue happens on Safari browser, which doesn't enforce the time format as it does on Chrome: \r\n\r\n\r\nTrying to find for a fix for this usecase.\r\n"... | 2021-03-22T07:39:05 | 2021-04-02T16:26:41 | 2021-04-02T16:26:41 | NONE | null | /kind bug
**What steps did you take and what happened:**
<img width="634" alt="image" src="https://user-images.githubusercontent.com/5370798/107193399-1a0b9900-69ef-11eb-839b-64c803abe362.png">
When I select whole value in the text input, and press "1" (meaning that I want to write full time by hand), the whole page crashes, and only top banner and white page is displayed
<img width="1440" alt="image" src="https://user-images.githubusercontent.com/5370798/107193737-7373c800-69ef-11eb-88fd-a646711847f0.png">
Same thing happens if I just delete the `:` character, without even losing the focus on the input
Also, "Date" fields are broken in a same way as well .. as soon as you delete the `-` character, whole page crashes again
Attaching the console log as well, but this is so easy, and 100% reproducible issue.
**What did you expect to happen:**
I expected a proper validation error displayed, after I leave the field, not while typing it.
Also, why not just use some time picker?
Because validation is obviously just completely wrong .. if you type `09:92`, no error is displayed ...
**Anything else you would like to add:**
Safari console log:
```
[Error] Failed to load resource: the server responded with a status of 400 () (runs, line 0)
[Error] Error: Invalid picker format
xi — TriggerUtils.ts:146
(anonymous function) — Trigger.tsx:408
bi — react-dom.production.min.js:131:466
_i — react-dom.production.min.js:131:200
_l — react-dom.production.min.js:252
_l
(anonymous function) — scheduler.production.min.js:18:437
vl — react-dom.production.min.js:244:440
il — react-dom.production.min.js:223:423
il
(anonymous function) — react-dom.production.min.js:121:115
(anonymous function) — scheduler.production.min.js:18:437
Yo — react-dom.production.min.js:121
Ko — react-dom.production.min.js:120:496
(anonymous function) — react-dom.production.min.js:224:85
le — react-dom.production.min.js:285:132
pe — react-dom.production.min.js:27
Mn — react-dom.production.min.js:83:282
Bn — react-dom.production.min.js:84:473
Fn — react-dom.production.min.js:84:101
Fn
(anonymous function) — scheduler.production.min.js:18:437
se — react-dom.production.min.js:285
Tn — react-dom.production.min.js:82:280
Tn
ds (2.729a2889.chunk.js:2:2176548)
(anonymous function) (2.729a2889.chunk.js:2:2182311)
bi (2.729a2889.chunk.js:2:2149218)
_i (2.729a2889.chunk.js:2:2148952)
_l (2.729a2889.chunk.js:2:2200695)
_l
(anonymous function) (2.729a2889.chunk.js:2:2219939)
vl (2.729a2889.chunk.js:2:2196681)
il (2.729a2889.chunk.js:2:2187008)
il
(anonymous function) (2.729a2889.chunk.js:2:2144991)
(anonymous function) (2.729a2889.chunk.js:2:2219939)
Yo (2.729a2889.chunk.js:2:2144937)
Ko (2.729a2889.chunk.js:2:2144872)
(anonymous function) (2.729a2889.chunk.js:2:2214205)
le (2.729a2889.chunk.js:2:2214209)
pe (2.729a2889.chunk.js:2:2104545)
Mn (2.729a2889.chunk.js:2:2128859)
Bn (2.729a2889.chunk.js:2:2129787)
Fn (2.729a2889.chunk.js:2:2129075)
Fn
(anonymous function) (2.729a2889.chunk.js:2:2219939)
se (2.729a2889.chunk.js:2:2214040)
Tn (2.729a2889.chunk.js:2:2128498)
Tn
[Error] Error: Invalid picker format
Yo (2.729a2889.chunk.js:2:2145080)
Ko (2.729a2889.chunk.js:2:2144872)
(anonymous function) (2.729a2889.chunk.js:2:2214205)
le (2.729a2889.chunk.js:2:2214209)
pe (2.729a2889.chunk.js:2:2104545)
Mn (2.729a2889.chunk.js:2:2128859)
Bn (2.729a2889.chunk.js:2:2129787)
Fn (2.729a2889.chunk.js:2:2129075)
Fn
(anonymous function) (2.729a2889.chunk.js:2:2219939)
se (2.729a2889.chunk.js:2:2214040)
Tn (2.729a2889.chunk.js:2:2128498)
Tn
```
**Environment:**
- Kubeflow version: v1beta1
- kfctl version: kfctl v1.2.0-0-gbc038f9
- Kubernetes platform: EKS
- Kubernetes version: v1.18.9-eks-d1db3c
- OS: EKS managed nodes
Copied from https://github.com/kubeflow/kubeflow/issues/5587
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"@hilcj hello. I think this issue should be labeld to `area/backend`",
"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 ha... | 2021-03-22T05:56:41 | 2022-04-18T17:27:46 | 2022-04-18T17:27:46 | CONTRIBUTOR | null | ### What steps did you take:
Removed Recurring Run via python's KFP package.
(The Recurring Runs to be deleted are all those associated with a particular Experiment.)
### What happened:
The job information of the RecurringRun were removed from mysql, but the ScheduledWorkflow (CRD) was not removed.
### What did you expect to happen:
Both the job information of the RecurringRun on mysql and the ScheduledWorkflow (CRD) should be deleted.
### Environment:
kubeflow pipelines are running on GKE.
GKE version: 1.18.12-gke.1210
mysql version: MySQL 8.0
How did you deploy Kubeflow Pipelines (KFP)?
It is built on an infrastructure environment managed by terraform.
Deploying a resource generated by manifest( of gcp-blue prints) with some editing.
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
1.2
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
(pypi)kfp 1.3.0
### Anything else you would like to add:
I read the source code, and there is no mechanism to rollback when the process fails. This causes inconsistencies between the DB and other resources such as CRD, so I think a transactional mechanism should be implemented.
Also, I think there should be a mechanism to synchronize the ScheduledWorkflow (CRD) with the job information in mysql.
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"I think the main reason for the error is that you're not using full paths in `file_outputs`. The system cannot find those files.\r\nCan you try specifying the full paths everywhere?\r\n\r\nAdditionally I see couple of other issues (although they should not be leading to errors).\r\n\r\n* Directly constructing `Con... | 2021-03-22T03:38:33 | 2021-04-10T04:09:16 | 2021-04-10T04:09:16 | NONE | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
**I upload a pipeline to load data,the function's code like this**
**Dockerfile**
And the pipeline's code like this
### What happened:
The load-data pipeline was uploaded successful,but when I create a run based on the load-data pipeline,it occur this error
### Environment:
<!-- Please fill in those that seem relevant. -->
My environment is
docker version :1.13.1
k8s : v1.19.4
kubeflow:1.0.2
pipelines:0.2.5
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Bump @pvaneck ",
"Just a note that any of the PodSpec fields can be used with the component if you pass in yaml directly:\r\n```python\r\n isvc_yaml = '''\r\n apiVersion: \"serving.kubeflow.org/v1beta1\"\r\n kind: \"InferenceService\"\r\n metadata:\r\n name: \"sklearn-iris2\"\r\n spec:\r\n ... | 2021-03-20T16:41:13 | 2021-04-08T03:25:57 | 2021-04-08T03:25:57 | MEMBER | null | /kind bug
/kind feature
Hi. Recently the KFServing component for the pipeline was updated to v0.5.1 supporting v1beta1 API. This component is still missing the `timeout` field in PredictorSpec which specifies the number of seconds to wait before timing out a request to the component. A couple of issues were raised in this regard:
- [https://github.com/kubeflow/kfserving/issues/892]( https://github.com/kubeflow/kfserving/issues/892)
- [https://github.com/kubeflow/kfserving/issues/1129](https://github.com/kubeflow/kfserving/issues/1129)
I would be happy to submit a PR as seems like a very small change. :heart:
Proposed solution:
Add an extra args named `requestTimeout` to component yaml and use this as parameter in the `create_predictor_spec` function. | {
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"HI @rexshang,\r\n\r\nI can think of three ways to improve:\r\n* set up a retry strategy, so that the pipeline can auto retry a few times when the instability happens (you should always expect other services may fail)\r\n* use managed storage, gcs is probably stabler than minio\r\n* upgrade minio server, the defaul... | 2021-03-19T06:40:14 | 2022-04-18T15:26:52 | null | NONE | null | ### What steps did you take:
I'm running kubeflow 1.0.4 in GCP. There is a recurring job that runs every 15 min. It would fail about 1 time a day and generate the following error. Does anyone have any pointers to help me troubleshoot?
### What happened:
"time="2021-03-18T04:55:22Z" level=warning msg="Failed to put file: Get http://minio-service.default:9000/kubeflow-artifact/?location=: dial tcp x.x.x.x:9000: connect: connection refused"
"time="2021-03-18T04:55:22Z" level=error msg="executor error: timed out waiting for the condition"
### Environment:
Google Cloud Platform
Kubeflow 1.0.4
How did you deploy Kubeflow Pipelines (KFP)?
using GCP AI Platform Pipelines "NEW INSTANCE" process
KFP version: 1.0.4
KFP SDK version: N/A
### Anything else you would like to add:
/kind bug
/area backend
<!--
// /area frontend
// /area sdk
// /area testing
// /area engprod
-->
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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",
"superceded by https://github.com/kubeflow/pipelines/issues/6204"
] | 2021-03-19T01:01:48 | 2021-08-25T06:35:13 | 2021-08-25T06:35:13 | CONTRIBUTOR | null | We are missing some doc to introduce how to configure KFP to run stably in a production environment. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5329 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5329/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5329/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5329/events | https://github.com/kubeflow/pipelines/issues/5329 | 834,862,040 | MDU6SXNzdWU4MzQ4NjIwNDA= | 5,329 | Database Connection Errors | {
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"Thank you for the suggestion @maganaluis ! It makes sense to change the lifetime for DB connection, and make it configurable. Welcome to contribute this feature!",
"@zijianjoy I created the PR. After changing this configuration we did not experience any of these errors anymore, so seems to be the correct approac... | 2021-03-18T14:11:06 | 2023-05-14T21:59:38 | 2021-08-04T21:28:23 | CONTRIBUTOR | null | ### What steps did you take:
We recently switched from using the MySQL database that Kubeflow Pipelines comes with, to a managed database on Azure. We started observing errors such as the one below, the errors are not consistent but they do happen often enough to impact our workloads.
I have not been able to replicate the issue outside of Kubeflow, more specifically this ```unexpected EOF``` error. As it is suggested by methane https://github.com/go-sql-driver/mysql/issues/674#issuecomment-345661869 the general advice is to set ```DB.SetConnMaxLifetime(time.Second)``` to something small like 1-3 minutes, but this is not a setting that's enabled by env variables by Kubeflow Pipelines. I believe it should be because I'm sure others will run into the same issue as Kubeflow gains more Enterprise Adoption.
We are going to test if this change reduces or hopefully eliminates these errors, but at least from the research I've done it looks like the right approach.
```golang
func (c *ClientManager) init() {
glog.Info("Initializing client manager")
db := initDBClient(common.GetDurationConfig(initConnectionTimeout))
db.SetConnMaxLifetime(time.Second * 120)
```
```
[mysql] 2021/03/16 07:27:11 packets.go:36: unexpected EOF
[mysql] 2021/03/16 07:27:11 connection.go:141: write tcp 10.244.24.107:50550->40.71.8.203:3306: write: broken pipe
E0316 07:27:11.976783 6 experiment_store.go:78] Failed to start transaction to list jobs
I0316 07:27:11.976819 6 error.go:243] invalid connection
InternalServerError: Failed to list experiments: invalid connection
github.com/kubeflow/pipelines/backend/src/common/util.NewInternalServerError
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:142
github.com/kubeflow/pipelines/backend/src/apiserver/storage.(*ExperimentStore).ListExperiments.func1
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/storage/experiment_store.go:49
github.com/kubeflow/pipelines/backend/src/apiserver/storage.(*ExperimentStore).ListExperiments
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/storage/experiment_store.go:79
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).ListExperiments
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:145
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:162
github.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:619
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:621
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.28.0/server.go:1082
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.28.0/server.go:1405
google.golang.org/grpc.(*Server).serveStreams.func1.1
/go/pkg/mod/google.golang.org/grpc@v1.28.0/server.go:746
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
List experiments failed.
```
### What happened:
After switching Kubeflow to a managed database, we have been experiencing sporadic connection errors in the Kubeflow API service.
### What did you expect to happen:
I would expect not to have any database connection issues.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
- Kustomize
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
~ 1.3
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
~ 1.3
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Thank you @AntPeixe for this suggestion! Currently we are able to filter by pipeline name, but we might need to take an action to give some examples about how to use it, and improve reference documentation.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be cl... | 2021-03-18T13:13:06 | 2022-04-18T17:27:49 | 2022-04-18T17:27:49 | NONE | null | /kind feature
**Why you need this feature:**
Right now it isn't possible to (easily) get the pipeline ID by it's name. When creating a new pipeline it may happen that a name already exists and the POST request returns an error. I'd like to be able to then get ID of such pipeline using it's name.
I think currently there is a workaround by getting all pipelines and the filtering from the list of results but this isn't as handy.
**Describe the solution you'd like:**
A new API endpoint to find a single pipeline ID by providing it's name.
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"Duplicate of https://github.com/kubeflow/pipelines/issues/3763",
"Can you confirm?",
"Confirmed this fixes the issue\r\n`kubectl delete pod ml-pipeline-persistenceagent-xxxxxxx-xxxx -n kubeflow`\r\n\r\nAlso observed that the persistence-agent was looping over an old copy of pipelines possibly from a previous d... | 2021-03-18T09:12:12 | 2021-03-19T02:58:36 | 2021-03-19T02:58:36 | NONE | null | This is a deployment of Kubeflow on Kubernetes. Previously, Kubeflow was running fine. From one day, all newly created runs cannot get any other status than 'Unknown'. Inside the run, the 'Graph' tab is stuck at loading forever. Run outputs stay at none, and the 'Config' tab shows everything as expected.
From the Argo UI, every run is still functioning correctly, and the logs and artifacts are accessible as normal.
Kubeflow version (the version of kubeflow source code specified in the manifest file used to deploy kubeflow) is around 1.1
If the graph is left to load, from the console, there are a few error requests, if it is relevant:
GET http://[ip]:31380/pipeline/apis/v1beta1/runs/01d0ca69-931d-42c2-afa7-4bf0f72f08cf 500
Utils.tsx:32 Response for path: system/project-id was not 'ok'
Response was: upstream request timeout
What communication could have gone wrong? | {
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"/cc @zijianjoy ",
"Example pipeline\r\n\r\n```\r\nHOST = '<redacted>'\r\n\r\nimport kfp\r\nimport kfp.components as comp\r\n\r\n\r\ndef fail():\r\n import sys\r\n println(\"failed\")\r\n sys.exit(1)\r\n\r\n\r\ndef echo():\r\n println(\"hello world\")\r\n\r\n\r\ndef main():\r\n fail_op = comp.func_... | 2021-03-18T05:29:16 | 2021-03-24T08:28:23 | 2021-03-24T08:28:23 | CONTRIBUTOR | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
### What happened:
It seems there was a change that all skipped nodes in the pipeline are also added to workflow status, so they also render in KFP UI now. However, it doesn't show properly.
TODO: I'll upload a screenshot and give an example here.
### What did you expect to happen:
The skipped nodes should either be hidden, or we remove the unfinished sign after the last unfinished node.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Duplicate of https://github.com/kubeflow/pipelines/issues/5223",
"No one maintains Kubeflow 1.2 istio-dex distribution"
] | 2021-03-18T02:23:18 | 2021-03-19T01:06:49 | 2021-03-19T01:06:49 | NONE | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
I'm just installing Kubeflow with kfctl_istio_dex.yaml config in my Kubernetes local cluster following [this instruction](https://www.kubeflow.org/docs/started/k8s/kfctl-istio-dex/). Every pods are running successfully except `ml-pipeline-ui` and `ml-pipeline-visualizationserver`. The containers are running and ready except `istio-proxy` container
### What happened:
Here is the log of `istio-proxy` from `ml-pipeline-ui` pod
```
[2021-03-18 02:16:55.627][21][warning][config] [bazel-out/k8-opt/bin/external/envoy/source/common/config/_virtual_includes/grpc_stream_lib/common/config/grpc_stream.h:87] gRPC config stream closed: 14, no healthy upstream
[2021-03-18 02:16:55.627][21][warning][config] [bazel-out/k8-opt/bin/external/envoy/source/common/config/_virtual_includes/grpc_stream_lib/common/config/grpc_stream.h:50] Unable to establish new stream
2021-03-18T02:16:55.733771Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:16:57.734035Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:16:59.733651Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:17:01.733671Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:17:03.733793Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:17:05.733660Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:17:07.733940Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
```
And here is log of `istio-proxy` from `ml-pipeline-visualizationserver` pod
```
[2021-03-18 02:19:24.289][22][warning][config] [bazel-out/k8-opt/bin/external/envoy/source/common/config/_virtual_includes/grpc_stream_lib/common/config/grpc_stream.h:87] gRPC config stream closed: 14, all SubConns are in TransientFailure, latest connection error: connection error: desc = "transport: Error while dialing dial tcp 10.99.171.76:8060: connect: connection timed out"
2021-03-18T02:19:26.055989Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:28.056171Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:30.056271Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:32.056020Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:34.056093Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
[2021-03-18 02:19:35.179][22][warning][config] [bazel-out/k8-opt/bin/external/envoy/source/common/config/_virtual_includes/grpc_stream_lib/common/config/grpc_stream.h:87] gRPC config stream closed: 14, upstream connect error or disconnect/reset before headers. reset reason: connection failure
2021-03-18T02:19:36.056009Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
[2021-03-18 02:19:37.414][22][warning][config] [bazel-out/k8-opt/bin/external/envoy/source/common/config/_virtual_includes/grpc_stream_lib/common/config/grpc_stream.h:87] gRPC config stream closed: 14, upstream connect error or disconnect/reset before headers. reset reason: connection failure
2021-03-18T02:19:38.056105Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:40.056148Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
[2021-03-18 02:19:41.892][22][warning][config] [bazel-out/k8-opt/bin/external/envoy/source/common/config/_virtual_includes/grpc_stream_lib/common/config/grpc_stream.h:87] gRPC config stream closed: 2, failed to get root cert
2021-03-18T02:19:42.056084Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:44.055754Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
2021-03-18T02:19:46.056156Z info Envoy proxy is NOT ready: config not received from Pilot (is Pilot running?): cds updates: 0 successful, 0 rejected; lds updates: 0 successful, 0 rejected
```
### What did you expect to happen:
Every pods are running and ready
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)? I'm just following [this instruction](https://www.kubeflow.org/docs/started/k8s/kfctl-istio-dex/)
KFP version: 1.2.0
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"> i think the kubeflow is triggering the pipelines to be in a single node. \r\n\r\nAre you sure this is the case? KFP compiler creates and Argo Workflow object and Argo controller just creates Kubernetes Pods. Do you see anything strange with the pods that Argo creates? Perhaps the pods are not allowed to be sched... | 2021-03-17T20:53:31 | 2022-03-03T00:05:15 | null | NONE | null | When i run many pipelines from kubeflow UI. some of the pipelines are crashing with the error statement "This step is in Failed state with this message: failed with exit code 137". I think that this is a out of memory error. when we investigated from kubernetes side , we see that all the pipelines are running on a single node. Even though if we have enabled autoscaling between the nodes for kubeflow pipelines , they are getting into the single node. i think the kubeflow is triggering the pipelines to be in a single node. I don't know if this is a bug, if not ,I want to know how can we autoscale the pipelines between the nodes from kubeflow side
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"Hello!\r\n\r\nThere are two old issues about this, so let's link to them:\r\n* https://github.com/kubeflow/pipelines/issues/3226\r\n* https://github.com/kubeflow/pipelines/issues/3731\r\n\r\nMoreover, to fix this I suggest we rely on existing pagination support of the backend, thus making the the UI backwards comp... | 2021-03-17T16:45:07 | 2021-03-18T17:38:03 | 2021-03-18T17:38:03 | COLLABORATOR | null | ### What is the issue
Discussion: https://github.com/kubeflow/pipelines/pull/5311#pullrequestreview-613890507
It is hard to scale when there are many artifacts to show on [Artifact list](https://github.com/zijianjoy/pipelines/blob/master/frontend/src/pages/ArtifactList.tsx). We should implement something that API can accept pageSize and pageToken like [listPipelines](https://github.com/zijianjoy/pipelines/blob/master/frontend/src/pages/PipelineList.tsx#L174-L179).
It might require update on backend API, or we can implement this during KFPv2 implementation, because it touches metadata store communication.
/kind bug
/area frontend
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"Moving this to v2 compatible project, because this especially affects MLMD artifacts etc, they are important in v2 compatible mode"
] | 2021-03-17T16:40:04 | 2021-07-15T20:36:38 | 2021-07-15T20:36:38 | COLLABORATOR | null | ### Issue description
Discussion: https://github.com/kubeflow/pipelines/pull/5311#pullrequestreview-613890507
We received report from users that the clickable items on Artifact list page are hard to discover, because they are grey color. You can only discover that they are clickable when you hover over to that item, which will turn the buttons into blue and underlined. But they are actually darker grey while non-clickable items are lighter grey.
Our mitigation is to add underline strictly to clickable items on this page, but it breaks the existing styling consistency with other pages.
### What decision we want to make
We need to determine a clickable styling and apply to all pages: This style needs to be `accessible`, `discoverable` and `visual friendly`.
/kind bug
/area frontend
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"I'm not sure this is related to Kubeflow Pipelines. Have you checked how the created pod looks like? You can also replace the command with `sleep 9999` and then use `kubectl exec` to probe and debug the pod.\r\n\r\nWe advice our users to avoid using volumes directly (and creating ContainerOp directly as well).\r\n... | 2021-03-16T00:45:04 | 2022-04-18T17:27:50 | 2022-04-18T17:27:50 | NONE | null | More like a question kind
Currently, I have a pipeline contains a op like:
```
def preprocess_op(input_path, output_path):
op = dsl.ContainerOp(
name='process data',
image='image:latest',
command=[
'sh',
'-c',
],
arguments=[
f'cd /home && python test.py'
f'--input-path {input_path}'
f'--output-path {output_path}'
],
file_outputs={}
)
op.apply(onprem.mount_pvc(pvc_name="flow-test", volume_name="pipeline", volume_mount_path="/mnt/data"))
return op
```
I have a pvc called flow-test already bound
When I run:
`client.create_run_from_pipeline_func(test_pipeline, arguments={'input_path': '/mnt/data', 'output_path': '/'}, namespace='namespaceName')`
it shows `No such file or directory` so it seems /mnt/data cannot be found by the ContainerOp. So just wondering is it the correct way to get data from pvc inside the pipeline? If not, could anyone share what would be the correct way? I am using kubeflow for on-prem servers with k8s deployed on them.
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"This example is no longer maintained in this repo. I tried to fix it for kubeflow 1.0 at https://github.com/kubeflow/pipelines/pull/4126 a while ago, but seems like the original author is no longer active",
"cc @andreyvelich : What is the current status of this component? Should we perform a fix or remove this e... | 2021-03-15T21:58:34 | 2021-07-27T18:22:44 | 2021-07-27T18:22:44 | NONE | null | ### What steps did you take:
Run the e2e mnist notebook
### What happened:
Got TypeError: Katib - Launch Experiment() got an unexpected keyword argument 'parallel_trial_count'
Trying to execute
```
katib_experiment_launcher_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/katib-launcher/component.yaml')
op1 = katib_experiment_launcher_op(
experiment_name=name,
experiment_namespace=namespace,
parallel_trial_count=3,
max_trial_count=12,
objective=str(objectiveConfig),
algorithm=str(algorithmConfig),
trial_template=str(trialTemplate),
parameters=str(parameters),
metrics_collector=str(metricsCollectorSpec),
# experiment_timeout_minutes=experimentTimeoutMinutes,
delete_finished_experiment=False)
```
### What did you expect to happen:
Not throwing an error
### Environment:
kubeflow 1.2
How did you deploy Kubeflow Pipelines (KFP)?
part of kubeflow 1.2 on IBM cloud
KFP SDK version:
kfp 1.0.4
kfp-pipeline-spec 0.1.6
kfp-server-api 1.4.1
kfp-tekton 0.4.0
### Anything else you would like to add:
Seems affected by this PR https://github.com/kubeflow/pipelines/pull/4798
/kind bug
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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"
] | 2021-03-15T17:58:13 | 2022-04-18T17:27:48 | 2022-04-18T17:27:48 | CONTRIBUTOR | null | {
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"/assign",
"@zijianjoy should we aim for making [all arguments](https://cloud.google.com/sdk/gcloud/reference/ml-engine/versions/create) available for the user or keep a selected few(I see that we currently only support some options)? ",
"After looking in to this I believe this is actually already possible @Amr... | 2021-03-15T14:37:51 | 2022-03-03T00:05:29 | null | NONE | null | Hi,
I am using the ml-engine component to deploy a ML model to AI Platform Prediction (from a Kubeflow deployment in AI Platform Pipelines), however there is no available parameter to deploy the model into a particular region. This results in the model deployment defaulting to the us-central1 region. Is there any way to deploy a model using this component to a specific region?
Any help on this will be much appreciated!.
Thanks.
Link to component: https://github.com/kubeflow/pipelines/tree/master/components/gcp/ml_engine/deploy
**What steps did you take:**
1 - Trained a model using AI Platform Pipelines
2 - Saved this model to Google Cloud Storage as a .pkl file
3 - Tried to use the deploy component to deploy this model to AI Platform Prediction, however
**What happened:**
There is no region parameter for this deploy component hence the deployment happens in the default region (us-central1)
**What did you expect to happen:**
Be able to specify the region parameter in the input for this component. (Since we can do this when deploying the model from either gcloud commands/GCP Console
**Anything else you would like to add:**
/kind feature
/kind component | {
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"Hi @SaschaHeyer, thank you for the report!\n\nWhat is your TFX version? KFP cache shouldn't run on TFX pipelines. This is a bug.",
"Hi @Bobgy \r\nissue can be reproduced with 0.27.0 and 0.28.0",
"@SaschaHeyer can you provide a minimum reproducible TFX pipeline with a requirements.txt for all dependencies versi... | 2021-03-15T13:10:14 | 2021-07-14T05:24:59 | 2021-07-14T05:24:59 | NONE | null | ### What steps did you take:
1. deploy pipeline with one component
2. run pipeline with one component (👍 works)
3. add another component
4. run the pipeline (this time the output is taken from cache) (👎 fails)
### What happened:
The pipeline runs into the following error
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/opt/conda/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.7/site-packages/tfx/orchestration/kubeflow/container_entrypoint.py", line 360, in <module>
main()
File "/opt/conda/lib/python3.7/site-packages/tfx/orchestration/kubeflow/container_entrypoint.py", line 353, in main
execution_info = launcher.launch()
File "/opt/conda/lib/python3.7/site-packages/tfx/orchestration/launcher/base_component_launcher.py", line 198, in launch
self._exec_properties)
File "/opt/conda/lib/python3.7/site-packages/tfx/orchestration/launcher/base_component_launcher.py", line 167, in _run_driver
component_info=self._component_info)
File "/opt/conda/lib/python3.7/site-packages/tfx/dsl/components/base/base_driver.py", line 270, in pre_execution
driver_args, pipeline_info)
File "/opt/conda/lib/python3.7/site-packages/tfx/dsl/components/base/base_driver.py", line 158, in resolve_input_artifacts
producer_component_id=input_channel.producer_component_id)
File "/opt/conda/lib/python3.7/site-packages/tfx/orchestration/metadata.py", line 948, in search_artifacts
pipeline_info)
RuntimeError: Pipeline run context for PipelineInfo(pipeline_name: sentiment4, pipeline_root: gs://sascha-playground-doit-kubeflowpipelines-default/sentiment4, run_id: sentiment4-qnknl) does not exist
```
Assume the second component doesn't find the cached data because the component did not exist in the first run.
First run:
<img width="1216" alt="1run" src="https://user-images.githubusercontent.com/1991664/111158263-1e9c1200-8598-11eb-969f-370aec9c4be2.png">
Second run with additional component
<img width="1216" alt="2run" src="https://user-images.githubusercontent.com/1991664/111158281-22c82f80-8598-11eb-8e2a-ec3b070163f0.png">
### What did you expect to happen:
Pipeline run completes without errors
### Environment:
AI Platform Pipelines
How did you deploy Kubeflow Pipelines (KFP)?
AI Platform Pipelines
KFP version: https://github.com/kubeflow/pipelines/commit/d79071c0bef19442483abc101769a0d893e72f42
KFP SDK version: no pip in AI Platform Pipelines
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
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https://api.github.com/repos/kubeflow/pipelines/issues/5302 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5302/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5302/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5302/events | https://github.com/kubeflow/pipelines/issues/5302 | 831,792,710 | MDU6SXNzdWU4MzE3OTI3MTA= | 5,302 | Kubeflow / AI Platform Pipelines component visualization missing | {
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"Duplicate of #5303",
"This has the same root cause, so I am closing it"
] | 2021-03-15T13:06:45 | 2021-03-27T00:58:41 | 2021-03-27T00:58:41 | NONE | null | ### What steps did you take:
1. run the pipeline (visualization is rendered)
2. run the pipeline again (visualization is missing)
### What happened:
On the second run, the StatisticsGen component has missing static HTML and metadata.
This issue can be reproduced only if the run output is taken from the cache.
Runs not taken from cache work as expected.
I assume this is an issue related to the cache.
<img width="1211" alt="1" src="https://user-images.githubusercontent.com/1991664/111157846-a59cba80-8597-11eb-9d35-b95f77582e09.png">
<img width="1201" alt="11" src="https://user-images.githubusercontent.com/1991664/111157853-a6cde780-8597-11eb-99d0-66fdbac7c6b4.png">
### What did you expect to happen:
The visualization for the TFX StatisticsGen component is rendered
### Environment:
Google AI Platform Pipelines
How did you deploy Kubeflow Pipelines (KFP)?
Using Google AI Platform Pipelines
KFP version: https://github.com/kubeflow/pipelines/commit/d79071c0bef19442483abc101769a0d893e72f42
KFP SDK version: no pip in AI Platform Pipelines
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
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"/assign @Ark-kun @chensun "
] | 2021-03-13T12:41:59 | 2021-03-19T04:10:17 | 2021-03-19T04:10:17 | CONTRIBUTOR | null | ### What happened:
After https://github.com/kubeflow/pipelines/pull/5266, I noticed in tests, the warning
> Cannot validate the compiled workflow. Found the argo program in PATH, but it's not usable. argo v2.4.3 should work.
### What did you expect to happen:
KFP SDK can use latest argo CLI to validate workflows locally.
### Environment:
<!-- Please fill in those that seem relevant. -->
KFP SDK version: 1.4.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
/area sdk
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The root cause seems to be that, KFP SDK tries to lint empty input using argo CLI to verify if argo CLI works.
However, the default behavior of Argo CLI changed, it gives an error when validating empty string.
We should update to validate an empty workflow instead.
See https://github.com/kubeflow/pipelines/blob/21e3ef8311308c748c0b1656a5f8a06df0ea3045/sdk/python/kfp/compiler/compiler.py#L1094 | {
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"description": "The issue / pull request is stale, any activities remove this label.... | closed | false | null | [] | null | [
"We currently have the following support for time formatting: https://github.com/kubeflow/pipelines/blob/0795597562e076437a21745e524b5c960b1edb68/backend/src/crd/samples/scheduledworkflow/parameterized.yaml#L29-L33.\r\n\r\nDoes it fit your use case?",
"Hi @zijianjoy tried exploring the documentation. \r\n\r\nLet... | 2021-03-13T01:59:16 | 2022-04-18T17:27:53 | 2022-04-18T17:27:53 | NONE | null | We are trying to use KFP's [ScheduledTime] parameter in TFX Pipelines runtime parameters. [We have discussed](https://github.com/tensorflow/tfx/issues/3354) with @1025KB from the TFX project and he suggested we file this ticket to enable this functionality on the KFP side by allowing users to be able to specify transforms on KFP runtime parameters like [ScheduledTime], and have these transformed values accessible by the components in the running pipeline.
e.g. a user could specify a transform like iso_date_to_tfx_span("[ScheduledTime]") which would convert the scheduled time in ISO format to an integer representing the corresponding TFX span using the TFX utility libraries. This is an example interface, and will leave the details for the interface and the actual parameter storage and retrieval interface to TFX/KFP folks to suss out.
cc @casassg | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5292 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5292/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5292/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5292/events | https://github.com/kubeflow/pipelines/issues/5292 | 830,284,073 | MDU6SXNzdWU4MzAyODQwNzM= | 5,292 | Feature request: don't start run if obligatory parameters are missing | {
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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"
] | 2021-03-12T16:41:48 | 2022-04-18T17:28:01 | 2022-04-18T17:28:01 | NONE | null | /kind feature
**Why you need this feature**:
Currently it is possible to start a run even if some parameters that are obligatory in a step, are missing. It would be easier to have the option to make some parameters obligatory to begin with (just like pipeline, pipeline version, run name, experiment), making it impossible to start a run if these obligatory parameters are not passed. This would avoid creating a whole container that simply immediately raises an error because a parameter is missing.
**Describe the solution you'd like:**
Have an option to make a parameter obligatory before starting a run. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5288 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5288/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5288/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5288/events | https://github.com/kubeflow/pipelines/issues/5288 | 829,727,011 | MDU6SXNzdWU4Mjk3MjcwMTE= | 5,288 | [Bug] KeyValueStore fails to check the cached data with new data | {
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"cc @Ark-kun ",
"Thank you @lynnmatrix ! This bug makes sense and would you like to contribute a PR for the fix?",
"@zijianjoy, My colleague @michaelliqx has fixed it in PR #5290.",
"Thank you for the discovery and the fix.\r\nI wonder how this worked before....?\r\n\r\nUpdate: I see that this code was somew... | 2021-03-12T03:37:16 | 2021-03-24T06:08:06 | 2021-03-24T00:03:44 | MEMBER | null | Typo bug.
```
def store_value_bytes(self, key: str, data: bytes) -> str:
...
if cache_value_file_path.exists():
old_data = cache_value_file_path.write_bytes()
...
```
should be:
```
def store_value_bytes(self, key: str, data: bytes) -> str:
...
if cache_value_file_path.exists():
old_data = cache_value_file_path.read_bytes()
...
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5285 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5285/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5285/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5285/events | https://github.com/kubeflow/pipelines/issues/5285 | 829,102,215 | MDU6SXNzdWU4MjkxMDIyMTU= | 5,285 | Orchestration - Flakiness in small samples when using PNS executor | {
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"/assign @chensun @neuromage \r\n/cc @Ark-kun @capri-xiyue \r\n\r\nI think we need to discuss this problem, from KFP side, we could automatically mount emptyDir for users.",
"/cc @jessesuen @alexec\r\nDo you have any suggestions? Is my above understanding of the workarounds accurate?",
"Hmm, strange, I'm not se... | 2021-03-11T12:22:54 | 2021-08-22T07:03:03 | 2021-08-22T07:03:03 | CONTRIBUTOR | null | In https://github.com/kubeflow/pipelines/pull/5273, I switched to PNS executor by default. After that, it seems lightweight component sample fail more frequently than before.
(It failed 3 times consecutively in an example I found, but it seems to me that the last two failures should be fixed by https://github.com/kubeflow/pipelines/pull/5284, we can observe the actual flakiness rate after the change)
Symptom, pipeline components that run too fast fail with:
> failed to save outputs: could not chroot into main for artifact collection: container may have exited too quickly
https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/4147/kubeflow-pipeline-sample-test/1369916972707352576#1:build-log.txt%3A5681
Root cause seems to be: https://github.com/argoproj/argo-workflows/issues/1256#issuecomment-481471276
And workarounds can be:
* (hacky) let the main container sleep for a while
* (stable fix) mount the artifacts on an emptyDir | {
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"> Examples:\r\n> \\# Print the logs of a workflow:\r\n> \r\n> argo logs my-wf"
] | 2021-03-11T11:53:09 | 2021-03-12T03:39:24 | 2021-03-12T03:39:24 | CONTRIBUTOR | null | > [36msample-test-bpnv9-4034248067: unknown shorthand flag: 'w' in -w[0m
See https://storage.googleapis.com/oss-prow/pr-logs/pull/kubeflow_pipelines/4147/kubeflow-pipeline-sample-test/1369916972707352576/build-log.txt in https://github.com/kubeflow/pipelines/pull/4147.
It seems after certain versions, argo CLI no longer have the `-w` flag. It directly accepts workflow name as a positional argument.
(Note, the code path is only triggered when a pipeline fails, after we fix this issue, when pipeline samples fail, there should be detailed error log there)
/cc @chensun @zijianjoy @neuromage
/assign
I'll try to fix this. | {
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"@Bobgy is it possible to confirm what the expected behavior is with optional params?",
"/assign @chensun @Ark-kun ",
"Thank you for the detailed issue report.\r\n\r\nThe behavior is by design. There is no special/different behavior for multiple optional arguments. What you see is mostly the way you program par... | 2021-03-11T07:36:42 | 2022-03-13T01:10:31 | 2022-03-03T06:05:32 | NONE | null | ### What steps did you take:
When an input param is given as `optional: True` in the component YAML, and the Python program uses a CLI library such as argparse or click to accept options, the optional params get passed as **flags** instead of as **omitted arguments** as expected.
Given a `component.yaml` file such as:
```
name: myprint
inputs:
- {name: A, optional: true, type: String}
- {name: B, optional: true, type: String}
implementation:
container:
image: gcr.io/.../mycomp
command: [python3, /src/myprint.py]
args: [
--param1, {inputValue: A},
--param2, {inputValue: B},
]
```
And `myprint.py` that was built into the image above:
```
import argparse, json
def myprint(param_1: str, param_2: str) -> float:
print(json.dumps(locals(), indent=4))
parser = argparse.ArgumentParser()
parser.add_argument('--param1', type=str, default="default1")
parser.add_argument('--param2', type=str, default="default2")
args = parser.parse_args()
myprint(args.param1, args.param2)
```
### What happened:
**1) When we omit both arguments in the pipeline**
(Using the component spec from above)
```
myprint_op = kfp.components.load_component_from_file("component.yaml")
@dsl.pipeline()
def myprint_pipeline():
first_add_task = myprint_op() # neither input `a` nor `b` are supplied to myprint_op
run = client.create_run_from_pipeline_func(myprint_pipeline, arguments=arguments)
```
The run is compiled and submitted successfully.
But we see from the output that both params were passed in as **flags** instead of **arguments**, and **worse, `--param1` got passed in as the _value_ of `--param1`.**
```
# console output
{
"param_1": "--param2",
"param_2": null
}
```
By indicating both inputs as optional in the YAML spec, it seemed to have instead passed both arguments in as flags instead of arguments. i.e. equivalent of: `python3 /src/myprint.py --param1 --param2` instead of `python3 /src/myprint.py` (which would set param1 and param2 __argument__ values to None, allowing argparse to assign default values.)
**2. Programs with one optional argument**
In cases where our `.py` program only has only one optional argument, say `--param1`, and described as: `{name: param1, optional: true, type: String}`.
If we omit the param in the pipeline similar to before, the run compiles successfully. But during component runtime, we get the error:
```
Error: --param1 option requires an argument
```
**3. Adding defaults to optional inputs**
It seems that it's not possible to set a default value of null for any inputs via the YAML file.
There seems to be some incongruence between how non-null and null default values are treated:
1a. `{name: A, optional: false, default: 40}` - Pipeline compiles successfully when A is omitted, assigns 40 to A, as expected.
1b. `{name: A, optional: false, default: null}` - Pipeline fails to compile, complaining A is missing; but if it's in keeping with the previous case, the expected behaviour should be to assign null as the value of A
2a. `{name: A, optional: true, default: 40}` - Pipeline compiles successfully when A is omitted, and assigns 40 to A - this seems to be the exact same behaviour as in the case of 1a.?
2b. `{name: A, optional: true, default: null}` - Compiles successfully, but passes in --A as a flag; in this case it seems `{name: A, optional: true, default: null}` and `{name: A, optional: true}` behave in exactly in the same way.
In general, there doesn't seem to be an agreement as to when default values are assigned when optional is set to true or false. And this is especially the case when we attempt to set `default: null` (or `default: ~`).
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
KFP version:
https://github.com/kubeflow/pipelines/commit/743746b96e6efc502c33e1e529f2fe89ce09481c
KFP SDK version:
```
kfp 1.4.0
kfp-pipeline-spec 0.1.6
kfp-server-api 1.4.1
```
### Anything else you would like to add:
**All in all,** there seems to be two main ways of resolving this:
1. Change in the KFP library: Allow that when `optional: true`, and no default is set, do NOT send a flag to the component program - so that argparse/click within the component program can handle empty arguments properly.
2. With argparse/click library: Find a way to somehow have these CLI libraries parse an input as both a flag and an option. So that when KFP passes a flag into the component program, the CLI library can interpret the flag as if it's an option with a None value. Have dug around on this possibility, but this doesn't seem to be something that these libraries support.
Finally a temporary workaround we've been using:
3. I've created a wrapper around the .py component, and used the wrapper component as the entrypoint instead to the component in the YAML spec. The wrapper effectively removes any flags it receives, before invoking the actual component. e.g. assuming we omitted param1 and param2, KFP now calls `myprint_wrapper.py` with `python3 /src/myprint_wrapper.py --param1 --param2`. The wrapper then removes the flags --param1 --param2, before invoking the actual component without those erroneous flags: `python3 /src/myprint.py`
/kind bug
/area sdk
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Interesting. The hydra command-line format is different from the normal way of specifying command-line arguments that 99% of other command-line tools use (separating flag and value, instead of joining them with `=`).\r\n\r\nHowever, the `ComponentSpec`/`component.yaml` format supports such command lines via the `c... | 2021-03-11T06:13:45 | 2021-03-15T17:59:03 | 2021-03-15T17:57:21 | COLLABORATOR | null | Using `ContainerOp` to define component, users can use [hydra](https://hydra.cc/) arguments like shown below.
```
import kfp.dsl as dsl
import kfp.gcp as gcp
import kfp.compiler as compiler
....
transform = dsl.ContainerOp(
name="transform",
image="gcr.io/{}/image:latest".format(PROJECT),
arguments=[
"component={}/transformer".format(TEMPLATE_PRJ),
"common={}".format(ENV),
"common.template_prj={}".format(TEMPLATE_PRJ),
"common.project_api_key={}".format(project_api_key),
"common.data_api_key={}".format(data_api_key),
"common.model_id={}".format(model_id),
],
)
```
However, this is not easily achievable via YAML component. The following code results compile error when loading the component YAML.
```
name: TransformOp
inputs:
- {name: project_api_key, type: String}
- {name: data_api_key, type: String}
- {name: model_id, type: String}
implementation:
container:
image: gcr.io/%s/kubeflow-projects/causal_discovery
args:
- component=%s/transformer
- common=%s
- common.template_prj=%s
- common.project_api_key={inputValue: project_api_key}
- common.data_api_key={inputValue: data_api_key}
- common.model_id={inputValue: model_id}
```
This is because when we load and parse the YAML, `common.project_api_key={inputValue` is taken as a key of a dictionary, while `project_api_key}` is taken as the the value for the key, which is apparently wrong.
So we have a limitation here that a placeholder like `{inputValue: project_api_key}` must be on its own line (the list item).
/kind feature
/area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5280 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5280/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5280/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5280/events | https://github.com/kubeflow/pipelines/issues/5280 | 828,767,334 | MDU6SXNzdWU4Mjg3NjczMzQ= | 5,280 | Constructing dsl.ContainerOp instance directly in user code doesn't always raise the warning. | {
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"/assign @chensun \r\n/cc @Ark-kun ",
"Hmm.\r\nInteresting. We had this issue before and @munagekar fixed it by changing the warning type from `DeprecationWarning` to `FutureWarning`. https://github.com/kubeflow/pipelines/pull/4658\r\n\r\nIt looks like that warning is still not show to the users even when we see ... | 2021-03-11T05:38:46 | 2021-03-30T09:56:18 | 2021-03-30T09:56:18 | COLLABORATOR | null | ### What steps did you take:
Compile the following pipeline
```
import kfp
def _sample_component_op(
name: str, run_id: str, sample_metric_value: float
) -> kfp.dsl.ContainerOp:
arguments = [run_id, sample_metric_value]
return kfp.dsl.ContainerOp(
name=name,
image='library/hello-world:latest',
arguments=arguments,
)
@kfp.dsl.pipeline(name='minimul')
def sample_pipeline(
run_id: str = kfp.dsl.RUN_ID_PLACEHOLDER,
sample_metric_value: float = 0.5,
):
"""Execute main procedure of the pipeline."""
_sample_component_op("component", run_id, sample_metric_value)
```
### What happened:
No warning raised on using `ContainerOp` directly.
### What did you expect to happen:
Seeing the following warning:
```
/usr/local/lib/python3.7/dist-packages/kfp/dsl/_container_op.py:1039: FutureWarning: Please create reusable components instead of constructing ContainerOp instances directly. Reusable components are shareable, portable and have compatibility and support guarantees. Please see the documentation: https://www.kubeflow.org/docs/pipelines/sdk/component-development/#writing-your-component-definition-file The components can be created manually (or, in case of python, using kfp.components.create_component_from_func or func_to_container_op) and then loaded using kfp.components.load_component_from_file, load_component_from_uri or load_component_from_text: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_file
category=FutureWarning,
```
KFP SDK version: 1.4.0
### Anything else you would like to add:
For comparison, compiling the following example raises the warning as expected.
```
import kfp
@kfp.dsl.pipeline(name='hello-world-pipeline')
def sample_pipeline():
kfp.dsl.ContainerOp(
name='hello-world',
image='library/hello-world:latest',
arguments=[],
)
```
/kind bug
/area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5279 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5279/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5279/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5279/events | https://github.com/kubeflow/pipelines/issues/5279 | 828,299,878 | MDU6SXNzdWU4MjgyOTk4Nzg= | 5,279 | Python func component renames function arguments | {
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"/assign @Ark-kun @neuromage @chensun ",
"There seems to be an error in your function definition. I'm not fully sure what is the behavior you've intended, but you probably should not be using the `InputPath` annotation given your component function code:\r\nJust use `def hydrate_schema(synced_local_path: str`. Or... | 2021-03-10T20:32:53 | 2022-04-18T17:27:32 | 2022-04-18T17:27:32 | NONE | null | ### What steps did you take:
Converting a python func to compoent:
```python
import kfp
def hydrate_schema(
synced_local_path: kfp.components.InputPath(str), data_schema: str
) -> str:
import re
if not synced_local_path.endswith("/"):
synced_local_path += "/"
return re.sub(r"s3:.+\/", synced_local_path, data_schema)
hydrate_schema_op = kfp.components.create_component_from_func(hydrate_schema, output_component_file="replace_schema.yaml")
```
In the generated component yaml
```yaml
name: Hydrate schema
inputs:
- {name: synced_local, type: String}
- {name: data_schema, type: String}
outputs:
- {name: Output, type: String}
implementation:
container:
image: python:3.7
command:
- sh
- -ec
- |
program_path=$(mktemp)
echo -n "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |
def hydrate_schema(
synced_local_path, data_schema
):
import re
if not synced_local_path.endswith("/"):
synced_local_path += "/"
return re.sub(r"s3:.+\/", synced_local_path, data_schema)
def _serialize_str(str_value: str) -> str:
if not isinstance(str_value, str):
raise TypeError('Value "{}" has type "{}" instead of str.'.format(str(str_value), str(type(str_value))))
return str_value
import argparse
_parser = argparse.ArgumentParser(prog='Hydrate schema', description='')
_parser.add_argument("--synced-local", dest="synced_local_path", type=str, required=True, default=argparse.SUPPRESS)
_parser.add_argument("--data-schema", dest="data_schema", type=str, required=True, default=argparse.SUPPRESS)
_parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)
_parsed_args = vars(_parser.parse_args())
_output_files = _parsed_args.pop("_output_paths", [])
_outputs = hydrate_schema(**_parsed_args)
_outputs = [_outputs]
_output_serializers = [
_serialize_str,
]
import os
for idx, output_file in enumerate(_output_files):
try:
os.makedirs(os.path.dirname(output_file))
except OSError:
pass
with open(output_file, 'w') as f:
f.write(_output_serializers[idx](_outputs[idx]))
args:
- --synced-local
- {inputPath: synced_local}
- --data-schema
- {inputValue: data_schema}
- '----output-paths'
- {outputPath: Output}
```
The argument `synced_local_path` is replaced with `synced_local`.
Therefore, when using the component as
```python
local_data_schema = hydrate_schema_op(
data_schema=data_schema, synced_local_path=data_sync.output
)
```
Compiler complains that
```
TypeError: Hydrate schema() got an unexpected keyword argument 'synced_local_path'
```
### What happened:
The python func argument `synced_local_path` is renamed to `synced_local`
### What did you expect to happen:
The argument names are preserved after conversion.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
Local cluster deployment using Kind
KFP version: 1.2.0 <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: 1.2.0 <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"For long term 3 sound like the way to go. ",
"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... | 2021-03-10T16:51:51 | 2022-04-18T17:27:53 | 2022-04-18T17:27:53 | CONTRIBUTOR | null | ## The problem
Realized the problem from https://github.com/kubeflow/pipelines/pull/5273.
Argo artifact repository config is highly coupled to minio, so it's best put inside minio's manifests and get it reused in manifests envs that use minio.
However, if we save the config as a patch, it's only allowed to use patches in the kustomization.yaml 's same or sub-folders. So if we do not include the minio-argo-artifact-repository patch by default, all downstream manifests that use argo with minio will have to duplicate the config patch.
For Kubeflow, we have set up best practices for always using `--load_restrictor none`, so that there's no such restriction.
However, the argument is not available in `kubectl kustomize`.
## Potential Solutions
### Solution 1 - Wait for kubectl to upgrade kustomize (from upstream issue, looks like there will soon be a release)
Pros:
* consistent with Kubeflow
Cons:
* requiring an extra argument is still a common confusion, and sort of hacky
* we need to wait for next kubectl release and people using the latest kubectl version (can be a quarter)
### Solution 2 - Use configmap called `artifact-repositories` to choose default artifact repository.
Ref: https://argoproj.github.io/argo-workflows/configure-artifact-repository/
Pros:
* we no longer need to reuse the config as a patch, it can be a resource, so not limited by load_restrictor anymore
Cons:
* it only takes effect in the namespace that has it, so multi-user mode KFP doesn't work very well with this approach
* the feature was added in argo 3.0, so we need to upgrade first
### Solution 3 - Use kustomize components
https://kubectl.docs.kubernetes.io/guides/config_management/components/
this seems like exactly the canonical solution
Pros:
* this kustomize feature is exactly built to solve this problem
Cons:
* we need to upgrade to kustomize 3.7+, so we can no longer ask users to `kubectl apply -k manifests`
* kustomize components seem to be an alpha API: `kustomize.config.k8s.io/v1alpha1`
### Analysis of the solutions
Solution 1 & 2 has fatal problems.
It seems we should take Solution 3, which should work long term (but it's also an alpha API).
A prerequisite is either of the following:
* Wait for kubectl release that includes latest kustomize
* Update all documentation that were using `kubectl apply -k` to `kubectl apply -f <prehydrated-manifest>` or `kustomize build xxx | kubectl apply -f -` (when the manifest needs some var config, so it requires local hydration) and introduce kustomize installation process.
Maybe the only acceptable short term solution is to default workflow-controller-configmap's artifact repository config to use minio, so that most of the manifests do not need to change it. | {
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"For aiplatform, here is a sample of test failure log:\r\n\r\n```\r\n\r\nsample-test-92wvs-4170907803: Using endpoint [https://ml.googleapis.com/]\r\nsample-test-92wvs-4170907803: ERROR: (gcloud.ai-platform.predict) HTTP request failed. Response: {\r\nsample-test-92wvs-4170907803: \"error\": {\r\nsample-test-92wv... | 2021-03-10T00:58:54 | 2021-08-22T07:00:13 | 2021-08-22T07:00:13 | CONTRIBUTOR | null | kubeflow-pipeline-postsubmit-integration-test has been failing consecutively.
I did a quick check, the following samples are both flaky:
* aiplatform
* container_build
Creating this issue to track solving this
/assign @zijianjoy | {
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"Good catch.\r\nSome time ago the return value of the component function was not passed completely correctly. So the components had to whap output in a tuple when outputting a single list. The problem was fixed in the SDK, but the test was not updated."
] | 2021-03-09T21:47:37 | 2021-03-11T22:06:25 | 2021-03-11T22:06:25 | MEMBER | null | ### What steps did you take:
Run the parallelfor_item_argument_resolving compiler test example on Kubeflow Pipeline, the pipeline will fail because item arguments are wrapped into list of list of items instead of list of items
https://github.com/kubeflow/pipelines/blob/master/sdk/python/tests/compiler/testdata/parallelfor_item_argument_resolving.py
### What happened:
The `func_to_container_op` wraps the containerOp result into a list. Because the containerOp output is already a list of items, `func_to_container_op` warps the output into a list of list of items. e.g.
```python
@func_to_container_op
def produce_list_of_dicts() -> list:
return ([{"aaa": "aaa1", "bbb": "bbb1"}, {"aaa": "aaa2", "bbb": "bbb2"}],)
# Actual output in containerOp: [[{"aaa": "aaa1", "bbb": "bbb1"}, {"aaa": "aaa2", "bbb": "bbb2"}]]
```
<img width="1076" alt="Screen Shot 2021-03-09 at 1 44 30 PM" src="https://user-images.githubusercontent.com/10889249/110542224-a3d97f80-80dd-11eb-8b0a-134d04ec8870.png">
### What did you expect to happen:
The compiler example could be a typo and only need to remove the `[ ]` from the return statement.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. --> 1.4.0
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp --> 1.4.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"I'm also getting this error in the main landing page:\r\n```json\r\n{error: \"Invalid input error: ListRuns must filter by resource reference in multi-user mode.\",…}\r\ncode: 3\r\ndetails: [{@type: \"type.googleapis.com/api.Error\",…}]\r\n0: {@type: \"type.googleapis.com/api.Error\",…}\r\n@type: \"type.googleapis... | 2021-03-09T18:29:51 | 2022-03-03T06:05:33 | 2022-03-03T06:05:33 | NONE | null | ### What steps did you take:
I'm running a multiuser pipeline environment with kubeflow 1.2.0. From what I am seeing the `ml-pipeline-ui` service calls the `ml-pipeline` service when some actions on the ui are take like for example: start a pipeline run or create an experiment
I'm getting this error on the ui:
```
{"error":"Failed to authorize the request.: Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header.","message":"Failed to authorize the request.: Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header.","code":10,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Request header error: there is no user identity header.","error_details":"Failed to authorize the request.: Failed to authorize with API resource references: Bad request.: BadRequestError: Request header error: there is no user identity header.: Request header error: there is no user identity header."}]}
```
and I can see this on the `ml-pipeline` pod log:
```
I0309 16:31:31.191130 6 error.go:227] Request header error: there is no user identity header.
github.com/kubeflow/pipelines/backend/src/apiserver/server.getUserIdentity
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/util.go:304
github.com/kubeflow/pipelines/backend/src/apiserver/server.isAuthorized
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/util.go:377
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:74
```
from what I understand the `ml-pipline-ui service` should forward the userid via the header configured in the `kubeflow-config` configmap, but that does not seem to be happening.
Is there a flag to enable this behavior? Am I missing something?
### What did you expect to happen:
If the userid header field was passed I'd expect not to get the error and to be able to launch a pipeline.
### Environment:
kubeflow 1.2.0 on OCP 4.6
How did you deploy Kubeflow Pipelines (KFP)?
I used kfctl, with the `kfctl_k8s_istio.v1.2.0.yaml` config with a few adjustment for OCP.
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
/area frontend
/area backend
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```
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[34msample-test-bg6w4-565595884: [0;32m/usr/local/lib/python3.7/dist-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/_component_bridge.py[0m in [0;36m_attach_v2_specs[0;34m(task, component_spec, arguments)[0m[0m
[34msample-test-bg6w4-565595884: [1;32m 345[0m input_name, component_spec.inputs)[0m
[34msample-test-bg6w4-565595884: [1;32m 346[0m importer_specs[input_name] = importer_node.build_importer_spec([0m
[34msample-test-bg6w4-565595884: [0;32m--> 347[0;31m input_type_schema=type_schema, constant_value=argument_value)[0m
[34msample-test-bg6w4-565595884: [0m[1;32m 348[0m [0;32melif[0m [0misinstance[0m[0;34m([0m[0margument_value[0m[0;34m,[0m [0mint[0m[0;34m)[0m[0;34m:[0m[0;34m[0m[0;34m[0m[0m[0m
[34msample-test-bg6w4-565595884: [1;32m 349[0m pipeline_task_spec.inputs.parameters[[0m
[34msample-test-bg6w4-565595884: [0m
[34msample-test-bg6w4-565595884: [0;32m/usr/local/lib/python3.7/dist-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/importer_node.py[0m in [0;36mbuild_importer_spec[0;34m(input_type_schema, pipeline_param_name, constant_value)[0m[0m
[34msample-test-bg6w4-565595884: [1;32m 42[0m """[0m
[34msample-test-bg6w4-565595884: [1;32m 43[0m assert bool(pipeline_param_name) != bool(constant_value), ([0m
[34msample-test-bg6w4-565595884: [0;32m---> 44[0;31m [0;34m'importer spec should be built using either pipeline_param_name or '[0m[0;34m[0m[0;34m[0m[0m[0m
[34msample-test-bg6w4-565595884: [0m[1;32m 45[0m 'constant_value.')[0m
[34msample-test-bg6w4-565595884: [1;32m 46[0m [0mimporter_spec[0m [0;34m=[0m [0mpipeline_spec_pb2[0m[0;34m.[0m[0mPipelineDeploymentConfig[0m[0;34m.[0m[0mImporterSpec[0m[0;34m([0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m[0m
[34msample-test-bg6w4-565595884: [0m
[34msample-test-bg6w4-565595884: [0;31mAssertionError[0m: importer spec should be built using either pipeline_param_name or constant_value.[0m
[34msample-test-bg6w4-565595884: KFP API host is 3edeebea8e9ac0e0-dot-us-east1.pipelines.googleusercontent.com[0m
[34msample-test-bg6w4-565595884: Run the sample tests...[0m
[34msample-test-bg6w4-565595884: Traceback (most recent call last):[0m
[34msample-test-bg6w4-565595884: File "/python/src/github.com/kubeflow/pipelines/test/sample-test/sample_test_launcher.py", line 260, in <module>[0m
[34msample-test-bg6w4-565595884: main()[0m
[34msample-test-bg6w4-565595884: File "/python/src/github.com/kubeflow/pipelines/test/sample-test/sample_test_launcher.py", line 256, in main[0m
[34msample-test-bg6w4-565595884: 'component_test': ComponentTest[0m
[34msample-test-bg6w4-565595884: File "/usr/local/lib/python3.7/dist-packages/fire/core.py", line 141, in Fire[0m
[34msample-test-bg6w4-565595884: component_trace = _Fire(component, args, parsed_flag_args, context, name)[0m
[34msample-test-bg6w4-565595884: File "/usr/local/lib/python3.7/dist-packages/fire/core.py", line 471, in _Fire[0m
[34msample-test-bg6w4-565595884: target=component.__name__)[0m
[34msample-test-bg6w4-565595884: File "/usr/local/lib/python3.7/dist-packages/fire/core.py", line 681, in _CallAndUpdateTrace[0m
[34msample-test-bg6w4-565595884: component = fn(*varargs, **kwargs)[0m
[34msample-test-bg6w4-565595884: File "/python/src/github.com/kubeflow/pipelines/test/sample-test/sample_test_launcher.py", line 183, in run_test[0m
[34msample-test-bg6w4-565595884: nbchecker.check()[0m
[34msample-test-bg6w4-565595884: File "/python/src/github.com/kubeflow/pipelines/test/sample-test/check_notebook_results.py", line 74, in check[0m
[34msample-test-bg6w4-565595884: experiment_id = client.get_experiment(experiment_name=experiment).id[0m
[34msample-test-bg6w4-565595884: File "/usr/local/lib/python3.7/dist-packages/kfp-1.4.1-py3.7.egg/kfp/_client.py", line 454, in get_experiment[0m
[34msample-test-bg6w4-565595884: raise ValueError('No experiment is found with name {}.'.format(experiment_name))[0m
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5257 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5257/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5257/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5257/events | https://github.com/kubeflow/pipelines/issues/5257 | 824,311,950 | MDU6SXNzdWU4MjQzMTE5NTA= | 5,257 | VolumeOp was not able to create PVC | {
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"/assign @elikatsis \r\nCan you help with this issue?",
"Kubeflow cache's steps, so given the same inputs, it skips the step and instead fetches outputs from minio. \r\nVolume op has no outputs in Minio so ideally VolumeOp should not be cached. \r\n\r\nIn this case you would need to change the name of the PVC. \r... | 2021-03-08T08:24:43 | 2022-03-02T18:05:18 | null | NONE | null | ### What steps did you take:
A simple pipeline with one vol_op and one simple step that mounts the pvc created.
```python
# minimal example
import kfp
@kfp.dsl.pipeline(
name="data download and upload",
description="Data IO test"
)
def volume_pipeline():
# shared vol
vop = kfp.dsl.VolumeOp(
name="volume_creation",
resource_name="sharedpvc",
size="5Gi",
modes=["RWO"]
)
# mount the vol
simple_task = kfp.dsl.ContainerOp(
name="simple task",
image="bash",
arguments=[
"echo",
"hello",
">/data/hello.text"
]
).add_pvolumes({
"/data": vop.volume
})
# run the pipeline
client = kfp.Client()
client.create_run_from_pipeline_func(volume_pipeline, arguments={})
```
### What happened:
The VolumeOp was not able to create the PVC, therefore the depending task complains about not finding the PVC.
`kubectl get pvc -n kubeflow | grep sharedpvc` didn't return any results.
### What did you expect to happen:
The VolumeOp shall create a PVC named `sharedpvc`.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
Deploying Kubeflow Pipelines on a local kind cluster
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: 1.2.0 <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: 1.4.0 <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
The log of the VolumneOp indicates
```
This step output is taken from cache.
```
I was trying to prevent it from using the cache but didn't succeed.
The manifest from the VolumneOp
```
apiVersion: v1 kind: PersistentVolumeClaim metadata: name: '{{workflow.name}}-sharedpvc' spec: accessModes: - RWO resources: requests: storage: 5Gi
```
The log of the depending task indicates
```
This step is in Pending state with this message: Unschedulable: persistentvolumeclaim "{{tasks.volume-creation.outputs.parameters.volume-creation-name}}" not found
```
Storage class used
``` yaml
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"storage.k8s.io/v1","kind":"StorageClass","metadata":{"annotations":{"storageclass.kubernetes.io/is-default-class":"true"},"name":"standard"},"provisioner":"rancher.io/local-path","reclaimPolicy":"Delete","volumeBindingMode":"WaitForFirstConsumer"}
storageclass.kubernetes.io/is-default-class: "true"
creationTimestamp: "2020-12-29T06:31:17Z"
name: standard
resourceVersion: "195"
selfLink: /apis/storage.k8s.io/v1/storageclasses/standard
uid: 5dbc1bff-b488-4d3a-b45f-e710cf96a415
provisioner: rancher.io/local-path
reclaimPolicy: Delete
volumeBindingMode: WaitForFirstConsumer
```
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5254 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5254/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5254/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5254/events | https://github.com/kubeflow/pipelines/issues/5254 | 823,672,141 | MDU6SXNzdWU4MjM2NzIxNDE= | 5,254 | Pip package kfp 1.4.1 and kfp-server-api 1.4.0 missing | {
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"/cc @chensun \n\nRecently, KFP sdk has been released in separate schedule than backend. There hasn't been enough communication about it.\n\nKfp-server-api 1.4.0 missing was a mistake, but I no longer see the need to release it when 1.4.1 is out.",
"Thanks for confirming this. This is something I wanted to clear ... | 2021-03-06T15:15:10 | 2021-05-10T00:50:48 | 2021-03-24T08:29:54 | CONTRIBUTOR | null | While updating some images I noticed a discrepancy with the releases of `kfp` and `kfp-server-api` on PyPi. `kfp` 1.4.1 is not available on PyPi but `kfp-server-api` 1.4.1 is available. In turn `kfp` 1.4.0 is available and `kfp-server-api` 1.4.1 is not available. Is this intentional (were kfp 1.4.1 and kfp-server-api 1.4.0 never released) or has something gone wrong with pushing the package?
/cc @Bobgy | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5252 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5252/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5252/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5252/events | https://github.com/kubeflow/pipelines/issues/5252 | 823,281,436 | MDU6SXNzdWU4MjMyODE0MzY= | 5,252 | I can't get sorted runs based on a metric using the kfp python package | {
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"u... | closed | false | null | [] | null | [] | 2021-03-05T17:28:43 | 2021-03-08T15:17:59 | 2021-03-08T15:17:59 | NONE | null | ### What steps did you take:
I am trying to get runs sorted on a particular metric using the kfp python package. I am calling the `list_runs` function and passing the `sort_by` parameter.
i.e. ```list_runs(sort_by='metric:accuracy_score asc')```
### What happened:
Getting the following error:
```
HTTP response body: {"error":"Failed to create list options: Invalid input error: Invalid sorting field: \"metric:accuracy_score\": %!s(\u003cnil\u003e)","message":"Failed to create list options: Invalid input error: Invalid sorting field: \"metric:accuracy_score\": %!s(\u003cnil\u003e)","code":3,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Invalid sorting field: \"metric:accuracy_score\": %!s(\u003cnil\u003e)","error_details":"Failed to create list options: Invalid input error: Invalid sorting field: \"metric:accuracy_score\": %!s(\u003cnil\u003e)"}]}
```
### Environment:
<!-- Please fill in those that seem relevant. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
kfp 1.4.0
kfp-pipeline-spec 0.1.6
kfp-server-api 1.4.1
### Anything else you would like to add:
I was expecting this https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/model/run.go#L107 to return true.
Any ideas what the problem may be?
/kind bug
/area backend
/area sdk
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"/assign @chensun @Ark-kun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Any update on this?",
"This issue has been automatically marked as stale because it has not had recen... | 2021-03-05T06:24:52 | 2022-03-02T10:06:30 | null | CONTRIBUTOR | null | ### What steps did you take:
I am trying to pass the output of one step to resourceOp instead of the actual manifest
```
c_op = dsl.ContainerOp(
name='patch',
image='docker.intuit.com/data-mlplatform/mlp-components/service/patch',
command=['python3','mlp/components/patch.py'],
arguments=[ "--manifest", manifest,
"--patch", patch],
file_outputs={'train': '/output.txt'}
)
#create training job
op = dsl.ResourceOp(
name="train",
action="create",
k8s_resource=c_op.outputs['train'],
success_condition="status.trainingJobStatus==Completed,status.secondaryStatus==Completed",
failure_condition="status.trainingJobStatus==Failed,status.secondaryStatus==Failed",
attribute_outputs={
"job-name": "{.status.sageMakerTrainingJobName}",
"model-path": "{.status.modelPath}",
"cloud-watch-url": "{.status.cloudWatchLogUrl}"
})
```
### What happened:
it throws following error during workflow validation
`Error: time="2021-03-04T22:09:12-08:00" level=fatal msg="/dev/stdin: templates.test-pipeline.tasks.train templates.train.resource.manifest must be a valid yaml"`
After checking the workflow manifest, manifest value is separated by | (see below) which is the actual reason behind the issue, workflow writer should not have appended this | in case if the manifest is parameterized.
```
- name: train
resource:
action: create
successCondition: status.trainingJobStatus==Completed,status.secondaryStatus==Completed
failureCondition: status.trainingJobStatus==Failed,status.secondaryStatus==Failed
manifest: |
'{{inputs.parameters.patch-train}}'
inputs:
parameters:
- {name: patch-train}
outputs:
parameters:
- name: train-cloud-watch-url
valueFrom: {jsonPath: '{.status.cloudWatchLogUrl}'}
- name: train-job-name
valueFrom: {jsonPath: '{.status.sageMakerTrainingJobName}'}
- name: train-manifest
valueFrom: {jsonPath: '{}'}
- name: train-model-path
valueFrom: {jsonPath: '{.status.modelPath}'}
- name: train-name
valueFrom: {jsonPath: '{.metadata.name}'}
```
### What did you expect to happen:
The compiler should write the value of manifest as `manifest: '{{inputs.parameters.patch-train}}'` in case if manifest value is parameterized.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
local
KFP version: 1.4.0
KFP SDK version: 1.4.0
### Anything else you would like to add:
When I fixed the workflow manually by removing | and ran the `argo lint` command which workflow validator does, it passes
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
pipeline.yaml
```
import os
from kfp import dsl, components
from training_component import mlp_train_op
training_manifest = """
apiVersion: sagemaker.aws.amazon.com/v1
kind: TrainingJob
metadata:
name: xgboost-mnist
spec:
roleArn: SAGEMAKER_EXECUTION_ROLE_ARN
region: us-east-1
algorithmSpecification:
trainingImage: 811284229777.dkr.ecr.us-east-1.amazonaws.com/xgboost:latest
trainingInputMode: File
outputDataConfig:
s3OutputPath: s3://BUCKET_NAME/xgboost-mnist/models/
inputDataConfig:
- channelName: train
dataSource:
s3DataSource:
s3DataType: S3Prefix
s3Uri: s3://BUCKET_NAME/xgboost-mnist/train/
s3DataDistributionType: FullyReplicated
contentType: text/csv
compressionType: None
- channelName: validation
dataSource:
s3DataSource:
s3DataType: S3Prefix
s3Uri: s3://BUCKET_NAME/xgboost-mnist/validation/
s3DataDistributionType: FullyReplicated
contentType: text/csv
compressionType: None
resourceConfig:
instanceCount: 1
instanceType: ml.m4.xlarge
volumeSizeInGB: 5
hyperParameters:
- name: max_depth
value: "5"
- name: eta
value: "0.2"
- name: gamma
value: "4"
- name: min_child_weight
value: "6"
- name: silent
value: "0"
- name: objective
value: multi:softmax
- name: num_class
value: "10"
- name: num_round
value: "10"
stoppingCondition:
maxRuntimeInSeconds: 86400
"""
@dsl.pipeline(
name="Test Pipeline",
description="Kubeflow test pipeline")
def pipeline():
train = mlp_train_op(manifest=training_manifest, patch='')
if __name__ == "__main__":
kfp.compiler.Compiler().compile(pipeline, __file__ + ".zip")
```
training_component.yaml
```
import logging
import os
import argparse
from kfp import dsl, components
import yaml
def mlp_train_op(manifest, patch):
c_op = dsl.ContainerOp(
name='patch',
image='docker.intuit.com/data-mlplatform/mlp-components/service/patch',
command=['python3','mlp/components/patch.py'],
arguments=[ "--manifest", manifest,
"--patch", patch],
file_outputs={'train': '/output.txt'}
)
#create training job
op = dsl.ResourceOp(
name="train",
action="create",
k8s_resource=c_op.outputs['train'],
success_condition="status.trainingJobStatus==Completed,status.secondaryStatus==Completed",
failure_condition="status.trainingJobStatus==Failed,status.secondaryStatus==Failed",
attribute_outputs={
"job-name": "{.status.sageMakerTrainingJobName}",
"model-path": "{.status.modelPath}",
"cloud-watch-url": "{.status.cloudWatchLogUrl}"
})
return op
```
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https://api.github.com/repos/kubeflow/pipelines/issues/5244 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5244/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5244/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5244/events | https://github.com/kubeflow/pipelines/issues/5244 | 822,627,271 | MDU6SXNzdWU4MjI2MjcyNzE= | 5,244 | Cache Webhook "disabled" by default in Azure | {
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{
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"node_id": "MDU6TGFiZWwxMDczMTUzOT... | closed | false | null | [] | null | [
"@dtzar @eedorenko @sudivate",
"In azure, the problem is that istio pods are not being injected to the pods in kubeflow. \r\n\r\nAzure enforces that the `MutatingWebhookConfiguration - istio-sidecar-injector` in kubeflow is being automatically edited by AKS to add the following match expression in the namespaceSe... | 2021-03-05T00:30:56 | 2022-03-11T06:04:49 | 2022-03-11T06:04:49 | NONE | null | ### What happened:
[A clear and concise description of what the bug is.]
Right now, mutating webhooks are used for components such as the cache-server. Previously there was an issue with the knative webhook, so the label "control-plane" was attached to prevent the webhook from triggering all the time. (Refer to https://github.com/kubeflow/kubeflow/issues/4511). However, Azure by default adds the below namespace selector to mutatingwebhooks to prevent applying to AKS internal namespaces. (https://github.com/Azure/AKS/issues/1771)
```
namespaceSelector:
matchExpressions:
- key: control-plane
operator: DoesNotExist
```
As the KF namespace comes with "control-plane: kubeflow", this causes the cache server to fail to mutate any pods in Kubeflow.
### What did you expect to happen:
It seems unfair to expect Kubeflow to fix this issue, as this dependency is inherently caused by Azure upstream. Perhaps we can update the Azure docs / default deploy to tell the users that these components won't work as intended?
### Environment:
<!-- Please fill in those that seem relevant. -->
Azure
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. --> 1.2
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp --> 1.4
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5240 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5240/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5240/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5240/events | https://github.com/kubeflow/pipelines/issues/5240 | 822,412,063 | MDU6SXNzdWU4MjI0MTIwNjM= | 5,240 | Presubmit test failing 03/04/2021 | {
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} | [] | closed | false | null | [] | null | [] | 2021-03-04T19:00:01 | 2021-03-04T20:43:48 | 2021-03-04T20:43:48 | COLLABORATOR | null | Error:
```
Traceback (most recent call last):
File "/samples/core/train_until_good/train_until_good.py", line 1, in <module>
import kfp; kfp.components.default_base_image_or_builder="gcr.io/google-appengine/python:2020-03-31-141326"
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/__init__.py", line 23, in <module>
from . import dsl
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/__init__.py", line 17, in <module>
from ._pipeline import Pipeline, pipeline, get_pipeline_conf, PipelineConf
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/_pipeline.py", line 20, in <module>
from kfp.dsl import _component_bridge
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/_component_bridge.py", line 26, in <module>
from kfp.dsl import component_spec as dsl_component_spec
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/component_spec.py", line 21, in <module>
from kfp.dsl import type_utils
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/type_utils.py", line 26, in <module>
'model': ontology_artifacts.Model.get_artifact_type(),
File "/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/ontology_artifacts.py", line 37, in get_artifact_type
with open(schema_file_path) as schema_file:
FileNotFoundError: [Errno 2] No such file or directory: '/usr/local/lib/python3.7/site-packages/kfp-1.4.1-py3.7.egg/kfp/dsl/ontology_type_schemas/model.yaml'
The command '/bin/sh -c set -e; < /samples/sample_config.json jq .[].file --raw-output | while read pipeline_yaml; do pipeline_py="${pipeline_yaml%.yaml}"; mv "$pipeline_py" "${pipeline_py}.tmp"; echo 'import kfp; kfp.components.default_base_image_or_builder="gcr.io/google-appengine/python:2020-03-31-141326"' | cat - "${pipeline_py}.tmp" > "$pipeline_py"; dsl-compile --py "$pipeline_py" --output "$pipeline_yaml" || python3 "$pipeline_py"; done' returned a non-zero code: 1
ERROR
ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: step exited with non-zero status: 1
```
The yaml file was added by #5197
I think the fix should be include these yaml files in setuptools.
/cc @dushyanthsc @capri-xiyue | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5238 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5238/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5238/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5238/events | https://github.com/kubeflow/pipelines/issues/5238 | 821,826,483 | MDU6SXNzdWU4MjE4MjY0ODM= | 5,238 | Issue in decoding the output artifact fetched using self._run_api.read_artifact() in Kubeflow client | {
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"@vishalsmb I think https://github.com/kubeflow/pipelines/issues/4327#issuecomment-687255001 is probably a good example"
] | 2021-03-04T07:01:24 | 2021-03-30T02:42:53 | 2021-03-30T02:42:53 | NONE | null | I'm fetching an artifact output using kubeflow client. When I try to decompress the returned value, its returning junk characters along with the actual content. Attaching the sample code. Please let me know if any is wrong with the decoding that I'm doing here.
Code :
`from kfp import Client
import base64
import gzip
kubeflowService = Client(host='')
artifact = kubeflowService ._run_api.read_artifact(run_id=run_id, node_id=key, artifact_name=artifact_name)
value = artifact.data
print(gzip.decompress(base64.b64decode(value))`
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https://api.github.com/repos/kubeflow/pipelines/issues/5236 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5236/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5236/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5236/events | https://github.com/kubeflow/pipelines/issues/5236 | 821,698,248 | MDU6SXNzdWU4MjE2OTgyNDg= | 5,236 | [Testing] postsubmit mkp test failure 2021.3.4 | {
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"The failure: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-mkp-e2e-test/1365452157549023232#1:build-log.txt%3A7816\r\n\r\nStep #1 - \"verify\": 37s Warning FailedScheduling pod/ml-pipeline-55bbc45946-m7s66 0/3 nodes are available: 2 Insufficient memo... | 2021-03-04T02:50:29 | 2021-04-01T02:07:40 | 2021-03-04T06:03:41 | CONTRIBUTOR | null | First failing mkp test: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-mkp-e2e-test/1365452157549023232
Root cause seems to be: https://github.com/kubeflow/pipelines/pull/5158 | {
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"Hi @andre-lx, is https://github.com/kubeflow/pipelines/issues/3938 the corresponding config?",
"I think: \r\n```\r\n.set_ttl_seconds_after_finished(seconds: int)\r\n```\r\nis what you are looking for if you like to remove the underlying pipeline after if is finished for a specific pipeline, reference posted by B... | 2021-03-03T12:02:46 | 2021-08-11T19:26:12 | 2021-03-24T14:41:52 | NONE | null | Hi.
I was trying to delete the pods of the pipelines after running.
This will help a lot, for example in gpu pipeline functions. As an example:
```python
def some_func():
some_task = (
kfp_generator().set_gpu_limit(1, 'nvidia')
```
In our enviorment, this pipeline step will start a gpu node in our pool, but, the only way of deleting this node is deleting the run.
In the argo project, I can see that this is possible using the `podGC`: https://github.com/Duske/argo/blob/bd4750fbb9413eea8ced0ca642664f54fb5b3c47/examples/pod-gc-strategy.yaml#L9-L15
Currently, there are any way of doing this throw the kfp pipeline in python?
Thanks | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5232 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5232/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5232/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5232/events | https://github.com/kubeflow/pipelines/issues/5232 | 820,897,119 | MDU6SXNzdWU4MjA4OTcxMTk= | 5,232 | Upgrade argo image to 2.12+ | {
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"/assign",
"I found that there's now a maintained tool to extract licenses from go libraries: https://github.com/google/go-licenses.\r\n\r\nWe can use it instead of our home-made tool.\r\n\r\nUPDATE: I took a look and it seems this new tool doesn't support go modules, so it could only get licenses using a library... | 2021-03-03T08:56:23 | 2021-03-11T06:03:11 | 2021-03-11T06:03:10 | CONTRIBUTOR | null | Part of https://github.com/kubeflow/pipelines/issues/4553
/cc @NikeNano | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5230 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5230/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5230/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5230/events | https://github.com/kubeflow/pipelines/issues/5230 | 820,769,343 | MDU6SXNzdWU4MjA3NjkzNDM= | 5,230 | 403 error or empty response when using KFP SDK to connect to AI Platform Pipelines / KFP standalone on GCP | {
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"@Bobgy I also found this problem today. The solution you suggested it solves well thanks.\r\n\r\nI want to know why this changes happened, and where I can find out before this kind of changes occurs.",
"Tnx @Bobgy , we also had the problem starting today. Your solution fixed it, though we had to use kfp==1.4.0\r... | 2021-03-03T05:50:35 | 2021-04-09T00:36:41 | 2021-04-09T00:36:41 | CONTRIBUTOR | null | The problem just started happening yesterday.
To resolve the problem, ensure you are doing BOTH of the following:
1. Use "kfp" SDK version 0.5.2 or >= 1.0.4:
e.g. `pip install kfp==0.5.2` or `pip install kfp==1.0.4` or `pip install --upgrade kfp`.
You can check which version you are using via `pip list | grep kfp`.
2. Use explicit https protocol, eg from python:
```
import kfp
client = kfp.Client(host='https://*.pipelines.googleusercontent.com')
```
when following [the documentation to connect to KFP host in AI Platform Pipelines / KFP standalone on GCP](https://cloud.google.com/ai-platform/pipelines/docs/connecting-with-sdk). | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5229 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5229/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5229/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5229/events | https://github.com/kubeflow/pipelines/issues/5229 | 820,125,839 | MDU6SXNzdWU4MjAxMjU4Mzk= | 5,229 | missing "hidden input" type for enabling credential input to pipline | {
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"@klaimane I'd suggest mounting k8s secret into related steps, so it's not handle by the pipeline system.",
"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 autom... | 2021-03-02T15:28:41 | 2022-04-28T18:00:19 | 2022-04-28T18:00:19 | NONE | null | I need to pass credentials to the pipe in order to authenticate, but currently all pipeline input parameters are visible in the UI and pipe logs. We need a dedicated input type that is hidden (e.g. displays asterisks) to enable users to authenticate securely without the credential showing up in the pipe UI or logs.. | {
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"Note most users of Kubeflow experiencing this bug cannot afford to wait 6-9 months for the next release which may or may not address this problem. This bug breaks this product. We need a solution immediately. This part of the product is considered STABLE. ",
"Kubernetes installed via Kubespray ",
"Hi @ReggieCa... | 2021-03-01T20:54:25 | 2023-05-14T21:58:36 | null | NONE | null | ### What steps did you take:
KFP in KF 1.2.0 with Dex on K8s 1.18.9 does not work. I receive an error in the KF dashboard when attempting to view pipelines:
Error: failed to retrieve list of pipelines. Click Details for more information. -> An error occured, no healthy upstream
### What happened:
Installed Kubeflow 1.2.0 on-prem as per installation instructions. Any attempt to see pipelines or use pipelines fails.
### What did you expect to happen:
I expected to be able to use Pipelines
### Environment:
Kubernetes version 1.18.9
Kubeflow version 1.2.0
Installed with Dex, configured after deploy to use LDAP.
ml-pipelines pod fails to start completely. Logs indicate
How did you deploy Kubeflow Pipelines (KFP)?
Installed Kubeflow Pipelines as part of Kubeflow installation for on-prem with dex.
KFP version: 1.0.4
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
I HAVEN'T GOTTEN FAR ENOUGH TO USE THIS!
### Anything else you would like to add:
ml-pipeline pod refuses to run:
```
$ kubectl get pods -n kubeflow
NAME READY STATUS RESTARTS AGE
admission-webhook-bootstrap-stateful-set-0 1/1 Running 0 4d20h
admission-webhook-deployment-5d9ccb5696-f6zs6 1/1 Running 0 4d20h
application-controller-stateful-set-0 1/1 Running 0 4d21h
argo-ui-684bcb587f-z84nh 1/1 Running 0 4d16h
cache-deployer-deployment-6667847478-7h2w8 2/2 Running 2 4d21h
cache-server-bd9c859db-755zj 2/2 Running 527 4d21h
centraldashboard-895c4c768-46xgc 1/1 Running 0 4d21h
jupyter-web-app-deployment-6588c6f544-c5m45 1/1 Running 0 3d3h
katib-controller-75c8d47f8c-5k2tr 1/1 Running 0 4d21h
katib-db-manager-6c88c68d79-cgxdh 1/1 Running 0 4d16h
katib-mysql-858f68f588-zvhnj 1/1 Running 0 4d21h
katib-ui-68f59498d4-bkscp 1/1 Running 0 4d21h
kfserving-controller-manager-0 2/2 Running 0 36h
kubeflow-pipelines-profile-controller-69c94df75b-xtpfj 1/1 Running 0 4d21h
metacontroller-0 1/1 Running 0 4d21h
metadata-db-757dc9c7b5-pt75k 1/1 Running 0 4d21h
metadata-envoy-deployment-6ff58757f6-57pjc 1/1 Running 0 4d21h
metadata-grpc-deployment-76d69f69c8-xcmjk 1/1 Running 3 4d21h
metadata-writer-6d94ffb7df-mhnxj 2/2 Running 1 4d21h
minio-66c9cd74c9-jrss8 1/1 Running 0 4d21h
ml-pipeline-54989c9946-s2f46 1/2 Running 926 4d21h
ml-pipeline-persistenceagent-7f6bf7646-ldct6 2/2 Running 0 4d21h
ml-pipeline-scheduledworkflow-66db7bcf5d-q244j 2/2 Running 0 4d16h
ml-pipeline-ui-756b58fb-gpwn9 2/2 Running 0 4d21h
ml-pipeline-viewer-crd-58f59f87db-dmj2l 2/2 Running 2 4d21h
ml-pipeline-visualizationserver-6f9ff4974-k4cf9 2/2 Running 0 4d21h
mpi-operator-77bb5d8f4b-w4dhj 1/1 Running 0 4d21h
mxnet-operator-68b688bb69-b5985 1/1 Running 0 4d16h
mysql-7694c6b8b7-jthp6 2/2 Running 0 4d17h
notebook-controller-deployment-58447d4b4c-6ll57 1/1 Running 0 4d21h
profiles-deployment-78d4549cbc-z9lld 2/2 Running 0 4d21h
pytorch-operator-b79799447-f8nnl 1/1 Running 0 4d21h
seldon-controller-manager-5fc5dfc86c-nh2qm 1/1 Running 0 4d21h
spark-operatorsparkoperator-67c6bc65fb-8tgn5 1/1 Running 0 4d21h
tf-job-operator-5c97f4bf7-g5vtw 1/1 Running 0 4d21h
workflow-controller-5c7cc7976d-5n6tb 1/1 Running 0 4d16h
```
```
$ kubectl logs -n kubeflow ml-pipeline-54989c9946-s2f46 ml-pipeline-api-server
I0301 20:22:00.153656 6 client_manager.go:134] Initializing client manager
I0301 20:22:00.153817 6 config.go:50] Config DBConfig.ExtraParams not specified, skipping
[mysql] 2021/03/01 20:22:01 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:02 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:04 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:07 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:10 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:13 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:16 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:23 packets.go:36: unexpected EOF
```
```
$ kubectl logs -n kubeflow mysql-7694c6b8b7-jthp6 mysql
...
MySQL init process done. Ready for start up.
2021-02-25 03:04:17 0 [Warning] TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp server option (see documentation for more details).
2021-02-25 03:04:17 0 [Note] mysqld (mysqld 5.6.44) starting as process 1 ...
2021-02-25 03:04:17 1 [Note] Plugin 'FEDERATED' is disabled.
2021-02-25 03:04:17 1 [Note] InnoDB: Using atomics to ref count buffer pool pages
2021-02-25 03:04:17 1 [Note] InnoDB: The InnoDB memory heap is disabled
2021-02-25 03:04:17 1 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins
2021-02-25 03:04:17 1 [Note] InnoDB: Memory barrier is not used
2021-02-25 03:04:17 1 [Note] InnoDB: Compressed tables use zlib 1.2.11
2021-02-25 03:04:17 1 [Note] InnoDB: Using Linux native AIO
2021-02-25 03:04:17 1 [Note] InnoDB: Using CPU crc32 instructions
2021-02-25 03:04:17 1 [Note] InnoDB: Initializing buffer pool, size = 128.0M
2021-02-25 03:04:17 1 [Note] InnoDB: Completed initialization of buffer pool
2021-02-25 03:04:17 1 [Note] InnoDB: Highest supported file format is Barracuda.
2021-02-25 03:04:17 1 [Note] InnoDB: 128 rollback segment(s) are active.
2021-02-25 03:04:17 1 [Note] InnoDB: Waiting for purge to start
2021-02-25 03:04:17 1 [Note] InnoDB: 5.6.44 started; log sequence number 1625997
2021-02-25 03:04:17 1 [Note] Server hostname (bind-address): '*'; port: 3306
2021-02-25 03:04:17 1 [Note] IPv6 is available.
2021-02-25 03:04:17 1 [Note] - '::' resolves to '::';
2021-02-25 03:04:17 1 [Note] Server socket created on IP: '::'.
2021-02-25 03:04:17 1 [Warning] Insecure configuration for --pid-file: Location '/var/run/mysqld' in the path is accessible to all OS users. Consider choosing a different directory.
2021-02-25 03:04:17 1 [Warning] 'proxies_priv' entry '@ root@mysql-7694c6b8b7-jthp6' ignored in --skip-name-resolve mode.
2021-02-25 03:04:17 1 [Note] Event Scheduler: Loaded 0 events
2021-02-25 03:04:17 1 [Note] mysqld: ready for connections.
Version: '5.6.44' socket: '/var/run/mysqld/mysqld.sock' port: 3306 MySQL Community Server (GPL)
```
Cache Server also is unable to connect to MYSQL
```
$ kubectl logs -n kubeflow cache-server-bd9c859db-755zj server
2021/03/01 20:19:21 Initing client manager....
[mysql] 2021/03/01 20:19:22 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:24 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:25 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:27 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:30 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:33 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:39 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:46 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:19:55 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:20:07 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:20:26 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:21:02 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:21:40 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:22:35 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:23:58 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:09 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:50 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:51 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:52 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:54 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:56 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:25:59 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:26:02 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:26:06 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:26:15 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:26:20 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:26:34 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:27:03 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:27:45 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:28:11 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:29:39 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:30:12 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:31:32 packets.go:36: unexpected EOF
[mysql] 2021/03/01 20:32:07 packets.go:36: unexpected EOF
F0301 20:32:07.437107 1 error.go:305] invalid connection
goroutine 1 [running]:
github.com/golang/glog.stacks(0xc000786600, 0xc0004790a0, 0x3f, 0x40)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:769 +0xd4
github.com/golang/glog.(*loggingT).output(0x237c4c0, 0xc000000003, 0xc000479080, 0x20d8f16, 0x8, 0x131, 0x0)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:720 +0x329
github.com/golang/glog.(*loggingT).printf(0x237c4c0, 0x3, 0x14ca0b3, 0x2, 0xc0006c58f8, 0x1, 0x1)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:655 +0x14b
github.com/golang/glog.Fatalf(0x14ca0b3, 0x2, 0xc0006c58f8, 0x1, 0x1)
/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:1148 +0x67
github.com/kubeflow/pipelines/backend/src/common/util.TerminateIfError(0x1649b00, 0xc0005eca40)
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:305 +0x79
main.initMysql(0x7ffefc6905bd, 0x5, 0x7ffefc6905cd, 0x5, 0x7ffefc6905dd, 0x4, 0x7ffefc6905ec, 0x7, 0x7ffefc6905fe, 0x4, ...)
/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:157 +0x466
main.initDBClient(0x7ffefc6905bd, 0x5, 0x7ffefc6905cd, 0x5, 0x7ffefc6905dd, 0x4, 0x7ffefc6905ec, 0x7, 0x7ffefc6905fe, 0x4, ...)
/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:71 +0x599
main.(*ClientManager).init(0xc0006c5db8, 0x7ffefc6905bd, 0x5, 0x7ffefc6905cd, 0x5, 0x7ffefc6905dd, 0x4, 0x7ffefc6905ec, 0x7, 0x7ffefc6905fe, ...)
/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:57 +0x80
main.NewClientManager(0x7ffefc6905bd, 0x5, 0x7ffefc6905cd, 0x5, 0x7ffefc6905dd, 0x4, 0x7ffefc6905ec, 0x7, 0x7ffefc6905fe, 0x4, ...)
/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:169 +0xab
main.main()
/go/src/github.com/kubeflow/pipelines/backend/src/cache/main.go:71 +0x367
```
Attempted suggestions for repair (ALL fail - please do not suggest)
1) ISTIO disable ISTIO_MUTUAL -> DISABLE : This allows the mysql db to be populated but the KFP UI will NOT startup.
2) ISTIO configure STRICT vs PERMISSIVE : Pipelines and Jupyter Notebooks will not come up.
The product as advertised online does not work on a vanilla on-prem, K8s installation. It appears to work on GCP, Azure, AwS, and possibly IBM.
Provided diagnostic tools are not compatible with an on-prem installation:
```
$ kfp diagnose_me
Google Cloud SDK is not installed, gcloud, gsutil and kubectl are required for this app to run. Please follow instructions at https://cloud.google.com/sdk/install to install the SDK.
```
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
/area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Thanks @boarder7395 for filing this issue. \r\n\r\nI think the solution for treating `mlpipeline-metadata-ui` and `metrics-ui artifacts` as special in this rewriter is reasonable. If you submit a PR, we can review it. You mentioned you had a couple of other options too. Would you mind outlining them in this issue?... | 2021-03-01T19:48:58 | 2022-04-18T17:27:44 | 2022-04-18T17:27:44 | CONTRIBUTOR | null | ### What steps did you take:
When using the experimental feature of data passing with a volume mlpipeline-metadata-ui artifacts, and metrics-ui artifacts do not work. I ran some local testing and have identified the issue to be in kfp/compiler/_data_passing_using_volume.py::rewrite_data_passing_to_use_volumes. Currently this function deletes all artifacts and replaces them with parameters. I believe instead this function should delete all artifacts except mlpipeline-metadata-ui, and metrics-ui artifacts.
### What happened:
Artifacts are not displayed in UI when using volume data passing.
### What did you expect to happen:
Artifacts to be displayed.
### Environment:
Running kfp on aws.
How did you deploy Kubeflow Pipelines (KFP)?
Full kubeflow deployment.
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
743746b
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
1.4.0
### Anything else you would like to add:
I have implemented the fix and tested on my end. Happy to make the PR to fix this just having some issues pushing my code to kfp github. I have signed the developer agreement. Are there additional steps I need to take to be able to contribute, from the documentation I don't believe I am missing anything.
/kind bug
/area sdk
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"cc @Udiknedormin @Tomcli @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",
"This issue has been automatically closed because it has not had recent activity. Please comment \... | 2021-03-01T16:53:57 | 2022-04-29T03:59:45 | 2022-04-29T03:59:45 | CONTRIBUTOR | null | https://docs.google.com/document/d/1B2CQgyouqd6oSnqXtGk9uXaUV7JKEvQxFERbyQcZjZw/edit?usp=sharing
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"Found in https://github.com/kubeflow/pipelines/pull/5196\r\n\r\nWhy did it require docker?",
"> Why did it require docker?\r\n\r\nThe test log seems to be messed up. The docker requirement is from another test which somehow didn't shown as \"FAILED\". That test explicitly asks for testing with `docker`: \r\nhttp... | 2021-03-01T03:46:08 | 2021-03-01T19:04:17 | 2021-03-01T19:04:17 | CONTRIBUTOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5196/kubeflow-pipelines-sdk-python36/1365228134722441216
/assign @chensun | {
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"KFP comes already with two deployment modes: `KUBEFLOW` and `MARKETPLACE`. See https://github.com/kubeflow/pipelines/blob/0795597562e076437a21745e524b5c960b1edb68/frontend/src/lib/Flags.ts#L1-L4 and https://github.com/kubeflow/pipelines/blob/0795597562e076437a21745e524b5c960b1edb68/frontend/server/handlers/index-h... | 2021-02-26T15:11:16 | 2021-03-09T15:02:26 | 2021-03-09T15:02:26 | MEMBER | null | There is an ongoing discussion about how web apps should behave when deployed as part Kubeflow. See https://github.com/kubeflow/kubeflow/issues/5566.
The main discussion point I would like to focus on for KFP UI is (quoting from the issue):
> There is a CentralDashboard which:
> - decides what Namespaces to show to the user and feeds to the app the selected Namespace
> - has a left hand sidebar, which can have subsections https://github.com/kubeflow/kubeflow/pull/5474. This side bar can be used for navigating the user between different pages of the underlying deployed app
Centraldashboard should act as the single place where all applications expose their pages. Centraldashboard's sidebar is very easily customizable with a ConfigMap and allows creating collapsible subsections.
Consider also the discussion here https://github.com/kubeflow/katib/issues/1437#issuecomment-782039859 around the sidebar of the Katib UI. The new Katib UI (https://github.com/kubeflow/katib/pull/1427) will not have such sidebar and instead expose Katib pages using Centraldashboard (when deployed in Kubeflow mode). | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5198 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5198/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5198/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5198/events | https://github.com/kubeflow/pipelines/issues/5198 | 817,152,750 | MDU6SXNzdWU4MTcxNTI3NTA= | 5,198 | bug in compilier.Compiler._validate_exit_handler | {
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"/assign @chensun ",
"@HaozhengAN , I cannot reproduce this. \r\n\r\nCan you maybe try again in a clean environment?\r\n\r\nHere's what I did for your reference:\r\n1. Create a new virtual environment\r\n`python3 -m venv ~/venv/kfp1.4.0`\r\n`source ~/venv/kfp1.4.0/bin/activate`\r\n2. Install kfp 1.4.0\r\n`pip3 in... | 2021-02-26T08:32:15 | 2022-04-19T08:27:52 | 2022-04-19T08:27:52 | NONE | null | ### What steps did you take:
```python
@dsl.pipeline(
name='Conditional execution pipeline with exit handler',
description='Shows how to use dsl.Condition() and dsl.ExitHandler().'
)
def flipcoin_exit_pipeline():
exit_task = print_op('Exit handler has worked!')
exit_task2 = print_op('Exit handler has worked!')
with dsl.ExitHandler(exit_task):
flip = flip_coin_op()
with dsl.Condition(flip.output == 'heads'):
random_num_head = get_random_int_op(0, 9)
with dsl.Condition(random_num_head.output > 5):
print_op('heads and %s > 5!')
with dsl.Condition(random_num_head.output <= 5):
print_op('heads and %s <= 5!' % random_num_head.output)
with dsl.Condition(flip.output == 'tails'):
random_num_tail = get_random_int_op(10, 19)
with dsl.Condition(random_num_tail.output > 15):
print_op('tails and %s > 15!' % random_num_tail.output)
with dsl.Condition(random_num_tail.output <= 15):
print_op('tails and %s <= 15!' % random_num_tail.output)
with dsl.Condition(flip.output == 'tails'):
fail_op(message="Failing the run to demonstrate that exit handler still gets executed.")
if __name__ == '__main__':
# Compiling the pipeline
kfp.compiler.Compiler().compile(flipcoin_exit_pipeline, __file__ + '.yaml')
```
### What happened:
it compiled success,
### What did you expect to happen:
raise Error such as follow:
```
Traceback (most recent call last):
File "control_structures.py", line 125, in <module>
kfp.compiler.Compiler().compile(flipcoin_exit_pipeline, __file__ + '.yaml')
File "/Users/anhaozheng/pipelines-master/sdk/python/kfp/compiler/compiler.py", line 984, in compile
self._create_and_write_workflow(
File "/Users/anhaozheng/pipelines-master/sdk/python/kfp/compiler/compiler.py", line 1037, in _create_and_write_workflow
workflow = self._create_workflow(
File "/Users/anhaozheng/pipelines-master/sdk/python/kfp/compiler/compiler.py", line 874, in _create_workflow
self._validate_exit_handler(dsl_pipeline)
File "/Users/anhaozheng/pipelines-master/sdk/python/kfp/compiler/compiler.py", line 779, in _validate_exit_handler
return _validate_exit_handler_helper(pipeline.groups[0], [], False)
File "/Users/anhaozheng/pipelines-master/sdk/python/kfp/compiler/compiler.py", line 777, in _validate_exit_handler_helper
_validate_exit_handler_helper(g, exiting_op_names, handler_exists)
File "/Users/anhaozheng/pipelines-master/sdk/python/kfp/compiler/compiler.py", line 770, in _validate_exit_handler_helper
raise ValueError('Only one global exit_handler is allowed and all ops need to be included.')
ValueError: Only one global exit_handler is allowed and all ops need to be included.
```
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!--1.4.0-->
1.4.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Worked around by https://github.com/kubeflow/pipelines/pull/5191.\r\n\r\nWe need to report the issue upstream.",
"> I see that both old and new fail to load the tensorflow_model_analysis.js script.\r\nBut then the old version succeeds in loading https://unpkg.com/tensorflow_model_analysis@0.21.5/dist/vulcanized_... | 2021-02-25T11:15:09 | 2022-04-28T18:00:03 | 2022-04-28T18:00:03 | CONTRIBUTOR | null | > I tried to upgrade some other TFX related deps, however, I'm getting the following errors in various places in parameterized_tfx_sample:
>
> * when clicking visualizations on `evaluator`, the visualization js simply crashes without any clear error message. I tried to take a look at browser console, but it only shows. EDIT: workarounded in #5191
>
> ```
> Uncaught Error: Script error for: tensorflow_model_analysis
> http://requirejs.org/docs/errors.html#scripterror
> at C (require.min.js:8)
> at HTMLScriptElement.onScriptError (require.min.js:29)
> ```
Originally posted in https://github.com/kubeflow/pipelines/issues/5137#issuecomment-785012674 | {
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"/cc @chensun \r\nDo you have bandwidth to take this?\r\n\r\nNot urgent, but I believe we should better have it in next release.",
"I would be happy to help out with this as well. ",
"/assign",
"blocked by: https://github.com/tensorflow/tfx/pull/3484",
"This issue has been automatically marked as stale beca... | 2021-02-25T11:13:01 | 2022-04-18T17:27:42 | 2022-04-18T17:27:42 | CONTRIBUTOR | null | TFX iris example was removed in new releases, it should be replaced by penguins example.
context: https://github.com/kubeflow/pipelines/pull/5189 | {
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"There are quite a few changes with the v1beta1 API that comes with KFServing v0.5.0. Here are some initial findings as I go over the component use case:\r\n\r\nKFServing Component Use Cases\r\n\r\n\r\n- Create InferenceService for available frameworks. General structure would look like:\r\n```\r\nV1beta1InferenceS... | 2021-02-24T18:28:56 | 2021-03-05T22:15:49 | 2021-03-05T22:15:49 | CONTRIBUTOR | null | Needs to be updated/upgraded/tested | {
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"\r\n\r\nNotice that, postsubmit test before the release commit passed, however the released version had a problematic tfx sample pipeline (ignore the failure, it was expected for a release commit).\r\n\r\n... | 2021-02-24T12:10:41 | 2022-03-03T06:05:29 | 2022-03-03T06:05:29 | CONTRIBUTOR | null | In #5137, tfx sample pipeline was broken, but it was not caught by postsubmit test.
> IIRC our integration test should cover this sample, do we know why this is not captured?
Originally posted in https://github.com/kubeflow/pipelines/issues/5137#issuecomment-783788084
I think this is a high priority issue, because it causes a lot of efforts after 1.4.0 was released.
/cc @numerology @chensun | {
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] | closed | false | null | [] | null | [] | 2021-02-24T08:00:51 | 2021-03-01T16:25:40 | 2021-03-01T16:25:40 | CONTRIBUTOR | null | ### What steps did you take:
Deployed metadata-writer Deployment
### What happened:
https://github.com/kubeflow/pipelines/blob/master/backend/metadata_writer/src/metadata_writer.py#L27 results in setting the namespace to watch as `default` as we are setting empty string or a particular namespace only.
### What did you expect to happen:
This results in the writer watching one particular namespace only, while it should watch for all namespaces for argo pods so that Output Artifact can be written for those cases
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
https://www.kubeflow.org/docs/started/k8s/kfctl-istio-dex/
Using Kubeflow 1.2
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
/area backend
// /area sdk
// /area testing
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"/cc @Ark-kun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reope... | 2021-02-23T19:57:31 | 2022-04-28T18:00:20 | 2022-04-28T18:00:20 | CONTRIBUTOR | null | (With KFP 1.4)
My subgraph component was constructed from other components that I loaded via `load_component_from_file()`.
So, the subgraph yaml has entries that look like this:
```
tasks:
Create dataset tabular bigquery sample:
componentRef: {digest: e185acfe42a7dd076c54a55ba368c772f72a602a2c5182754ac5ad33b0f2e106,
url: ./tables_create_dataset_component.yaml}
arguments:
...
```
Then, I get this error when compiling a pipeline based on that component:
`MissingSchema: Invalid URL './tables_create_dataset_component.yaml'`
(The file is available locally at that path).
After discussion with Alexey, we should support this.
Comment from Alexey: I think the issue is pretty simple - ComponentStore always tries to download components references with URIs using the requests library without checking for the schema.
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https://api.github.com/repos/kubeflow/pipelines/issues/5172 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5172/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5172/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5172/events | https://github.com/kubeflow/pipelines/issues/5172 | 814,727,369 | MDU6SXNzdWU4MTQ3MjczNjk= | 5,172 | client.create_experiment and client.get_experiment treat differences in case differently | {
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"Makes sense, I think we should distinguish case when creating an experiment too.\r\nWelcome contribution on this change.",
"/assign",
"The error originates from that `mysql` is [not case sensitve](https://dev.mysql.com/doc/refman/8.0/en/case-sensitivity.html) in the comparisons as I understand it for CHAR, VAR... | 2021-02-23T18:34:10 | 2021-03-28T11:14:51 | 2021-03-28T11:14:51 | NONE | null | ### What steps did you take:
The following code snippet tries to create two experiments. The experiment names only differ in case, e.g. `Untitled` vs `untitled`
```
import kfp
client = kfp.Client(host='http://.../pipeline')
# create experiment succeeds if there is no experiment named `untitled` (ignoring case)
experiment = client.create_experiment('Untitled', namespace='anonymous')
# create experiment fails
experiment = client.create_experiment('untitled', namespace='anonymous')
# output > Create experiment failed.: Already exist error: Failed to create a new experiment. The name untitled already exists. Please specify a new name.
experiment = client.get_experiment('untitled', namespace='anonymous')
# output > ValueError: No experiment is found with name untitled.
```
### What happened:
Create experiment fails because an experiment with the name already exists.
### What did you expect to happen:
`get_experiment` and `create_experiment` behave the same (ignore case or honor it).
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: 1.0.4
KFP SDK version: 1.3.0
### Anything else you would like to add:
Found https://github.com/kubeflow/pipelines/issues/2240 but the issue is stale.
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
/area sdk
// /area testing
// /area engprod
-->
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"Hi @muyajil, the code you mentioned is correct, because the _fetch function also returns a Promise, this function can just return it, and let its callsites to await for the promise.\r\n\r\nI'm not sure what the problem you hit, welcome more investigation to the root cause. You may follow https://github.com/kubeflo... | 2021-02-22T19:28:45 | 2022-04-18T17:27:38 | 2022-04-18T17:27:38 | NONE | null | ### What steps did you take?
Create UI metadata artifacts as inline web apps.
### What happened?
When trying to view the artifacts I receive the error:
`Could not parse metadata file at: artifacts/xxx/xxx/mlpipeline-ui-metadata.tgz. Error: SyntaxError: Unexpected end of JSON input`
Sometimes it works and sometimes it does not.
### What did you expect to happen
That I can see the generated web apps always.
### Environment:
Azure with OIDC
KFP version: 1.2.0
### Anything else you would like to add:
The error is triggered in this line:
https://github.com/kubeflow/pipelines/blob/61f9c2c328d245d89c9d9b8c923f24dbbd08cdc9/frontend/src/lib/OutputArtifactLoader.ts#L75
I think the error can be solved by awaiting the promise in this function:
https://github.com/kubeflow/pipelines/blob/61f9c2c328d245d89c9d9b8c923f24dbbd08cdc9/frontend/src/lib/Apis.ts#L212
However I am not sure about the implications and would appreciate some feedback. If this would fix the issue, I would happily submit a PR. | {
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"/cc @chensun ",
"I think this can be hardly solved without TFX IV providing more flexible configuration options to expose authentication capability when running on a more complex deployment.\r\n\r\nMaybe we shall port this to https://github.com/tensorflow/tfx ?",
"@numerology @Bobgy @chensun This has already b... | 2021-02-22T09:31:23 | 2021-04-07T08:00:39 | 2021-04-07T08:00:39 | CONTRIBUTOR | null | ### What steps did you take:
I'm running a TFX pipeline with an InfraValidator component (https://www.tensorflow.org/tfx/guide/infra_validator). This works by spinning up a TFServing pod using the k8s python client, and optionally querying it.
### What happened:
When InfraValidator tries to spin up TFServing using the CreateNamespacedPod it get a 500 error which seems to originate from istio. See logs below.
### What did you expect to happen:
Pod to be successfully created.
### Environment:
On-prem KubeFlow installation.
How did you deploy Kubeflow Pipelines (KFP)?
With KF 1.1.0 istio dex (e.g. multi-user).
KFP version: 1.0.0
KFP SDK version: 1.1.2
k8s version: 1.19
TFX Version: 0.27.0
Python version: 3.7
### Anything else you would like to add:
The issue seems to istio rejecting the attempt to create the pod. Logs from failure:
```
INFO:absl:Starting infra validation (attempt 1/5).
INFO:absl:Starting KubernetesRunner(image: docker.vby.svenskaspel.se:8181/tensorflow/serving:2.3.0, pod_name: None).
INFO:absl:Stopping KubernetesRunner(image: docker.vby.svenskaspel.se:8181/tensorflow/serving:2.3.0, pod_name: None).
INFO:absl:Deleting Pod (name=None)
WARNING:absl:Error occurred while deleting the Pod. Please run the following command to manually clean it up:
kubectl delete pod --namespace admin None
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/tfx/components/infra_validator/executor.py", line 356, in _ValidateOnce
runner.Start()
File "/usr/local/lib/python3.7/dist-packages/tfx/components/infra_validator/model_server_runners/kubernetes_runner.py", line 140, in Start
body=self._BuildPodManifest())
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/apis/core_v1_api.py", line 6115, in create_namespaced_pod
(data) = self.create_namespaced_pod_with_http_info(namespace, body, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/apis/core_v1_api.py", line 6206, in create_namespaced_pod_with_http_info
collection_formats=collection_formats)
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/api_client.py", line 344, in call_api
_return_http_data_only, collection_formats, _preload_content, _request_timeout)
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/api_client.py", line 178, in __call_api
_request_timeout=_request_timeout)
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/api_client.py", line 387, in request
body=body)
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/rest.py", line 266, in POST
body=body)
File "/usr/local/lib/python3.7/dist-packages/kubernetes/client/rest.py", line 222, in request
raise ApiException(http_resp=r)
kubernetes.client.rest.ApiException: (500)
Reason: Internal Server Error
HTTP response headers: HTTPHeaderDict({'Audit-Id': 'f16c63f3-113e-4eba-b50b-5f56f81c7599', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Wed, 17 Feb 2021 13:30:17 GMT', 'Content-Length': '457'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"Internal error occurred: failed calling webhook \"sidecar-injector.istio.io\": Post \"
https://istio-sidecar-injector.istio-system.svc:443/inject?timeout=30s
\": EOF","reason":"InternalError","details":{"causes":[{"message":"failed calling webhook \"sidecar-injector.istio.io\": Post \"https://istio-sidecar-injector.istio-system.svc:443/inject?timeout=30s\": EOF"}]},"code":500}
https://istio-sidecar-injector.istio-system.svc:443/inject?timeout=30s
\": EOF"}]},"code":500}
```
Any help would be appreciated!
/kind bug
/area backend | {
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"@Ark-kun Sorry I messed up with my push. Here is the PR https://github.com/kubeflow/pipelines/pull/5246 with correct changes."
] | 2021-02-22T06:58:49 | 2021-03-12T00:12:24 | 2021-03-12T00:12:24 | CONTRIBUTOR | null | The original issue is reported here https://github.com/kubeflow/kubeflow/issues/5605 | {
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"/cc @chensun @neuromage ",
"What is you main usage scenario for this feature request?\r\n\r\n\r\nUsually we advice out users to either use image tags (which are mutable) or use image replacement (in test scenarios). In KFP it's also possible to replace the container image after creating an instance of the compon... | 2021-02-22T06:12:24 | 2021-03-13T16:18:29 | 2021-03-13T09:44:58 | CONTRIBUTOR | null | Would it be possible to make the image name configurable in component.yaml specification, so that the end user could override the image. It is possible to write a function to modify the yaml before it is read by ` kfp.components.load_component_from_text`, however I think this would be a useful addition to component.yaml specification, this is something that was supported by ContainerOp.
For example the following component.yaml adds another additional inputValue to specify the docker image. However this does not work when loading the component.yaml.
```yaml
name: echo
description: Academic Component. Echoes input to output.
metadata:
annotations:
version: "0.0.1"
inputs:
- { name: Text, type: String, description: Text to be echoed }
- { name: Docker Image, type: String, description: Docker Image to be used, default: alpine }
outputs:
- { name: Echoed Text, type: String, description: Echoed Text }
implementation:
container:
image: {inputValue: Docker Image}
command: [sh, -c]
args: [
echo $0 && mkdir -p "$(dirname "$1")" &&echo $0 > $1,
{ inputValue: Text },
{ outputPath: Echoed Text}
]
```
However this does not work. Attempting to load this component gives an error.
```python
kfp.components.load_component_from_text(manifest.read_text())
```
# Traceback.
```
kfp.components.load_component_from_text(manifest.read_text())
File "/usr/local/lib/python3.8/site-packages/kfp/components/_components.py", line 115, in load_component_from_text
component_spec = _load_component_spec_from_component_text(text)
File "/usr/local/lib/python3.8/site-packages/kfp/components/_components.py", line 164, in _load_component_spec_from_component_text
component_spec = ComponentSpec.from_dict(component_dict)
File "/usr/local/lib/python3.8/site-packages/kfp/components/modelbase.py", line 285, in from_dict
return parse_object_from_struct_based_on_class_init(cls, struct, serialized_names=cls._serialized_names)
File "/usr/local/lib/python3.8/site-packages/kfp/components/modelbase.py", line 238, in parse_object_from_struct_based_on_class_init
args[python_name] = parse_object_from_struct_based_on_type(value, param_type)
File "/usr/local/lib/python3.8/site-packages/kfp/components/modelbase.py", line 158, in parse_object_from_struct_based_on_type
raise TypeError('\n'.join(exception_lines))
TypeError: Error: ContainerImplementation.from_dict(struct=OrderedDict([('container', OrderedDict([('image', OrderedDict([('inputValue', 'Docker Image')])), ('command', ['sh', '-c']), ('args', ['echo $0 && mkdir -p "$(dirname "$1")" &&echo $0 > $1', OrderedDict([('inputValue', 'Text')]), OrderedDict([('outputPath', 'Echoed Text')])])]))])) failed with exception:
Error: ContainerSpec.from_dict(struct=OrderedDict([('image', OrderedDict([('inputValue', 'Docker Image')])), ('command', ['sh', '-c']), ('args', ['echo $0 && mkdir -p "$(dirname "$1")" &&echo $0 > $1', OrderedDict([('inputValue', 'Text')]), OrderedDict([('outputPath', 'Echoed Text')])])])) failed with exception:
Error: Structure "OrderedDict([('inputValue', 'Docker Image')])" is incompatible with type "<class 'str'>". Structure is not the instance of the type, the type does not have .from_dict method and is not generic.
Error: GraphImplementation.from_dict(struct=OrderedDict([('container', OrderedDict([('image', OrderedDict([('inputValue', 'Docker Image')])), ('command', ['sh', '-c']), ('args', ['echo $0 && mkdir -p "$(dirname "$1")" &&echo $0 > $1', OrderedDict([('inputValue', 'Text')]), OrderedDict([('outputPath', 'Echoed Text')])])]))])) failed with exception:
__init__() got an unexpected keyword argument 'container'
Error: Structure "OrderedDict([('container', OrderedDict([('image', OrderedDict([('inputValue', 'Docker Image')])), ('command', ['sh', '-c']), ('args', ['echo $0 && mkdir -p "$(dirname "$1")" &&echo $0 > $1', OrderedDict([('inputValue', 'Text')]), OrderedDict([('outputPath', 'Echoed Text')])])]))])" is not None.
Error: Structure "OrderedDict([('container', OrderedDict([('image', OrderedDict([('inputValue', 'Docker Image')])), ('command', ['sh', '-c']), ('args', ['echo $0 && mkdir -p "$(dirname "$1")" &&echo $0 > $1', OrderedDict([('inputValue', 'Text')]), OrderedDict([('outputPath', 'Echoed Text')])])]))])" is incompatible with type "typing.Union[kfp.components._structures.ContainerImplementation, kfp.components._structures.GraphImplementation, NoneType]" - none of the types in Union are compatible.
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"/cc @Ark-kun @chensun ",
"I was wondering about this too\r\ndoes kfp have a Op which can start a container and just leave it there\r\n\r\nThanks",
"I've gotten around this by compiling the pipeline yaml, then adding the \"daemon: true\" line to the argo pipeline yaml before uploading to KFP server. This feel... | 2021-02-20T09:54:36 | 2022-04-18T17:27:45 | 2022-04-18T17:27:45 | NONE | null | # Argo Daemon Containers
I've looked through as many docs I could find and determined that Argo pipelines support daemon containers that suit my purposes pretty well (spinning up database server to use as a cache for a pipeline).
# Question:
1. Are there any examples of using daemon containers with KFP SDK in python?
2. If not, does someone have a 'solid' alternative to hacking the pipeline YAML?
Thanks a lot, also; I'm loving the work the team has done! | {
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"This wasn't notified, because after migration to gcp oss prow, I haven't set up testgrid and notification, that will be the last step during migration in https://github.com/kubernetes/test-infra/issues/14343",
"The logs showed that the periodic functional test failed because it encountered 403 error when creatin... | 2021-02-20T07:50:15 | 2021-02-25T02:35:29 | 2021-02-25T02:35:28 | CONTRIBUTOR | null | https://oss-prow.knative.dev/?repo=kubeflow%2Fpipelines
/assign @capri-xiyue | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5152 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5152/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5152/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5152/events | https://github.com/kubeflow/pipelines/issues/5152 | 811,948,463 | MDU6SXNzdWU4MTE5NDg0NjM= | 5,152 | [Testing] Postsubmit failing 2021 Feb 19 | {
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"Figured out some previous failures were because of test workflow timeout, it timeouts at 1 hour, but now it's exceeded:\r\n> [test-timing] It took 60m35.255s.\r\n\r\nWe need to increase timeout to make it stabler.\r\n\r\nSome recent failures was because of GKE instability.",
"Increased integration test timeout.\... | 2021-02-19T11:33:58 | 2021-02-20T01:11:02 | 2021-02-20T01:11:02 | CONTRIBUTOR | null | example: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1362660888636559360
it's been failing for a while since 1.4.0 release. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5151 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5151/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5151/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5151/events | https://github.com/kubeflow/pipelines/issues/5151 | 811,765,211 | MDU6SXNzdWU4MTE3NjUyMTE= | 5,151 | Default Notebook images cannot install KFP due to enum34 | {
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"Additional info:\r\nenum34 is installed by jupyter-http-over-ws==0.0.7. The latest version, 0.0.8, has added restrictions in setup.py to prevent installing enum34 for python3.4+. A brute force fix would be changing the pip installs in the image to the following:\r\n\r\n``RUN pip3 uninstall -y enum34 jupyter-http-o... | 2021-02-19T07:10:44 | 2021-07-04T07:24:22 | 2021-07-04T07:24:22 | NONE | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
I ran "pip install kfp" in the default notebook server image gcr.io/kubeflow-images-public/tensorflow-2.1.0-notebook-cpu:1.0.0.
### What happened:
...
ERROR: Command errored out with exit status 1:
command: /usr/bin/python3 /usr/local/lib/python3.6/dist-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-e1cn2jdf/overlay --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- 'setuptools>=40.8.0' wheel
cwd: None
Complete output (14 lines):
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/pip/__main__.py", line 16, in <module>
from pip._internal.main import main as _main # isort:skip # noqa
File "/usr/local/lib/python3.6/dist-packages/pip/_internal/main.py", line 8, in <module>
import locale
File "/usr/lib/python3.6/locale.py", line 16, in <module>
import re
File "/usr/lib/python3.6/re.py", line 142, in <module>
class RegexFlag(enum.IntFlag):
AttributeError: module 'enum' has no attribute 'IntFlag'
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/bin/python3 /usr/local/lib/python3.6/dist-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-e1cn2jdf/overlay --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- 'setuptools>=40.8.0' wheel Check the logs for full command output.
### What did you expect to happen:
It should install successfully. The above error happens because enum34 is installed. The notebook image uses python3.6, which does not need an enum backport. Also, I cannot uninstall inside the image without root privileges, which means the HEAD images need to be rebuilt.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
Azure with OIDC
KFP version: 1.2.0
KFP SDK version: Can't install..
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5214 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5214/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5214/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5214/events | https://github.com/kubeflow/pipelines/issues/5214 | 818,615,254 | MDU6SXNzdWU4MTg2MTUyNTQ= | 5,214 | 'kubeflow-pipelines-profile-controller' fails to deploy pods on profile creation when ResourceQuota is set in the profile. | {
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"This would be more like a KFP-specific issue\r\n\r\n/cc @Bobgy \r\n\r\nCan you help transfer the issue to kubeflow/pipelines?",
"Thanks @henrysecond1! That makes sense adding some default value there.\r\nManifests is currently migrating to kubeflow/pipelines repo. Would you want to open a PR after that?",
"Sur... | 2021-02-18T16:27:26 | 2021-03-13T03:26:47 | 2021-03-13T03:26:47 | CONTRIBUTOR | null | In multi-user mode, it seems like `kubeflow-pipelines-profile-controller` deploy below pods on Kubeflow profile creation.
- `ml-pipeline-ui-artifact`
- `ml-pipeline-visualizationserver`
When `ResourceQuota` is set in the profile, `kubeflow-pipelines-profile-controller` fails to deploy `ml-pipeline-ui-artifact` and `ml-pipeline-visualizationserver` with below error.
```
Warning FailedCreate 17m replicaset-controller
Error creating: pods "ml-pipeline-ui-artifact-684c5db68-s74w8" is forbidden: failed quota: kf-resource-quota: must specify cpu,memory
```
- Related code: [https://github.com/kubeflow/manifests/blob/master/apps/pipeline/upstream/installs/multi-user/pipelines-profile-controller/sync.py](https://github.com/kubeflow/manifests/blob/master/apps/pipeline/upstream/installs/multi-user/pipelines-profile-controller/sync.py)
- Cause: The container resource limit & request is not set on the pod specs, so the pods can not be deployed in the namespace (which has `ResourceQuota` ).
Since Kubeflow profile supports setting `ResourceQuota`, `kubeflow-pipelines-profile-controller` should set container resource requests & limits in pod specs to avoid above errors.
I confirmed that with below patch, ml-pipeline pods are successfully deployed.
```python
diff --git a/apps/pipeline/upstream/installs/multi-user/pipelines-profile-controller/sync.py b/apps/pipeline/upstream/installs/multi-user/pipelines-profile-controller/sync.py
index 75c6e5db..a0e71fbf 100644
--- a/apps/pipeline/upstream/installs/multi-user/pipelines-profile-controller/sync.py
+++ b/apps/pipeline/upstream/installs/multi-user/pipelines-profile-controller/sync.py
@@ -104,6 +104,16 @@ class Controller(BaseHTTPRequestHandler):
"ports": [{
"containerPort": 8888
}],
+ "resources": {
+ "requests": {
+ "cpu": "50m",
+ "memory": "200Mi"
+ },
+ "limits": {
+ "cpu": "500m",
+ "memory": "2Gi"
+ },
+ }
}],
"serviceAccountName":
"default-editor",
@@ -204,7 +214,17 @@ class Controller(BaseHTTPRequestHandler):
"IfNotPresent",
"ports": [{
"containerPort": 3000
- }]
+ }],
+ "resources": {
+ "requests": {
+ "cpu": "50m",
+ "memory": "200Mi"
+ },
+ "limits": {
+ "cpu": "500m",
+ "memory": "2Gi"
+ },
+ }
}],
"serviceAccountName":
"default-editor"
```
Please take a look, thanks. | {
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"Got some help from Sid Palas:\r\n\r\n```\r\nA couple of example request settings:\r\nml-pipeline (api server)\r\n requests:\r\n cpu: '2'\r\n memory: 4Gi\r\nml-pipeline-ui\r\n requests:\r\n cpu: 10m\r\n memory: 70Mi\r\nworkflow-controller (argo)\r\n requests:... | 2021-02-18T13:21:06 | 2021-04-01T03:52:15 | 2021-04-01T03:52:15 | CONTRIBUTOR | null | UPDATE: at the end, we decided to only add resource requirements, see discussion in https://github.com/kubeflow/pipelines/issues/5236#issuecomment-790301148
It's desirable to provide a set of default resource requirement & limits for KFP UI & system services, to make sure their QoS is `Guaranteed` by default.
https://kubernetes.io/docs/tasks/configure-pod-container/quality-service-pod/
I'm not exactly sure what will be reasonable, because if they are set too low, the services may stop operating when there are workloads reaching a limit.
But setting them to make QoS Guaranteed is also important, because otherwise when there are many other workloads, KFP UI & API services may be evicted because default QoS is BestEffort and BestEffort Pods are the first to be evicted by Kubernetes when it runs out of resources. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5147 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5147/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5147/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5147/events | https://github.com/kubeflow/pipelines/issues/5147 | 811,003,688 | MDU6SXNzdWU4MTEwMDM2ODg= | 5,147 | build_image_from_working_dir Kubernetes job creation failed. | {
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"Hi @Adedolapo-Akin , looks like your KFP is not deployed in kubeflow namespace.\r\nYou can configure the namespace when using build_image_from_working_dir: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.containers.html#kfp.containers.build_image_from_working_dir"
] | 2021-02-18T11:15:37 | 2021-03-12T01:11:19 | 2021-03-12T01:11:19 | NONE | null | ### What steps did you take:
kfp v. 1.4.0
I'm trying build_image_from_working_dir() as per https://github.com/kubeflow/pipelines/blob/master/samples/core/container_build/container_build.ipynb, from an AI Platform notebook.
### What happened:
code
# Building and pushing new container image
image_with_packages = build_image_from_working_dir(
#working_dir='.', # Optional. Default is the current directory
#base_image='google/cloud-sdk:latest', # Optional
#image_name='gcr.io/my-org/my-image:latest', # Optional. Default is gcr.io/<project_id>/<notebook_id>/kfp_container
)
# Creating component while explicitly specifying the newly-built base image
read_data_op = func_to_container_op(read_data, base_image=image_with_packages)
Error
ERROR:root:Exception when calling CoreV1Api->create_namespaced_pod: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Audit-Id': '49f84f01-752c-4780-adfa-afd0fbdc184a', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Thu, 18 Feb 2021 10:41:35 GMT', 'Content-Length': '196'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"namespaces \"kubeflow\" not found","reason":"NotFound","details":{"name":"kubeflow","kind":"namespaces"},"code":404}
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/kfp/containers/_k8s_job_helper.py", line 65, in _create_k8s_job
api_response = self._corev1.create_namespaced_pod(yaml_spec['metadata']['namespace'], pod)
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/api/core_v1_api.py", line 6174, in create_namespaced_pod
(data) = self.create_namespaced_pod_with_http_info(namespace, body, **kwargs) # noqa: E501
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/api/core_v1_api.py", line 6265, in create_namespaced_pod_with_http_info
collection_formats=collection_formats)
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 345, in call_api
_preload_content, _request_timeout)
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 176, in __call_api
_request_timeout=_request_timeout)
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/api_client.py", line 388, in request
body=body)
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/rest.py", line 278, in POST
body=body)
File "/opt/conda/lib/python3.7/site-packages/kubernetes/client/rest.py", line 231, in request
raise ApiException(http_resp=r)
kubernetes.client.rest.ApiException: (404)
Reason: Not Found
HTTP response headers: HTTPHeaderDict({'Audit-Id': '49f84f01-752c-4780-adfa-afd0fbdc184a', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'Date': 'Thu, 18 Feb 2021 10:41:35 GMT', 'Content-Length': '196'})
HTTP response body: {"kind":"Status","apiVersion":"v1","metadata":{},"status":"Failure","message":"namespaces \"kubeflow\" not found","reason":"NotFound","details":{"name":"kubeflow","kind":"namespaces"},"code":404}
### What did you expect to happen:
To build image
### Environment:
<!-- Please fill in those that seem relevant. -->
GCP
AI Platform Notebook
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https://api.github.com/repos/kubeflow/pipelines/issues/5145 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5145/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5145/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5145/events | https://github.com/kubeflow/pipelines/issues/5145 | 810,806,090 | MDU6SXNzdWU4MTA4MDYwOTA= | 5,145 | HTTP response body: OpenID Connect token expired: JWT has expired | {
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"Duplicate of https://github.com/kubeflow/pipelines/issues/4321"
] | 2021-02-18T06:34:00 | 2021-03-12T01:07:00 | 2021-03-12T01:07:00 | NONE | null | ### What steps did you take:
1. gcloud auth activate-service-account account --key-file=...
2. copy credentials.json ~/.config/kfp/
3. Client is a singleton: kfp.Client(host=KUBEFLOW_AUTHORIZATION.HOST,
client_id=KUBEFLOW_AUTHORIZATION.CLIENT_ID,
other_client_id=KUBEFLOW_AUTHORIZATION.OTHER_CLIENT_ID,
other_client_secret=KUBEFLOW_AUTHORIZATION.OTHER_CLIENT_SECRET)
4. after a few days, this problem occurred
### What happened:
### What did you expect to happen:
_is_refresh_token = True, The token should be refreshed automatically, but JWT has expired.
### Environment:
_**client:**_
ubuntu18.06
kfp 1.4.0
kfp-pipeline-spec 0.1.5
kfp-server-api 1.3.0
Python 3.7.7
_**service:**_
google cloud
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https://api.github.com/repos/kubeflow/pipelines/issues/5139 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5139/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5139/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5139/events | https://github.com/kubeflow/pipelines/issues/5139 | 809,598,565 | MDU6SXNzdWU4MDk1OTg1NjU= | 5,139 | Building component from Python function producing PendingDeprecationWarning | {
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"In the documentation [here](https://www.kubeflow.org/docs/pipelines/sdk/python-function-components/) I think you can find the information you are looking for. It also points to some examples that should help guide you in the right direction. Let me know if you still have some issues. ",
"I've written similar cod... | 2021-02-16T19:47:14 | 2021-04-02T01:00:24 | 2021-04-02T01:00:24 | CONTRIBUTOR | null | ### What happened:
I have been using the below ContainerOp in a pipeline for unit tests and with `kfp>1.1.0` I started getting `PendingDeprecationWarning` while Compiling the pipeline using ContainerOp.
```python3
@pytest.fixture
def add_op() -> ContainerOp:
def add_ints(a: int, b: int) -> int:
return a + b
add_comp = func_to_container_op(
func=add_ints, base_image="python:3.7-slim-buster"
)
return add_comp
@dsl.pipeline("test", "test pipeline")
def test_pipeline(a: int, b: int):
add = add_op(a, b)
add.set_display_name("Add Two Integers")
add.add_pod_label("component_name", "add_ints")
def test_pipeline_image_settings(self):
with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
Compiler().compile(pipeline_func=test_pipeline, package_path=f.name)
```
```console
PendingDeprecationWarning: dsl.ContainerOp.image will be removed in future releases. Use dsl.ContainerOp.container.image instead
```
### What did you expect to happen:
Could someone let me know what is an alternative method to use to build component from a python function?
### Environment:
Python 3.8
How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow on Kubernetes
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: 1.4.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind question
/area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5138 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5138/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5138/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5138/events | https://github.com/kubeflow/pipelines/issues/5138 | 808,439,548 | MDU6SXNzdWU4MDg0Mzk1NDg= | 5,138 | Add authentication with ServiceAccountToken | {
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"Thank you for the proposal! I'd love to see it getting it upstreamed too. It's a common request in #4440.",
"Hello!\r\n\r\nI'll provide an update here as I'll be pushing a PR covering the backend part very soon.\r\n\r\nAs mentioned in the first comment, we are adding a new authentication method: authentication u... | 2021-02-15T11:09:18 | 2023-03-06T19:27:10 | null | CONTRIBUTOR | null | ### Problem Statement
Clients in various namespaces (e.g., Notebooks) need to access the Pipelines API. However, there is currently no way for these clients to authenticate to the Pipelines API:
https://github.com/kubeflow/pipelines/issues/4440
https://github.com/kubeflow/pipelines/issues/4733
In-cluster clients need a way to authenticate to the KFP API Server.
### Proposed Solution
The correct way to do this is by using audience-scoped ServiceAccountTokens. In Arrikto's Kubeflow distribution, we have been successfully using this method for a long time, in numerous customer environments. We want to upstream this solution so the whole community can benefit as well, since we see this is an issue many users bump into.
Changes need to happen in 2 places:
- API Server, which needs to support authentication with ServiceAccountToken.
- KFP Client, to better support this authentication method.
/assign @yanniszark
cc @Bobgy | {
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