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https://api.github.com/repos/kubeflow/pipelines/issues/8313 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8313/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8313/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8313/events | https://github.com/kubeflow/pipelines/issues/8313 | 1,387,958,970 | I_kwDOB-71UM5SupK6 | 8,313 | [sdk] 403 error when API key is auto-refreshed with Out of Cluster kube-context | {
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"Hi @alexlatchford!\r\n\r\nIt seems that the `refresh_token` got expired or removed. Could you logout from your Google account and try repeating the steps you outlined?",
"Hey @gkcalat we're on EKS 😅",
"@alexlatchford, I am not an AWS expert, but it might have an option to set the expiration of the tokens. @su... | 2022-09-27T15:29:08 | 2022-10-04T21:33:33 | null | CONTRIBUTOR | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
We run KFP v1.8.1 via KF v1.5.0.
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
We run a forked version of KFP SDK which is at ~v1.9.0, mostly has a couple of patches for problems we've contributed back but haven't fully reincorporated.
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
From my investigation of the current code on `master` in the open source repo, this bug still exists and should be pretty easy to reproduce with a sufficiently long KFP run such that the API needs to be refreshed.
### Steps to reproduce
1. Instantiate a `Client` using a local Kube config, outside of a Kubernetes cluster.
2. Use a long running method like `wait_for_run_completion` (code [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L1302)).
3. Wait for long enough for the API key to expire.
4. You get a `403` error 😢
It gets a 403 because the URL it is hitting on the Kubernetes API service doesn't exist as we lose the "proxy path" that gets injected, more details below.
**PS. This has the potential to impact ANY KFP SDK call to the KFP API service from what I can tell when operating outside of a cluster.**
### Expected result
<!-- What should the correct behavior be? -->
It simply shouldn't error, it should refresh the `api_key` without needing to reload the `host`.
### Materials and Reference
The root cause of the 403 is that in these scenarios instead of making calls to:
`<Kube API Server Host>/api/v1/namespaces/<namespace>/services/ml-pipeline:http/proxy//apis/v1beta1/runs/<run id>`
Instead it calls (which doesn't exist, thus the 403):
`<Kube API Server Host>/apis/v1beta1/runs/<run id>`
Effectively it loses the `Client._KUBE_PROXY_PATH` that is injected [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L302).
Now why this is happens because:
1. During `Client.__init__` the `load_kube_config` method is (see [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L295)) called in the upstream `kubernetes` library.
- Under the hood eventually it sets the `refresh_api_key_hook` (see [here](https://github.com/kubernetes-client/python/blob/ada96faca164f5d5c018fb21b8ef2ecafbdf5e43/kubernetes/base/config/kube_config.py#L576)).
- In that `_refresh_api_key` method it calls `_set_config`, which will re-pull the `host` from the `~/.kube/config`.
- **Normally this would be fine but [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L302) KFP mutates that value to inject `Client._KUBE_PROXY_PATH` 😢**
2. Now for `wait_for_run_completion`, that under the hood calls the `kfp_server_api.api.run_service_api.RunService. get_run_with_http_info`.
3. The `get_run_with_http_info` method calls the `auth_settings` method, which in scenarios when the credentials expire it'll call the `refresh_api_key_hook`.
**How to potentially fix this?**
I guess my preference would be to work out some way to not mutate the `config.host` to append the `Client._KUBE_PROXY_PATH` as that "hacky" approach clearly has its shortcomings. Unclear though what the alternative is as this is quite a convenient solution most of the time!
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8312 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8312/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8312/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8312/events | https://github.com/kubeflow/pipelines/issues/8312 | 1,387,804,595 | I_kwDOB-71UM5SuDez | 8,312 | [sdk] <Bug Name> | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8308 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8308/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8308/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8308/events | https://github.com/kubeflow/pipelines/issues/8308 | 1,386,500,014 | I_kwDOB-71UM5SpE-u | 8,308 | [feature] Container start and end times associated with a Pod | {
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"Hi @ameya-parab, thank you for bringing up this feature request. We recommend using the API from k8s to collect pod info now, and we don't have plans to implement this functionality. Feel free to open this thread if you have more thoughts!"
] | 2022-09-26T18:05:01 | 2022-09-29T22:52:10 | 2022-09-29T22:52:09 | 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?
We want to track the start and end times of containers within a pod (Kubeflow Pipeline/Run task). In other words, for better observability, we would like to log the start and end times of the `init`, `wait` and `main` container which are triggered as part of a KFP pod. The `mlpipeline` metabase currently tracks total run and task duration but does not have information on container runtimes.
### What is the use case or pain point?
To improve the observability experience, we would like to track the initialization, wait, and execution time for a Kubeflow pipeline task (preferably in the metabase).
### Is there a workaround currently?
We are currently polling the Kubernetes API server periodically to retrieve container runtimes for a specific Pod (KFP task), but there is a major issue with short lived tasks (<10 seconds), where the Pod's container details are lost if the node on which the Pod was running is scaled down.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8307 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8307/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8307/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8307/events | https://github.com/kubeflow/pipelines/issues/8307 | 1,386,037,128 | I_kwDOB-71UM5SnT-I | 8,307 | [bug] Unable to produce pipeline with basic Component I/O | {
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"Hi @MartinRogmann, maybe consider upgrading the kfp SDK to 1.8.14 and see if the error persists? ",
"Hi @Linchin, that solved the problem. Thank you"
] | 2022-09-26T12:56:54 | 2022-09-30T08:19:53 | 2022-09-30T08:19:53 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
Installed kubeflow manifest **v1.6.0-rc.1** with **minikube v1.26.0** and **kubectl v1.21.14**
For detailed output:
minikube version: v1.26.0
commit: f4b412861bb746be73053c9f6d2895f12cf78565
kubectl version
Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.14", GitCommit:"0f77da5bd4809927e15d1658fb4aa8f13ad890a5", GitTreeState:"clean", BuildDate:"2022-06-15T14:17:29Z", GoVersion:"go1.16.15", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.14", GitCommit:"0f77da5bd4809927e15d1658fb4aa8f13ad890a5", GitTreeState:"clean", BuildDate:"2022-06-15T14:11:36Z", GoVersion:"go1.16.15", Compiler:"gc", Platform:"linux/amd64"}
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
Inside notebook: pip list | grep kfp
kfp 1.6.3
kfp-pipeline-spec 0.1.16
kfp-server-api 1.6.0
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
We tried to produce a basic pipeline that saves pandas dataframes in one component and uses it in the next component using artifacts. We experimented with both Output[Dataset] and OutputPath(str) and tried various samples from v1/v2 SDK without success.
To reproduce, create the following options:
**Imports**
```
import kfp
import kfp.dsl as dsl
from kfp.v2.dsl import component, Input, Output, InputPath, OutputPath, Dataset, Metrics, Model, Artifact
```
**Option A: Using csv**
```
@component(
packages_to_install = ["pandas", "sklearn"],
)
def load(data: Output[Dataset]):
import pandas as pd
from sklearn import datasets
dataset = datasets.load_iris()
df = pd.DataFrame(data=dataset.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"])
df.to_csv(data.path)
@component(
packages_to_install = ["pandas"],
)
def print_head(data: Input[Dataset]):
import pandas as pd
df = pd.read_csv(data.path)
print(df.head())
@dsl.pipeline(
name='Iris',
description='iris'
)
def pipeline():
load_task = load()
print_task = print_head(data=load_task.outputs["data"])
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
pipeline_func=pipeline,
package_path='iris_csv.yaml')
```
**Option B: Using pickle**
```
@component(
packages_to_install = ["pandas", "sklearn"],
)
def load(data: Output[Dataset]):
import pickle
import pandas as pd
from sklearn import datasets
dataset = datasets.load_iris()
df = pd.DataFrame(data=dataset.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"])
with open(data.path, "wb") as f:
pickle.dump(df, f)
@component(
packages_to_install = ["pandas"],
)
def print_head(data: Input[Dataset]):
import pickle
import pandas as pd
with open(data.path, 'rb') as f:
df = pickle.load(f)
print(df.head())
@dsl.pipeline(
name='Iris',
description='iris'
)
def pipeline():
load_task = load()
print_task = print_head(data=load_task.outputs["data"])
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
pipeline_func=pipeline,
package_path='iris_pickle.yaml')
```
**Option C: Using json**
```
@component(
packages_to_install = ["pandas", "sklearn"],
)
def load(data: Output[Dataset]):
import pandas as pd
from sklearn import datasets
dataset = datasets.load_iris()
df = pd.DataFrame(data=dataset.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"])
json_df = {"data": df}
with open(data.path, "wb") as f:
f.write(json.dumps(result, default=lambda df: json.loads(df.to_json())))
@component(
packages_to_install = ["pandas"],
)
def print_head(data: Input[Dataset]):
import pandas as pd
with open(data.path, 'rb') as f:
df = pd.DataFrame(json.loads(f.read())["data"])
print(df.head())
@dsl.pipeline(
name='Iris',
description='iris'
)
def pipeline():
load_task = load()
print_task = print_head(data=load_task.outputs["data"])
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
pipeline_func=pipeline,
package_path='iris_json.yaml')
```
All three options result in the following error:
```
time="2022-09-26T09:50:42.264Z" level=info msg="capturing logs" argo=true
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead:
https://pip.pypa.io/warnings/venv
Traceback (most recent call last):
File "/tmp/tmp.CjkMbFoRTh", line 826, in <module>
executor_main()
File "/tmp/tmp.CjkMbFoRTh", line 820, in executor_main
function_to_execute=function_to_execute)
File "/tmp/tmp.CjkMbFoRTh", line 549, in __init__
artifacts_list[0])
File "/tmp/tmp.CjkMbFoRTh", line 562, in _make_output_artifact
os.makedirs(os.path.dirname(artifact.path), exist_ok=True)
File "/usr/local/lib/python3.7/posixpath.py", line 156, in dirname
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
F0926 09:51:03.011047 26 main.go:56] Failed to execute component: exit status 1
time="2022-09-26T09:51:03.028Z" level=error msg="cannot save artifact /tmp/outputs/data/data" argo=true error="stat /tmp/outputs/data/data: no such file or directory"
Error: exit status 1
```
### Expected result
<!-- What should the correct behavior be? -->
Clearer examples in https://github.com/kubeflow/pipelines/tree/master/samples. As it is hard to distinguish which sample are actually using the v1 or v2 syntax and are compatible with the latest version.
Can you provide a minimal working example that ideally also includes the use of multiple inputs and outputs and the logging of metrics and models. The samples we found in the docs or on github either use the deprecated container_ops instead of components or could not be run by us.
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area testing -->
/area sdk
/area samples
/area components
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8302 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8302/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8302/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8302/events | https://github.com/kubeflow/pipelines/issues/8302 | 1,385,145,397 | I_kwDOB-71UM5Sj6Q1 | 8,302 | [sdk] KFPv2: Custom Container component set bool default value `False` to `0` which caused runtime failure | {
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* KFP version:
KFP 2.0.0-alpha.4
* KFP SDK version:
SDK 2.0.0-beta4
### Steps to reproduce
Create the following custom container component:
```
from kfp.dsl import Input, Output, Artifact, container_component, ContainerSpec
@dsl.container_component
def create_dataset(experiment_name: str, experiment_namespace: str, experiment_timeout_minutes: int,
experiment_spec_json: Input[Artifact], parameter_set: Output[Artifact], delete_finished_experiment: bool = False):
return ContainerSpec(
image='docker.io/kubeflowkatib/kubeflow-pipelines-launcher',
command=['python', 'src/launch_experiment.py'],
args=[
'--experiment-name', experiment_name,
'--experiment-namespace', experiment_namespace,
'--experiment-spec', experiment_spec_json.path,
'--experiment-timeout-minutes',experiment_timeout_minutes,
'--delete-after-done', delete_finished_experiment,
'--output-file', parameter_set.path,
])
```
Note that `delete_finished_experiment` has a default value `False`.
When compiling the pipeline, this component is converted to IR with the following format:
```
tasks:
create-dataset:
cachingOptions:
enableCache: true
componentRef:
name: comp-create-dataset
dependentTasks:
- create-katib-experiment-task
inputs:
artifacts:
experiment_spec_json:
taskOutputArtifact:
outputArtifactKey: experiment_spec_json
producerTask: create-katib-experiment-task
parameters:
delete_finished_experiment:
runtimeValue:
constant: 0
experiment_name:
componentInputParameter: name
experiment_namespace:
componentInputParameter: namespace
experiment_timeout_minutes:
runtimeValue:
constant: 60
taskInfo:
name: create-dataset
```
Note that `delete_finished_experiment` has a runtime value as 0.
### Expected result
What should the type of boolean default value be? I am expecting the value to be either of followings: bool `false`, bool `False` or string `False`. I didn't expect the value to be number 0.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8300 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8300/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8300/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8300/events | https://github.com/kubeflow/pipelines/issues/8300 | 1,384,498,692 | I_kwDOB-71UM5ShcYE | 8,300 | [sdk] Unable to import ServiceAccountTokenVolumeCredentials in KFPv2 SDK | {
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"u... | open | false | null | [] | null | [
"Thank you, @zijianjoy!"
] | 2022-09-24T03:09:25 | 2022-09-26T15:08:13 | null | COLLABORATOR | null | ### Environment
* KFP version:
KFP 2.0.0-alpha4
* KFP SDK version:
2.0.0-beta.4
### Steps to reproduce
Follow https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/#full-kubeflow-subfrom-inside-clustersub to set the default SA token path as empty. But `ServiceAccountTokenVolumeCredentials` cannot be imported.
From the `__init__.py` file, ServiceAccountTokenVolumeCredentials is not exported:
https://github.com/kubeflow/pipelines/blob/e14a784327c83e2d7f3e66dc09f4b3af3323cbc7/sdk/python/kfp/client/__init__.py#L17
If we don't import `ServiceAccountTokenVolumeCredentials`, that means by default it should use default SA token path when creating KFP client: `kfp.Client()`. However, this command has failed due to:
```
File /opt/conda/lib/python3.8/site-packages/kfp/client/client.py:372, in Client._get_config_with_default_credentials(self, config)
360 """Apply default credentials to the configuration object.
361
362 This method accepts a Configuration object and extends it with
363 some default credentials interface.
364 """
365 # XXX: The default credentials are audience-based service account tokens
366 # projected by the kubelet (ServiceAccountTokenVolumeCredentials). As we
367 # implement more and more credentials, we can have some heuristic and
(...)
370
371 # TODO: auth.ServiceAccountCredentials does not exist... dead code path?
--> 372 credentials = auth.ServiceAccountTokenVolumeCredentials()
373 config_copy = copy.deepcopy(config)
375 try:
AttributeError: module 'kfp.client.auth' has no attribute 'ServiceAccountTokenVolumeCredentials'
```
### Expected result
User can create KFP client using the default SA token path. User can also configure SA token path to be custom value so KFP SDK can read from a different location. User can configure SA token path to be None, in this case, KFP SDK should be able to use environment variable KF_PIPELINES_SA_TOKEN_PATH.
cc @chensun @connor-mccarthy
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8294 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8294/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8294/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8294/events | https://github.com/kubeflow/pipelines/issues/8294 | 1,382,977,963 | I_kwDOB-71UM5SbpGr | 8,294 | [feature] Stop recurring run when failed | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | closed | false | null | [] | null | [
"This feels like a custom logic that probably shouldn't be part of a scheduling system. Alternatives could be implementing the custom logic yourself either from client side or in component level like using exit handler.",
"@chensun I wonder wdym to implement this from client side? Scheduled runs after they are cr... | 2022-09-22T20:28:32 | 2022-09-23T00:51:46 | 2022-09-22T22:54:31 | CONTRIBUTOR | null | ### Feature Area
/area backend
### What feature would you like to see?
Support for stopping recurring runs (or retry N times) if a run fails.
### What is the use case or pain point?
At the moment, when a recurring run has a failed run, it will continue scheduling new runs independently of previous state. Ideally, we would like to define a custom behaviour were a scheduled run gets retried N times or it stops scheduling new runs after trying to schedule N runs.
This is mostly for cases where running for date X+1 is counterproducing when X is missing. For example for data processing where you want to make sure that no gaps are missing between segments.
### Is there a workaround currently?
Currently there is some sort of way which is to use an ExitHandler to stop the run by instrospecting into itself and finding the recurring_run id associated with current run. However that makes it complicated to support. Ideally we make the handling more transparent by doing this on the controller and configuring it from UI.
---
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"This is infeasible, as each task is a containerized app. KFP system doesn't have visibility into the progress of a task. \r\nOur suggestion would be use the log workaround as you described.",
"ok, thanks for answering."
] | 2022-09-22T13:33:29 | 2022-09-23T08:06:10 | 2022-09-22T22:47:37 | NONE | null | ### Feature Area
/area frontend
/area backend
/area sdk
### What feature would you like to see?
In the UI displaying the DAG of the pipeline, it would be great to see a progress bar on each task. This would allow to visually see how far the task is to completion. In the code of the component, we could provide information on the progress with a dedicated API in the SDK.
### What is the use case or pain point?
It is difficult to estimate how long a task will run. The only option I know, as of today, is to log the progress information.
### Is there a workaround currently?
I add some traces/logs in my task, but this implies looking regularly to the logs of each running task.
---
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"`google-cloud-pipeline-components` is currently not fully compatible with kfp 2.0*. We're working on the migration, and @chongyouquan may help provide a rough timeline.\r\n",
"Any update here?",
"Hi, the following `requirements.in` works:\r\n```\r\nkfp==2.0.0b10\r\nproto-plus==1.19.6\r\ngoogleapis-common-proto... | 2022-09-22T10:03:22 | 2023-01-11T08:51:00 | null | NONE | null | ### Environment
* KFP version:
2.0.0b1
### Steps to reproduce
Create a `requirements.in` file:
```
kfp==2.0.0b1
google-cloud-pipeline-components
```
Then run `pip-compile -v requirements.in`
### Result
```Using indexes:
https://pypi.org/simple
ROUND 1
Current constraints:
google-cloud-pipeline-components (from -r requirements.in (line 7))
kfp==2.0.0b1 (from -r requirements.in (line 6))
Finding the best candidates:
found candidate google-cloud-pipeline-components==1.0.22 (constraint was <any>)
found candidate kfp==2.0.0b1 (constraint was ==2.0.0b1)
Finding secondary dependencies:
kfp==2.0.0b1 requires absl-py<2,>=0.9, click<9,>=7.1.2, cloudpickle<3,>=2.0.0, Deprecated<2,>=1.2.7, docstring-parser<1,>=0.7.3, fire<1,>=0.3.1, google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5, google-auth<3,>=1.6.1, google-cloud-storage<3,>=2.2.1, jsonschema<4,>=3.0.1, kfp-pipeline-spec<0.2.0,>=0.1.14, kfp-server-api<3.0.0,>=2.0.0a0, kubernetes<19,>=8.0.0, protobuf<4,>=3.13.0, PyYAML<6,>=5.3, requests-toolbelt<1,>=0.8.0, strip-hints<1,>=0.1.8, tabulate<1,>=0.8.6, typer<1.0,>=0.3.2, uritemplate<4,>=3.0.1
google-cloud-pipeline-components==1.0.22 requires google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5, google-cloud-aiplatform<2,>=1.11.0, google-cloud-notebooks>=0.4.0, google-cloud-storage<2,>=1.20.0, kfp<2.0.0,>=1.8.9
New dependencies found in this round:
adding ('absl-py', '<2,>=0.9', [])
adding ('click', '<9,>=7.1.2', [])
adding ('cloudpickle', '<3,>=2.0.0', [])
adding ('deprecated', '<2,>=1.2.7', [])
adding ('docstring-parser', '<1,>=0.7.3', [])
adding ('fire', '<1,>=0.3.1', [])
adding ('google-api-core', '!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5', [])
adding ('google-auth', '<3,>=1.6.1', [])
adding ('google-cloud-aiplatform', '<2,>=1.11.0', [])
adding ('google-cloud-notebooks', '>=0.4.0', [])
adding ('google-cloud-storage', '<2,<3,>=1.20.0,>=2.2.1', [])
adding ('jsonschema', '<4,>=3.0.1', [])
adding ('kfp', '<2.0.0,>=1.8.9', [])
adding ('kfp-pipeline-spec', '<0.2.0,>=0.1.14', [])
adding ('kfp-server-api', '<3.0.0,>=2.0.0a0', [])
adding ('kubernetes', '<19,>=8.0.0', [])
adding ('protobuf', '<4,>=3.13.0', [])
adding ('pyyaml', '<6,>=5.3', [])
adding ('requests-toolbelt', '<1,>=0.8.0', [])
adding ('strip-hints', '<1,>=0.1.8', [])
adding ('tabulate', '<1,>=0.8.6', [])
adding ('typer', '<1.0,>=0.3.2', [])
adding ('uritemplate', '<4,>=3.0.1', [])
Removed dependencies in this round:
------------------------------------------------------------
Result of round 1: not stable
ROUND 2
Current constraints:
absl-py<2,>=0.9 (from kfp==2.0.0b1->-r requirements.in (line 6))
click<9,>=7.1.2 (from kfp==2.0.0b1->-r requirements.in (line 6))
cloudpickle<3,>=2.0.0 (from kfp==2.0.0b1->-r requirements.in (line 6))
Deprecated<2,>=1.2.7 (from kfp==2.0.0b1->-r requirements.in (line 6))
docstring-parser<1,>=0.7.3 (from kfp==2.0.0b1->-r requirements.in (line 6))
fire<1,>=0.3.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 (from kfp==2.0.0b1->-r requirements.in (line 6))
google-auth<3,>=1.6.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
google-cloud-aiplatform<2,>=1.11.0 (from google-cloud-pipeline-components==1.0.22->-r requirements.in (line 7))
google-cloud-notebooks>=0.4.0 (from google-cloud-pipeline-components==1.0.22->-r requirements.in (line 7))
google-cloud-pipeline-components (from -r requirements.in (line 7))
google-cloud-storage<2,<3,>=1.20.0,>=2.2.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
jsonschema<4,>=3.0.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
kfp<2.0.0,==2.0.0b1,>=1.8.9 (from -r requirements.in (line 6))
kfp-pipeline-spec<0.2.0,>=0.1.14 (from kfp==2.0.0b1->-r requirements.in (line 6))
kfp-server-api<3.0.0,>=2.0.0a0 (from kfp==2.0.0b1->-r requirements.in (line 6))
kubernetes<19,>=8.0.0 (from kfp==2.0.0b1->-r requirements.in (line 6))
protobuf<4,>=3.13.0 (from kfp==2.0.0b1->-r requirements.in (line 6))
PyYAML<6,>=5.3 (from kfp==2.0.0b1->-r requirements.in (line 6))
requests-toolbelt<1,>=0.8.0 (from kfp==2.0.0b1->-r requirements.in (line 6))
strip-hints<1,>=0.1.8 (from kfp==2.0.0b1->-r requirements.in (line 6))
tabulate<1,>=0.8.6 (from kfp==2.0.0b1->-r requirements.in (line 6))
typer<1.0,>=0.3.2 (from kfp==2.0.0b1->-r requirements.in (line 6))
uritemplate<4,>=3.0.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
Finding the best candidates:
found candidate absl-py==1.2.0 (constraint was >=0.9,<2)
found candidate click==8.1.3 (constraint was >=7.1.2,<9)
found candidate cloudpickle==2.2.0 (constraint was >=2.0.0,<3)
found candidate deprecated==1.2.13 (constraint was >=1.2.7,<2)
found candidate docstring-parser==0.15 (constraint was >=0.7.3,<1)
found candidate fire==0.4.0 (constraint was >=0.3.1,<1)
found candidate google-api-core==2.10.1 (constraint was >=1.31.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev)
found candidate google-auth==1.35.0 (constraint was >=1.6.1,<3)
found candidate google-cloud-aiplatform==1.17.1 (constraint was >=1.11.0,<2)
found candidate google-cloud-notebooks==1.4.2 (constraint was >=0.4.0)
found candidate google-cloud-pipeline-components==1.0.22 (constraint was <any>)
Could not find a version that matches google-cloud-storage<2,<3,>=1.20.0,>=2.2.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
Tried: 0.20.0, 0.20.0, 0.21.0, 0.21.0, 0.22.0, 0.22.0, 0.23.0, 0.23.0, 0.23.1, 0.23.1, 1.0.0, 1.0.0, 1.1.0, 1.1.0, 1.1.1, 1.1.1, 1.2.0, 1.2.0, 1.3.0, 1.3.0, 1.3.1, 1.3.1, 1.3.2, 1.3.2, 1.4.0, 1.4.0, 1.5.0, 1.5.0, 1.6.0, 1.6.0, 1.7.0, 1.7.0, 1.8.0, 1.8.0, 1.9.0, 1.9.0, 1.10.0, 1.10.0, 1.11.0, 1.11.0, 1.11.1, 1.11.1, 1.12.0, 1.12.0, 1.12.1, 1.12.1, 1.13.0, 1.13.0, 1.13.1, 1.13.1, 1.13.2, 1.13.2, 1.13.3, 1.13.3, 1.14.0, 1.14.0, 1.14.1, 1.14.1, 1.15.0, 1.15.0, 1.15.1, 1.15.1, 1.15.2, 1.15.2, 1.16.0, 1.16.0, 1.16.1, 1.16.1, 1.16.2, 1.16.2, 1.17.0, 1.17.0, 1.17.1, 1.17.1, 1.18.0, 1.18.0, 1.18.1, 1.18.1, 1.19.0, 1.19.0, 1.19.1, 1.19.1, 1.20.0, 1.20.0, 1.21.0, 1.21.0, 1.22.0, 1.22.0, 1.23.0, 1.23.0, 1.24.0, 1.24.0, 1.24.1, 1.24.1, 1.25.0, 1.25.0, 1.26.0, 1.26.0, 1.27.0, 1.27.0, 1.28.0, 1.28.0, 1.28.1, 1.28.1, 1.29.0, 1.29.0, 1.30.0, 1.30.0, 1.31.0, 1.31.0, 1.31.1, 1.31.1, 1.31.2, 1.31.2, 1.32.0, 1.32.0, 1.33.0, 1.33.0, 1.34.0, 1.34.0, 1.35.0, 1.35.0, 1.35.1, 1.35.1, 1.36.0, 1.36.0, 1.36.1, 1.36.1, 1.36.2, 1.36.2, 1.37.0, 1.37.0, 1.37.1, 1.37.1, 1.38.0, 1.38.0, 1.39.0, 1.39.0, 1.40.0, 1.40.0, 1.41.0, 1.41.0, 1.41.1, 1.41.1, 1.42.0, 1.42.0, 1.42.1, 1.42.1, 1.42.2, 1.42.2, 1.42.3, 1.42.3, 1.43.0, 1.43.0, 1.44.0, 1.44.0, 2.0.0, 2.0.0, 2.1.0, 2.1.0, 2.2.0, 2.2.0, 2.2.1, 2.2.1, 2.3.0, 2.3.0, 2.4.0, 2.4.0, 2.5.0, 2.5.0
There are incompatible versions in the resolved dependencies:
google-cloud-storage<3,>=2.2.1 (from kfp==2.0.0b1->-r requirements.in (line 6))
google-cloud-storage<2,>=1.20.0 (from google-cloud-pipeline-components==1.0.22->-r requirements.in (line 7))
```
### Expected result
An output with compatible package versions.
### Possible solution
Bump the google-cloud-storage version to include `<3,>=2.2.1` in https://github.com/kubeflow/pipelines/blob/2.0.0b4/components/google-cloud/dependencies.py
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https://api.github.com/repos/kubeflow/pipelines/issues/8283 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8283/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8283/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8283/events | https://github.com/kubeflow/pipelines/issues/8283 | 1,378,737,179 | I_kwDOB-71UM5SLdwb | 8,283 | [backend] metadata-writer cannot save metadata of S3 artifacts with argo v3.1+ | {
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"@tktest1234 Are you running on AWS and want to use KFP with S3 as storage backend?\r\n\r\n",
"Yes, I install kubeflow1.4 on AWS(EKS) and want to use KFP with S3 as storage backend.\r\nIsn't it expected?",
"Hey @tktest1234 please follow the instructions on AWS distribution of Kubeflow to install KFP with S3 as ... | 2022-09-20T02:39:36 | 2022-12-28T03:18:38 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
kubeflow 1.4 manifest (git::https://github.com/kubeflow/manifests.git?ref=v1.4.0 )
and manually change config for S3
* KFP version:
1.7.0 (kubeflow 1.4)
* KFP SDK version:
1.7.1
### Steps to reproduce
1. setting to save artifacts to S3 (change configmap for workflow-controller)
1. Run pipeline: [Tutorial] Data passing in python components
metadata-writer output log
```
Kubernetes Pod event: ADDED file-passing-pipelines-txmqj-2254123803 14598940
Traceback (most recent call last):
File "/kfp/metadata_writer/metadata_writer.py", line 238, in <module>
artifact_uri = argo_artifact_to_uri(argo_artifact)
File "/kfp/metadata_writer/metadata_writer.py", line 106, in argo_artifact_to_uri
provider=get_object_store_provider(s3_artifact['endpoint']),
KeyError: 'endpoint'
```
result:
- Artifact in metadb is empty
### Expected result
metadata for the artifacts is logged into metadb
### Materials and Reference
I think it is caused by changes for argo Key-Only Artifacts.
In https://github.com/kubeflow/pipelines/blob/1.7.0/backend/metadata_writer/src/metadata_writer.py#L318, metadata-writer read "workflows.argoproj.io/outputs", but endpoint and bucket are not passed by argo3.1+. So https://github.com/kubeflow/pipelines/blob/1.7.0/backend/metadata_writer/src/metadata_writer.py#L105 cannot set the correct URL.
Related frontend issue: https://github.com/kubeflow/pipelines/issues/5930
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8269 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8269/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8269/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8269/events | https://github.com/kubeflow/pipelines/issues/8269 | 1,374,042,311 | I_kwDOB-71UM5R5jjH | 8,269 | Cannot get key for artifact location | {
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"@SeibertronSS, do other pipelines have this error? Have you successfully run a pipeline on this deployment?\r\n\r\nIt seems like there may be an issue with the deployment related to the [minio artifact secret](https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/manifests/kustomize/t... | 2022-09-15T07:11:35 | 2022-09-15T22:52:43 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
I deploy the Kubeflow Pipelines (standalone) by Kubeflow Manifests (https://github.com/kubeflow/manifests)
* KFP version:
1.7.0
* KFP SDK version:
1.8.2
When I submit a pipeline, I will get the error `This step is in Error state with this message: Error (exit code 1): key unsupported: cannot get key for artifact location, because it is invalid`
The code is as follows
```
import kfp
from kfp.components import create_component_from_func
client = kfp.Client()
def add(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
return a + b
add_op = create_component_from_func(
add, output_component_file='add_component.yaml')
import kfp.dsl as dsl
@dsl.pipeline(
name='Addition pipeline',
description='An example pipeline that performs addition calculations.'
)
def add_pipeline(
a='1',
b='7',
):
# Passes a pipeline parameter and a constant value to the `add_op` factory
# function.
first_add_task = add_op(a, 4)
# Passes an output reference from `first_add_task` and a pipeline parameter
# to the `add_op` factory function. For operations with a single return
# value, the output reference can be accessed as `task.output` or
# `task.outputs['output_name']`.
second_add_task = add_op(first_add_task.output, b)
# Specify argument values for your pipeline run.
arguments = {'a': '7', 'b': '8'}
# Create a pipeline run, using the client you initialized in a prior step.
client.create_run_from_pipeline_func(add_pipeline, arguments=arguments, run_name="add-8")
print("submit pipeline")
```
This problem seems to be related to Minio.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8267 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8267/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8267/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8267/events | https://github.com/kubeflow/pipelines/issues/8267 | 1,373,732,443 | I_kwDOB-71UM5R4X5b | 8,267 | [sdk] ssl.SSLError: [SSL: WRONG_VERSION_NUMBER] wrong version number | {
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"u... | open | false | null | [] | null | [
"Feels like something is wrong with your kubernetes/kubeflow setup.\r\nCan you see the pipelines UI or does it also show this error?",
"@BroderPeters Thank you very much for your answer. I can see pipelines UI. \r\n\r\nIt's a weird error. The error does not happen to me. It happens to my work colleague sometimes.... | 2022-09-15T00:20:34 | 2022-09-15T23:52:59 | null | NONE | null | ### Environment
* KFP version: 1.8.1
* KFP SDK version: 1.6.2
* All dependencies version:
kfp 1.6.2
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
It happens on GCP, on a Kubeflow 1.5 cluster
To reproduce you should execute the following code:
```
from kfp import Client
kfp_client = Client(
host="",
client_id="",
other_client_id="",
other_client_secret="",
namespace="aNamespace"
)
kfp_client.set_user_namespace(namespace="aNamespace")
pipeline_id = kfp_client.get_pipeline_id(name="aPipelineName")
print(pipeline_id)
```
### Expected result
I should get a pipeline id
Instead I get
Traceback (most recent call last):
File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connectionpool.py", line 1040, in _validate_conn
conn.connect()
File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connection.py", line 416, in connect
self.sock = ssl_wrap_socket(
File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/util/ssl_.py", line 449, in ssl_wrap_socket
ssl_sock = _ssl_wrap_socket_impl(
File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/util/ssl_.py", line 493, in _ssl_wrap_socket_impl
return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
File "/Users/user/.pyenv/versions/3.9.9/lib/python3.9/ssl.py", line 500, in wrap_socket
return self.sslsocket_class._create(
File "/Users/user/.pyenv/versions/3.9.9/lib/python3.9/ssl.py", line 1040, in _create
self.do_handshake()
File "/Users/user/.pyenv/versions/3.9.9/lib/python3.9/ssl.py", line 1309, in do_handshake
self._sslobj.do_handshake()
ssl.SSLError: [SSL: WRONG_VERSION_NUMBER] wrong version number (_ssl.c:1129)
---------------------
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8257 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8257/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8257/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8257/events | https://github.com/kubeflow/pipelines/issues/8257 | 1,370,642,028 | I_kwDOB-71UM5RslZs | 8,257 | [feature] Allow GCP ModelUploadOp to upload a model version | {
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"received_events_url": "https://api.github.com/users/parthmishra/received_events",
"type": "User",
"site_admin": false
} | [
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"id": 1126834402,
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"url": "https://api.github.com/repos/kubeflow/pipelines/labels/area/components",
"name": "area/components",
"color": "d2b48c",
"default": false,
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"node_id": "MDU6TGFiZWwxMjg5NTg... | closed | false | null | [] | null | [
"/cc @chongyouquan ",
"Similarly other Ops that can generate a model (Custom*TrainingJobRunOp, AutoML*TrainingJobRunOp etc) should support adding new versions of a model as well.",
"> \r\n\r\n+1",
"This should now be supported by the latest release, via the parent_model parameter. Please reopen if you see it'... | 2022-09-12T23:21:34 | 2023-05-31T09:55:18 | 2023-01-26T17:50:51 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
/area components
### What feature would you like to see?
The ability to upload a Vertex Model version to the model registry
### What is the use case or pain point?
In the REST API for[ model upload](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.models/upload#request-body), the request body has several fields including `parentModel` which can be supplied if uploading a new model version.
The current version of [`ModelUploadOp`](https://github.com/kubeflow/pipelines/blob/1739434f4ad2000105e0a8feff41b587e3c9de8c/components/google-cloud/google_cloud_pipeline_components/container/v1/gcp_launcher/upload_model_remote_runner.py#L55) only supplies the `model` field in the request:
```python
upload_model_request = {
# TODO(IronPan) temporarily remove the empty fields from the spec
'model':
json_util.recursive_remove_empty(
append_unmanaged_model_artifact_into_payload(
executor_input, model_spec))
}
```
### Is there a workaround currently?
I believe this can be done by creating a custom component that uses the `gcloud` sdk and then `gcloud ai model upload ... --parent-model ... --version-aliases ` but I haven't tried it yet.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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"followers_url": "https://api.githu... | null | [
"I'm experiencing the same issue. It seems that the issue was introduced in the alpha.3 release. Metrics are successfully picked up when downgrading the ml-pipeline deployment to the previous image `gcr.io/ml-pipeline/api-server:2.0.0-alpha.2`",
"@Tobiasgoerke and myself can reproduce this too. @kimwnasptd @annaj... | 2022-09-12T09:36:20 | 2022-09-26T19:27:53 | 2022-09-21T17:31:09 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow/manifest v1.6
AKS v1.22
<!-- For more information, see an overview of KFP installation options:
https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: ml-pipeline/api-server:2.0.0-alpha.3
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: '1.8.13'
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
```
from kfp.components import InputPath, OutputPath, create_component_from_func
from kfp import dsl
def produce_metrics(
# Note when the `create_component_from_func` method converts the function to a component, the function parameter "mlpipeline_metrics_path" becomes an output with name "mlpipeline_metrics" which is the correct name for metrics output.
mlpipeline_metrics_path: OutputPath('Metrics'),
):
import json
accuracy = 0.1
metrics = {
'metrics': [{
'name': 'accuracy-score', # The name of the metric. Visualized as the column name in the runs table.
'numberValue': accuracy, # The value of the metric. Must be a numeric value.
'format': "PERCENTAGE", # The optional format of the metric. Supported values are "RAW" (displayed in raw format) and "PERCENTAGE" (displayed in percentage format).
}]
}
with open(mlpipeline_metrics_path, 'w') as f:
json.dump(metrics, f)
produce_metrics_op = create_component_from_func(
produce_metrics,
base_image='python:3.7',
packages_to_install=[],
output_component_file='component.yaml',
)
from kfp import dsl
import os, kfp
@dsl.pipeline(
name='KF iris PoC',
description='Simple TF'
)
def test():
task=produce_metrics_op()
import os,kfp
with open(os.environ['KF_PIPELINES_SA_TOKEN_PATH'], "r") as f:
TOKEN = f.read()
client = kfp.Client(host='http://ml-pipeline.kubeflow.svc.cluster.local:8888',existing_token=TOKEN)
client.create_run_from_pipeline_func(
test,
mode=kfp.dsl.PipelineExecutionMode.V1_LEGACY,
arguments={}
)
```
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
I cannot see the pipeline metrics in the UI but the pipeline artifact is logged i am getting this error in ml-pipeline
```
I0912 09:12:02.846997 8 error.go:259] Invalid input error: Unknown execution spec
InternalServerError: failed to unmarshal workflow
```
### 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 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/8255 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8255/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8255/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8255/events | https://github.com/kubeflow/pipelines/issues/8255 | 1,369,054,485 | I_kwDOB-71UM5Rmh0V | 8,255 | [frontend] Can't see log field in UI (SDK v2) | {
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"We do not support main.logs in KFP v2 compatible mode. We are no longer supporting KFP v2 compatible mode.\r\n\r\nWe support main.logs in KFP v1 and are working toward support in KFP v2."
] | 2022-09-11T20:24:44 | 2022-09-15T22:49:39 | 2022-09-15T22:49:39 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
from kubeflow/manifests v1.4
* KFP version: 1.7.0
### Steps to reproduce
QQ
When I try to run a simple pipeline on SDK 2, I don't see the main.log fields in the output artifact.
Although I can see the log saved in minio.
UI:
Minio:
Log:
### Expected result
Like this:
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8252 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8252/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8252/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8252/events | https://github.com/kubeflow/pipelines/issues/8252 | 1,367,373,279 | I_kwDOB-71UM5RgHXf | 8,252 | [bug] GCP ModelBatchPredictOp is never cached | {
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"u... | open | false | null | [] | null | [] | 2022-09-09T06:56:51 | 2022-09-09T06:56:55 | null | NONE | null | ### Environment
* How do you deploy Kubeflow Pipelines (KFP)?
Vertex-AI pipelines
* KFP version:
I don't know (vertex ai)
* KFP SDK version:
1.8.12
### Steps to reproduce
Create a pipeline that uses the ModelBatchPredictOp, and run it several times. The task is never cached, but systematically executed.
### Expected result
The task should be cached when the input parameters do not change (dataset, model, gcs source uri, gcs destination prefix, and machine type).
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
According to the documentation, the gcs output uri is created with the following pattern: prediction-model-display-name-job-create-time. The usage of the job creation time here prevents any caching of this operator.
https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.16/google_cloud_pipeline_components.aiplatform.html?highlight=ModelUploadOp#google_cloud_pipeline_components.aiplatform.ModelUploadOp
### Labels
/area sdk
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | open | false | null | [] | null | [
"/cc @Linchin\r\n\r\n"
] | 2022-09-06T20:05:43 | 2022-09-08T22:46:11 | 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?
Extend the api endpoint for listing runs to allow selecting or filtering runs by pipeline ID.
### What is the use case or pain point?
This feature will be helpful when it is necessary to easily gather all runs associated with a given pipeline
### Is there a workaround currently?
No
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8240 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8240/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8240/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8240/events | https://github.com/kubeflow/pipelines/issues/8240 | 1,361,857,532 | I_kwDOB-71UM5RLEv8 | 8,240 | when using the emissary executor you must either explicitly specify the command, or list the image's command in the index: https://argoproj.github.io/argo-workflows/workflow-executors/#emissary-emissary[bug] <Bug Name> | {
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"using kubeflow , machine learning model deployed in to kubernetes,then i'm getting the attached error message",
"Hello @kotalakshman ,\r\n\r\nThere are multiple information in here, I am assuming you are using container component in your pipeline:\r\n\r\n1. In the issue title, this is by design because you need ... | 2022-09-05T12:20:37 | 2022-09-08T23:04:39 | 2022-09-08T23:04:38 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.7.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:1.7.1
<!-- Specify the output of the following shell command: $pip list | grep kfp -->DEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. pip 21.0 will drop support for Python 2.7 in January 2021. More details about Python 2 support in pip can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support pip 21.0 will remove support for this functionality.
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8236 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8236/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8236/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8236/events | https://github.com/kubeflow/pipelines/issues/8236 | 1,359,557,825 | I_kwDOB-71UM5RCTTB | 8,236 | [bug] Disabling caching for a step by set_caching_options lead to disabling caching for subsequent steps | {
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"Hello @ysk24ok \r\n\r\n1. It is likely that `download_task` generates new output every time, even if the content is the same. As a result, if the upstream is not cached, then the downstream task will not be able to use cache.\r\n2. We have deprecated V2_COMPATIBLE mode, please use 2.0.0-XXX and remove the use of `... | 2022-09-02T00:02:18 | 2022-09-08T23:00:28 | null | CONTRIBUTOR | null | I'm not sure if it's a bug or an expected behavior, but let me mark as a bug for now.
### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? full Kubeflow deployment
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.8.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.8.13
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
```py
from kfp import components, dsl
from kfp.v2.dsl import Dataset, Input, component
download_op = components.load_component_from_url(
"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/contrib/google-cloud/storage/download_blob/component.yaml"
)
@component
def print_file_content_op(file: Input[Dataset]) -> None:
with open(file.path) as f:
print(f.read())
@dsl.pipeline(name="caching-test")
def pipeline(path: str) -> None:
download_task = download_op(path)
download_task.set_caching_options(False)
print_file_content_task = print_file_content_op(download_task.outputs["data"])
```
Submit the pipeline using `create_run_from_pipeline_func` with `V2_COMPATIBLE` mode two times.
We'll see `print_file_content_task` is not cached and executed on the second run.
The behavior is counterintuitive as I don't call `set_caching_options(False)` for `print_file_content_task`.
Why I call `download_task.set_caching_options(False)` because the file at the GCS path might be replaced at some point.
I know it would be better to upload a file to a different path instead of replacing the current file and make the step deterministic, but to do so we need to put some efforts to update our existing system.
### Expected result
<!-- What should the correct behavior be? -->
`print_file_content_task` is cached and skipped if the file content is the same as before.
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8230 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8230/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8230/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8230/events | https://github.com/kubeflow/pipelines/issues/8230 | 1,358,783,253 | I_kwDOB-71UM5Q_WMV | 8,230 | [bug] Kubeflow Pipeline UI not rendering Pipeline Metrics (again?) | {
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"Ignore. I found that the value inside the metrics json was screwed up.. I was passing a dict unintentionally in one of the metric where I had to pass the number.\r\n\r\nI think KFP Compiler can show this as warning when ml-pipeline metrics doesn't have an expected return output schema (or it can be shown in UI so ... | 2022-09-01T12:39:16 | 2022-09-01T22:55:36 | 2022-09-01T22:55:36 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Ans: Full Kubeflow Deployment
* KFP version:
For some reason the sidebar shows : **build version dev_local** but was installed using YAMLs at: https://github.com/kubeflow/manifests
Docker Image and Tag info:
`ml-pipeline-api-server`: `gcr.io/ml-pipeline/api-server:1.8.1`
`ml-pipeline-ui`: `gcr.io/ml-pipeline/frontend:1.8.1`
* KFP SDK version:
KFP SDK version: 1.8.13
### Steps to reproduce
Built component (part of the pipeline and is the last component as well) using KFP SDK v1 API
My Component looks like following:
```py
def train_model(customer_id: str,
start_time: str,
end_time: str,
take_target_log: bool,
log_addition: float,
model: OutputPath('Model'),
x_test: OutputPath('CSV'),
y_test: OutputPath('CSV'),
mlpipeline_metrics_path: OutputPath('Metrics')):
metrics = {
'metrics': [
{
'name': 'best_iteration',
'numberValue': 101,
'format': 'RAW'
},
{
'name': 'best_score',
'numberValue': 1.2,
'format': 'RAW'
}
]
}
with open(mlpipeline_metrics_path, 'w') as f:
json.dump(metrics, f)
```
Above component is added in Pipeline using `kfp.components.create_component_from_func` function.
1. Changed metrics argument in above `train_model` signature to
- `mlpipeline_metrics_path: OutputPath('Metrics')`
- `mlpipeline_metrics: OutputPath('Metrics')`
- `mlpipeline_metrics: OutputPath()`
All of them yield YAML with
```yaml
outputs:
artifacts:
- {name: mlpipeline-metrics, path: /tmp/outputs/mlpipeline_metrics/data}
```
`train_model` component in UI shows the Artifacts as :
Weird thing to note here is the `Type` of the artifact.
2. I tried returning `NamedTuple` as :
```py
def train_model(customer_id: str,
start_time: str,
end_time: str,
take_target_log: bool,
log_addition: float,
model: OutputPath('Model'),
x_test: OutputPath('CSV'),
y_test: OutputPath('CSV')) -> NamedTuple('Outputs', [('mlpipeline_metrics', 'Metrics')]):
metrics = {
'metrics': [
{
'name': 'best_iteration',
'numberValue': 101,
'format': 'RAW'
},
{
'name': 'best_score',
'numberValue': 1.2,
'format': 'RAW'
}
]
}
return [json.dumps(metrics)]
```
Above generates YAML with
```yaml
outputs:
artifacts:
- {name: mlpipeline-metrics, path: /tmp/outputs/mlpipeline_metrics/data}
```
No diff in component spec, but container args had `----output-paths` extra arguments in the yaml
```yaml
'----output-paths', /tmp/outputs/mlpipeline_metrics/data
```
### Expected result
Expected to see ML Pipeline Metrics (like `best_score` and `best_iterations` ) in following screenshot
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8224 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8224/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8224/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8224/events | https://github.com/kubeflow/pipelines/issues/8224 | 1,357,755,047 | I_kwDOB-71UM5Q7bKn | 8,224 | [backend] metadata-grpc-deployment cannot connect to mysql | {
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"Hi @jielou !\r\nCould you please try a newer version of KFP standalone deployment (e.g. 1.8.5) and let us know if you still encounter the problem.",
"@gkcalat Hi. I tried to install 1.8.5, but `metadata-grpc-deployment` still failed. Logs showed `MetadataStore cannot be created with the given connection config`.... | 2022-08-31T19:15:15 | 2022-10-07T15:45:38 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
follow the standalone deployment doc, but run `kubectl create -k platform-agnostic' instead.
* KFP version:
1.5
* KFP SDK version:
NA
* EKS cluster k8s version: 1.19
### Steps to reproduce
I follow the guideline the deploy the standalone kubeflow pipelines. However, the metadata-grpc-deployment pod always crush, and logs shows
```
F ml_metadata/metadata_store/metadata_store_server_main.cc:220] Non-OK-status: status status: Internal: mysql_real_connect failed: errno: 2005, error: Unknown MySQL server host ‘mysql’ (-3)MetadataStore cannot be created with the given connection config.
```
mysql pod is running fine.
```
mysql: ready for connections.
```
also meta-writer, ml-pipeline,ml-pipeline-persistenceagent also has crashloopbackoff or 0/1 ready status.
### Expected result
pods are running ok.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8205 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8205/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8205/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8205/events | https://github.com/kubeflow/pipelines/issues/8205 | 1,354,090,761 | I_kwDOB-71UM5QtckJ | 8,205 | [feature] import python function in component | {
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"Which SDK version are you using?\r\n\r\nIn both KFP v1 (v2 namespace) and KFP v2, this is provided by the containerized component functionality. Try `kfp component build --help` on the command line and view the reference docs for `kfp.v2.dsl.component` and `kfp.dsl.component` in v1 and v2, respectively. Reference ... | 2022-08-29T10:51:27 | 2022-12-27T17:05:26 | 2022-12-27T17:05:05 | NONE | null | for example:
I have a file utils.py which defined some functions.
How can I import those functions in my components?
Any ideas? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8203 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8203/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8203/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8203/events | https://github.com/kubeflow/pipelines/issues/8203 | 1,353,271,685 | I_kwDOB-71UM5QqUmF | 8,203 | [sdk] Can't use pipeline's input object in dsl.Condition | {
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"Thanks for reporting this @ittus. This is a known bug.",
"Any update on this? We'd like to use the dsl.Condition to trigger some alert ops and can't whilst this is open.",
"This has not yet been fixed in KFP v1.\r\n\r\nThis was fixed at least as early as `kfp==2.0.0b1`. The following runs without issue:\r\n\r\... | 2022-08-28T07:22:29 | 2023-02-01T02:50:31 | null | CONTRIBUTOR | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Full Kubeflow deployment
* KFP version:
1.8.1
* KFP SDK version:
1.8.12
### Steps to reproduce
```python
@dsl.pipeline(name="Test pipeline", description=__doc__)
def test_pipeline(
output_config: Optional[dict] = {
"s3_bucket": "my-s3-bucket",
"s3_prefix": "my-s3-prefix"
}
) -> None:
with dsl.Condition(output_config is not None):
(
# run a container op
)
```
Then there is an error when create the pipeline
```bash
if isinstance(group.condition.operand1, dsl.PipelineParam):
AttributeError: 'bool' object has no attribute 'operand1'
```
If I change the condition to
```
with dsl.Condition(output_config != None)
```
then when running, there is another error
```
This step is in Error state with this message: Invalid 'when' expression '"{"s3_bucket": "my-s3-bucket", "s3_prefix": "my-s3-prefix}" != "None"': Cannot transition token types from STRING [{] to VARIABLE [s3_bucket]
```
### Expected result
- Able to use dsl.Condition with pipeline's input
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8201 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8201/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8201/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8201/events | https://github.com/kubeflow/pipelines/issues/8201 | 1,352,355,575 | I_kwDOB-71UM5Qm073 | 8,201 | [sdk] sdk documentation of kfp.v2 | {
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"follower... | null | [
"@verhoek, thanks for raising this. We are working on a significant documentation refresh that should help with this. In the meantime, maybe I can help here.\r\n\r\nWhich version of the SDK are you using?\r\n\r\n> Some functionality from the package kfp.v2.google seems to be referenced on the web/from the vertex ai... | 2022-08-26T14:35:40 | 2023-04-04T17:52:25 | 2023-04-04T17:52:25 | NONE | null | I am having difficulties regarding the documentation of the python SDK of pipelines, in particular the kfp.v2 package of the 1.8.x releases.
Some functionality from the package kfp.v2.google seems to be referenced on the web/from the vertex ai docu, yet I cannot find any official referenced documentation of it from the project itself, for example on https://kubeflow-pipelines.readthedocs.io .
I understand by browsing through the issues that apparently kfp.v2.google contains deprecated code such as the AIPlatformClient.
The effect is that for older versions of kfp there is no SDK documentation available of all that was once/is in kfp.v2 release 1.8.x
that is later actually removed in kfp release 2.x . I think this is very confusing for people new to kfp, for which the stable release is still 1.8.x, but from tutorials from vertex ai use code from kfp.v2. I would expect this documentation, even now deprecated, is still available in the SDK documentation.
PS: Also, what was the original intent of the v2 package? Already making available v2 sdk functionality from 1.8.x ?
In the 2.x versions the kfp.v2 seems to point back to the main package kfp itself, which probably makes sense.
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https://api.github.com/repos/kubeflow/pipelines/issues/8200 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8200/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8200/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8200/events | https://github.com/kubeflow/pipelines/issues/8200 | 1,351,897,426 | I_kwDOB-71UM5QlFFS | 8,200 | [backend] Metadata writer pod always restarting | {
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"Hello @andre-lx , does this issue happen during start-up time, or does it happen when you are running specific pipeline? If there is more information about how to reproduce this issue, it will help us to investigate the problem. \r\n\r\n/assign @chensun ",
"> Hello @andre-lx , does this issue happen during start... | 2022-08-26T07:53:41 | 2023-05-16T20:08:33 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Manifests in k8s
* K8S Version:
1.21
* KFP version:
1.8.1/1.8.2/1.8.3/1.8.4
### Steps to reproduce
Hi.
Since release 1.8.1 (can't be sure about older versions) our metadata-writer pod is always restarting infinitely with the following message error:
```
metadata-writer-78fc7d5bb8-ph9kj 2/2 Running 299 78d
```
```
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 697, in _update_chunk_length
self.chunk_left = int(line, 16)
ValueError: invalid literal for int() with base 16: b''
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 438, in _error_catcher
yield
File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 764, in read_chunked
self._update_chunk_length()
File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 701, in _update_chunk_length
raise InvalidChunkLength(self, line)
urllib3.exceptions.InvalidChunkLength: InvalidChunkLength(got length b'', 0 bytes read)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/kfp/metadata_writer/metadata_writer.py", line 157, in <module>
for event in pod_stream:
File "/usr/local/lib/python3.7/site-packages/kubernetes/watch/watch.py", line 144, in stream
for line in iter_resp_lines(resp):
File "/usr/local/lib/python3.7/site-packages/kubernetes/watch/watch.py", line 48, in iter_resp_lines
for seg in resp.read_chunked(decode_content=False):
File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 793, in read_chunked
self._original_response.close()
File "/usr/local/lib/python3.7/contextlib.py", line 130, in __exit__
self.gen.throw(type, value, traceback)
File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 455, in _error_catcher
raise ProtocolError("Connection broken: %r" % e, e)
urllib3.exceptions.ProtocolError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read))
```
We already try the most recent versions of version 1.8 (we did not try version 2.0.0).
The pipelines are working very well, and we don't have any problems till now because of this, but this only happens with this pod.
This happens in our multiple clusters with multiple installations, so don't look like an issue of a specific cluster.
### Expected result
The pod should stop restarting.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8189 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8189/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8189/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8189/events | https://github.com/kubeflow/pipelines/issues/8189 | 1,349,923,063 | I_kwDOB-71UM5QdjD3 | 8,189 | [backend] Cannot list artifacts | {
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"Have you checked that both `ml-pipeline-ui` deployment in `kubeflow` namespace and the `ml-pipeline-ui-artifact` deployment in user namespaces are all using `ml-pipeline/frontend:1.8.1`?",
"@zijianjoy Yes, I have\r\n\r\n```\r\nName: ml-pipeline-ui\r\nNamespace: kubeflow\r\nCreation... | 2022-08-24T19:28:06 | 2023-09-13T08:24:47 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Using https://www.kubeflow.org/docs/distributions/gke/deploy/upgrade/
* KFP version:
ml-pipeline/frontend:1.8.1
ml-pipeline/api-server:1.8.1
### Steps to reproduce
Upgrade from Kubeflow 1.3 to Kubeflow 1.5 allows to replicate the problem
### Expected result
I expect to be able to see a list of artifacts when I access myClusterURL/pipeline/artifacts. Instead I get this https://user-images.githubusercontent.com/74205824/186285977-cba538c2-e496-416e-8f27-67fa4950b4cc.png
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8185 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8185/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8185/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8185/events | https://github.com/kubeflow/pipelines/issues/8185 | 1,347,536,775 | I_kwDOB-71UM5QUceH | 8,185 | [feature] add delete button for runs to KFP UI | {
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"Hi @tingshua-yts , you can archive the runs first and then delete them in the archived list."
] | 2022-08-23T08:19:39 | 2022-08-25T22:47:17 | 2022-08-25T22:47:17 | 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?
can add delete button for runs ? Currently, there is no delete button. Is there any design concern？
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
I submitted a batch of tasks and found that they did not meet expectations. I hope to delete them
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
I use kubectl delete workflow xxxx
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8184 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8184/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8184/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8184/events | https://github.com/kubeflow/pipelines/issues/8184 | 1,347,443,703 | I_kwDOB-71UM5QUFv3 | 8,184 | [backend] disabling task caching doesn't appear to work | {
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"I think this is because this function only works when using SDK V2.\r\n\r\nTo disable the cache for a component in V1, use:\r\n\r\n```python\r\ncontainer_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\r\n```",
"Thanks! That appears to work.",
"> Thanks! That appears to work.\r\n\r\nPlease ... | 2022-08-23T07:05:07 | 2022-09-06T10:20:22 | 2022-09-06T10:20:22 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
```
$ kubectl version --short
Client Version: v1.24.4
Kustomize Version: v4.5.4
Server Version: v1.20.2
```
- minikube v1.18.1
- kubernetes v1.20.2
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: Deployed using [kubeflow/manifests v1.5.0](https://github.com/kubeflow/manifests/archive/refs/tags/v1.5.0.tar.gz)
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
$ pip list | grep kfp
kfp 1.8.13
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.4
kfp-tekton 1.3.1
```
### Steps to reproduce
Create a ContainerOp and use [`set_caching_options(False)`](https://kubeflow-pipelines.readthedocs.io/en/1.8.13/source/kfp.dsl.html#kfp.dsl.ContainerOp.set_caching_options) to disable task caching
```
from kfp import dsl
from kfp.compiler import Compiler
@dsl.pipeline(name='ContainerOp caching test', description='')
def pipeline():
co = dsl.ContainerOp(
name='sample ContainerOp',
image='busybox',
command=['echo', 'Hello KFP!'],
)
co.set_caching_options(False)
if __name__ == '__main__':
Compiler().compile(pipeline, __file__ + '.yaml')
```
Compile the pipeline. The compiled pipeline looks as follows:
```
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: containerop-caching-test-
annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.13, pipelines.kubeflow.org/pipeline_compilation_time: '2022-08-23T08:20:01.620863',
pipelines.kubeflow.org/pipeline_spec: '{"name": "ContainerOp caching test"}'}
labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.13}
spec:
entrypoint: containerop-caching-test
templates:
- name: containerop-caching-test
dag:
tasks:
- {name: sample-containerop, template: sample-containerop}
- name: sample-containerop
container:
command: [echo, Hello KFP!]
image: busybox
metadata:
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.8.13
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/enable_caching: "false"
arguments:
parameters: []
serviceAccountName: pipeline-runner
```
Run the pipeline multiple times and review the run info in the Central Dashboard.
fwiw the following labels are defined on the pod:
```
Labels: pipeline/runid=40313ed7-32c0-4d01-9588-7c1f033b1b14
pipelines.kubeflow.org/cache_enabled=true
pipelines.kubeflow.org/cache_id=813
pipelines.kubeflow.org/enable_caching=false
pipelines.kubeflow.org/kfp_sdk_version=1.8.13
pipelines.kubeflow.org/metadata_execution_id=28
pipelines.kubeflow.org/metadata_written=true
pipelines.kubeflow.org/pipeline-sdk-type=kfp
pipelines.kubeflow.org/reused_from_cache=true
workflows.argoproj.io/completed=true
workflows.argoproj.io/workflow=lambda-4mk5p
```
Note `pipelines.kubeflow.org/cache_enabled=true` and `pipelines.kubeflow.org/enable_caching=false`
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
Task results are not re-used across multiple pipeline runs. Not sure what (if anything) I am missing. Disabling caching for entire pipelines or the entire KFP deployment is not a viable option since the caching requirements vary. Please advise. Thank you!
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8176 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8176/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8176/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8176/events | https://github.com/kubeflow/pipelines/issues/8176 | 1,345,406,619 | I_kwDOB-71UM5QMUab | 8,176 | [sdk] Missing outputs field in generated component yaml | {
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"u... | closed | false | null | [] | null | [
"Close as this is resolved by updating the kfp SDK to v1.8.13 and above. Seems like a minor bug in the v1 to v2 migration that got fixed."
] | 2022-08-21T07:09:12 | 2022-08-22T15:22:17 | 2022-08-22T15:21:00 | NONE | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.8.3
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
I'm writing a component for the KFP v2 Pipeline backend, I wrote it in Python with the following definition:
@component
def train(
dataset: Input[Dataset],
config: Input[Artifact],
model: Output[Model],
bucket_name: str)
then, I tried to package it for later usages, using
from kfp.v2.components.component_factory import create_component_from_func
create_component_from_func(
func=train,
base_image="[docker.io/](http://docker.io/)...", output_component_file="component_v2.yaml"
)
A file showed up without any error, but weirdly it's missing my outputs artifacts, only showing inputs and implementations instead. I dont khow whether this is expected or I did it wrong somehow?
### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"Hi @r-matsuzaka, \r\n\r\nSorry about the confusion here. The doc you referred to is about KFP v1, and the master branch code is about KFP v2. The code for v1 can be found in the `sdk/release-1.8` branch: https://github.com/kubeflow/pipelines/blob/0ad4cdfaabebbb566dad2b9523267d886942f2ad/sdk/python/kfp/compiler/com... | 2022-08-21T07:02:29 | 2022-08-25T23:05:40 | 2022-08-25T23:05:39 | NONE | null | ### Environment
#### How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
- minikube latest (kubernets 1.21.14)
- kustomize 3.2.0
- kubectl 1.21.14
- kubeflow/manifests v1.6-branch
```shell
git clone https://github.com/kubeflow/manifests.git -b v1.6-branch
cd manifests
bash hack/setup-kubeflow.sh
```
* KFP version:
```
$ print(kfp.__version__)
1.8.13
```
I tried both:
```
pip install kfp==1.8.13
pip install --pre kfp
```
### Steps to reproduce
https://www.kubeflow.org/docs/components/pipelines/sdk/build-pipeline/#getting-started-building-a-pipeline
### Expected result
I cannot get kubeflow pipeline yaml file by following method. And I got `TypeError: compile() got an unexpected keyword argument 'pipeline_parameters'`
```
kfp.compiler.Compiler().compile(
pipeline_func=my_pipeline,
package_path='pipeline.yaml',
pipeline_parameters={'url': 'https://storage.googleapis.com/ml-pipeline-playground/iris-csv-files.tar.gz'})
```
From this implementation, I should use `pipeline_parameters` argument for `kfp.compiler.Compiler().compile`
https://github.com/kubeflow/pipelines/blob/3b1bd08ce0188605cff90e3f14563d55436aac96/sdk/python/kfp/compiler/compiler.py#L62
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. | {
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"u... | open | false | null | [] | null | [] | 2022-08-19T20:10:55 | 2022-08-19T20:10:55 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.3
* KFP SDK version:
2.0.0b2
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
1. Open https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler/test_data/pipelines/two_step_pipeline_containerized.py
2. Call `.set_display_name('name1')` on `component1` and `.set_display_name('name2')` on `component2` when defining `my_pipeline`
3. Run the file to compile a yaml IR
4. Submit the IR to KFP standalone
### Expected result
The pipeline is not able to run as the second component couldn't find the component `component1`, which is renamed.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler/test_data/pipelines/two_step_pipeline_containerized.py
https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler/test_data/pipelines/two_step_pipeline_containerized.yaml
---
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* KFP version:
1.8.12
GCP Vertex AI pipelines.
* KFP SDK version:
GCP Vertex AI pipelines.
* All dependencies version:
```shell
kfp 1.8.12
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.4
```
```shell
google-cloud-pipeline-components 1.0.16
```
### Steps to reproduce
Using yaml configuration for components, one of the inputs is `JsonObject`type i.e.:
```yaml
inputs:
- {name: Server resources, type: JsonObject, description: 'A dict describing the amount of resources required to run the server', default: '{"cpu": "1", "memory": "3Gi"}'}
```
Also, it can be `String` type:
```yaml
inputs:
- {name: Server resources, type: String, description: 'A dict describing the amount of resources required to run the server', default: '{"cpu": "1", "memory": "3Gi"}'}
```
To build a pipeline run we are going threw these stages:
1. Load definition using KFP.
2. Update definition using `create_custom_training_job_from_component` from `google-cloud-pipeline-components` package.
3. Submitting run threw Vertex AI SDK.
The component fails due to an error: `Failed to parse the container spec json payload to requested prototype.`. In the Payload the invalid part is this: `"{"cpu": "1", "memory": "3Gi"}"`.
PS.: If building pipelines run remove the second step (2. Update definition using `create_custom_training_job_from_component` from `google-cloud-pipeline-components` package.), the component doesn't get incompatible JSON format error and it starts.
### Expected result
Component job is provisioned/starts.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8165 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8165/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8165/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8165/events | https://github.com/kubeflow/pipelines/issues/8165 | 1,342,431,886 | I_kwDOB-71UM5QA-KO | 8,165 | [bug] Recurring runs not working after re-installing kubeflow-pipelines modules | {
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"I figured out that registering a recurring run creates a scheduled workflow crd instance. Removing the pipeline modules from the k8s cluster, also destroyed those crd instances, so the remaining metadata is meaningless. Is there a safer and more efficient way to upgrade the kubeflow-pipeline module without losing ... | 2022-08-18T01:17:17 | 2022-08-25T22:44:07 | 2022-08-25T22:44:07 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
Using Kubeflow 1.5.1 on EKS and it is updated from version 1.3.1
I did `kustomize build apps/pipeline | kubectl delete -f -` and
`kustomize build apps/pipeline | kubectl apply -f -`
registering a pipeline and calling a run works fine but registered recurring runs not working
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
gcr.io/ml-pipeline/api-server:1.8.2
gcr.io/ml-pipeline/metadata-writer:1.8.2
gcr.io/ml-pipeline/persistenceagent:1.8.2
gcr.io/ml-pipeline/scheduledworkflow:1.8.2
gcr.io/ml-pipeline/frontend:1.8.2
gcr.io/ml-pipeline/viewer-crd-controller:1.8.2
gcr.io/ml-pipeline/visualization-server:1.8.2
gcr.io/ml-pipeline/workflow-controller:v3.2.3-license-compliance
gcr.io/tfx-oss-public/ml_metadata_store_server:1.5.0
gcr.io/ml-pipeline/cache-server:1.8.2
gcr.io/ml-pipeline/metadata-envoy:1.8.2
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Hi, is there anyone experienced registered recurring runs not working after updating and installing kubeflow-pipeline?
I re-deployed kubeflow-pipeline to update the version from 1.3.1 to 1.5.1 Recurring runs registered previously are not being updated but I see new recurring runs are being updated normally
Tested query:
SELECT uuid, displayname, name, namespace, serviceaccount, description, from_unixtime(CreatedAtInSec) CreatedAtInSec, from_unixtime(UpdatedAtInSec) UpdatedAtInSec, from_unixtime(CronScheduleStartTimeInSec) CronScheduleStartTimeInSec, from_unixtime(CronScheduleEndTimeInSec) CronScheduleEndTimeInSec FROM mlpipeline.jobs;
It seems like a certain demon in pipeline module misses records to trace.
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
registered recurring runs updated and triggered normally after upgrading kubeflow-pipeline module
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8164 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8164/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8164/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8164/events | https://github.com/kubeflow/pipelines/issues/8164 | 1,342,377,594 | I_kwDOB-71UM5QAw56 | 8,164 | [bug] <kubeflow/pipelines mysql && workflow-controller pods failing 'Running' stuck in 'Pending' State with several other pods crashing consistently on Apple M1 architecture Deployed in Kind> | {
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<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? Deploy in Kind --image kindest/node:v1.21.2 (Apple M1 Silicon Architecture BigSur)
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.7.0 (Also Failed with 1.8.13 i.e. replace with in command sequence -> % export PIPELINE_VERSION=1.8.13)
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.8.13
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Activate Python Virtual Environment and run following commands:
% kind create cluster --image kindest/node:v1.21.2
% export PIPELINE_VERSION=1.7.0
% kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION&timeout=300"
% kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
% kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION&timeout=300"
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
All pods/deployments should be Successfully Running and Stable.
### Materials and reference
https://community.deeplearning.ai/t/c4w3-lab1-kubeflowpipelines-kubectl-f-apply-fail/176728/19
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
area backend
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
area components
---
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Impacted by this bug? Give it a 👍. | {
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"followers_url": "https://api.githu... | null | [] | 2022-08-17T12:05:54 | 2022-08-18T22:34:14 | null | NONE | null | ### Environment
* KFP version: 1.8.12
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 1.8.12
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.4
### Steps to reproduce
Run the following code snippet (not necessarily fully-working, but enough to demonstrate this issue)
```
import kfp
from google_cloud_pipeline_components.v1.custom_job import (
create_custom_training_job_from_component,
)
component = kfp.components.load_component_from_text(
"""
name: my-component-name
inputs:
- { name: Columns, type: String }
implementation:
container:
image: my-custom-image
command: [ echo ]
args:
- if:
cond: { isPresent: Columns }
then:
- --columns
- { inputValue: Columns }
"""
)
job = create_custom_training_job_from_component(component)
```
This is the result I get:
`
TypeError: Got unexpected placeholder type for IfPlaceholder(if_structure=IfPlaceholderStructure(condition=IsPresentPlaceholder(input_name='Columns'), then_value=['--columns', InputValuePlaceholder(input_name='Columns')], else_value=None))
`
### Expected result
Expected the conditional argument to be parsed without errors. Seems like the issue comes from here: https://github.com/kubeflow/pipelines/blob/1.8.13/sdk/python/kfp/dsl/dsl_utils.py#L132 - the `IfPlaceholder` type is not handled
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8155 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8155/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8155/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8155/events | https://github.com/kubeflow/pipelines/issues/8155 | 1,340,854,745 | I_kwDOB-71UM5P69HZ | 8,155 | Critical CVEs found in gcr.io/ml-pipeline/visualization-server:1.8.4 | {
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"@andresmascl, thank you for your contribution!\r\n\r\nAs these vulnerabilities are not marked as critical on GCR, we will keep this in backlog for now.",
"Closing this as we have released KFP v2.0.0"
] | 2022-08-16T20:37:31 | 2023-08-04T23:01:52 | 2023-08-04T23:01:52 | NONE | null | ### Environment
Critical CVEs reported by the `anchore` scanning service: CVE-2020-36242, GHSA-57wx-m983-2f88, GHSA-8vj2-vxx3-667w, GHSA-9j59-75qj-795w , GHSA-cq27-v7xp-c356, GHSA-h6gw-r52c-724r , GHSA-pw3c-h7wp-cvhx
* KFP version:
`1.8.4`
### Materials and Reference
---
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"@andresmascl, thank you for your contribution!\r\n\r\nAs these vulnerabilities are not marked as critical on GCR, we will keep this in backlog for now.",
"Closing this as we have released KFP v2.0.0"
] | 2022-08-16T20:34:35 | 2023-08-04T23:01:27 | 2023-08-04T23:01:27 | NONE | null | ### Environment
Critical CVEs found by the `anchore` scanning service: CVE-2020-3681, CVE-2022-28391, GHSA-896r-f27r-55mw, GHSA-xvch-5gv4-984h,
* KFP version:
`1.8.4`
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---
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"@andresmascl, thank you for your contribution!\r\n\r\nAs these vulnerabilities are not marked as critical on GCR, we will keep this in backlog for now.",
"Resolving as we have released v2.0.0"
] | 2022-08-16T20:26:18 | 2023-08-04T22:09:46 | 2023-08-04T22:09:46 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Critical Vulnerabilities reported by `anchore` : CVE-2015-20107, CVE-2017-18342, CVE-2021-30473, CVE-2021-30474, CVE-2021-30475, GHSA-8q59-q68h-6hv4
* KFP version:
`1.8.4`
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---
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https://api.github.com/repos/kubeflow/pipelines/issues/8141 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8141/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8141/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8141/events | https://github.com/kubeflow/pipelines/issues/8141 | 1,337,255,332 | I_kwDOB-71UM5PtOWk | 8,141 | [feature] Print deep links outside of Jupyter notebooks | {
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"u... | closed | false | null | [] | null | [] | 2022-08-12T14:08:39 | 2022-09-09T00:00:30 | 2022-09-09T00:00:30 | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
Currently, `client.py` has 5 blocks that look like this:
```python
if self._is_ipython():
import IPython
html = \
f'<a href="{self._get_url_prefix()}/#/experiments/details/{experiment.id}" target="_blank" >Experiment details</a>.'
IPython.display.display(IPython.display.HTML(html))
```
This is a really helpful feature but it's only available when interacting with the SDK from IPython. It would be nice (and trivial to implement) if links were printed outside of IPython.
I'll submit a PR momentarily that implements this.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8133 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8133/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8133/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8133/events | https://github.com/kubeflow/pipelines/issues/8133 | 1,335,040,025 | I_kwDOB-71UM5PkxgZ | 8,133 | [frontend] Inaccurate two-level dropdown selection | {
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"followers_url": "https://a... | null | [] | 2022-08-10T18:47:09 | 2022-08-10T18:48:52 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.3
### Steps to reproduce
1. Create a run which has two Confusion Matrix artifacts of the same type on the same execution
- One artifact has the name `Artifact ID #<id>`, and the other artifact has no name but an ID of `<id>`
- These confusion matrices should be different
- _Note: This may be difficult to accomplish, and is not presented here, but the issue is shown by understanding the underlying code and implementation._
2. Go to the "Runs" tab on KFP
3. Select that run and one other, and click "Compare Runs"
4. Select the "Confusion Matrix" tab under "Metrics"
5. Select both confusion matrices and view the compare results
### Expected result
The Confusion Matrices should be displayed as expected, but instead one of the confusion matrices will be shown twice.
### Materials and Reference
On the two-panel layout for Confusion Matrices, HTML, and Markdown, we need to improve the differentiation between display name and ID, likely by adding an underlying value to each dropdown item (rather than requiring each name be unique).
This is mentioned in [this GitHub discussion](https://github.com/kubeflow/pipelines/pull/7966#discussion_r913346458); essentially, if an Execution has a displayName of exact value “Execution ID #1” and another Execution has no displayName but has an ID of 1, we run into mis-identified artifacts and dropdown items due to the logic implemented at [getLinkedArtifactFromSelectedItem](https://github.com/kubeflow/pipelines/blob/e47a784035820c0d322c5e50562df676fd81ce4c/frontend/src/components/viewers/MetricsDropdown.tsx#L335-L357).
---
<!-- 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/8132 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8132/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8132/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8132/events | https://github.com/kubeflow/pipelines/issues/8132 | 1,335,029,616 | I_kwDOB-71UM5Pku9w | 8,132 | [frontend] Dropdown and HTML / Markdown artifacts are misaligned | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | open | false | null | [] | null | [] | 2022-08-10T18:38:40 | 2023-01-18T10:29:44 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.3
### Steps to reproduce
1. Go to the "Runs" tab on KFP
2. Select multiple runs which have HTML or Markdown artifacts, and click "Compare Runs"
3. Select the "HTML" or "Markdown" tab under "Metrics"
4. Select an artifact which has large width (see "Materials and Reference")
### Expected result
The HTML and Markdown artifacts are not left-aligned on the two-panel layout along with the two-level dropdown component.
### Materials and Reference
The HTML and Markdown artifacts are not left-aligned alongside the two-level dropdown component.
The below image shows the artifact is currently aligned in the center of the panel of this two-panel layout.
---
<!-- 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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* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.3
### Steps to reproduce
1. Go to the "Runs" tab on KFP
2. Select multiple runs which have many artifacts (for instance, some over 100+), and click "Compare Runs"
### Expected result
You should see the page load all of the artifacts efficiently and accurately. Instead, the page currently does not load the artifacts past 100 per run, and the loading time will likely be slower in relation to comparing artifacts with few to no runs.
### Materials and Reference
The main issue is shown in the `TODO` below:
https://github.com/kubeflow/pipelines/blob/a7fddf000d5da254ee872574677a974e4ff4b4db/frontend/src/pages/CompareV2.tsx#L315-L316
However, there is also a broader issue of scalability; though we implemented scalable methods and designed the page to handle a large quantity of artifacts, we have not thoroughly tested this page against runs with 100s of artifacts. Although the design and loading states should be able to provide a nice UX, the scalability of this page is nevertheless an important question to answer.
Lastly, this is also related to the issue of fetching very large HTML and Markdown files in an efficient manner, as the KFPv2 Run Comparison page will need to do this repeatedly. The existing implementation and `TODO` for this is available here: https://github.com/kubeflow/pipelines/blob/a7fddf000d5da254ee872574677a974e4ff4b4db/frontend/src/components/viewers/MetricsVisualizations.tsx#L890
---
<!-- 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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* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.3
### Steps to reproduce
1. Go to the "Runs" tab on KFP
2. Select multiple runs which have Confusion Matrices, and click "Compare Runs"
3. Select the "Confusion Matrix" tab under "Metrics"
4. Click the two-level dropdown
### Expected result
You should see the two-level dropdown with some explanation as to the naming schema, but no headers or explanation for this component are provided.
### Materials and Reference
The below image shows the existing two-level dropdown used for Confusion Matrices, HTML, and Markdown on the two-panel layout of the KFPv2 Run Comparison page.
This below image shows one proposed solution to clarify the two-level dropdown naming schema. This styling is not finalized, and another solution would be to add a text explanation above the dropdown component itself.
---
<!-- 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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"followers_url": "https://a... | null | [] | 2022-08-10T18:03:41 | 2022-08-10T18:31:49 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
2.0.0-alpha.3
### Steps to reproduce
1. Go to the "Runs" tab on KFP
2. Select multiple runs which have Confusion Matrices, and click "Compare Runs"
3. Select the "Confusion Matrix" tab under "Metrics"
4. Select any confusion matrix
### Expected result
You should see the confusion matrix with correct formatting, but it is instead surrounded by some unused space.
### Materials and Reference
The below image shows the space in question which is unused, and could be removed so the scroll bar has a more accurate state.
This should be achievable by conditionally removing padding on the component here: https://github.com/kubeflow/pipelines/blob/bdff332ac69db0589c8b9eb55e7cdf7e3bee710d/frontend/src/components/viewers/MetricsVisualizations.tsx#L755-L767
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8126 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8126/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8126/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8126/events | https://github.com/kubeflow/pipelines/issues/8126 | 1,333,948,492 | I_kwDOB-71UM5PgnBM | 8,126 | [feature] Implement comprehensive unit tests for the KFP SDK Client class | {
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"Hi @droctothorpe feel free to contribute ? JFYI, here're some example code on how we tested the v2 Vertex client (which was moved out of KFP package in the master branch): https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/sdk/python/kfp/v2/google/client"
] | 2022-08-10T01:28:47 | 2022-08-11T22:54:28 | null | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
We would like to see comprehensive unit tests for the KFP SDK [Client](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L82) class to improve confidence in code changes.
The existing coverage looks [quite sparse](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client_test.py).
My team and I would be interested in contributing this to the upstream but want to make sure that we're not stepping on anyone's toes / it makes sense to core maintainers.
### What is the use case or pain point?
Reduced confidence in code changes / releases.
### Is there a workaround currently?
No.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8125 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8125/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8125/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8125/events | https://github.com/kubeflow/pipelines/issues/8125 | 1,333,944,091 | I_kwDOB-71UM5Pgl8b | 8,125 | [feature]Add a KubeFlow pipeline for TensorFlowJS/JavaScript | {
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"Hi @shivaylamba, KFP support running containerized app, meaning you can always package your code into a container, and we support running any container as a KFP component. Would that work for your case?",
"/cc @zichuan-scott-xu "
] | 2022-08-10T01:20:30 | 2022-08-11T22:50:16 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area javascript
/area samples
/area pipelines
### What feature would you like to see?
We would like to see a dedicated pipeline for TensorFlowJS or even a pipeline for JavaScript. Today TensorFlowJS is being used in a lot of production use cases. And it makes sense to have dedicated KubeFlow Pipeline for JavaScript/NodeJS.
### What is the use case or pain point?
Today TensorFlowJS is being used in a lot of production use cases. But we don't have any supported KubeFlow pipeline for JavaScript.
### Is there a workaround currently?
Today there isn't a KubeFlow pipeline supported for TensorFlowJS / NodeJS. The workaround could be to have a self serve container. But it would be great to have a dedicated KubeFlow pipeline.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8121 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8121/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8121/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8121/events | https://github.com/kubeflow/pipelines/issues/8121 | 1,333,774,038 | I_kwDOB-71UM5Pf8bW | 8,121 | [frontend] Table sort mechanism broken on multiple tabs | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | open | false | null | [] | null | [] | 2022-08-09T20:50:06 | 2022-08-09T20:50:06 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
`HEAD`
### Steps to reproduce
1. Go to the `Runs`, `Artifacts`, or `Executions` tab on KFP
2. Try to sort by any of the desired and sortable columns
### Expected result
You should see the table sort itself based on that column, but either nothing changes or the table contents disappear.
### Materials and Reference
The below video shows how the sorting mechanism works on the `Pipelines`, `Experiments`, and `Recurring Runs` tabs, but does not work on the `Runs`, `Artifacts`, or `Executions` tabs.
https://user-images.githubusercontent.com/7987279/183758095-4b937e5e-b7fb-4610-b763-e600b90beef7.mp4
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8119 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8119/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8119/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8119/events | https://github.com/kubeflow/pipelines/issues/8119 | 1,333,206,696 | I_kwDOB-71UM5Pdx6o | 8,119 | [sdk] kfp==2.0.0b1 is incompatible with google-cloud-pipeline-components | {
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"Hi @GytisBraz , this is a known issue and we're working on migrating `google-cloud-pipeline-components` to be compatible with kfp 2.0.",
"any updates on this? Also will there be any breaking changes?",
"Interested a solution",
"Any updates? This incompatibility is impacting me quite badly as a new user. We a... | 2022-08-09T12:45:53 | 2023-04-05T17:04:05 | null | NONE | null | ### Environment
* KFP version: `2.0.0b1`
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
### Steps to reproduce
`pip3 install kfp==2.0.0b1`
followed by
`pip3 install google-cloud-pipeline-components`
### Expected result
I expect both packages to be compatible. `google-cloud-pipeline-components` is an [SDK](https://pypi.org/project/google-cloud-pipeline-components/) for interacting with Vertex AI, and `kfp>=2.0.0b1` is the version that is suggested/needed to work with Vertex AI - link [here](https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template#before-you-begin)
The issue seems to stems from this [line](https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/dependencies.py#L24)
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8117 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8117/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8117/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8117/events | https://github.com/kubeflow/pipelines/issues/8117 | 1,332,686,916 | I_kwDOB-71UM5PbzBE | 8,117 | [sdk] SlicedClassificationMetrics Artifact load_confusion_matrix has incorrect call | {
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* KFP version: N/A
* KFP SDK version: `1.8.13`
### Steps to reproduce
Use a `SlicedClassificationMetrics` Artifact and try to call `load_confusion_matrix`. Internally this calls `log_confusion_matrix_cell` on the `ClassificationMetrics` object instead of `log_confusion_matrix`, which results in a type error:
`TypeError: log_confusion_matrix_cell() missing 1 required positional argument: 'value'`
### Expected result
Calling `load_confusion_matrix` on `SlicedClassificationMetrics` doesn't break, and correctly adds the confusion matrix to the right slice in the metadata.
### Materials and Reference
Full reproducible sample:
```python
from kfp.v2.dsl import component, Output, SlicedClassificationMetrics
from kfp.v2 import compiler
import kfp
@component()
def simple_metric_op(sliced_eval_metrics: Output[SlicedClassificationMetrics]):
sliced_eval_metrics._sliced_metrics = {}
sliced_eval_metrics.load_confusion_matrix('a slice',
categories=['cat1', 'cat2'],
matrix=[[1,0],[2,4]]
)
@kfp.dsl.pipeline(name="test-sliced-metric")
def metric_test_pipeline():
m_op = simple_metric_op()
compiler.Compiler().compile(
pipeline_func=metric_test_pipeline,
package_path="evaluation_pipeline.json",
)
```
See the incorrect line of code here:
https://github.com/kubeflow/pipelines/blob/061905b6df397c40fbcc4ffafa24d7b3b9daf439/sdk/python/kfp/components/types/artifact_types.py#L452-L464
Where
`self._sliced_metrics[slice].log_confusion_matrix_cell(
categories, matrix)`
Should be
`self._sliced_metrics[slice].log_confusion_matrix(
categories, matrix)`
Since `self._sliced_metrics[slice]` is of type `ClassificationMetrics` after the call to `self._upsert_classification_metrics_for_slice(slice)`
As an aside - `_sliced_metrics` is never initialised in the class, leaving it up to the user of the Artifact class, which isn't very intuitive.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8111 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8111/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8111/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8111/events | https://github.com/kubeflow/pipelines/issues/8111 | 1,332,053,763 | I_kwDOB-71UM5PZYcD | 8,111 | [frontend] Artifact API Does not support S3 Regions | {
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"My bad, I've found that it actually uses `AWS_REGION` variable here: https://github.com/kubeflow/pipelines/blob/e8abec24fed4c4f8be6f527207b1cec9811ce3e7/frontend/server/configs.ts#L129. Will add that to deployment to test if it works",
"It works as expected if I specify `AWS_REGION`, `AWS_SECRET_ACCESS_KEY`, and... | 2022-08-08T15:39:02 | 2022-09-01T13:57:30 | null | NONE | null | <img width="1727" alt="Screenshot 2022-08-08 at 18 16 17" src="https://user-images.githubusercontent.com/832185/183455928-0c6c6c10-da25-4c2b-b19f-13573e43bafe.png">
<img width="1719" alt="Screenshot 2022-08-08 at 18 16 02" src="https://user-images.githubusercontent.com/832185/183455906-ba856df3-d657-4bbb-922f-572c7faf2aae.png">
Artifact fetching API does not seem to support region for s3 resources. If we look here, https://github.com/kubeflow/pipelines/blob/e8abec24fed4c4f8be6f527207b1cec9811ce3e7/frontend/server/minio-helper.ts#L50, the region parameter is not being passed and it is not a part of the artefact API as well. However, MINIO Client API allows you to specify region when creating a client: https://docs.min.io/docs/javascript-client-api-reference.html. I think API should fetch default region using `MINIO_REGION` environment variable which should be passed as a part of `ml-pipeline-ui-artifact` k8s deployment `env` map.
### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
[kubeflow-manifests](https://github.com/awslabs/kubeflow-manifests)
* KFP version:
1.5.1
### Steps to reproduce
1. Launch any pipeline
2. Go to Run UI
3. Try to download any output
4. Observe the error: Failed to get object in bucket [bucket] at path [path]: S3Error: The authorization header is malformed; the region 'us-east-1' is wrong; expecting '[actual bucket region]'
### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/8104 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8104/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8104/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8104/events | https://github.com/kubeflow/pipelines/issues/8104 | 1,328,941,966 | I_kwDOB-71UM5PNguO | 8,104 | [feature] Make managing the artifact storage and cache available in the UI | {
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"Is it also possible to do this from the runs page? E.g. if you delete a run it tries to delete the artifacts and if you delete an experiment it deletes all runs and therefore all corresponding artifacts. This is what most datascientists expect when they delete a run. You are using ml-metadata which is not namespac... | 2022-08-04T17:05:33 | 2022-08-23T09:20:52 | null | NONE | null | I'd like users to be able to manage the artifacts their pipelines have created. This involves disabling or invalidating the cache for certain artifacts so that a pipeline can be re-executed without having to touch code.
I've first mentioned this feature [here](https://github.com/kubeflow/pipelines/issues/7939#issuecomment-1204986272)(#7939) and posted the following GIF, exemplifying how such a feature could look like.
Also, [I've created a design document](https://docs.google.com/document/d/1WdPjTgoDj5AdPNYVzqYwVIWKsWjPPphYD3Ck4DFxSLA/edit#heading=h.c1mjwae174cw) that addresses all details. If you like the feature (or don't), please leave comments here on directly in the document.
### Feature Area
/area frontend
/area backend
/area components
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8103 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8103/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8103/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8103/events | https://github.com/kubeflow/pipelines/issues/8103 | 1,328,918,911 | I_kwDOB-71UM5PNbF_ | 8,103 | [backend] JSONDecodeError: in BigQuery `bigquery_create_model_job` execution | {
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"Hi Team,\r\nany suggestions here?"
] | 2022-08-04T16:47:19 | 2022-08-08T15:36:08 | null | NONE | null | ### What steps did you take ###
Executing big query create model functionality through pipeline using
image: gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.13
command: [python3, -u, -m, google_cloud_pipeline_components.container.v1.gcp_launcher.launcher]
with type: `BigqueryCreateModelJob`,
Also followed the component.yaml format as mentioned here:
https://github.com/kubeflow/pipelines/blob/google-cloud-pipeline-components-1.0.13/components/google-cloud/google_cloud_pipeline_components/v1/bigquery/create_model/component.yaml
I get below error when executing the pipeline:
`Traceback (most recent call last):
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/bigquery_job_remote_runner.py", line 332, in bigquery_create_model_job
artifact_util.update_output_artifacts(executor_input, [bqml_model_artifact])
File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/utils/artifact_util.py", line 51, in update_output_artifacts
executor_input_json = json.loads(executor_input)
File "/opt/python3.7/lib/python3.7/json/__init__.py", line 348, in loads
return _default_decoder.decode(s)
File "/opt/python3.7/lib/python3.7/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/opt/python3.7/lib/python3.7/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)`
This is because of the `--executor_input, "{{$}}",` param in the component.yaml file
It is not an input parameter so it should take and proceed but it fails.
### What did you expect to happen: ###
That the pipeline would run successfully
Please provide any suggestion/solution how to bypass the error and make the pipeline run success | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8097 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8097/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8097/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8097/events | https://github.com/kubeflow/pipelines/issues/8097 | 1,326,753,840 | I_kwDOB-71UM5PFKgw | 8,097 | [sdk] Compilation with V2_COMPATIBLE mode fails when inputPath/outputPath name contains an empty space | {
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"Hi @ysk24ok. We no longer support v2-compatible mode. If you'd like to use this compilation style, please consider using the official KFP v2 pre-release (`pip install kfp --pre`). Otherwise, please use a name without a space for v2 compatible mode.",
"> please use a name without a space for v2 compatible mode. ... | 2022-08-03T06:42:59 | 2022-08-24T05:37:13 | null | CONTRIBUTOR | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.8.13
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
kfp 1.8.13
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.3
```
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
```py
import kfp
from kfp.v2 import dsl
from kfp.v2.dsl import component
from kfp.v2.dsl import Artifact, Output
@component
def file_generator(path: Output[Artifact]):
with open(path, "w") as f:
f.write("Hello World!")
input_path_op = kfp.components.load_component_from_text("""
name: Input path consumer
inputs:
- {name: input path}
implementation:
container:
image: alpine
command:
- sh
- -exc
- cat $0
- inputPath: input path
""")
output_path_op = kfp.components.load_component_from_text("""
name: output path generator
outputs:
- {name: output path}
implementation:
container:
image: alpine
command:
- sh
- -exc
- echo Hello World! > $0
- outputPath: output path
""")
@dsl.pipeline(name='my-pipeline')
def my_pipeline():
file_generator_task = file_generator()
input_path_op(file_generator_task.outputs["path"])
# Comment this line out when you want to test the behavior of a component with outputs
# output_path_op()
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
pipeline_func=my_pipeline,
package_path='pipeline.yaml')
```
---
Run the code and we will get the following error.
```console
% python3 pipeline.py
/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py:79: UserWarning: V2_COMPATIBLE execution mode is at Beta quality. Some pipeline features may not work as expected.
warnings.warn('V2_COMPATIBLE execution mode is at Beta quality.'
Traceback (most recent call last):
File "/Users/y-nishioka/repos/test_kfp/pipeline.py", line 48, in <module>
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1175, in compile
self._create_and_write_workflow(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1227, in _create_and_write_workflow
workflow = self._create_workflow(pipeline_func, pipeline_name,
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1058, in _create_workflow
workflow = self._create_pipeline_workflow(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 786, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 751, in _create_dag_templates
v2_compat.update_op(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/v2_compat.py", line 158, in update_op
"metadataPath": op.input_artifact_paths[artifact_name],
KeyError: 'input path'
```
`op.input_artifact_paths` is `{'input-path': '/tmp/inputs/input_path/data'}` and `artifact_name` is `input path` so we're getting this error.
---
In the case of `output_path_op`, we will get the following error.
```console
% python3 pipeline.py
/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py:79: UserWarning: V2_COMPATIBLE execution mode is at Beta quality. Some pipeline features may not work as exp
ected.
warnings.warn('V2_COMPATIBLE execution mode is at Beta quality.'
Traceback (most recent call last):
File "/Users/y-nishioka/repos/test_kfp/pipeline.py", line 48, in <module>
kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1175, in compile
self._create_and_write_workflow(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1227, in _create_and_write_workflow
workflow = self._create_workflow(pipeline_func, pipeline_name,
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1058, in _create_workflow
workflow = self._create_pipeline_workflow(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 786, in _create_pipeline_workflow
templates = self._create_dag_templates(pipeline, op_transformers)
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 751, in _create_dag_templates
v2_compat.update_op(
File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/v2_compat.py", line 187, in update_op
"metadataPath": op.file_outputs[artifact_name],
KeyError: 'output path'
```
`op.file_outputs` is `{'output-path': '/tmp/outputs/output_path/data'}` and `artifact_name` is `output path` so we're getting this error.
### Expected result
<!-- What should the correct behavior be? -->
Compilation succeeds.
[v2 SDK doc](https://www.kubeflow.org/docs/components/pipelines/sdk-v2/component-development/#using-your-component-in-a-pipeline) says
> Input and output names are converted to Pythonic names (spaces and symbols are replaced with underscores and letters are converted to lowercase). For example, an input named Input 1 is converted to input_1.
So using a name with empty spaces should be allowed in v2.
### 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 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8096 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8096/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8096/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8096/events | https://github.com/kubeflow/pipelines/issues/8096 | 1,326,701,089 | I_kwDOB-71UM5PE9oh | 8,096 | container not ready status showing | {
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"u... | closed | false | null | [] | null | [
"@kotalakshman, could you please provide some more information or screenshots to help us diagnose this issue?",
"issue got resolved.\r\n\r\nThanks"
] | 2022-08-03T05:33:06 | 2022-08-09T05:22:32 | 2022-08-09T05:22:32 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.7.1
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8095 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8095/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8095/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8095/events | https://github.com/kubeflow/pipelines/issues/8095 | 1,326,656,451 | I_kwDOB-71UM5PEyvD | 8,095 | [bug] TypeError: Object of type PipelineParam is not JSON serializable | {
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{
"id": 1073153908,
"node_id": "MDU6TGFiZWwxMDczMTUzOTA4",
"url": "https://api.github.com/repos/kubeflow/pipelines/labels/kind/bug",
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"default": false,
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"id": 1118896905,
"node_id": "MDU6TGFiZWwxMTE4ODk2OTA1",
"u... | closed | false | null | [] | null | [
"Hi @lightingghost. Using a component within a list comprehension is not a permitted component usage pattern in the KFP SDK and will result in unpredictable behavior. This is the source of the error.\r\n\r\n`outputs = {i: compute(i).output for i in range(10)}`"
] | 2022-08-03T04:18:31 | 2022-08-04T22:46:55 | 2022-08-04T22:46:55 | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* KFP SDK version:
1.8.13
### Steps to reproduce
```
import kfp
import kfp.v2.dsl as dsl
@dsl.component
def compute(x: int) -> int:
return x
@dsl.component
def print_op(results: dict):
print(results)
@dsl.pipeline(name='pipeline')
def pipeline():
outputs = {i: compute(i).output for i in range(10)}
print_op(outputs)
kfp.v2.compiler.Compiler().compile(
pipeline_func=pipeline,
package_path="/tmp/test_pipeline.json"
)
```
it give error of `TypeError: Object of type PipelineParam is not JSON serializable`
### Expected result
it compiles without an error
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
/area backend
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8094 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8094/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8094/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8094/events | https://github.com/kubeflow/pipelines/issues/8094 | 1,326,651,262 | I_kwDOB-71UM5PExd- | 8,094 | [feature] Nested component | {
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"node_id": "MDU6TGFiZWwxMjg5NTg... | closed | false | null | [] | null | [
"Hi @lightingghost. Thanks for your feature request. This is concept of using a pipeline as a task is actually currently in development. You can look forward to this in an upcoming release.",
"heyyyy @connor-mccarthy, are there any updates or planned release date for that particular feature?\r\nHow could we help ... | 2022-08-03T04:10:03 | 2023-08-30T17:14:25 | 2023-03-21T17:48:59 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
/area components
### What feature would you like to see?
I would like to be able to do nested call to components, for example
```python
@component
def foo():
pass
@component
def bar():
foo()
```
### What is the use case or pain point?
* It helps reuse the code.
### Is there a workaround currently?
No.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8087 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8087/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8087/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8087/events | https://github.com/kubeflow/pipelines/issues/8087 | 1,324,028,715 | I_kwDOB-71UM5O6xMr | 8,087 | failed to authenticate with kubeflow pipeline [bug] <HTTP 500 error> | {
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"the second error message :\r\n\r\n HTTP response body: {\"error\":\"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common... | 2022-08-01T08:23:46 | 2022-08-15T09:12:42 | null | NONE | null | * How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow is deployed on MiniKF locally downloaded on windows using vagrant and Virtual Box
downloading steps : [here](https://v0-7.kubeflow.org/docs/started/workstation/getting-started-windows/)
* KFP version:
i cant fin it, neither on the dahsboard nor on the minikf window.
### Steps to reproduce
The problem can be reproduced just by using Kale to create a pipeline and after dividing any notebook into different steps, clicking on compile and run shows an error that i can find by going to the pipeline window logs,
the logs show me this :
the second error message is really long, if someone needs it to maybe understand my issue you will find it as a comment to this post
### Expected result
After seeing a lot of videos that implements kale and uses it to create pipelines, the expected result is just a pipeline that runs every cells of the notebook contained in each step using the data of the step before.
Thank you in advance for your help.
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8076 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8076/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8076/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8076/events | https://github.com/kubeflow/pipelines/issues/8076 | 1,320,093,507 | I_kwDOB-71UM5OrwdD | 8,076 | [bug] Kubeflow Pipelines Standalone Kustomize incompatible with K8s 1.22+ | {
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"I suspect that upgrading from KFP 1.7.1 fixes this. Never mind.",
"Hi, Im facing the same issue with Kubernetes 1.22, doesnt seem like KFP 1.7.1+ fixes this"
] | 2022-07-27T20:34:01 | 2023-05-03T10:03:40 | 2022-07-27T20:42:12 | NONE | null | KFP Kustomize setup does not work with Kubernetes 1.22+. The [kustomization.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/cluster-scoped-resources/kustomization.yaml) references an API that was removed (apiextensions.k8s.io/v1beta1).
### Environment
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines Standalone
* KFP version:
Version: [1.7.1](https://www.github.com/kubeflow/pipelines/commit/1d53de98c731d3cf6ef9017ad19bbfcf19787de3)
### Steps to reproduce
```
> kubectl diff --kustomize "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=1.7.1"
error: resource mapping not found for name: "applications.app.k8s.io" namespace: "" from "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=1.7.1": no matches for kind "CustomResourceDefinition" in version "apiextensions.k8s.io/v1beta1"
ensure CRDs are installed first
```
### Expected result
It should diff with my current kfp state.
### Materials and reference
The beta CustomResourceDefinition API (apiextensions.k8s.io/v1beta1) has been removed. See [Kubernetes 1.22 API changes](https://kubernetes.io/blog/2021/07/14/upcoming-changes-in-kubernetes-1-22/).
### Labels
backend
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8074 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8074/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8074/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8074/events | https://github.com/kubeflow/pipelines/issues/8074 | 1,319,210,049 | I_kwDOB-71UM5OoYxB | 8,074 | [feature] Add authorization to all functions in ReportServer | {
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"cc: @juliusvonkohout ",
"/assign @difince ",
"For the next KFP meeting ;-)",
"Hello @difince , the `ReportWorkflow` and `ReportScheduledWorkflow` are used by single persistent-agent instance for monitoring the status of workflow. persistent-agent itself cannot and shouldn't authenticate as a user. \r\n/assig... | 2022-07-27T08:33:39 | 2023-08-17T19:23:24 | 2023-08-17T19:23:24 | MEMBER | null | ### Feature Area
There are two API-server endpoints that still miss authorization - [ReportWorkflow](https://github.com/kubeflow/pipelines/blob/d48776fb2d9752ca48af14ea6256791d2b167a71/backend/src/apiserver/server/report_server.go#L32) and [ReportScheduledWorkflow](https://github.com/kubeflow/pipelines/blob/d48776fb2d9752ca48af14ea6256791d2b167a71/backend/src/apiserver/server/report_server.go#L45). This is a security issue. Each endpoint should validate that the user has permission to call them.
**persistent-agent** service calls these endpoints. Once authorization is enabled, the persistent-agent need to authorize itself by providing user information in the request headers.
This issue is a related/ follow-up issue to PR https://github.com/kubeflow/pipelines/pull/7819
<!-- 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. -->
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
no. A security issue exists in the current implementation.
<!-- Without this feature, how do you accomplish your task today? -->
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8067 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8067/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8067/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8067/events | https://github.com/kubeflow/pipelines/issues/8067 | 1,316,994,341 | I_kwDOB-71UM5Of70l | 8,067 | [question] How does `importer` work under the hood? | {
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"/assign @chensun ",
"I'm not certain if the current KFP backend implementation on importer is correct or complete at this point. Concept-wise, importer doesn't download an artifact per se, it only retrieves or registers (if not found) an artifact record from MLMD. \r\nIt is launcher that handles downloading from... | 2022-07-25T15:19:13 | 2022-08-09T22:12:36 | 2022-08-09T22:12:36 | CONTRIBUTOR | null | I'm trying to find/understand the underlying Argo YAML for an `importer`. Is https://github.com/kubeflow/pipelines/blob/3e734ed19146f569e910f75627d12239ec2e86dc/backend/src/v2/compiler/argocompiler/testdata/importer.yaml the right place to look (for an example, of how https://github.com/kubeflow/pipelines/blob/c6125ffc44df0eced27ae83451c6243b8fc8d73f/sdk/python/kfp/compiler/test_data/pipelines/pipeline_with_importer.py gets converted)? If so, where is the logic to actually download the artifact from Google Cloud Storage? I feel like, looking at definitions for importer nodes and importer specs, I'm not able to trace back to the place where the downloading is actually happening.
My current thought is it's happening on some `kfp-launcher` Docker image, but I'm not sure if that's right, and--even if so--I would like to be able to see the underlying logic getting called.
edit: I've looked into this more over the past few days, and I've understood that:
1. [The `artifactUri` is extracted from the `ImporterSpec` and returned from `ImportSpecToMLMDArtifact`](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/component/importer_launcher.go#L191-L207).
2. [Find and return a matched artifact (if any)](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/component/importer_launcher.go#L159-L164). Is the implementation of finding the matched artifact dependent on the execution backend? I got a bit lost trying to trace back in there.
3. [Execute](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/component/importer_launcher.go#L143-L145).
I'm still not sure, for example, if you have an `importer` like https://github.com/kubeflow/pipelines/blob/c6125ffc44df0eced27ae83451c6243b8fc8d73f/sdk/python/kfp/compiler/test_data/pipelines/pipeline_with_importer.py#L55-L59
* How does it know how to download an artifact from `gs://` vs `s3://` vs `http://`, etc.? I'm sure there must be some logic to determine protocol, etc., but it's happening somewhere I can't identify.
* Is there a reasonable approximation in Argo? Maybe something like:
```yaml
arguments:
parameters:
- name: raw_green_data_url
value: https://raw.githubusercontent.com/deepyaman/azureml-examples/main/sdk/jobs/pipelines/2c_nyc_taxi_data_regression/data/greenTaxiData.csv
templates:
- name: nyc-taxi-data-regression
steps:
- - name: download-raw-green-data
template: importer
arguments:
artifacts:
- name: uri
http: # The artifact parameter (this line and the one below) have to be generated programmatically by parsing the URI string.
url: "{{workflow.parameters.raw_green_data_url}}"
- name: importer
inputs:
artifacts:
- name: uri
path: /tmp/data
outputs:
artifacts:
- name: artifact
path: /tmp/data
container:
image: hello-world
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8061 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8061/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8061/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8061/events | https://github.com/kubeflow/pipelines/issues/8061 | 1,314,925,604 | I_kwDOB-71UM5OYCwk | 8,061 | [bug] open /tmp/outputs/Output/data: permission denied | {
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"Hello @szymonbcoding, `V2_COMPATIBLE` is not supported any more. You can start using KFP SDK 2.0.0-beta and see if this resolves your problem. Note that there are breaking changes when you use the new v2.0+ SDK. Breaking change: https://docs.google.com/document/d/1nCUUVRXexXbQ0LDkGHsMIBDSu1WvJA9Upy1JzybNVMk/edit",... | 2022-07-22T12:08:01 | 2023-02-22T17:43:49 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
with Kubeflow on prem
* KFP version:
No info, just "Build: dev local | Dashboard v0.0.2- | Isolation-Mode: multi-user" in left corner of Kubeflow UI.
I got non-admin access to Kubeflow platform.
* KFP SDK version:
1.8.13
### Steps to reproduce
I created some KFP's components:
https://gist.github.com/szymonbcoding/faa2b5c3d46a4fd99ba1659410c06ffc
And then upload created pipeline's yaml file.
Unfortunatelly I got error during run of my pipeline:
_This step is in Failed state with this message: Error (exit code 64): failed to open /tmp/outputs/Output/data: open /tmp/outputs/Output/data: permission denied_
Does it mean that I have to root permission to use KFP?
### Expected result
Successfully execution of my run
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https://api.github.com/repos/kubeflow/pipelines/issues/8060 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8060/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8060/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8060/events | https://github.com/kubeflow/pipelines/issues/8060 | 1,314,332,015 | I_kwDOB-71UM5OVx1v | 8,060 | This step is in Error state with this message: Error (exit code 1): failed to put file: You did not provide the number of bytes specified by the Content-Length HTTP header. | {
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"/assign @Linchin \r\n\r\nIt might be something related to argoworkflow: https://github.com/argoproj/argo-workflows/issues?q=is%3Aissue+%22+failed+to+put+file%22+is%3Aclosed.",
"We deployed a Kubeflow system with Minio on a NFS Storage and the same error happened.\r\nIt might be related to slow disk speed on Mini... | 2022-07-22T05:44:30 | 2023-08-28T20:58:53 | null | NONE | null | My pipeline contains several components, these components have OutputPath type variables, so there will be files stored in minio, but when running, a random component will generate an error:
`This step is in Error state with this message: Error (exit code 1): failed to put file: You did not provide the number of bytes specified by the Content-Length HTTP header.`
Sometimes Pipeline can run normally again, how is this? What's the matter?
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https://api.github.com/repos/kubeflow/pipelines/issues/8053 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8053/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8053/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8053/events | https://github.com/kubeflow/pipelines/issues/8053 | 1,311,620,760 | I_kwDOB-71UM5OLb6Y | 8,053 | [feature] Re-enable extra_code parameter for Python components. | {
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"/assign @connor-mccarthy ",
"@casassg, thanks for creating this issue. Since our previous discussion, I want to provide a bit more context about how this functionality will be supported in the v2 KFP SDK, but through a slightly different approach than the `extra_code` parameter.\r\n\r\nIn v2, we're adding suppor... | 2022-07-20T18:02:17 | 2022-07-22T17:18:31 | null | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
In KFP v1, there's an option to extend lightweight python components via `extra_code` which enables to have non-primitive values as defaults supported. However in KFPv2 this seems to be largely unsupported. Even in KFP v1, seems to be discouraged to use as `create_component_from_func` does not support it and `func_to_container_op` seems to be officially discouraged by maintainers (see: #7794 )
### What is the use case or pain point?
Extending python lightweigh decorator functionality to use custom defaults that are not primitives.
### Is there a workaround currently?
`func_to_container_op` in KFP v1 does seem to work fine. However this ask is more related to keep this functionality moving forward.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/8052 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8052/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8052/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8052/events | https://github.com/kubeflow/pipelines/issues/8052 | 1,310,920,049 | I_kwDOB-71UM5OIw1x | 8,052 | [feature] Ability to add Runtime Class name to Kubeflow Pipeline | {
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"Hi @jayanthp-intellect, can you provide the reference for changing the default Runtime?",
"/assign @chensun ",
"@jlyaoyuli Refer to the following containerd config file auto-generated and managed by KOPS 1.23.x ([KOPS GPU support](https://github.com/kubernetes/kops/blob/master/docs/gpu.md)). This is on a GPU n... | 2022-07-20T11:45:08 | 2023-04-12T10:33:59 | null | NONE | null | ### Feature Area
/area backend
/area sdk
### What feature would you like to see?
Would like to have a separate method in kfp py package to add Runtime class (https://kubernetes.io/docs/concepts/containers/runtime-class/) to KF Pipeline Config and/or KFP Components. This is already supported in kubernetes py package (https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1RuntimeClass.md)
### What is the use case or pain point?
The latest upgrade to kops 1.23.x is altering the default runtime in containerd to **runc** instead of **nvidia**. This is forcing us to use the Runtime Class name to choose **nvidia** runtime for GPU workloads.
### Is there a workaround currently?
We manually override the default runtime set in the Containerd to nvidia and recycle the Containerd service (using some script customisation in the Kops Instance Group config)
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"anyone else faced this issue ?",
"/cc @jagadeeshi2i Would you like to help with this issue? Thank you!",
"@kanwaljitkhurmi did the launcher start worker and master pods ? Can you share the logs or describe the pod.\r\n\r\nI could launch pytorch job for the example - https://github.com/kubeflow/pipelines/blob/m... | 2022-07-20T05:08:37 | 2022-08-16T06:13:16 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
As part of the Kubeflow Manifest 1.4
* KFP version:
* KFP SDK version:
1.8.4
### Steps to reproduce
Trying to use Kubeflow PyTorchJob launcher component in the kubeflow pipeline ,however the pipeline component endlessly waits at the main thread with the following logs and does not proceed further with creation of main and worker pods.
```
Generating job template.
Creating launcher client.
Submitting CR.
Creating kubeflow.org/pytorchjobs pytorch-cnn-dist-file-c3 in namespace kubeflow-user-example-com.
Created kubeflow.org/pytorchjobs pytorch-cnn-dist-file-c3 in namespace kubeflow-user-example-com.
Monitoring job until status is any of ['Succeeded', 'Failed'].
```
Code:
```
!pip install kfp==1.8.4
pytorch_job_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/pytorch-launcher/component.yaml')
@dsl.pipeline(name="PyTorch Training pipeline", description="Sample training job test")
def pytorch_cnn_n_b_yaml(
namespace=kanwal_namespace
):
train_task = pytorch_job_op(
name='pytorch-cnn-dist-file-c3',
namespace='kubeflow-user-example-com',
master_spec='{ \
"replicas": 1, \
"restartPolicy": "OnFailure", \
"template": { \
"metadata": { \
"annotations": { \
"sidecar.istio.io/inject": "false" \
} \
}, \
"spec": { \
"containers": [ \
{ \
"name": "pytorch1", \
"image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.12.0-gpu-py38-cu116-ubuntu20.04-e3", \
"args": [ \
"python", \
"./sc-claim-dlc/mnist.py", \
"--epochs", "5", \
"--seed", "7", \
"--log-interval", "60" \
], \
"resources": { \
"limits": { \
"nvidia.com/gpu": 2 \
} \
}, \
"volumeMounts": [ \
{ \
"mountPath": "/sc-claim-dlc", \
"name": "sc-claim-dlc" \
} \
] \
} \
], \
"volumes": [ \
{ \
"name": "sc-claim-dlc", \
"persistentVolumeClaim": { \
"claimName": "sc-claim-dlc" \
} \
} \
] \
} \
} \
}',
worker_spec='{ \
"replicas": 1, \
"restartPolicy": "OnFailure", \
"template": { \
"metadata": { \
"annotations": { \
"sidecar.istio.io/inject": "false" \
} \
}, \
"spec": { \
"containers": [ \
{ \
"name": "pytorch2", \
"image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.12.0-gpu-py38-cu116-ubuntu20.04-e3", \
"args": [ \
"python", \
"./sc-claim-dlc/mnist.py", \
"--epochs", "5", \
"--seed", "7", \
"--log-interval", "60" \
], \
"resources": { \
"limits": { \
"nvidia.com/gpu": 1 \
} \
}, \
"volumeMounts": [ \
{ \
"mountPath": "/sc-claim-dlc", \
"name": "sc-claim-dlc" \
} \
] \
} \
], \
"volumes": [ \
{ \
"name": "sc-claim-dlc", \
"persistentVolumeClaim": { \
"claimName": "sc-claim-dlc" \
} \
} \
] \
} \
} \
}',
delete_after_done=False
)
```
Can you help ?
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/8038 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8038/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8038/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8038/events | https://github.com/kubeflow/pipelines/issues/8038 | 1,306,982,279 | I_kwDOB-71UM5N5veH | 8,038 | Unable to set memory for a Kubeflow component using yaml | {
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"In the interim, it appears that I can set the memory in the pipeline json.",
"/assign @chensun ",
"Hi @patrickmead, are you using KFP SDK v1.8.*? \r\nYou can still set memory limit for components load from file. Example:\r\nhttps://github.com/kubeflow/pipelines/blob/d5bc8ddd6250d90b38cff5759e856f73e71e7d03/sdk... | 2022-07-17T04:14:45 | 2022-07-28T23:09:37 | null | NONE | null | I'm using yaml for my Kubeflow components which run on Vertex AI. I know that I have alternatives for creating components such as the python SDK, but I would like to use yaml directly if possible (rather than generating it). I see methods like set_memory_limit and set_memory_request for the ContainerOp but when I use methods such as load_component_from_file I have a TaskSpec.
Is there a way to set memory and other resources via the Kubeflow component yaml?
Thank you. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8034 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8034/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8034/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8034/events | https://github.com/kubeflow/pipelines/issues/8034 | 1,305,520,428 | I_kwDOB-71UM5N0Kks | 8,034 | [frontend] Couldn't get Logs, Pod, and Events of any pipeline components in Runs | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | closed | false | null | [] | null | [
"There is a similar issue https://github.com/kubeflow/kubeflow/issues/5605 \r\nAfter updating kubernetes-client, it works in his case, but for me, even though I upgraded it to 0.11.2 which supports IPv6, it didn't work.",
"Hi @vsk2015 , do you have any idea about this issue?",
"Hi @ConverJens , do you have any ... | 2022-07-15T03:11:38 | 2022-12-27T06:06:11 | 2022-07-21T11:25:17 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Following https://github.com/kubeflow/manifests/tree/v1.3.1
* Versions:
Kubeflow:1.3.1,
Pipeline:1.5.1,
"@kubernetes/client-node": "^0.11.2". Original version is "^0.8.2", tried to upgrade to "^0.11.2", but still the same errors.
"portable-fetch": "^3.0.0", https://github.com/kubeflow/pipelines/blob/1.5.1/frontend/server/package.json#L16
"node-fetch":1.7.3, portable-fetch require node-fetch:1.7.3 that seems not support IPv6
BTW, using IPv6 in the cluster.
I could see Pipelines, Experiment, and Runs list, but couldn't see Logs, Pod, and Events of any pipeline components in Runs.
### Steps to reproduce
<img width="1439" alt="Screen Shot 2022-12-27 at 14 51 16" src="https://user-images.githubusercontent.com/37242439/209619418-2e4aebe2-ebb8-4a8a-8c96-aff826ec5ab8.png">
<img width="1418" alt="Screen Shot 2022-12-27 at 14 52 45" src="https://user-images.githubusercontent.com/37242439/209619426-3eab6885-f3d3-4e2c-a12e-58c2347d616f.png">
<img width="1495" alt="Screen Shot 2022-12-27 at 14 53 36" src="https://user-images.githubusercontent.com/37242439/209619432-79ca88b8-7ee5-47f8-b3ad-9fce91f91edc.png">
### Expected result
See Logs, Pod, and Events of pipeline components in Runs
### Materials and Reference
npm list in ml-pipeline-ui pod
Error logs of ml-pipeline-ui pod
Hostname/IP does not match certificate's altnames: Host: **240b**
```
{
argo: {
archiveArtifactory: 'minio',
archiveBucketName: 'mlpipeline',
archiveLogs: false,
archivePrefix: 'logs'
},
artifacts: 'Artifacts config contains credentials, so it is omitted',
metadata: {
envoyService: { host: '[ipv6]', port: '9090' }
},
pipeline: { host: '[ipv6]', port: '8888' },
server: {
apiVersionPrefix: 'apis/v1beta1',
basePath: '/pipeline',
deployment: 'KUBEFLOW',
hideSideNav: true,
port: 3000,
staticDir: '/client'
},
viewer: {
tensorboard: { podTemplateSpec: [Object], tfImageName: 'tensorflow/tensorflow' }
},
visualizations: { allowCustomVisualizations: true },
gkeMetadata: { disabled: false },
auth: {
enabled: true,
kubeflowUserIdHeader: 'kubeflow-userid',
kubeflowUserIdPrefix: ''
}
}
[HPM] Proxy created: [Function] -> /artifacts
[HPM] Proxy created: / -> http://[ipv6]:9090
[HPM] Proxy created: / -> http://127.0.0.1
[HPM] Subscribed to http-proxy events: [ 'error', 'close' ]
[HPM] Proxy created: / -> http://127.0.0.1
[HPM] Subscribed to http-proxy events: [ 'error', 'close' ]
[HPM] Proxy created: / -> http://[ipv6]:8888
[HPM] Subscribed to http-proxy events: [ 'proxyReq', 'error', 'close' ]
[HPM] Proxy created: / -> http://[ipv6]:8888
[HPM] Subscribed to http-proxy events: [ 'proxyReq', 'error', 'close' ]
Server listening at http://localhost:3000
GET /apis/v1beta1/healthz
...
GET /pipeline/k8s/pod?podname=rflow-demo-07-qfjnq-3523444001&podnamespace=rflow-test
Could not get pod demo-07-qfjnq-3523444001 in namespace test: Hostname/IP does not match certificate's altnames: Host: 240b. is not in the cert's altnames: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster.local, DNS:uhn7kls2drbms001.test.local, IP Address:240B:C0E0:202:5404:B4D4:2:0:8001, IP Address:240B:..., IP Address:240B:... Error [ERR_TLS_CERT_ALTNAME_INVALID]: Hostname/IP does not match certificate's altnames: Host: 240b. is not in the cert's altnames: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster.local, DNS:uhn7kls2drbms001.test.local, IP Address:240B:...:8001, IP Address:240B:..., IP Address:240B:...
at Object.checkServerIdentity (tls.js:283:12)
at TLSSocket.onConnectSecure (_tls_wrap.js:1331:27)
at TLSSocket.emit (events.js:223:5)
at TLSSocket._finishInit (_tls_wrap.js:794:8)
at TLSWrap.ssl.onhandshakedone (_tls_wrap.js:608:12) {
reason: "Host: 240b. is not in the cert's altnames: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster.local, DNS:uhn7kls2drbms001.test.local, IP Address:240B:...:8001, IP Address:240B:..., IP Address:240B:...",
host: '240b',
cert: {
...
```
config.ts
https://github.com/kubeflow/pipelines/blob/1.5.1/frontend/server/configs.ts
```
const { staticDir, port } = parseArgs(argv);
/** All configurable environment variables can be found here. */
const {
/** minio client use these to retrieve minio objects/artifacts */
MINIO_ACCESS_KEY = 'minio',
MINIO_SECRET_KEY = 'minio123',
MINIO_PORT = '9000',
MINIO_HOST = 'minio-service',
MINIO_NAMESPACE = 'kubeflow',
MINIO_SSL = 'false',
/** minio client use these to retrieve s3 objects/artifacts */
AWS_ACCESS_KEY_ID,
AWS_SECRET_ACCESS_KEY,
/** http/https base URL */
HTTP_BASE_URL = '', // tried to use '[::]', but make no difference.
/** http/https fetch with this authorization header key (for example: 'Authorization') */
HTTP_AUTHORIZATION_KEY = '',
/** http/https fetch with this authorization header value by default when absent in client request at above key */
HTTP_AUTHORIZATION_DEFAULT_VALUE = '',
/** API service will listen to this host */
ML_PIPELINE_SERVICE_HOST = '[::]',
/** API service will listen to this port */
ML_PIPELINE_SERVICE_PORT = '8888',
/** path to viewer:tensorboard pod template spec */
VIEWER_TENSORBOARD_POD_TEMPLATE_SPEC_PATH,
/** Tensorflow image used for tensorboard viewer */
VIEWER_TENSORBOARD_TF_IMAGE_NAME = 'tensorflow/tensorflow',
/** Whether custom visualizations are allowed to be generated by the frontend */
ALLOW_CUSTOM_VISUALIZATIONS = 'false',
/** Envoy service will listen to this host */
METADATA_ENVOY_SERVICE_SERVICE_HOST = '[::]',
/** Envoy service will listen to this port */
METADATA_ENVOY_SERVICE_SERVICE_PORT = '9090',
/** Is Argo log archive enabled? */
ARGO_ARCHIVE_LOGS = 'false',
/** Use minio or s3 client to retrieve archives. */
ARGO_ARCHIVE_ARTIFACTORY = 'minio',
/** Bucket to retrive logs from */
ARGO_ARCHIVE_BUCKETNAME = 'mlpipeline',
/** Prefix to logs. */
ARGO_ARCHIVE_PREFIX = 'logs',
/** Should use server API for log streaming? */
STREAM_LOGS_FROM_SERVER_API = 'false',
/** Disables GKE metadata endpoint. */
DISABLE_GKE_METADATA = 'true',
/** Enable authorization checks for multi user mode. */
ENABLE_AUTHZ = 'false',
/** Deployment type. */
DEPLOYMENT: DEPLOYMENT_STR = '',
/**
* Set to true to hide the SideNav. When DEPLOYMENT is KUBEFLOW, HIDE_SIDENAV
* defaults to true if not explicitly set to false.
*/
HIDE_SIDENAV,
/**
* A header user requests have when authenticated. It carries user identity information.
* The default value works with Google Cloud IAP.
*/
KUBEFLOW_USERID_HEADER = 'x-goog-authenticated-user-email',
/**
* KUBEFLOW_USERID_HEADER's value may have a prefix before user identity.
* Use this header to specify what the prefix is.
*
* e.g. a valid header value for default values can be like `accounts.google.com:user@gmail.com`.
*/
KUBEFLOW_USERID_PREFIX = 'accounts.google.com:',
} = env;
```
---
```
kind: Deployment
apiVersion: apps/v1
metadata:
name: ml-pipeline-ui
namespace: kubeflow
labels:
app: ml-pipeline-ui
app.kubernetes.io/component: ml-pipeline
app.kubernetes.io/instance: ml-pipeline
app.kubernetes.io/name: kubeflow-pipelines
application-crd-id: kubeflow-pipelines
annotations:
…
managedFields:
- manager: argocd-application-controller
operation: Update
apiVersion: apps/v1
time: '2022-04-13T06:18:49Z'
fieldsType: FieldsV1
fieldsV1:
f:metadata:
f:annotations:
.: {}
f:kubectl.kubernetes.io/last-applied-configuration: {}
f:labels:
.: {}
f:app: {}
f:app.kubernetes.io/component: {}
f:app.kubernetes.io/instance: {}
f:app.kubernetes.io/name: {}
f:application-crd-id: {}
f:spec:
f:progressDeadlineSeconds: {}
f:replicas: {}
f:revisionHistoryLimit: {}
f:selector: {}
f:strategy:
f:rollingUpdate:
.: {}
f:maxSurge: {}
f:maxUnavailable: {}
f:type: {}
f:template:
f:metadata:
f:annotations:
.: {}
f:cluster-autoscaler.kubernetes.io/safe-to-evict: {}
f:labels:
.: {}
f:app: {}
f:app.kubernetes.io/component: {}
f:app.kubernetes.io/name: {}
f:application-crd-id: {}
f:spec:
f:containers:
k:{"name":"ml-pipeline-ui"}:
.: {}
f:env:
.: {}
k:{"name":"ALLOW_CUSTOM_VISUALIZATIONS"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"ARTIFACTS_SERVICE_PROXY_ENABLED"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"ARTIFACTS_SERVICE_PROXY_NAME"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"ARTIFACTS_SERVICE_PROXY_PORT"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"DEPLOYMENT"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"ENABLE_AUTHZ"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"KUBEFLOW_USERID_HEADER"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"KUBEFLOW_USERID_PREFIX"}:
.: {}
f:name: {}
k:{"name":"MINIO_ACCESS_KEY"}:
.: {}
f:name: {}
f:valueFrom:
.: {}
f:secretKeyRef:
.: {}
f:key: {}
f:name: {}
k:{"name":"MINIO_NAMESPACE"}:
.: {}
f:name: {}
f:valueFrom:
.: {}
f:fieldRef:
.: {}
f:apiVersion: {}
f:fieldPath: {}
k:{"name":"MINIO_SECRET_KEY"}:
.: {}
f:name: {}
f:valueFrom:
.: {}
f:secretKeyRef:
.: {}
f:key: {}
f:name: {}
k:{"name":"VIEWER_TENSORBOARD_POD_TEMPLATE_SPEC_PATH"}:
.: {}
f:name: {}
f:value: {}
f:livenessProbe:
.: {}
f:exec:
.: {}
f:command: {}
f:failureThreshold: {}
f:initialDelaySeconds: {}
f:periodSeconds: {}
f:successThreshold: {}
f:timeoutSeconds: {}
f:name: {}
f:ports:
.: {}
k:{"containerPort":3000,"protocol":"TCP"}:
.: {}
f:containerPort: {}
f:protocol: {}
f:readinessProbe:
.: {}
f:exec:
.: {}
f:command: {}
f:failureThreshold: {}
f:initialDelaySeconds: {}
f:periodSeconds: {}
f:successThreshold: {}
f:timeoutSeconds: {}
f:resources:
.: {}
f:requests:
.: {}
f:cpu: {}
f:memory: {}
f:terminationMessagePath: {}
f:terminationMessagePolicy: {}
f:volumeMounts:
.: {}
k:{"mountPath":"/etc/config"}:
.: {}
f:mountPath: {}
f:name: {}
f:readOnly: {}
f:dnsPolicy: {}
f:restartPolicy: {}
f:schedulerName: {}
f:securityContext: {}
f:serviceAccount: {}
f:serviceAccountName: {}
f:terminationGracePeriodSeconds: {}
f:volumes:
.: {}
k:{"name":"config-volume"}:
.: {}
f:configMap:
.: {}
f:defaultMode: {}
f:name: {}
f:name: {}
- manager: kubectl-edit
operation: Update
apiVersion: apps/v1
time: '2022-04-19T04:22:29Z'
fieldsType: FieldsV1
fieldsV1:
f:spec:
f:template:
f:spec:
f:containers:
k:{"name":"ml-pipeline-ui"}:
f:env:
k:{"name":"METADATA_ENVOY_SERVICE_SERVICE_HOST"}:
.: {}
f:name: {}
f:value: {}
k:{"name":"ML_PIPELINE_SERVICE_HOST"}:
.: {}
f:name: {}
f:value: {}
f:imagePullPolicy: {}
- manager: kubectl-rollout
operation: Update
apiVersion: apps/v1
time: '2022-05-09T02:59:57Z'
fieldsType: FieldsV1
fieldsV1:
f:spec:
f:template:
f:metadata:
f:annotations:
f:kubectl.kubernetes.io/restartedAt: {}
- manager: dashboard
operation: Update
apiVersion: apps/v1
time: '2022-07-15T01:59:57Z'
fieldsType: FieldsV1
fieldsV1:
f:spec:
f:template:
f:spec:
f:containers:
k:{"name":"ml-pipeline-ui"}:
f:env:
k:{"name":"HTTP_BASE_URL"}:
.: {}
f:name: {}
f:value: {}
f:image: {}
- manager: kube-controller-manager
operation: Update
apiVersion: apps/v1
time: '2022-07-15T02:01:01Z'
fieldsType: FieldsV1
fieldsV1:
f:metadata:
f:annotations:
f:deployment.kubernetes.io/revision: {}
f:status:
f:availableReplicas: {}
f:conditions:
.: {}
k:{"type":"Available"}:
.: {}
f:lastTransitionTime: {}
f:lastUpdateTime: {}
f:message: {}
f:reason: {}
f:status: {}
f:type: {}
k:{"type":"Progressing"}:
.: {}
f:lastTransitionTime: {}
f:lastUpdateTime: {}
f:message: {}
f:reason: {}
f:status: {}
f:type: {}
f:observedGeneration: {}
f:readyReplicas: {}
f:replicas: {}
f:updatedReplicas: {}
spec:
replicas: 1
selector:
matchLabels:
app: ml-pipeline-ui
app.kubernetes.io/component: ml-pipeline
app.kubernetes.io/name: kubeflow-pipelines
application-crd-id: kubeflow-pipelines
template:
metadata:
creationTimestamp: null
labels:
app: ml-pipeline-ui
app.kubernetes.io/component: ml-pipeline
app.kubernetes.io/name: kubeflow-pipelines
application-crd-id: kubeflow-pipelines
annotations:
cluster-autoscaler.kubernetes.io/safe-to-evict: 'true'
kubectl.kubernetes.io/restartedAt: '2022-07-13T06:30:53Z'
spec:
volumes:
- name: config-volume
configMap:
name: ml-pipeline-ui-configmap
defaultMode: 420
containers:
- name: ml-pipeline-ui
image: >-
frontend:1.5.1-build10
ports:
- containerPort: 3000
protocol: TCP
env:
- name: VIEWER_TENSORBOARD_POD_TEMPLATE_SPEC_PATH
value: /etc/config/viewer-pod-template.json
- name: HTTP_BASE_URL
value: abcd
- name: ML_PIPELINE_SERVICE_HOST
value: '[IPv6 address]'
- name: METADATA_ENVOY_SERVICE_SERVICE_HOST
value: '[IPv6 address]'
- name: DEPLOYMENT
value: KUBEFLOW
- name: ARTIFACTS_SERVICE_PROXY_NAME
value: ml-pipeline-ui-artifact
- name: ARTIFACTS_SERVICE_PROXY_PORT
value: '80'
- name: ARTIFACTS_SERVICE_PROXY_ENABLED
value: 'true'
- name: ENABLE_AUTHZ
value: 'true'
- name: KUBEFLOW_USERID_HEADER
value: kubeflow-userid
- name: KUBEFLOW_USERID_PREFIX
- name: MINIO_NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: MINIO_ACCESS_KEY
valueFrom:
secretKeyRef:
name: mlpipeline-minio-artifact
key: accesskey
- name: MINIO_SECRET_KEY
valueFrom:
secretKeyRef:
name: mlpipeline-minio-artifact
key: secretkey
- name: ALLOW_CUSTOM_VISUALIZATIONS
value: 'true'
```
<!-- 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/8014 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8014/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8014/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8014/events | https://github.com/kubeflow/pipelines/issues/8014 | 1,302,410,636 | I_kwDOB-71UM5NoTWM | 8,014 | [feature] Add primary key to DB tables | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw"... | closed | false | {
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"/assign @Linchin ",
"@ca-scribner Hi, we do create each DB with a column as primary key, see examples [here](https://github.com/kubeflow/pipelines/blob/061905b6df397c40fbcc4ffafa24d7b3b9daf439/backend/src/apiserver/model/run.go#L26) and [here](https://github.com/kubeflow/pipelines/blob/061905b6df397c40fbcc4ffafa... | 2022-07-12T17:28:22 | 2022-07-26T09:03:18 | 2022-07-26T09:03:18 | 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?
That kfp create [its database](https://github.com/kubeflow/pipelines/tree/5a56a9399908033ce97573abf85098fd77f649ae/backend/src/apiserver/model) with a primary key (probably [here](https://github.com/kubeflow/pipelines/blob/5a56a9399908033ce97573abf85098fd77f649ae/backend/src/apiserver/model/default_experiment.go#L18) and [here](https://github.com/kubeflow/pipelines/tree/5a56a9399908033ce97573abf85098fd77f649ae/backend/src/apiserver/model), with something like:
```
DefaultExperimentId string `gorm:"column:DefaultExperimentId; not null; primary key"`
```
### What is the use case or pain point?
Primary keys are required for group replication (high availability) of tables in mysql. Enabling this would enable using more resilient data storage.
### Is there a workaround currently?
I don't know if this is safe, but perhaps I can separately create the database following kfp's schema (but with a primary key) and then have kfp connect to that existing DB? I'm not sure what the consequences of this would be.
**update:** This appears to work (application starts without errors, etc). Haven't stress tested it yet
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/8012 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8012/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8012/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8012/events | https://github.com/kubeflow/pipelines/issues/8012 | 1,301,678,349 | I_kwDOB-71UM5NlgkN | 8,012 | [bug] executor attempts to kill already terminated kfp-launcher init container | {
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"Hi @jazzsir , can you provide what kubernetes version you used? We are moving toward the emissary executor. Here is the doc for executor: https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/"
] | 2022-07-12T07:49:14 | 2022-07-21T22:44:57 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? [standalone-deployment](https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deploying-kubeflow-pipelines)
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.8.2
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version: 1.8.2
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
The KFPv2 pipelines are completed, but errors occur as shown below, it seems to be a problem caused by attempting to terminate the already terminated the kfp-launcher init container.
```
$ kubectl -n kubeflow-pipeline logs -f pipeline-test-1-gtvhr-2680780413 -c wait
time="2022-07-12T06:48:44.957Z" level=info msg="Starting Workflow Executor" executorType=docker version=v3.2.3
time="2022-07-12T06:48:44.961Z" level=info msg="Creating a docker executor"
time="2022-07-12T06:48:44.961Z" level=info msg="Executor initialized" deadline="0001-01-01 00:00:00 +0000 UTC" includeScriptOutput=false namespace=kubeflow-pipeline podName=pipeline-test-1-gtvhr-2680780413 template="{\"name\":\"get-data\",\"inputs\":{\"parameters\":[{\"name\":\"pipeline-name\",\"value\":\"pipeline/pipeline-test-1\"},{\"name\":\"pipeline-root\",\"value\":\"\"}]},\"outputs\":{\"artifacts\":[{\"name\":\"get-data-dataset_test\",\"path\":\"/tmp/outputs/dataset_test/data\"},{\"name\":\"get-data-dataset_train\",\"path\":\"/tmp/outputs/dataset_train/data\"}]},\"metadata\":{\"annotations\":{\"pipelines.kubeflow.org/component_ref\":\"{}\",\"pipelines.kubeflow.org/v2_component\":\"true\",\"sidecar.istio.io/inject\":\"false\"},\"labels\":{\"pipelines.kubeflow.org/cache_enabled\":\"true\",\"pipelines.kubeflow.org/enable_caching\":\"false\",\"pipelines.kubeflow.org/kfp_sdk_version\":\"1.8.2\",\"pipelines.kubeflow.org/pipeline-sdk-type\":\"kfp\",\"pipelines.kubeflow.org/v2_component\":\"true\"}},\"container\":{\"name\":\"\",\"image\":\"python:3.7\",\"command\":[\"/kfp-launcher/launch\",\"--mlmd_server_address\",\"$(METADATA_GRPC_SERVICE_HOST)\",\"--mlmd_server_port\",\"$(METADATA_GRPC_SERVICE_PORT)\",\"--runtime_info_json\",\"$(KFP_V2_RUNTIME_INFO)\",\"--container_image\",\"$(KFP_V2_IMAGE)\",\"--task_name\",\"get-data\",\"--pipeline_name\",\"pipeline/pipeline-test-1\",\"--run_id\",\"$(KFP_RUN_ID)\",\"--run_resource\",\"workflows.argoproj.io/$(WORKFLOW_ID)\",\"--namespace\",\"$(KFP_NAMESPACE)\",\"--pod_name\",\"$(KFP_POD_NAME)\",\"--pod_uid\",\"$(KFP_POD_UID)\",\"--pipeline_root\",\"\",\"--enable_caching\",\"$(ENABLE_CACHING)\",\"--\",\"--\"],\"args\":[\"sh\",\"-c\",\"(python3 -m ensurepip || python3 -m ensurepip --user) \\u0026\\u0026 (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'pandas' 'sklearn' 'kfp==1.8.2' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'pandas' 'sklearn' 'kfp==1.8.2' --user) \\u0026\\u0026 \\\"$0\\\" \\\"$@\\\"\",\"sh\",\"-ec\",\"program_path=$(mktemp -d)\\nprintf \\\"%s\\\" \\\"$0\\\" \\u003e \\\"$program_path/ephemeral_component.py\\\"\\npython3 -m kfp.v2.components.executor_main --component_module_path \\\"$program_path/ephemeral_component.py\\\" \\\"$@\\\"\\n\",\"\\nfrom kfp.v2.dsl import *\\nfrom typing import *\\n\\ndef get_data(\\n dataset_train: Output[Dataset],\\n dataset_test: Output[Dataset]\\n\\n):\\n\\n from sklearn import datasets\\n from sklearn.model_selection import train_test_split as tts\\n import pandas as pd\\n # import some data to play with\\n\\n data_raw = datasets.load_breast_cancer()\\n data = pd.DataFrame(data_raw.data, columns=data_raw.feature_names)\\n data[\\\"target\\\"] = data_raw.target\\n\\n train, test = tts(data, test_size=0.3)\\n\\n train.to_csv(dataset_train.path)\\n test.to_csv(dataset_test.path)\\n\\n\",\"--executor_input\",\"{{$}}\",\"--function_to_execute\",\"get_data\"],\"envFrom\":[{\"configMapRef\":{\"name\":\"metadata-grpc-configmap\",\"optional\":true}}],\"env\":[{\"name\":\"KFP_POD_NAME\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.name\"}}},{\"name\":\"KFP_POD_UID\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.uid\"}}},{\"name\":\"KFP_NAMESPACE\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.namespace\"}}},{\"name\":\"WORKFLOW_ID\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.labels['workflows.argoproj.io/workflow']\"}}},{\"name\":\"KFP_RUN_ID\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.labels['pipeline/runid']\"}}},{\"name\":\"ENABLE_CACHING\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.labels['pipelines.kubeflow.org/enable_caching']\"}}},{\"name\":\"KFP_V2_IMAGE\",\"value\":\"python:3.7\"},{\"name\":\"KFP_V2_RUNTIME_INFO\",\"value\":\"{\\\"inputParameters\\\": {}, \\\"inputArtifacts\\\": {}, \\\"outputParameters\\\": {}, \\\"outputArtifacts\\\": {\\\"dataset_test\\\": {\\\"schemaTitle\\\": \\\"system.Dataset\\\", \\\"instanceSchema\\\": \\\"\\\", \\\"schemaVersion\\\": \\\"0.0.1\\\", \\\"metadataPath\\\": \\\"/tmp/outputs/dataset_test/data\\\"}, \\\"dataset_train\\\": {\\\"schemaTitle\\\": \\\"system.Dataset\\\", \\\"instanceSchema\\\": \\\"\\\", \\\"schemaVersion\\\": \\\"0.0.1\\\", \\\"metadataPath\\\": \\\"/tmp/outputs/dataset_train/data\\\"}}}\"}],\"resources\":{},\"volumeMounts\":[{\"name\":\"kfp-launcher\",\"mountPath\":\"/kfp-launcher\"}]},\"volumes\":[{\"name\":\"kfp-launcher\"}],\"initContainers\":[{\"name\":\"kfp-launcher\",\"image\":\"gcr.io/ml-pipeline/kfp-launcher:1.8.2\",\"command\":[\"launcher\",\"--copy\",\"/kfp-launcher/launch\"],\"resources\":{},\"mirrorVolumeMounts\":true}],\"archiveLocation\":{\"archiveLogs\":true,\"s3\":{\"endpoint\":\"minio-service.kubeflow-pipeline:9000\",\"bucket\":\"mlpipeline\",\"insecure\":true,\"accessKeySecret\":{\"name\":\"clops-minio-artifact\",\"key\":\"accessKey\"},\"secretKeySecret\":{\"name\":\"clops-minio-artifact\",\"key\":\"secretKey\"},\"key\":\"pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413\"}}}" version="&Version{Version:v3.2.3,BuildDate:2021-10-27T01:09:16Z,GitCommit:e5dc961b7846efe0fe36ab3a0964180eaedd2672,GitTag:v3.2.3,GitTreeState:clean,GoVersion:go1.16.9,Compiler:gc,Platform:linux/amd64,}"
time="2022-07-12T06:48:44.961Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:44.961Z" level=info msg="Starting deadline monitor"
time="2022-07-12T06:48:45.006Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Created {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:45.006Z" level=info msg="mapped container name \"wait\" to container ID \"b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c\" (created at 2022-07-12 06:48:44 +0000 UTC, status Up)"
time="2022-07-12T06:48:45.006Z" level=info msg="mapped container name \"kfp-launcher\" to container ID \"3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b\" (created at 2022-07-12 06:48:43 +0000 UTC, status Exited)"
time="2022-07-12T06:48:45.006Z" level=info msg="mapped container name \"main\" to container ID \"e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c\" (created at 2022-07-12 06:48:44 +0000 UTC, status Created)"
time="2022-07-12T06:48:45.962Z" level=info msg="docker wait e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c"
time="2022-07-12T06:48:46.007Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:46.036Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:47.036Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:47.068Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:48.069Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:48.099Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:49.100Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:49.130Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:50.131Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:50.172Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:51.173Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:51.204Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:52.204Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:52.231Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:53.231Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:53.265Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:54.266Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:54.294Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:55.295Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:55.328Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:56.328Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:56.357Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:57.357Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:57.386Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:58.387Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:58.421Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:48:59.422Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:48:59.449Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:00.449Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:00.477Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:01.477Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:01.530Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:02.530Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:02.565Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:03.565Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:03.607Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:04.607Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:04.638Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:05.640Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:05.693Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:06.693Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:06.749Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:07.749Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:07.783Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:08.784Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:08.848Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:09.849Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:09.883Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:10.884Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:10.934Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:11.935Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:11.963Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:12.964Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:12.997Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:13.997Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:14.027Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:15.028Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:15.058Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:22.093Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:22.195Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Exited {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:22.195Z" level=info msg="Main container completed"
time="2022-07-12T06:49:22.195Z" level=info msg="No Script output reference in workflow. Capturing script output ignored"
time="2022-07-12T06:49:22.195Z" level=info msg="Saving logs"
time="2022-07-12T06:49:22.195Z" level=info msg="[docker logs e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c]"
time="2022-07-12T06:49:22.247Z" level=info msg="S3 Save path: /tmp/argo/outputs/logs/main.log, key: pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/main.log"
time="2022-07-12T06:49:22.247Z" level=info msg="Creating minio client using static credentials" endpoint="minio-service.kubeflow-pipeline:9000"
time="2022-07-12T06:49:22.247Z" level=info msg="Saving file to s3" bucket=mlpipeline endpoint="minio-service.kubeflow-pipeline:9000" key=pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/main.log path=/tmp/argo/outputs/logs/main.log
time="2022-07-12T06:49:24.735Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/logs/main.log
time="2022-07-12T06:49:24.735Z" level=info msg="Successfully saved file: /tmp/argo/outputs/logs/main.log"
time="2022-07-12T06:49:24.735Z" level=info msg="No output parameters"
time="2022-07-12T06:49:24.735Z" level=info msg="Saving output artifacts"
time="2022-07-12T06:49:24.735Z" level=info msg="Staging artifact: get-data-dataset_test"
time="2022-07-12T06:49:24.735Z" level=info msg="Copying /tmp/outputs/dataset_test/data from container base image layer to /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz"
time="2022-07-12T06:49:24.735Z" level=info msg="Archiving main:/tmp/outputs/dataset_test/data to /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz"
time="2022-07-12T06:49:24.735Z" level=info msg="sh -c docker cp -a e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c:/tmp/outputs/dataset_test/data - | gzip > /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz"
time="2022-07-12T06:49:24.915Z" level=info msg="Archiving completed"
time="2022-07-12T06:49:24.915Z" level=info msg="S3 Save path: /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz, key: pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_test.tgz"
time="2022-07-12T06:49:24.915Z" level=info msg="Creating minio client using static credentials" endpoint="minio-service.kubeflow-pipeline:9000"
time="2022-07-12T06:49:24.915Z" level=info msg="Saving file to s3" bucket=mlpipeline endpoint="minio-service.kubeflow-pipeline:9000" key=pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_test.tgz path=/tmp/argo/outputs/artifacts/get-data-dataset_test.tgz
time="2022-07-12T06:49:25.058Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:25.089Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Exited {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:26.424Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/artifacts/get-data-dataset_test.tgz
time="2022-07-12T06:49:26.424Z" level=info msg="Successfully saved file: /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz"
time="2022-07-12T06:49:26.424Z" level=info msg="Staging artifact: get-data-dataset_train"
time="2022-07-12T06:49:26.424Z" level=info msg="Copying /tmp/outputs/dataset_train/data from container base image layer to /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz"
time="2022-07-12T06:49:26.424Z" level=info msg="Archiving main:/tmp/outputs/dataset_train/data to /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz"
time="2022-07-12T06:49:26.424Z" level=info msg="sh -c docker cp -a e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c:/tmp/outputs/dataset_train/data - | gzip > /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz"
time="2022-07-12T06:49:26.559Z" level=info msg="Archiving completed"
time="2022-07-12T06:49:26.571Z" level=info msg="S3 Save path: /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz, key: pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_train.tgz"
time="2022-07-12T06:49:26.571Z" level=info msg="Creating minio client using static credentials" endpoint="minio-service.kubeflow-pipeline:9000"
time="2022-07-12T06:49:26.571Z" level=info msg="Saving file to s3" bucket=mlpipeline endpoint="minio-service.kubeflow-pipeline:9000" key=pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_train.tgz path=/tmp/argo/outputs/artifacts/get-data-dataset_train.tgz
time="2022-07-12T06:49:28.158Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/artifacts/get-data-dataset_train.tgz
time="2022-07-12T06:49:28.158Z" level=info msg="Successfully saved file: /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz"
time="2022-07-12T06:49:28.158Z" level=info msg="Annotating pod with output"
time="2022-07-12T06:49:28.211Z" level=info msg="Patch pods 200"
time="2022-07-12T06:49:28.218Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413"
time="2022-07-12T06:49:28.250Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Exited {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]"
time="2022-07-12T06:49:28.250Z" level=info msg="Killing sidecars [\"kfp-launcher\"]"
time="2022-07-12T06:49:28.252Z" level=info msg="Get pods 403"
time="2022-07-12T06:49:28.252Z" level=warning msg="Non-transient error: pods \"pipeline-test-1-gtvhr-2680780413\" is forbidden: User \"system:serviceaccount:kubeflow-pipeline:default\" cannot get resource \"pods\" in API group \"\" in the namespace \"kubeflow-pipeline\""
time="2022-07-12T06:49:28.252Z" level=info msg="docker kill --signal TERM 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b"
time="2022-07-12T06:49:28.306Z" level=error msg="`docker kill --signal TERM 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b` failed: Error response from daemon: Cannot kill container: 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b: Container 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b is not running\n"
time="2022-07-12T06:49:28.306Z" level=warning msg="Ignored error from 'docker kill --signal TERM': Error response from daemon: Cannot kill container: 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b: Container 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b is not running"
time="2022-07-12T06:49:28.306Z" level=info msg="[docker wait 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b]"
time="2022-07-12T06:49:28.307Z" level=info msg="Timed out (0s) for containers to terminate gracefully. Killing forcefully"
time="2022-07-12T06:49:28.307Z" level=info msg="[docker kill --signal KILL 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b]"
time="2022-07-12T06:49:28.405Z" level=warning msg="Ignored error from 'docker kill --signal KILL': exit status 1"
time="2022-07-12T06:49:28.405Z" level=error msg="executor error: Error response from daemon: Cannot kill container: 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b: Container 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b is not running\ngithub.com/argoproj/argo-workflows/v3/errors.Wrap\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:88\ngithub.com/argoproj/argo-workflows/v3/errors.InternalWrapError\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:71\ngithub.com/argoproj/argo-workflows/v3/workflow/executor/docker.(*DockerExecutor).Kill\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/docker/docker.go:410\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).KillSidecars\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:989\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:35\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:72\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.NewWaitCommand.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:18\ngithub.com/spf13/cobra.(*Command).execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.2.1/command.go:860\ngithub.com/spf13/cobra.(*Command).ExecuteC\n\t/go/pkg/mod/github.com/spf13/cobra@v1.2.1/command.go:974\ngithub.com/spf13/cobra.(*Command).Execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.2.1/command.go:902\nmain.main\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/main.go:15\nruntime.main\n\t/usr/local/go/src/runtime/proc.go:225\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1371"
time="2022-07-12T06:49:28.405Z" level=info msg="Alloc=6364 TotalAlloc=22190 Sys=73297 NumGC=6 Goroutines=15"
```
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
- my workflow-controller-configmap
```
apiVersion: v1
data:
artifactRepository: |
archiveLogs: true
s3:
accessKeySecret:
key: accesskey
name: mlpipeline-minio-artifact
bucket: mlpipeline
endpoint: minio-service.kubeflow-pipeline:9000
insecure: true
secretKeySecret:
key: secretkey
name: mlpipeline-minio-artifact
containerRuntimeExecutor: docker
executor: |
imagePullPolicy: IfNotPresent
resources:
limits:
cpu: "0.5"
memory: 512Mi
requests:
cpu: "0.01"
memory: 32Mi
parallelism: "50"
kind: ConfigMap
metadata:
name: workflow-controller-configmap
namespace: kubeflow-pipeline
```
- KFPv2 example I tried: https://github.com/kubeflow/pipelines/blob/master/samples/v2/lightweight_python_functions_v2_pipeline/lightweight_python_functions_v2_pipeline.py
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
/area sdk
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/8001 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8001/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8001/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8001/events | https://github.com/kubeflow/pipelines/issues/8001 | 1,300,882,413 | I_kwDOB-71UM5NiePt | 8,001 | Does Kubeflow (1.2) has dependency with any specific kfp python library version? | {
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"/assign @connor-mccarthy ",
"@JebasinghLuccas, I do not believe there is any strict dependency on a KFP SDK version, so long as you're using a kfp SDK 1.x version, as we've adhered closely to the semantic versioning standards. The most recent is `1.8.13`.\r\n\r\nContext: Kubeflow 1.2 uses Kubeflow Pipelines 1.0.... | 2022-07-11T15:32:55 | 2022-07-22T15:54:48 | 2022-07-22T15:54:48 | NONE | null | i am wondering if there any dependencies in kfp python library version when i want to use it in kubeflow | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7995 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7995/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7995/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7995/events | https://github.com/kubeflow/pipelines/issues/7995 | 1,298,512,597 | I_kwDOB-71UM5NZbrV | 7,995 | [frontend] AWS S3 artifacts handler hardcode for s3 endpoint. | {
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"I've created a PR for this change here: https://github.com/kubeflow/pipelines/pull/8033\r\n"
] | 2022-07-08T05:34:00 | 2022-08-02T22:54:44 | 2022-08-02T22:54:44 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Install with Kubeflow
* KFP version:
1.8.2
### Steps to reproduce
Access the S3 artifaces locate in China regions(Beijing&Ningxia) the UI will run into errors:
```
S3Error: The provided token is malformed or otherwise invalid.
at Object.parseError (/server/node_modules/minio/dist/main/xml-parsers.js:86:11)
at /server/node_modules/minio/dist/main/transformers.js:156:22
at DestroyableTransform._flush (/server/node_modules/minio/dist/main/transformers.js:80:10)
at DestroyableTransform.prefinish (/server/node_modules/readable-stream/lib/_stream_transform.js:138:10)
at DestroyableTransform.emit (events.js:400:28)
at prefinish (/server/node_modules/readable-stream/lib/_stream_writable.js:619:14)
at finishMaybe (/server/node_modules/readable-stream/lib/_stream_writable.js:627:5)
at endWritable (/server/node_modules/readable-stream/lib/_stream_writable.js:638:3)
at DestroyableTransform.Writable.end (/server/node_modules/readable-stream/lib/_stream_writable.js:594:41)
at IncomingMessage.onend (internal/streams/readable.js:670:10) {
code: 'InvalidToken',
'token-0': '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',
requestid: '5QP07VQ89YKHCCJP',
hostid: 'ESrdZWeNF0v1J0iHKGzswkjkXgoIOpt7P1oViJf89PMeKZStjrhNtlK2RjBpjMaovANV+dIBj6o=',
amzRequestid: null,
amzId2: null,
amzBucketRegion: null
}
```
### Expected result
Use the pipeline UI to explore the S3 artifacts in China regions.
### Materials and Reference
I think [this line](https://github.com/kubeflow/pipelines/blob/086f0c960dcae0a7fd3d75b05e3e4f93445b2341/frontend/server/configs.ts#L127) should not be hardcoded.
<!-- 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/7990 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7990/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7990/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7990/events | https://github.com/kubeflow/pipelines/issues/7990 | 1,296,556,062 | I_kwDOB-71UM5NR-Ae | 7,990 | [backend] discussion - make jobs and runs top level resources? | {
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"node_id": "MDU6TGFiZWwxNjgyNzE3Mzc3"... | open | false | null | [] | null | [
"/cc @zijianjoy @chensun @difince @IronPan @annajung \r\nWould be great to see your thoughts on this.",
"The decision is already taken in v2beta design [doc](https://docs.google.com/document/d/19oL11PfTZyuevgoMR00JlwMdJuR6U8to0N3CoMmtqeg/edit#) \r\nThe choisen approach is the experiment to be passed as an argumen... | 2022-07-06T22:28:53 | 2022-12-08T12:16:12 | null | COLLABORATOR | null | Currently we have jobs and runs as sub-resources of experiments, i.e., a job or a run must belong to one and only one experiment. This is reflected in creating runs and jobs by specifying resource references. In v2 API, we are planning to [retire resource references](https://docs.google.com/document/d/19OfU-hIsY4xBKA6b_F3dYIbSgrLMrVshucUSsyqotQ4/edit#), and reflect the resources (mostly `namespace` and `experiment`) in URLs.
We are considering two options to specify experiments:
Option 1: `GET "/apis/v2beta1/experiments/{exp_id}/runs"`
Option 2: `GET "/apis/v2beta1/runs?experiment={exp_id}"`
[Option 1] implies hierarchy in the URL call, while [Option 2] has experiment id as a required parameter. [Option 2] gives us more leeway in future changes in case we want to alter the hierarchical structure between jobs/runs and experiments (For example, maybe runs/jobs do not have to belong to any experiment). I wanted to gauge interest in the community in having runs/jobs as top level resources, and see if there is any further complication that needs to be addressed.
---
<!-- 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/7986 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7986/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7986/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7986/events | https://github.com/kubeflow/pipelines/issues/7986 | 1,295,339,675 | I_kwDOB-71UM5NNVCb | 7,986 | [emissary] LibreOffice fails with emissary runtime. | {
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"With emissary executor, container cannot run commands with root privilege. Is that possible that `libreoffice` relies on some root privilege to run? Seems like this is out of control of KFP, and you have created https://github.com/argoproj/argo-workflows/issues/9117. "
] | 2022-07-06T07:26:51 | 2022-07-28T06:41:25 | 2022-07-14T23:12:28 | NONE | null | ## Summary
What happened/what you expected to happen?
I am need to use libreoffice headless to convert docx file to pdf. This is working execellent in Vanilla k8s and Databricks but when i do the same in Kubeflow which uses argo workflow at its backend it does not produce any output.
What version are you running?
argoproj.io/v1alpha1
Kubeflow 1.4
## Diagnostics
Paste the smallest workflow that reproduces the bug. We must be able to run the workflow.
```
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: libreoffice-pv-claim
spec:
storageClassName: gp2
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 2Gi
---
apiVersion: v1
kind: Pod
metadata:
name: libreoffice
spec:
containers:
- name: libreoffice-container
image: domnulnopcea/libreoffice-headless:latest
command: ["libreoffice", "--headless", "--convert-to","pdf" ,"/tests/288.pptx","--outdir", "/tests"]
volumeMounts:
- mountPath: "/tests"
name: libreoffice-storage
volumes:
- name: libreoffice-storage
persistentVolumeClaim:
claimName: libreoffice-pv-claim
tolerations:
- key: project
operator: Equal
value: cd-msr
effect: NoSchedule
---
apiVersion: v1
kind: Pod
metadata:
name: libreoffice-bash
spec:
containers:
- name: libreoffice-container
image: ubuntu:18.04
command: ["/bin/sleep", "3650d"]
volumeMounts:
- mountPath: "/tests"
name: libreoffice-storage
volumes:
- name: libreoffice-storage
persistentVolumeClaim:
claimName: libreoffice-pv-claim
tolerations:
- key: project
operator: Equal
value: cd-msr
effect: NoSchedule
```
This is the yaml I am using.
I am then manually copying the input files
```
kubectl cp ./288.pptx libreoffice-bash:/tests/
kubectl cp ./dummy.pptx libreoffice-bash:/tests/
```
This is working but when I tries to do the same in Kubeflow it doesn't was. The script executes without producing any output file.
```
import kfp
import kfp.components as components
import kfp.dsl as dsl
from kfp.components import InputPath, OutputPath
@components.create_component_from_func
def download_file(s3_folder_path,object_name):
input_file_path=s3_folder_path+"/"+object_name
import subprocess
subprocess.run('pip install boto3'.split())
# Download file
import boto3
s3=boto3.client('s3')
s3.download_file('qa-cd-msr-20220524050318415700000001', input_file_path, '/tmp/input.pptx')
print(input_file_path + " file is downloaded...Executing libreoffice conversion")
subprocess.run("ls -ltr /tmp".split())
def convert_to_pdf():
import subprocess
def exec_cmd(cmd)->(any,str):
print("Executing "+cmd)
result=subprocess.run(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout=result.stdout.decode('utf-8') + '\n'+ result.stderr.decode('utf-8')
print("stdout: "+stdout)
return stdout
exec_cmd("libreoffice --headless --convert-to pdf /files/input.pptx --outdir /files")
exec_cmd("ls -ltr /files")
convert_to_pdf_op = components.func_to_container_op(convert_to_pdf, base_image= "domnulnopcea/libreoffice-headless:latest")
@dsl.pipeline(
name="Libreoffice",
description="Libreoffice",
)
def sample_pipeline(s3_folder_path:str="/mpsr/decks", object_name:str="Adcetris_master_40.pptx"):
vop = dsl.VolumeOp(
name="create-pvc",
resource_name="my-pvc",
modes=dsl.VOLUME_MODE_RWO,
size="1Gi"
)
download = download_file(s3_folder_path,object_name).add_pvolumes({"/tmp": vop.volume})
convert = convert_to_pdf_op().add_pvolumes({"/files": download.pvolume})
convert.execution_options.caching_strategy.max_cache_staleness = "P0D"
convert.after(download)
client = kfp.Client()
experiment = client.create_experiment(
name="Libreoffice",
description="Libreoffice",
namespace="cd-msr"
)
client.create_run_from_pipeline_func(
sample_pipeline,
arguments={"s3_folder_path":"/mpsr/decks","object_name":"dummy1.pptx"},
run_name="libreoffice",
experiment_name="Libreoffice"
)
```
Output :
<img width="565" alt="image" src="https://user-images.githubusercontent.com/95207359/177474695-f31b195e-2358-4823-abe3-0b4276a5bd63.png">
ignore the error here. I was also getting this in vanilla k8s but it gives the output there.
It succeed with docker runtime.
<img width="766" alt="image" src="https://user-images.githubusercontent.com/95207359/177497023-83c67169-de05-43ff-bcbb-531a14e89d9e.png">
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"I found this PR in `kubeflow/training-operator` repo about similar problem.\r\nhttps://github.com/kubeflow/training-operator/pull/1281\r\n\r\nI think those changes above should be applied here and also to `nikenano/launchernew` container image.\r\nhttps://github.com/kubeflow/pipelines/blob/127dab4d4671849d596c455f... | 2022-07-06T02:58:07 | 2022-10-06T20:40:44 | 2022-07-13T07:37:03 | CONTRIBUTOR | null | https://github.com/kubeflow/pipelines/blob/127dab4d4671849d596c455f4619ea807d22f6ea/components/kubeflow/launcher/component.yaml#L19
When `delete_finished_tfjob` flag is `True`, TFJob launcher task fails with error below.
```
Traceback (most recent call last):
File "/ml/launch_tfjob.py", line 136, in <module>
main()
File "/ml/launch_tfjob.py", line 133, in main
tfjob.delete(args.name, args.namespace)
File "/ml/launch_crd.py", line 115, in delete
body)
TypeError: delete_namespaced_custom_object() takes exactly 6 arguments (7 given)
```
I think it's some kind of kubernetes client SDK version issue in `nikenano/launchernew:latest` container image. | {
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"think this is a dup of https://github.com/kubeflow/pipelines/pull/7801#issuecomment-1151064795 ?\r\nWith that said, any chance of getting a rough ETA of when 1.8.13 will be cut? =)",
"Can prob close this, kfp 1.8.13 was cut yesterday. \r\nThanks!"
] | 2022-07-05T16:34:35 | 2022-07-12T15:54:51 | 2022-07-12T15:54:51 | NONE | null | Currently, the latest `kfp=1.8.12` pins `typing_extensions to ` 'typing-extensions>=3.7.4,<4;python_version<"3.9"',`. This is a bit of a problem for us, since we have another dependency (PyTorch Lightning) that requires `typing-extensions >= 4`. Would it be possible to cut a new release that supports the latest typing extensions? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7982 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7982/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7982/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7982/events | https://github.com/kubeflow/pipelines/issues/7982 | 1,294,232,642 | I_kwDOB-71UM5NJGxC | 7,982 | [backend] DeleteExperiment does not clean up all relevant children objects from the DB | {
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"I just saw this [comment](https://github.com/kubeflow/pipelines/blob/127dab4d4671849d596c455f4619ea807d22f6ea/backend/api/experiment.proto#L84-L87) in the code. It looks that the current behavior is intended to behave this way - it is not a bug. But IMO, the server should self-validate that all runs/jobs are delet... | 2022-07-05T12:27:54 | 2022-07-14T22:57:48 | null | MEMBER | null | ### Steps to reproduce
When [deleteExperiment](https://github.com/kubeflow/pipelines/blob/127dab4d4671849d596c455f4619ea807d22f6ea/backend/src/apiserver/server/experiment_server.go#L173) is called, not all children objects ( like Runs/ resource_references) are deleted from MySQL.
DeleteExperiment cannot be called from the front-end/GUI, but the user still could execute a cURL request.
After an experiment is deleted the DB could get in an inconsistent state - having Runs that belong to an un-existing experiment - this leads to following up 404 exceptions in the frond-end.
### Proposed fix / behaviour
- IMO, delete experiments should be enabled only if the experiments are in an **Archived** state.
So, validation needs to be added prior experiment to be deleted
- In order to clean up all Experiment's resource_references :
[s.resourceReferenceStore.DeleteResourceReferences(tx, id, common.Run)](https://github.com/kubeflow/pipelines/blob/e3678e1aadcb3be7bbd4f27059ae64db5631222e/backend/src/apiserver/storage/experiment_store.go#L238) > should be changed to `s.resourceReferenceStore.DeleteResourceReferences(tx, id, common.Experiment) `
- All related Runs to be cleaned up.
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
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https://api.github.com/repos/kubeflow/pipelines/issues/7972 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7972/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7972/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7972/events | https://github.com/kubeflow/pipelines/issues/7972 | 1,290,369,750 | I_kwDOB-71UM5M6XrW | 7,972 | [backend] Caching does not work when using volumes for the data_passing_method | {
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"u... | open | false | null | [] | null | [] | 2022-06-30T16:18:17 | 2022-07-08T08:25:48 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Install the Kubeflow Manifests manually via manifests (https://github.com/kubeflow/manifests#installation), install with a single command
* KFP version: 1.8.2
* KFP SDK version:
* kfp 1.8.12
* kfp-server-api 1.8.2
### Steps to reproduce
If I change the method to use volumes and pass my Artifacts with OutputPath/InputPath, the cache mechanism does not work when I re-run the exact same pipeline. The cache keys of the pipeline steps are not identical.
The cache works as expected when using the Artifact Storage.
### Expected result
Caching should also work for data passing via volumes.
### Materials and Reference
<b>Sample code</b>
Note: "my-volume" should be replaced with existing PVC
``` python
from kubernetes.client.models import V1Volume, V1PersistentVolumeClaimVolumeSource
import kfp
import kfp.dsl as dsl
from kfp.dsl import data_passing_methods
from kfp.components import OutputPath, InputPath, func_to_container_op
# arbitrary python functions
def load(data_path: OutputPath()):
from sklearn import datasets
import pandas as pd
data = datasets.load_iris()
df = pd.DataFrame(data=data.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"])
df.to_csv(data_path)
def print_out(data_path: InputPath()):
import pandas as pd
data = pd.read_csv(data_path)
print(data.head())
# convert to ops
load_op = func_to_container_op(load, base_image='python:3.9.7', packages_to_install=["scikit-learn","pandas"])
print_out_op = func_to_container_op(print_out, base_image='python:3.9.7', packages_to_install=["pandas"])
# select existing volume for data passing
volume_based_data_passing_method = data_passing_methods.KubernetesVolume(
volume=V1Volume(
name="my-volume",
persistent_volume_claim=V1PersistentVolumeClaimVolumeSource(
claim_name="my-volume"),
),
path_prefix='artifacts/',
)
# pipeline
def pipeline():
load_task = load_op()
printing_task = print_out_op(data=load_task.outputs ["data"])
# select data passing method
kfp.dsl.get_pipeline_conf().data_passing_method = volume_based_data_passing_method
client = kfp.Client()
# run pipeline
client.create_run_from_pipeline_func(
pipeline, experiment_name="data_passing", run_name="via_volume", arguments={},
enable_caching=True
)
```
**Further information:**
The caching works as expected if the data passing method is not changed.
Only when you change it to using volumes it doesn't work.
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7971 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7971/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7971/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7971/events | https://github.com/kubeflow/pipelines/issues/7971 | 1,290,199,934 | I_kwDOB-71UM5M5uN- | 7,971 | Metrics using SDK | {
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"@bertinma how did you resolve the issue?",
"I create a dictionary with my metrics and save to a minIO output path like this \r\n\r\n`metrics = {\r\n 'metrics': [\r\n {\r\n 'name': 'loss',\r\n 'numberValue': self.metrics_dict['loss'],\r\n ... | 2022-06-30T14:13:21 | 2022-09-02T13:34:42 | 2022-06-30T15:52:29 | NONE | null | Hi,
I'm. using kip to build my kubeflow pipeline following this method/tutorial : https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/
So I have a python code, component.yaml, Dockerfile and build_image.sh files for each pipeline step.
I want to track metrics produced during evaluation step.
My codes are like that :
* eval.py
metrics': [
{
'name': 'loss',
'numberValue': self.metrics_dict['loss'],
'format': 'RAW',
},
{
'name': 'iou_score',
'numberValue': self.metrics_dict['iou_score'],
'format': 'PERCENTAGE',
},
{
'name': 'f1-score',
'numberValue': self.metrics_dict['f1-score'],
'format': 'PERCENTAGE'
},
]
}
print(args.metrics)
with open(Path(args.metrics) / "mlpipeline-metrics.json", 'w') as f:
json.dump(metrics, f)
* component.yaml
name : eval_seg
description: Evaluate segmentation model
outputs:
- {name: MLPipeline Metrics, type: Metrics, description: "metrics path"}
implementation:
container:
image: gcr.io/.../eval_seg@sha256:...
command: [
python3.8, eval.py,
--metrics,
{outputPath: MLPipeline Metrics}
]
The step finished correctly but no metrics and artifacts were found for this step in "Visualizations" and for the whole pipeline in "Run output" tab.
Where I am wrong ? Is there a method to get metrics using this kubeflow method or I have to change how to build my pipeline ?
Thanks for your help !
###################################
kfp version : 1.8.12 (installed with pip)
kubeflow : 1.7.1
Kubernetes : 1.21.11-gke.900
Cloud deployment : GCP AI Pipelines
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https://api.github.com/repos/kubeflow/pipelines/issues/7963 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7963/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7963/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7963/events | https://github.com/kubeflow/pipelines/issues/7963 | 1,288,081,345 | I_kwDOB-71UM5Mxo_B | 7,963 | [backend] Terminate Behavior Regression between Workflow Controllers 2.x vs 3.x (HEAD) | {
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"Do note that the expected behavior was true for Argo < 3, as the same function would fall through. I.e https://github.com/argoproj/argo-workflows/blob/v2.12.13/workflow/controller/exec_control.go#L89.",
"This will be resolved after we upgrade argo to version 3.3.",
"Is there a timeline for this? For this upgra... | 2022-06-29T02:03:52 | 2023-08-28T20:59:04 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
1.8.2
* KFP SDK version:
1.8.2
### Steps to reproduce
Currently, Kubeflow's terminate function works by setting activeDeadlineSeconds to 0. However, argo's controller loop only checks this for Pending pods, so a long running pod must run to completion before terminating. This has since been fixed on Argo's workflow controller with [commit](https://github.com/argoproj/argo-workflows/pull/8065). However, Argo 3.3 is fundamentally incompatible with Kubeflow as updates to workflows are now done by WorkflowTaskResult instead of patching pods.
### Expected result
Ideally, clicking terminate instantly terminates the workflow and any running pod. This behavior is honored by the above commit. However, due to other changes there is no feasible route to upgrade ATM. If Kubeflow wishes to stay on 3.2.3, perhaps it should be considered to cherrypick out the patch?
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
https://github.com/argoproj/argo-workflows/pull/8065
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7947 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7947/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7947/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7947/events | https://github.com/kubeflow/pipelines/issues/7947 | 1,286,171,330 | I_kwDOB-71UM5MqWrC | 7,947 | [sdk] The component method .after() not working properly with multiple components from the same spec.yaml file | {
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"Could you write them as graph components and see if that works? I'm not sure using two \".after()\"s consecutively will work. You may also try wrapping the first two steps into a single component, and execute the third one after it. See example here:\r\nhttps://github.com/kubeflow/pipelines/blob/master/samples/con... | 2022-06-27T18:11:08 | 2022-06-30T22:37:57 | null | NONE | null | ### Environment
* KFP SDK version:
2.0.0a5
### Purpose
I am trying to build and run a pipeline using a reusable component, leveraging an image hosted in Google Container Registry, a `component-spec.yaml` file as well as the `load_component_from_file()` function. The pipeline consists of three components, where the first two should be executed independently (and loaded from the same reusable file), and the third one should be executed after the two first have been completed.
For context, this component takes as input argument a tablename (which exists in our data warehouse (DWH)), and dumps it in a Google Cloud Storage Bucket (GCS). Because of this, having it as a reusable component is very convenient.
### Steps to reproduce
I define a pipeline as such:
```python
from kfp import dsl, compiler
from kfp.components import load_component_from_file
@dsl.pipeline(
name='my-pipeline',
description='Pipeline with a reusable component',
pipeline_root='gs://my-bucket/pipeline'
)
def pipeline(
tablename_1: str = 'table1',
tablename_2: str = 'table2',
some_file_path: str = 'gs://my-bucket/some_folder/'
):
# Snowflake queries and dump data
dwh_to_gcs_0 = load_component_from_file("dwh-to-gcs.yaml")
dwh_to_gcs_0(
tablename=tablename_1
)
dwh_to_gcs_1 = load_component_from_file("dwh-to-gcs.yaml")
dwh_to_gcs_1(
tablename=tablename_2
)
final_component = load_component_from_file("final-component.yaml")
final_component(
file_path=some_file_path
) \
.after(dwh_to_gcs_0) \
.after(dwh_to_gcs_1)
if __name__ == '__main__':
compiler.Compiler().compile(
pipeline_func=pipeline,
package_path="./pipeline.json"
)
```
With the following component specification yaml-files:
`dwh-to-gcs.yaml` ("Reusable"):
```yaml
name: DWHtoGCS
description: Dumps a table in the DWH as a csv-file into GS.
inputs:
- {name: tablename, type: String}
implementation:
container:
image: eu.gcr.io/my-project/my-image:latest
command: [
python3, main.py,
--tablename, {inputValue: tablename}
]
```
and `final-component.yaml`:
```yaml
name: FinalComponent
description: Makes some computations from some files.
inputs:
- {name: file_path, type: String}
implementation:
container:
image: eu.gcr.io/my-project/my-image:latest
command: [
python3, main.py,
--file_path, {inputValue: file_path}
]
```
When compiling the pipeline, by running `python3 pipeline.py`, I get the following `pipeline.json` file:
```json
{
"components": {
"comp-dwhtogcs": {
"executorLabel": "exec-dwhtogcs",
"inputDefinitions": {
"parameters": {
"tablename": {
"parameterType": "STRING"
}
}
}
},
"comp-dwhtogcs-2": {
"executorLabel": "exec-dwhtogcs-2",
"inputDefinitions": {
"parameters": {
"tablename": {
"parameterType": "STRING"
}
}
}
},
"comp-finalcomponent": {
"executorLabel": "exec-finalcomponent",
"inputDefinitions": {
"parameters": {
"file_path": {
"parameterType": "STRING"
}
}
}
}
},
"defaultPipelineRoot": "gs://my-bucket/pipeline",
"deploymentSpec": {
"executors": {
"exec-dwhtogcs": {
"container": {
"command": [
"python3",
"main.py",
"--tablename",
"{{$.inputs.parameters['tablename']}}"
],
"image": "eu.gcr.io/my-project/my-image:latest"
}
},
"exec-dwhtogcs-2": {
"container": {
"command": [
"python3",
"main.py",
"--tablename",
"{{$.inputs.parameters['tablename']}}"
],
"image": "eu.gcr.io/my-project/my-image:latest"
}
},
"exec-finalcomponent": {
"container": {
"command": [
"python3",
"main.py",
"--file_path",
"{{$.inputs.parameters['file_path']}}"
],
"image": "eu.gcr.io/my-project/my-image:latest"
}
}
}
},
"pipelineInfo": {
"name": "my-pipeline"
},
"root": {
"dag": {
"tasks": {
"dwhtogcs": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-dwhtogcs"
},
"inputs": {
"parameters": {
"tablename": {
"componentInputParameter": "tablename_1"
}
}
},
"taskInfo": {
"name": "dwhtogcs"
}
},
"dwhtogcs-2": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-dwhtogcs-2"
},
"inputs": {
"parameters": {
"tablename": {
"componentInputParameter": "tablename_2"
}
}
},
"taskInfo": {
"name": "dwhtogcs-2"
}
},
"finalcomponent": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-finalcomponent"
},
"dependentTasks": [
"dwhtogcs"
],
"inputs": {
"parameters": {
"file_path": {
"componentInputParameter": "some_file_path"
}
}
},
"taskInfo": {
"name": "finalcomponent"
}
}
}
},
"inputDefinitions": {
"parameters": {
"some_file_path": {
"defaultValue": "gs://my-bucket/some_folder/",
"parameterType": "STRING"
},
"tablename_1": {
"defaultValue": "table1",
"parameterType": "STRING"
},
"tablename_2": {
"defaultValue": "table2",
"parameterType": "STRING"
}
}
}
},
"schemaVersion": "2.1.0",
"sdkVersion": "kfp-2.0.0-alpha.5"
}
```
Within this pipeline definition, note that the final component only has **one** dependant task:
```yaml
...
"finalcomponent": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-finalcomponent"
},
"dependentTasks": [
"dwhtogcs"
],
"inputs": {
"parameters": {
"file_path": {
"componentInputParameter": "some_file_path"
}
}
},
"taskInfo": {
"name": "finalcomponent"
}
}
...
```
### Expected result
To clarify further, I specify in my `pipeline.py` file that the final component should be after **BOTH** previous steps;
```python
final_component(
file_path=some_file_path
) \
.after(dwh_to_gcs_0) \
.after(dwh_to_gcs_1)
```
I expect that to be reflected in the pipeline.json definition, but there, only **ONE** is listed (the name (lower-case) of the reusable component);
```yaml
"dependentTasks": [
"dwhtogcs"
],
```
Moreover, to verify that the pipeline is also behaving this way if executed, I submitted it to Vertex AI, where it also displays the same behaviour.
**Is this a bug, or am I somehow using the SDK in a non-ideal way?**
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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Kubeflow 1.5.1
### Steps to reproduce
The cache server wrongly states that the step is cached even if the artifacts have been deleted from S3. We propose https://github.com/kubeflow/pipelines/pull/7938 that checks whether the folder actually still exists on S3. If the folder does not exist we do not wrongfully pretend that it is cached and instead delete the cachedb entry and let the pipeline step run again.
---
<!-- 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/7929 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7929/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7929/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7929/events | https://github.com/kubeflow/pipelines/issues/7929 | 1,280,850,698 | I_kwDOB-71UM5MWDsK | 7,929 | [frontend] Input/Output tab doesn't show information. | {
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"https://github.com/kubeflow/pipelines/blob/master/frontend/src/components/tabs/InputOutputTab.tsx#L149-L170\r\nhttps://github.com/kubeflow/pipelines/blob/master/frontend/src/mlmd/MlmdUtils.ts#L213-L228",
"Execution details:\r\nhttps://screenshot.googleplex.com/7E9SGJGD6hbrC7v\r\n\r\nPattern(RegExp) & Key in Exec... | 2022-06-22T20:34:33 | 2022-07-27T03:22:08 | 2022-07-27T03:22:08 | COLLABORATOR | null | We expect to see detailed information about the input and output in the Input/Output tab of components.
Currently it is not able to display the info.
https://screenshot.googleplex.com/3doJNfZJQ6AzmVb | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7923 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7923/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7923/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7923/events | https://github.com/kubeflow/pipelines/issues/7923 | 1,278,934,192 | I_kwDOB-71UM5MOvyw | 7,923 | [feature] Implement an agnostic way to initialize the database | {
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"KFP uses gorm which already provides abstraction. it might already be supported, or easy to tweak the config and support \r\nhttps://gorm.io/docs/connecting_to_the_database.html",
"That's right, but there is something in the code that end up with a switch-case where if MySQL is not the driver used, it will retur... | 2022-06-21T19:16:59 | 2022-07-06T17:33:32 | null | NONE | null | ### Feature Area
Currently, Kubeflow Pipelines relies on a specific database vendor (MySQL) which restrict users on database choice.
<!-- 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?
My proposal is a change in the code to make this database calls agnostic from the vendor.
### What is the use case or pain point?
Suppose the user already uses PostgreSQL, and there is already a database running in his environment. My idea could make the user just point to the existing database instead of deploying another one.
### Is there a workaround currently?
The is no workaround so far
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/7917 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7917/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7917/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7917/events | https://github.com/kubeflow/pipelines/issues/7917 | 1,277,581,296 | I_kwDOB-71UM5MJlfw | 7,917 | ask 'no2-pipeline-xxg59.marge-pos-neg-data' errored: Pod "no2-pipeline-xxg59-3831270412" is invalid: spec.volumes[3].name: Duplicate value: "no2-pvc" | {
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"Maybe it's because the two `pvolumes` used in `marge_pos_neg_data` actually point to the same persistent volume? As in this line:\r\n```\r\n pvolumes = {\"/mnt/positive/\": load_negative_data.pvolume, \"/mnt/negative/\": load_positive_data.pvolume}\r\n```\r\n"
] | 2022-06-21T01:16:25 | 2022-06-30T23:20:39 | null | NONE | null | Hi, I am trying to run a Kubeflow pipeline.
Two steps will run in parallel and dump data to two different folders in PVC, then the third component will collect data from those t folders and merge them together and dump the merged data to another PVC folder.
`vop = dsl.VolumeOp(
name='no2-pvc',
resource_name = "no2-pvc",
size="100Gi",
modes = dsl.VOLUME_MODE_RWO
)
##LOADING POSITIVE DATA##
load_positive_data = dsl.ContainerOp(
name='load_positive_data',
image=load_positive_data_image,
command="python",
arguments=[
"/app/load_positive_data.py",
],
pvolumes={"/mnt/positive/": vop.volume}).apply(gcp.use_gcp_secret("user-gcp-sa"))
##LOADING NEGATIVE DATA##
load_negative_data = dsl.ContainerOp(
name='load_negative_data',
image=load_negative_data_image,
command="python",
arguments=[
"/app/load_negative_data.py",
],
pvolumes={"/mnt/negative/": vop.volume}).apply(gcp.use_gcp_secret("user-gcp-sa"))
##MERGING POSITIVE AND NEGATIVE DATA##
marge_pos_neg_data = dsl.ContainerOp(
name='marge_pos_neg_data',
image=marged_data_image,
command="python",
arguments=[
"/app/merge_neg_pos.py"
],
pvolumes = {"/mnt/positive/": load_negative_data.pvolume, "/mnt/negative/": load_positive_data.pvolume}
#volumes={'/mnt': vop.after(load_negative_data, load_positive_data)}
).apply(gcp.use_gcp_secret("user-gcp-sa")).after(load_positive_data, load_negative_data)
##PROCESSING MARGED DATA##
process_marged_data = dsl.ContainerOp(
name='process_data',
image=perpare_merged_data_image,
command="python",
arguments=[
"/app/prepare_all_dataset.py"
],
pvolumes = {"/mnt/pos_neg": marge_pos_neg_data.pvolume}
).apply(gcp.use_gcp_secret("user-gcp-sa")).after(marge_pos_neg_data)`
load-positive-data and load-negative-data are working fine but the marge-pos-neg-data step is giving the following error:
```
This step is in Error state with this message:
task 'no2-pipeline-x5kpd.marge-pos-neg-data'
errored: Pod "no2-pipeline-x5kpd-2954674781" is invalid:
spec.volumes[3].name: Duplicate value: "no2-pvc"
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7915 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7915/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7915/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7915/events | https://github.com/kubeflow/pipelines/issues/7915 | 1,275,832,061 | I_kwDOB-71UM5MC6b9 | 7,915 | Failed to copy data to Persistant Volume (PV) from GCS in Kubeflow Pipeline | {
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} | [] | closed | false | null | [] | null | [] | 2022-06-18T16:40:31 | 2022-06-20T21:09:48 | 2022-06-20T21:09:48 | NONE | null | Its been a long time since I am struggling to run a Kubeflow pipeline. I am trying this code to collect data from GCS and after filtering pass those data to Persistent Volume (PV) (/mnt/positive) to use as input in the next step:
```
def get_pos_data(analyte_name: str = "no2", run_name: str = "no2"):
raw_files_path = 'postive_data_file/'
# Checking for edge cases
if raw_files_path == None or analyte_name == None or run_name == None:
raise ValueError('Reading positive data failed: Component Configuration Invalid!')
if not gstore.is_dir(raw_files_path):
raise ValueError('Reading positive data failed: Raw files path do not exist')
# Read files
analyte_files = gstore.list_files(raw_files_path, recurse=True)
if len(analyte_files) == 0:
raise ValueError('Reading positive data failed: Raw files folder empty')
sensor_cols = ["s"+ str(i) for i in range(1, 33)]
removed_files = []
#Output path
#output_file_path = 'output_positive_data_file'
output_file_path='/mnt/positive'
print("Num of {} files before filter: {}".format(analyte_name, len(analyte_files)))
print('Processing files...')
for analyte_file in analyte_files:
content = pd.read_csv(f'gs://{gstore.get_bucket_name()}/{analyte_file}')
if len(content[analyte_name].unique()) < 10:
removed_files.append(analyte_file)
continue
if not('run_name' in content.columns):
removed_files.append(analyte_file)
continue
if not('_'+ run_name + '_' in content.run_name[0]):
removed_files.append(analyte_file)
continue
for an_input in sensor_cols:
if an_input not in content.columns:
removed_files.append(analyte_file)
break
analyte_filtered_files = [x for x in analyte_files if x not in removed_files]
print("Num of {} files after filter: {}".format(analyte_name, len(analyte_filtered_files)))
pos_files = analyte_filtered_files
for x in pos_files:
print(x)
file = f'gs://{gstore.get_bucket_name()}/{x}'
# Create temp local directory
#local_file_path = os.path.join(output_file_path, os.path.basename(x) )
shutil.copy(file, f'{output_file_path}/{os.path.basename(x)}')
#gstore.download_file_local(file, f'{output_file_path}/{os.path.basename(x)}')
print('Moving Postive Data Completed!')
```
But getting follow error in Kubeflow Pipeline:
```
Num of no2 files before filter: 1832
Processing files...
Num of no2 files after filter: 1832
Traceback (most recent call last):
File "/app/load_positive_data.py", line 83, in <module>
get_pos_data()
File "/app/load_positive_data.py", line 77, in get_pos_data
shutil.copy(path, output_file_path)
File "/usr/local/lib/python3.7/shutil.py", line 248, in copy
copyfile(src, dst, follow_symlinks=follow_symlinks)
File "/usr/local/lib/python3.7/shutil.py", line 120, in copyfile
with open(src, 'rb') as fsrc:
FileNotFoundError: [Errno 2] No such file or directory: 'gs://pipe_ml-data/postive_data_file/IAS_2106091729_100083_e868608d.csv'
```
How may I overcome this issue and copy data from GCS to PVC? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7909 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7909/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7909/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7909/events | https://github.com/kubeflow/pipelines/issues/7909 | 1,275,339,713 | I_kwDOB-71UM5MBCPB | 7,909 | [bug] Minio S3 Gateway results in 'pipeline spec is invalid' | {
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] | open | false | null | [] | null | [
"Could you please share the pipeline template that you used?",
"It says in the description: [Tutorial] Data passing in python components",
"I am facing the same error but just for tutorial pipeline example , I can create runs for pipelines I created"
] | 2022-06-17T18:22:22 | 2022-10-10T12:14:13 | null | NONE | null | ### What steps did you take
I updated minio on Kubeflow version 1.5.0 standard manifest install on a generic cluster to use an s3 gateway.
I also updated the bucket name on pipeline-install-config which was reflected in the workflow-controller-configmap
### What happened:
Minio came up correctly, and was able to manage s3 buckets through the minio GUI
When I try to run `[Tutorial] Data passing in python components`, it fails with:
```{"error":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid","code":3,"message":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid","details":[{"@type":"type.googleapis.com/api.Error","error_message":"unknown template format","error_details":"Failed to create a new run.: InvalidInputError: unknown template format: pipeline spec is invalid"}]}```
### What did you expect to happen:
The pipeline executes correctly
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
From the manifests directly, I patched it to include the new config, and the minio config.
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
1.5.0 kubeflow and all the standard versions
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/7904 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7904/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7904/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7904/events | https://github.com/kubeflow/pipelines/issues/7904 | 1,273,919,121 | I_kwDOB-71UM5L7naR | 7,904 | [backend] GCR images not accessible: `Consumer 'project:ml-pipeline' has been suspended.` | {
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"u... | closed | false | null | [] | null | [
"Okay, this feels real, I can't access any of the images from this repo: `ml-pipeline`\r\n\r\nAnd confirmed that other gcr.io pulls work fine, for example: `gcr.io/google-containers/busybox@sha256:d8d3bc2c183ed2f9f10e7258f84971202325ee6011ba137112e01e30f206de67`",
"This is definitely an issue our cluster is havin... | 2022-06-16T18:09:31 | 2022-06-16T22:26:40 | 2022-06-16T22:26:40 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Standalone installation with Kustomize on EKS.
* KFP version: 1.7.0
My KFP runs are failing and when I tried to pull this image: `gcr.io/ml-pipeline/argoexec:v3.1.14-license-compliance` manually from another unrelated machine, that failed with the following error:
```
Error response from daemon: Head https://gcr.io/v2/ml-pipeline/argoexec/manifests/v3.1.14-license-compliance: denied: Permission denied: Consumer 'project:ml-pipeline' has been suspended.
```
I apologize if this is something from my side, but it seems serious if it is not.
Edit:
## Suggestions
1. Your jobs might not actually be failing. For us, the `main` container runs without issue. Only the `wait` container with the `gcr.io/ml-pipeline/argoexec:v3.1.14-license-compliance` image is stuck. [This comment](https://github.com/kubeflow/pipelines/issues/7904#issuecomment-1158019599) details how to deal with that temporarily.
2. If you have your core Kubeflow Pipelines components on machines that have the potential to roll. On spot instance, for example, then:
a. Try freezing them if you can. For example, certain K8s scaling systems allow you to restrict scale down on certain node groups.
b. Copy over the core images to one of your private container registries as soon as possible. After that, you should be able to update your Kubeflow Pipelines manifests with these private images.
---
<!-- 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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"u... | closed | false | null | [] | null | [
"Hello @davidxia , the SDK versions in Pypi are not the same as GitHub release tags. KFP package in Pypi (SDK) has its own release cycle. The GitHub release tags are for KFP backend (docker images)."
] | 2022-06-15T14:26:23 | 2022-06-23T22:45:07 | 2022-06-23T22:45:07 | CONTRIBUTOR | null | The 1.8.1 git tag is [here](https://github.com/kubeflow/pipelines/commits/1.8.1/sdk/python/requirements.in) on Nov 23, 2021. The `kfp` package on Pypi [here](https://pypi.org/project/kfp/#history) was published on Sep 10, 2021. I suspect the git tag is misplaced and should be moved back to the correct commit that created the 1.8.1 PyPI package. Having misplaced git tags will create debugging confusion for users.
I haven't checked the correctness of other git tags.
---
<!-- 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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F0614 17:22:43.063393 18 main.go:74] KFP driver: driver.RootDAG(pipelineName=my-test-pipeline-beta, runID=8aa7dfc3-519f-4548-b886-ff1a11a37a4e, runtimeConfig, componentSpec) failed: Failed PutContext(name="8aa7dfc3-519f-4548-b886-ff1a11a37a4e", type="system.PipelineRun", typeid=16): rpc error: code = Unavailable desc = error reading from server: EOF
`
```
v2 sample tests keep failing and block presubmit. Possibly a result of using kfp 1.8.2 in test infra. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/7891 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7891/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7891/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7891/events | https://github.com/kubeflow/pipelines/issues/7891 | 1,270,469,758 | I_kwDOB-71UM5LudR- | 7,891 | [bug] Considerable delay in showing steps in Kubeflow Pipelines UI after navigating to Runs | {
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"Could you check the resource usage in your cluster, and see if any limit was reached? You may also consider upgrading to 1.8.",
"@Linchin No limit was reached when it comes to resource usage. "
] | 2022-06-14T08:50:53 | 2022-07-18T10:16:46 | null | NONE | null | ### What steps did you take
Create a pipeline with working ML algorithm.
### What happened:
We encountered considerable delay (lasting more than 2 minutes) in showing steps in Kubeflow UI after navigating to Runs. Initially, after opening Runs, Kubeflow Pipelines UI shows only a blank window. The algorithm's steps in Kubeflow Pipelines appear only after these 2 requests: `ml_metadata.MetadataStoreService/GetContextByTypeAndName` fail. It seems that it's rather a core Kubeflow's internal bug, similar to the one reported here: https://github.com/kubeflow/pipelines/issues/3763. This issue affects only the Kubeflow UI and does not affect the algorithms' deployments nor operations. We can still access pipelines details by using an external software as UI.
<img width="1532" alt="Kubeflow_140_170_working_slow" src="https://user-images.githubusercontent.com/107420134/173522832-e0f7cfca-8385-4abb-8dde-d77d998d9e91.png">
### What did you expect to happen:
We didn't expect any significant delay.
### Environment:
* How do you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines as [part of a full Kubeflow deployment](https://www.kubeflow.org/docs/components/pipelines/installation/overview/#full-kubeflow-deployment).
Kubeflow itself is installed using kustomize, according to the [documentation](https://github.com/kubeflow/manifests) (agnostic environment).
* KF version: 1.4.0
* KFP version: 1.7.0
* KFP SDK version: 1.8.11
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
What would be the estimated time of resolution for this issue?
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7887 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7887/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7887/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7887/events | https://github.com/kubeflow/pipelines/issues/7887 | 1,269,825,243 | I_kwDOB-71UM5Lr_7b | 7,887 | [feature] google-cloud component for generic container jobs | {
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"/assign @connor-mccarthy ",
"@defoishugo, thanks for reaching out. This is on our development roadmap as a \"Bring your own container\" feature. I believe this will meet your needs once implemented.",
"@connor-mccarthy perfect!\r\n\r\nCurrent workaround: using YML file with a container implementation. The issu... | 2022-06-13T18:53:58 | 2023-08-10T20:19:38 | 2023-08-10T20:19:38 | NONE | null | ### Feature Area
/area sdk
/area samples
/area components
### What feature would you like to see?
The ability to create custom container jobs, not only for training.
In my mind, we should be able to output user-defined artifacts (e.g.: metrics, datasets, two models, ...).
### What is the use case or pain point?
**USE CASE:** As a user, I have an existing on-premise application with which is a docker container with different pipelines:
- split: split the dataset into train/test/val with customized strategy (not random)
- train: train a model
- evaluate: displays and save some metrics
When it comes to calling the training pipeline, I can use the `CustomContainerTrainingJob` which produces a `VertexModel`. It works pretty well and thank you for that!
But for the split and evaluate commands, I could not use the `CustomContainerTrainingJob` since it produces only `VertexModel` and not arbitrary `Artifact`.
### Is there a workaround currently?
Currently, I do not have a working workaround. However, I am right now playing with `convert_method_to_component` and with the `CustomContainerTrainingJob` and I will find a solution for that.
The first idea that comes to my min would be to generate the train, test and val datasets in the _AIP_MODEL_DIR_ folder. Then, instead of creating a model, I will generate an artifact for each file with an URI to the files.
Do I like this workaround? NO. It needs a lot of edits in different parts of the code. The solution seems dirty and too much homemade. If you have any other idea, please tell me.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7880 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7880/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7880/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7880/events | https://github.com/kubeflow/pipelines/issues/7880 | 1,267,821,647 | I_kwDOB-71UM5LkWxP | 7,880 | [CI] release-1.8 branch build-each-commit error | {
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"The issue appears to be caused by go 1.18 adds additional info which are not recognized by the license check tool. Found a similar issue: https://github.com/uw-labs/lichen/issues/14\r\n\r\nIn the latest go-licenses tool, we removed the feature of checking licenses from binary file, so this issue shouldn't be a blo... | 2022-06-10T17:21:55 | 2022-06-16T22:56:22 | 2022-06-16T22:56:22 | COLLABORATOR | null | CRD viewer build failure on the go-licenses check
```
Step 8/17 : RUN go-licenses csv /bin/controller > /tmp/licenses.csv && diff /tmp/licenses.csv backend/third_party_licenses/viewer.csv && go-licenses save /tmp/licenses.csv --save_path /tmp/NOTICES
---> Running in 8c96b00fe722
listModulesInGoBinary(path="/bin/controller"): unrecognised line: build -compiler=gc
The command '/bin/sh -c go-licenses csv /bin/controller > /tmp/licenses.csv && diff /tmp/licenses.csv backend/third_party_licenses/viewer.csv && go-licenses save /tmp/licenses.csv --save_path /tmp/NOTICES' returned a non-zero code: 1
```
https://github.com/kubeflow/pipelines/runs/6498434354 | {
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"Actually, I just discovered that this request is not needed. It appears that this is already supported, but I ran into this issue because I was working outside of the compiler. To others who stumble upon this issue: it appears that I do in fact get ContainerOps back from `kfp.components.create_component_from_func`... | 2022-06-10T05:37:07 | 2022-06-12T05:00:04 | 2022-06-12T05:00:04 | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
/area components
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Allow ops created from `kfp.components.create_component_from_func` to support ".after(...)" like `kfp.dsl.ContainerOp`s. This would help declare dependencies that don't rely on output passing.
Currently, the following is not possible:
```python
import kfp
@kfp.components.create_component_from_func
def training_but_no_outputs():
# do stuff
pass
task_A = kfp.dsl.ContainerOp(name='other', image='python:3.6')
task_B = training_but_no_outputs()
task_A.after(task_B) # AttributeError: 'TaskSpec' object has no attribute 'name'
task_B.after(task_A) # AttributeError: 'TaskSpec' object has no attribute 'after'
```
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
We're still using pipelines "v1" and rely on `kfp.components.create_component_from_func` and `kfp.dsl.ContainerOp`s. We have been able to set dependencies between ContainerOps with `ContainerOp.after(*ops)`, but we cannot do so with `TaskSpec`s, which appear to be the output of create_component_from_func.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
I haven't tested this, but it seems like a workaround could be to emit an output from the python function decorated with `create_component_from_func` and pass it to a dependent op:
```python
import kfp
@kfp.components.create_component_from_func
def training_but_no_outputs(dummy_input: int) -> int:
# do stuff
return 4 # dummy output
task_A = training_but_no_outputs(4)
task_B = kfp.dsl.ContainerOp(name='other', image='python:3.6', command=['sh', '-c'], arguments=f'echo {task_A.output}')
task_C = kfp.dsl.ContainerOp(name='other', image='python:3.6', command=['sh', '-c'], arguments=f'echo 4 > /file', file_outputs={'file': '/file'})
task_D = training_but_no_outputs(str(task_C.output)) # oddly the stringify-ing of the pipelineparam is necessary
```
If this works, it seems like it would bloat the code.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/7878 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7878/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7878/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7878/events | https://github.com/kubeflow/pipelines/issues/7878 | 1,266,758,583 | I_kwDOB-71UM5LgTO3 | 7,878 | [feature] Seek alternative for object store solution other than MinIO | {
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"I would clarify it a bit more\r\n\r\n- It is open sourced and uses license that can be accepted by KFP, something similar to Apache 2.0.\r\n- Orchestration engine (like Argoworkflow) can interact with this object store (S3 compatibility).\r\n- It has fine-grained S3 permission control e.g. ${aws:username} https://... | 2022-06-09T23:15:32 | 2023-08-23T14:06:44 | null | COLLABORATOR | null | ### Feature Area
/area backend
/area frontend
### What is the use case or pain point?
Currently, KFP is using MinIO as the default object store for Artifact payload and Pipeline template. However, MinIO has fully changed its license to AGPLv3 since 2021. This change has prevented KFP from upgrading to the latest of MinIO: using AGPL software requires that anything it links to must also be licensed under the AGPL. Since we are not able to adopt the latest change from MinIO, we are seeking alternatives to replace it in future KFP deployment.
### What feature would you like to see?
This new object store solution should have the following nature:
1. It is open sourced and uses license that can be accepted by KFP.
2. Orchestration engine (like Argoworkflow) can interact with this object store.
3. It is compatible with S3.
4. It has fine-grained permission control.
5. Same or similar function set with MinIO.
### Exploration
We are currently considering following options, we would like to hear your opinions as well.
- https://www.zenko.io/cloudserver/
- https://longhorn.io/
- https://github.com/openstack/swift
- [ceph-rook](https://rook.io/)
- https://github.com/cubeFS/cubefs
- https://github.com/openebs/openebs
---
cc @chensun @IronPan @james-jwu
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/7861 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7861/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7861/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7861/events | https://github.com/kubeflow/pipelines/issues/7861 | 1,264,805,012 | I_kwDOB-71UM5LY2SU | 7,861 | [feature] google-cloud component for loading existing VertexDataset | {
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"Thanks, @defoishugo. This development is currently in progress and should be released with an upcoming v2 alpha release!",
"Thank you @connor-mccarthy.\r\n\r\nJust to let you know, I found this workaround for my task:\r\n\r\n```\r\n@component(base_image=\"python:3.9\", packages_to_install=[\"google-cloud-aiplatf... | 2022-06-08T14:02:34 | 2023-01-26T14:11:27 | null | NONE | null | ### Feature Area
/area sdk
/area samples
/area components
### What feature would you like to see?
A new component to load existing VertexDataset.
Related to #7792
### What is the use case or pain point?
As a user, I have one existing dataset in VertexAI. I am doing several experiments with different models. Each of my experiment is represented by a pipeline.
When developing a kubeflow pipeline for VertexAI, I would like to be able to load an existing VertexDataset instead of using the dataset creation component. But today, the dataset reading component is not existing so I am not able to do it.
### Is there a workaround currently?
Today, i am not able to do the task. I tried the following:
```
@component(base_image="python:3.9", packages_to_install=["google-cloud-aiplatform"])
def get_data(
project: str,
region: str,
bucket: str,
dataset: Output[VertexDataset]
):
from google.cloud import aiplatform
dataset = aiplatform.datasets._Dataset(TEST_ID, project=project, location=region)
```
This one is dropping the following error: `NameError: name 'VertexDataset' is not defined`.
---
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/7860 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7860/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7860/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7860/events | https://github.com/kubeflow/pipelines/issues/7860 | 1,264,647,569 | I_kwDOB-71UM5LYP2R | 7,860 | Passing big data between components SDK[V2] | {
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"Are you sure you've included the whole code?\r\n\r\nI'm not sure this can work: `output_path.path`.\r\nYou should replace it with just `output_path`.\r\n\r\nYour second issue is that your 2nd component is getting the texts without `InputPath`.\r\nReplace `docs: List[str]` with `docs_path: InputPath(List[str])`.\r\... | 2022-06-08T12:12:32 | 2022-08-09T22:13:48 | null | NONE | null | ### What steps did you take
1. Read text data from file on minio, write it to `Outputpath` to pass it to next step
`@component(base_image='wafaaabdelhafez/tensorflow-2')
def load_docs(output_path: OutputPath(List[str])):
from minio import Minio
import json
client = Minio()
obj = client.get_object("mlpipeline", "dataset/model_docs.txt")
for data in obj:
content = json.loads(data)
with open(output_path.path, 'w') as f:
f.write(content)`
2. Read data from the previous task to process it
`@component(base_image='wafaaabdelhafez/tensorflow-2')
def encode_docs(docs: List[str], output_path: OutputPath(List[str])):
import json
import tensorflow as tf
from keras.preprocessing.text import one_hot
vocab_size = 50000
encoded_docs = []
for d in docs:
res = one_hot(d, vocab_size)
encoded_docs.append(res)
with open(output_path, 'w') as f:
f.write(json.dumps(encoded_docs))`
<!-- A clear and concise description of what the bug is.-->
I got an error in the first step (reading data and write to outputpath) is it's exceed limits , data too long
### What happened:
cannot write data to `Outputhpath `
### What did you expect to happen:
can read and write big amount of data and pass it through steps of pipeline link in V1.x
### Environment:
Kubeflow 1.4
python 3.7
working on kubeflow notebook
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/7859 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7859/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7859/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7859/events | https://github.com/kubeflow/pipelines/issues/7859 | 1,264,512,907 | I_kwDOB-71UM5LXu-L | 7,859 | Deploy a new version of a model | {
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"Hello @clausagerskov \r\nKFP SDK doesn't have client to talk to Vertex AI service. Would you mind creating another issue under this repo: https://github.com/googleapis/python-aiplatform ? Thank you."
] | 2022-06-08T10:12:42 | 2022-06-16T22:48:04 | 2022-06-16T22:48:04 | NONE | null | ### Feature Area
As per this issue in the vertex-ai-samples repo,
https://github.com/GoogleCloudPlatform/vertex-ai-samples/issues/585
Vertex AI has model versions numbers that increase when manually importing models with the GUI.
Is updating models to higher versions supported when doing CI/CD using kubeflow pipelines for GCP Vertex AI?
/area backend
/area sdk
/area samples
/area components
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/7858 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/7858/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/7858/comments | https://api.github.com/repos/kubeflow/pipelines/issues/7858/events | https://github.com/kubeflow/pipelines/issues/7858 | 1,264,490,612 | I_kwDOB-71UM5LXph0 | 7,858 | KFP SDK should support other cloud platforms other than GCP for storing image artifacts built via "build_image_from_working_dir" | {
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"`build_image_from_working_dir` is deprecated, you can use the following KFP CLI to build component for KFPv2:\r\n\r\n```\r\nkfp component build --help\r\n```\r\n\r\ncc @chensun @connor-mccarthy "
] | 2022-06-08T09:54:27 | 2022-06-23T23:00:45 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Currently, KFP SDK in order to work with containers codebase uses GCP to store temporary files. But our need is to store the same in other cloud providers or on-prem cluster, Please help us in a way to achieve these. If its a new feature request then please help us understand the code base to modify the code accordingly
SDK Code trying-
`build_image_from_working_dir(working_dir=".", base_image="pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime", image_name="mnistpytorch:proxy")`
ERROR log - `ValueError: Cannot get the Google Cloud project ID, please specify the gcs_staging argument.`
Kubeflow(v.1.4.0) - On-prem setup
KFP - 1.8.1
### Is there a workaround currently? - No
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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