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https://api.github.com/repos/kubeflow/pipelines/issues/5137 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5137/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5137/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5137/events | https://github.com/kubeflow/pipelines/issues/5137 | 808,427,374 | MDU6SXNzdWU4MDg0MjczNzQ= | 5,137 | Problems upgrading to TFX 0.27.0 | {
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"@rafaascensao Which version of TFX are you using? the tfx.dsl package was introduced in TFX 0.25.0 I think, and if you are using an older version this error would pop up. Try running updating TFX to latest by running `python3 -m pip install -U tfx`. Anyway, this is a package import issue and most likely has nothin... | 2021-02-15T10:53:53 | 2021-02-25T11:09:29 | 2021-02-25T11:09:29 | NONE | null | ### What steps did you take:
Installed Kubeflow Pipelines on GCP via kustomize manifests.
Tried to run the Taxi TFX Demo.
### What happened:
On the first step, I got the error "No module named 'tfx.dsl.components'"
### What did you expect to happen:
To successfully run the TFX Taxi Demo.
### Environment:
How did you deploy Kubeflow Pipelines (KFP)?
Via the kustomize manifests in GCP.
KFP version: 1.4.0-rc.1
/kind bug
/area backend | {
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"/cc @zijianjoy @StefanoFioravanzo ",
"Another partially related problem is the Page abstraction: https://github.com/kubeflow/pipelines/blob/1f32e90ecd1fe8657c084d623b2fcd23ead13c48/frontend/src/pages/Page.tsx#L40-L99\r\n\r\n## Recap of what problem the Page abstraction tries to solve\r\n\r\nThis is a standard KF... | 2021-02-15T04:57:02 | 2021-08-24T13:52:30 | null | CONTRIBUTOR | null | https://reactjs.org/docs/refs-and-the-dom.html
It's recommended to avoid using ref when possible, because the data/event flow mechanism conflicts with React's recommendation of one-way data flow philosophy (see https://reactjs.org/docs/thinking-in-react.html).
> For example, instead of exposing open() and close() methods on a Dialog component, pass an isOpen prop to it.
Similar to this example, I think to avoid exposing `refresh()` as a public method, we could have a prop called sth like `refreshCounter: number`, the child components can do a refresh whenever it sees an update in refreshCounter -- using `componentDidUpdate` or `useEffect react hook`. In this way, we no longer need to add refs all the way and trigger a method to refresh components, one refresh would be as simple as a state update to the refreshCounter.
We'd also need a `onLoad` callback, that will be triggered by child components when a page finishes loading.
After avoiding using ref to get component instances, we can write components using functions and react hooks: https://reactjs.org/docs/hooks-overview.html. It's a nice way to make abstraction on state changes (and a lot more!).
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/pull/5040#discussion_r569956232_ | {
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```
Traceback (most recent call last):
File "<string>", line 3, in <module>
File "/usr/local/lib/python3.6/site-packages/kfp/__init__.py", line 24, in <module>
from ._client import Client
File "/usr/local/lib/python3.6/site-packages/kfp/_client.py", line 31, in <module>
from kfp.compiler import compiler
File "/usr/local/lib/python3.6/site-packages/kfp/compiler/__init__.py", line 17, in <module>
from ..containers._component_builder import build_python_component, build_docker_image, VersionedDependency
File "/usr/local/lib/python3.6/site-packages/kfp/containers/_component_builder.py", line 32, in <module>
from kfp.containers import entrypoint
File "/usr/local/lib/python3.6/site-packages/kfp/containers/entrypoint.py", line 23, in <module>
from kfp.containers import entrypoint_utils
File "/usr/local/lib/python3.6/site-packages/kfp/containers/entrypoint_utils.py", line 23, in <module>
from kfp.pipeline_spec import pipeline_spec_pb2
File "/usr/local/lib/python3.6/site-packages/kfp/pipeline_spec/pipeline_spec_pb2.py", line 23, in <module>
create_key=_descriptor._internal_create_key,
AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key'
```
Looks like the `protobuf` version is not matching in this case. @Bobgy are you aware of this error? Thanks.
_Originally posted by @Tomcli in https://github.com/kubeflow/pipelines/pull/5059#issuecomment-777656530_
/cc @numerology @chensun @Ark-kun
Can you take a look at this issue? I have seen multiple reports, this error seems to fail consistently. | {
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"Thank you!\n\n> I understand that the installs folder would probably be transferred under env in this repo.\n\nLet me think about it, there might be too many types and they are not conceptually consistent. Current manifests in env all include mysql and minio, but those in kubeflow/manifest/.../installs don't.\n\n>... | 2021-02-12T20:01:48 | 2021-03-10T01:51:48 | 2021-03-10T01:51:48 | CONTRIBUTOR | null | ### Background
Umbrella-Issue: https://github.com/kubeflow/manifests/issues/1740
As part of the work of wg-manifests for 1.3
(https://github.com/kubeflow/manifests/issues/1735), we are moving manifests
development to upstream repos. This gives the application developers full
ownership of their manifests, tracked in a single place.
To give an example from the `notebook-controller`, the diagram shows the current
situation. Manifests are in two repos, blurring separation of responsibilities
and making things harder for application developers.
Instead, we will copy all manifests from the manifests repo back to each
upstream's repo. From there, they will be imported in the manifests repo. The
following diagram presents the desired state:
### Current State
Kubeflow Pipelines has manifests:
- In the manifests repo, under folder `apps/pipeline/upstream`.
- In upstream repo (https://github.com/kubeflow/pipelines) under folder `manifests/kustomize`.
The current state of the manifests repo and the upstream repo is the following:
The kubeflow/manifests part of pipelines consists of the following:
- An `upstream` folder, which contains the copied upstream manifests from kubeflow/manifests.
- A kustomization for the pipelines cache (cache folder).
- The `installs` folder, which contains various installation profiles for pipelines (generic, multi-user, tekton, multi-user-tekton). The kustomizations under `installs` refer to the `cache` folder and the `upstream` folder.
- A bunch of folders (`api-service`, `minio`, `mysql`, `persistent-agent`, `pipeline-visualization-service`, `pipelines-runner`, `pipelines-ui`, `pipelines-viewer`, `scheduledworkflow`) which seem unused, since those manifests already exist upstream (most are in kubeflow/pipelines under `manifests/kustomize/base/pipeline`, `minio` and `mysql` are under `manifests/kustomize/env/platform-agnostic`)
I understand that the `installs` folder would probably be transferred under `env` in this repo.
What about all the other folders? Are they maintained by KFP? Are they abandoned? cc @Bobgy
### Desired State
The proposed folder in the upstream repo to receive the manifests is:
`manifests/kustomize`.
The goal is to consolidate all manifests development in the upstream repo.
The manifests repo will include a copy of the manifests under `apps/pipeline/upstream`.
This copy will be synced periodically.
### Success Criteria
The manifests in `apps/pipeline/upstream` should be a copy of the upstream manifests
folder. To do that, the application manifests in `kubeflow/manifests` should be
moved to the upstream application repo.
/assign @yanniszark
cc @kubeflow/wg-pipeline-leads | {
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"/area pipelines\r\n@Bobgy maybe you can move this over to the pipelines repo",
"Any updates on this issue? I'll have to name my metrics `1. dropsPerHour` and `2. unscheduled` for them to appear at the top of the list for now, but I fear the gods of software design are going to be angry with me. 😄 \r\n\r\nThanks... | 2021-02-12T16:51:23 | 2021-05-27T02:38:17 | 2021-05-27T02:38:17 | NONE | null | /kind bug
I have a pipeline that exposes pipeline metrics following [this example](https://www.kubeflow.org/docs/pipelines/sdk/pipelines-metrics/). It's working great, but it seems that the metrics always appear sorted alphabetically. I would like to make use of the fact that the first two metrics are displayed next to each run in the run list view, but the two metrics I'm interested in are not the first two in alphabetical order.
Note: I need to expose more than these two metrics, as I am using these metrics to aggregate results across multiple runs.
Am I doing something wrong? Is there a way to not sort the metrics alphabetically?
For context, here is the code I'm using to expose the metrics:
```
def write_metrics_data(kpis: dict):
metrics = {
'metrics': [
{
'name': name,
'numberValue': value,
'format': "PERCENTAGE" if 'percent' in name else "RAW",
}
for name, value in kpis.items()
]
}
myjson.write(metrics, Path('/mlpipeline-metrics.json'))
```
and this is the header row as displayed in the _Run Output_ tab:
```
activeTrips | dropsPerHour | idleHours | kilometresPerDrop | kilometresPerHour | percentReloads | ...
```
which seems to be in alphabetical order. However, if I do:
```
print(kpis.keys())
```
I get this:
```
['dropsPerHour', 'unscheduled', 'activeTrips', 'totalHours', ...]
```
which isn't in alphabetical order. And the two metrics I'm interested in are the first two ones in this list.
**Environment:**
- Kubeflow version: 1.0.4
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"Hey @BorFour, sorry about the inconvenience, there was a short period the image was not built yet.\r\n\r\nYou should be able to use it now.\r\n\r\nThe best source is to look at github releases, if the release note isn't appended, that means the release was not yet finished."
] | 2021-02-12T12:32:56 | 2021-02-25T08:34:33 | 2021-02-25T08:34:33 | NONE | null | I've been using a component that uses the image `gcr.io/ml-pipeline/ml-pipeline-gcp:1.4.0`. When I try to use it, the component's pod seems to be stuck with the `ImagePullBackOff` status and, eventually, the pipeline times out. I then tried to run the image locally like so:
```bash
docker pull gcr.io/ml-pipeline/ml-pipeline-gcp:1.4.0
```
And then I get this error message:
```bash
Error response from daemon: manifest for gcr.io/ml-pipeline/ml-pipeline-gcp:1.4.0 not found: manifest unknown: Failed to fetch "1.4.0" from request "/v2/ml-pipeline/ml-pipeline-gcp/manifests/1.4.0".
```
I then tried to pull `gcr.io/ml-pipeline/ml-pipeline-gcp:1.3.0` locally and it works just fine. My workaround was to use the same component but with the repo tag `1.3.0`, as it uses this image, but I can't figure out why 1.4.0 doesn't work.
| {
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"... | closed | false | null | [] | null | [
"Hi, this is a nice idea! Sounds useful to me\r\n\r\nWelcome contribution on this!\r\n\r\nyou can find frontend contribution guide in https://github.com/kubeflow/pipelines/tree/master/frontend,\r\nand the create run page is implemented in https://github.com/kubeflow/pipelines/blob/master/frontend/src/pages/NewRun.t... | 2021-02-12T11:07:27 | 2022-04-18T17:27:55 | 2022-04-18T17:27:55 | CONTRIBUTOR | null | ### What steps did you take:
I'd like to get multi-line bash script from user and pass it into ContainerOp.
### What happened:
but for now, Pipeline frontend only support two types of pipeline parameter - one line text input, json editor.
of course I can do it by appending all lines with semi-colon or by using list type and joining dynamically, but it's not intuitive.
but I'm not sure how to make pipeline code tell frontend to use textarea input. beacuse there is no type hint for multi line string.
maybe we could use custom type or use textarea input as default
### Environment:
kfp_istio_dex_1.2.0 on Windows Docker Desktop
/kind feature
/area frontend
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https://api.github.com/repos/kubeflow/pipelines/issues/5126 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5126/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5126/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5126/events | https://github.com/kubeflow/pipelines/issues/5126 | 806,604,398 | MDU6SXNzdWU4MDY2MDQzOTg= | 5,126 | Can not load pipeline | {
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"Can you answer the environment questions? I need more information to scope down the problem",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically clos... | 2021-02-11T17:41:17 | 2022-04-28T18:00:33 | 2022-04-28T18:00:33 | NONE | null | ### What steps did you take:
When I tried to upload the pipeline which is in format of "pipeline.tar.gz" and there is always an error and can not load the pipeline.
### What happened:
Pipeline version creation failed
upstream connect error or disconnect/reset before headers. reset reason: connection termination
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5125 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5125/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5125/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5125/events | https://github.com/kubeflow/pipelines/issues/5125 | 806,211,828 | MDU6SXNzdWU4MDYyMTE4Mjg= | 5,125 | Upgrade gorm | {
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"@Bobgy \r\nBy the way, I noticed that we are not using the main gorm repository ```\"gorm.io/gorm\"```, this will lead to conflict in the future. It should probably become a future refactoring task for the KFP team. \r\n\r\nFor example the way to add a composite unique index is shows as below, but this gave me a s... | 2021-02-11T09:23:12 | 2022-04-18T17:27:59 | 2022-04-18T17:27:59 | CONTRIBUTOR | null |
@capri-xiyue The creation of a unique index with (Namespace, Name) is done in the client manager. The reason we can't do this as part of the Pipeline struct definition is because the KFP repository is using a branch of the gorm package ```"github.com/jinzhu/gorm"``` which does not support it, the refactoring for this is not in scope.
_Originally posted by @maganaluis in https://github.com/kubeflow/pipelines/pull/4835#discussion_r571721941_ | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5124 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5124/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5124/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5124/events | https://github.com/kubeflow/pipelines/issues/5124 | 805,513,534 | MDU6SXNzdWU4MDU1MTM1MzQ= | 5,124 | Data Versioning with Kubeflow | {
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"Hi @VindhyaSRajan!\r\n\r\nDid you look into https://github.com/pachyderm/pachyderm? I think they also have some integration with KFP.\r\nKFP is very flexible to work with any external system by components.\r\n\r\nAny feedback on gaps?",
"Can KFP be set to take kubernetes snapshots after each step, and then pass ... | 2021-02-10T13:28:33 | 2023-03-10T17:14:53 | null | NONE | null | Hello,
I am working on setting up an in-house ML infrastructure for my company and we decided to go with Kubeflow. We need to ensure that the pipelines have the provision for data versioning as well. I understand from the official documentation of Kubeflow that it is possible with Rok Data Management. However, we are interested in exploring other options as well. Thus, my question comes in two parts:
1. Is there any alternative to using Rok for data versioning with Kubeflow pipelines?
2. Is it possible to use DVC for data versioning with Kubeflow?
Thanks :) | {
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"I have checked if the run has been correctly inserted into the `mlpipeline` database. I see both the run and the correct namespace.\r\n```\r\nkubectl -nkubeflow exec -it mysql-7694c6b8b7-nxn2h -- bash\r\nroot@mysql-7694c6b8b7-nxn2h:/# mysql\r\nmysql> use mlpipeline;\r\nmysql> select uuid, DisplayName, namespace, S... | 2021-02-10T12:49:34 | 2021-02-26T01:08:10 | 2021-02-26T01:08:10 | NONE | null | ### What steps did you take:
I have a notebook server in a multiuser environment with kale.
After fixing several bugs based on community comments (see below) I run into a new issue.
Added `ServiceRoleBinding` and `EnvoyFilter` as mentioned in https://github.com/kubeflow/pipelines/issues/4440#issuecomment-733702980
```
export NAMESPACE=mynamespace
export NOTEBOOK=mynotebook
export USER=myuser@domain.com
cat > ./envoy_filter.yaml << EOM
apiVersion: rbac.istio.io/v1alpha1
kind: ServiceRoleBinding
metadata:
name: bind-ml-pipeline-nb-${NAMESPACE}
namespace: kubeflow
spec:
roleRef:
kind: ServiceRole
name: ml-pipeline-services
subjects:
- properties:
source.principal: cluster.local/ns/${NAMESPACE}/sa/default-editor
---
apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
name: add-header
namespace: ${NAMESPACE}
spec:
configPatches:
- applyTo: VIRTUAL_HOST
match:
context: SIDECAR_OUTBOUND
routeConfiguration:
vhost:
name: ml-pipeline.kubeflow.svc.cluster.local:8888
route:
name: default
patch:
operation: MERGE
value:
request_headers_to_add:
- append: true
header:
key: kubeflow-userid
value: ${USER}
workloadSelector:
labels:
notebook-name: ${NOTEBOOK}
EOM
```
Added the namespace to `.config/kfp/context.json` as metioned in https://github.com/kubeflow-kale/kale/issues/210#issuecomment-727018461
Added `RoleBinding` as mentioned in https://github.com/kubeflow-kale/kale/issues/210#issuecomment-697231513
```
cat <<EOF | kubectl apply -f -
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: allow-workflow-nb-mynamespace
namespace: mynamespace
subjects:
- kind: ServiceAccount
name: default-editor
namespace: mynamespace
roleRef:
kind: ClusterRole
name: argo
apiGroup: rbac.authorization.k8s.io
EOF
```
### What happened:
The pipeline pods run successfully but I get an error in the Jupyter server. Additionally, in the experiment section I see the run but no graph is displayed.
```
2021-02-10 08:39:54 run:114 [[INFO]] [TID=82upm9iyob] [/home/jovyan/data-vol-1/examples/base/candies_sharing.ipynb] Executing RPC function 'get_run(run_id=e5b4e73c-2709-41b0-af75-f9b9dcb372f2)'
2021-02-10 08:39:54 run:125 [[ERROR]] [TID=82upm9iyob] [/home/jovyan/data-vol-1/examples/base/candies_sharing.ipynb] RPC function 'get_run' raised an unhandled exception
Traceback (most recent call last):
...
Reason: Internal Server Error
HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'trailer': 'Grpc-Trailer-Content-Type', 'date': 'Wed, 10 Feb 2021 08:39:54 GMT', 'x-envoy-upstream-service-time': '1', 'server': 'envoy', 'transfer-encoding': 'chunked'})
HTTP response body: {"error":"Failed to authorize the request.: Failed to authorize with the run Id.: Failed to get namespace from run id.: InternalServerError: Failed to get run: invalid connection: invalid connection","message":"Failed to authorize the request.: Failed to authorize with the run Id.: Failed to get namespace from run id.: InternalServerError: Failed to get run: invalid connection: invalid connection","code":13,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal Server Error","error_details":"Failed to authorize the request.: Failed to authorize with the run Id.: Failed to get namespace from run id.: InternalServerError: Failed to get run: invalid connection: invalid connection"}]}
```
### What did you expect to happen:
I receive a status feedback when the pipeline runs successfully and see the graph in the experiment section.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
Full Kubeflow Deployment on an on-premise cluster.
KFP version: 1.0.4<!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: kfp 1.4.0, kfp-pipeline-spec 0.1.5, kfp-server-api 1.3.0<!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
`kfp pipeline list` executed on the notebook server.
```
kfp pipeline list
+--------------------------------------+-------------------------------------------------+---------------------------+
| Pipeline ID | Name | Uploaded at |
+======================================+=================================================+===========================+
| 9f04bfad-cad5-4967-bad3-bf7e2f0fe156 | candies-sharing-0s70d | 2021-02-10T08:39:05+00:00 |
+--------------------------------------+-------------------------------------------------+---------------------------+
```
#### Are there any plans to automatically create the currently manually created ServiceRoleBinding, EnvoyFilter and the RoleBinding in the future?
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"u... | closed | false | null | [] | null | [
"@uhhc Are you willing to take over this issue? In addition, if we change it to single quote, will it affects users using other mysql mode?",
"> @uhhc Are you willing to take over this issue? In addition, if we change it to single quote, will it affects users using other mysql mode?\r\n\r\nOkay, I'll commit a PR... | 2021-02-10T05:39:04 | 2021-02-24T01:10:15 | 2021-02-24T01:10:15 | CONTRIBUTOR | null | ### What steps did you take:
I'm installing KFP in a new k8s cluster.
### What happened:
In pod console log, it shows error as follows:
```
Failed to query pipeline_version table's indices. Error: Error 1054: Unknown column 'idx_pipeline_version_uuid_name' in 'where clause'
```
But it successfully deployed in the past. So we checked the difference between them and found the sql_mode in MySQL are not the same. The newer set 'ANSI_QUOTES' in sql_mode value, and no errors occur when remove this value.
The [MySQL doc](https://dev.mysql.com/doc/refman/5.7/en/sql-mode.html#sqlmode_ansi_quotes) says:
> ANSI_QUOTES
> Treat " as an identifier quote character (like the ` quote character) and not as a string quote character. You can still use ` to quote identifiers with this mode enabled. With ANSI_QUOTES enabled, you cannot use double quotation marks to quote literal strings because they are interpreted as identifiers.
When I checked the [source code](https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/client_manager.go#L271), I found you use **double quotation marks** in raw SQL clause:
```
show index from pipeline_versions where Key_name="idx_pipeline_version_uuid_name"
```
and it would be **not compatible with different sql_mode**.
Change the double quotation marks to single quotes will solve this problem.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
Through my Helm/Charts
KFP version: `0.1.38` (This problem affects all the versions include the latest master)
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
/area backend
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https://api.github.com/repos/kubeflow/pipelines/issues/5119 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5119/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5119/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5119/events | https://github.com/kubeflow/pipelines/issues/5119 | 805,171,142 | MDU6SXNzdWU4MDUxNzExNDI= | 5,119 | Invalid pipeline is generated if its name contains non-ascii chars only | {
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"A workaround is to contain at least one ASCII char in the pipeline name."
] | 2021-02-10T05:02:54 | 2021-02-19T02:20:31 | 2021-02-19T02:20:31 | CONTRIBUTOR | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
Suppose there is a pipeline with non-ascii name:
```py
# sample.py
from kfp import dsl
@dsl.pipeline(name="サンプル")
def sample():
...
```
Then compile it:
```bash
dsl-compile --py sample.py --out sample.yaml
```
The sample.yaml becomes:
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: Pipeline-
...
```
Then upload the pipeline and create run.
### What happened:
Failed because `Pipeline-` contains upper case char (`P`)
```json
{
"error":"Failed to create a new run.: InternalServerError: Failed to create a workflow for (): Workflow.argoproj.io \"Pipeline-45z6j\" is invalid: [metadata.generateName: Invalid value: \"Pipeline-\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*'), metadata.name: Invalid value: \"Pipeline-45z6j\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*')]",
"message":"Failed to create a new run.: InternalServerError: Failed to create a workflow for (): Workflow.argoproj.io \"Pipeline-45z6j\" is invalid: [metadata.generateName: Invalid value: \"Pipeline-\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*'), metadata.name: Invalid value: \"Pipeline-45z6j\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*')]",
"code":13,
"details":[
{
"@type":"type.googleapis.com/api.Error",
"error_message":"Internal Server Error",
"error_details":"Failed to create a new run.: InternalServerError: Failed to create a workflow for (): Workflow.argoproj.io \"Pipeline-45z6j\" is invalid: [metadata.generateName: Invalid value: \"Pipeline-\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*'), metadata.name: Invalid value: \"Pipeline-45z6j\": a DNS-1123 subdomain must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character (e.g. 'example.com', regex used for validation is '[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*')]"
}
]
}
```
### What did you expect to happen:
Successfully create run.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)? AI Platform Pipelines
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. --> 1.0.4
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp --> 1.3.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
The string `'Pipeline'` comes from
https://github.com/kubeflow/pipelines/blob/3d40bba9a3e14b170a2c05e41a0b0bee64d399b8/sdk/python/kfp/compiler/compiler.py#L692
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5118 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5118/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5118/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5118/events | https://github.com/kubeflow/pipelines/issues/5118 | 804,825,708 | MDU6SXNzdWU4MDQ4MjU3MDg= | 5,118 | [Discuss] Kubeflow Pipelines frontend development best practice. | {
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"### Suggested Practice\r\n\r\nPrefer using `react-testing-library` in testing, avoid using `enzyme` when possible.\r\n\r\n### Reason\r\n\r\n> The more your tests resemble the way your software is used, the more confidence they can give you. \r\n\r\n`react-testing-library` allows you to focus on the user facing ele... | 2021-02-09T18:58:27 | 2022-05-09T19:53:29 | null | COLLABORATOR | null | ## Topic
Inspired by https://github.com/kubeflow/pipelines/pull/5040#discussion_r569956232 : Determine the pattern and anti-pattern for frontend development on Kubeflow Pipelines. There are some existing development styles which are either out-of-date, or not recommended in the codebase. For example, enzyme vs react-testing-library, Ref vs Hook/State Update.
## Request
Everyone is welcome to add their opinions on best practice for frontend development. Please use the following format in your comment for better reviewing:
```
### Suggested Practice
// Write down the pattern or anti-pattern you suggest, prefer a concise statement with necessary link.
### Reason
// Write down the reason why a pattern is preferred, or why certain pattern should be avoided. You can be very verbose here.
### Example (Optional)
// Give an entry point for people to understand how to adopt this practice, or point out the existing file in codebase where we should improve or should follow.
```
## Action
After collecting feedback, frontend reviewers will make a call on whether to apply such practice, then update the [README](https://github.com/kubeflow/pipelines/blob/master/frontend/README.md).
/kind discussion
/area frontend
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https://api.github.com/repos/kubeflow/pipelines/issues/5117 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5117/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5117/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5117/events | https://github.com/kubeflow/pipelines/issues/5117 | 804,746,653 | MDU6SXNzdWU4MDQ3NDY2NTM= | 5,117 | What is the Meaning for KFP_FLAGS.DEPLOYMENT | {
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"Hi @kudla, it's used to distinguish the three installation modes in https://www.kubeflow.org/docs/pipelines/installation/overview/.\r\n\r\nAnd as you already understood, the major difference is multi user mode. It's not really used much for other ways.\r\n\r\n",
"Question answered, feel free to raise new issues ... | 2021-02-09T17:14:24 | 2021-02-22T07:51:21 | 2021-02-22T07:51:20 | NONE | null | Please. Just can't find any info on `KFP_FLAGS.DEPLOYMENT` for pipelines implementation
According to
[front](https://github.com/kubeflow/pipelines/blob/1.1.2/frontend/src/index.tsx#L59)
```jsx
ReactDOM.render(
KFP_FLAGS.DEPLOYMENT === Deployments.KUBEFLOW ? (
<NamespaceContextProvider>{app}</NamespaceContextProvider>
) : (
// Uncomment the following for namespace switch during development.
// <NamespaceContext.Provider value='your-namespace'>{app}</NamespaceContext.Provider>
<NamespaceContext.Provider value={undefined}>{app}</NamespaceContext.Provider>
),
document.getElementById('root'),
);
```
and [api server](https://github.com/kubeflow/pipelines/blob/1.2.0/backend/src/apiserver/server/experiment_server.go#L136)
```js
if common.IsMultiUserMode() {
if refKey == nil || refKey.Type != common.Namespace {
return nil, util.NewInvalidInputError("Invalid resource references for experiment. ListExperiment requires filtering by namespace.")
}
namespace := refKey.ID
if len(namespace) == 0 {
return nil, util.NewInvalidInputError("Invalid resource references for experiment. Namespace is empty.")
}
resourceAttributes := &authorizationv1.ResourceAttributes{
Namespace: namespace,
Verb: common.RbacResourceVerbList,
}
err = s.canAccessExperiment(ctx, "", resourceAttributes)
if err != nil {
return nil, util.Wrap(err, "Failed to authorize with API resource references")
}
} else {
if refKey != nil && refKey.Type == common.Namespace && len(refKey.ID) > 0 {
return nil, util.NewInvalidInputError("In single-user mode, ListExperiment cannot filter by namespace.")
}
// In single user mode, apply filter with empty namespace for backward compatibile.
filterContext = &common.FilterContext{
ReferenceKey: &common.ReferenceKey{Type: common.Namespace, ID: ""},
}
}
```
codebases it seems `KFP_FLAGS.DEPLOYMENT` correlates tightly with `MULTIUSER` support. I.e. only `KUBEFLOW` deployment supports `MULTIUSER` mode. And vice versa for a Single User Mode the `KUBEFLOW` one should not be used.
But what is a wider scope for this `KFP_FLAGS.DEPLOYMENT`?
How this supposed to be really used for a Single User mode?
Or where can I read on this
Thanks | {
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"... | open | false | null | [] | null | [
"I think the question is: \"Should we allow **Active** Runs in **Archived** Experiments\"?\r\n\r\nIf the answer was no, I can think of the following implications:\r\n\r\n- Archiving an Experiment would imply archiving of ALL the Runs belonging to the Experiment. We could prompt users of course, saying that the Expe... | 2021-02-09T02:10:45 | 2022-04-21T00:23:16 | null | COLLABORATOR | null |
### What happened:
During the PR #5040, which allows user to see Active/Archive run lists of an experiment, regardless of whether that experiment is active or archived. There is discussion on whether a user can restore a `Run` from an `Archived Experiment`. See https://github.com/kubeflow/pipelines/pull/5040#issuecomment-773952554 for the initial question. And https://github.com/kubeflow/pipelines/pull/5040#issuecomment-774061658 for the argument. Currently this behavior is allowed on the latest code.
### Decision Making
The question: Can a user restore a Run from an Archived Experiment? If so, what is the use case? If not, why it is discouraged?
### Follow-up items
* If this behavior is allowed, should we update UI to call out the state of Experiment in ExperimentDetail.tsx file?
* If this behavior is not allowed, what is the error message we should show to users?
/kind discussion
/area frontend
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5111 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5111/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5111/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5111/events | https://github.com/kubeflow/pipelines/issues/5111 | 803,624,425 | MDU6SXNzdWU4MDM2MjQ0MjU= | 5,111 | Boolean parameters cannot be passed to components defined with the component.yaml specifications | {
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"I fixed this problem by using type \"Boolean\" instead of \"Bool\" in the component definition, although that goes directly against https://www.kubeflow.org/docs/pipelines/reference/component-spec/",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if n... | 2021-02-08T14:38:19 | 2022-12-16T20:07:59 | 2022-04-28T18:00:31 | CONTRIBUTOR | null | ### What steps did you take:
Boolean parameters cannot be passed to components defined with the component.yaml specifications:
Example pipeline:
```python
# test_case.py
from kfp import dsl, components
@dsl.pipeline(name="Pipeline", description="Pipeline")
def pipeline(
param: bool = False
) -> None:
some_op = components.load_component_from_text(
"""
name: Pipeline
inputs:
- name: param
type: Bool
implementation:
container:
image: some/container
args: [
/opt/conda/envs/py37/bin/python, script.py,
--job-name, {inputValue: param},
]
"""
)
some_op(param)
```
```bash
dsl-compile --py test_case.py --output test_case.yaml
```
### What happened:
The dsl-compile step throws an error:
```
kfp.dsl.types.InconsistentTypeException: Incompatible argument passed to the input "param" of component "Pipeline": Argument type "Boolean" is incompatible with the input type "Bool"
```
### What did you expect to happen:
The pipeline should compile succesfully
### Environment:
KFP version: N/A
KFP SDK version:
```
kfp 1.4.0
kfp-pipeline-spec 0.1.2
kfp-server-api 1.3.0
```
/kind bug
/area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5109 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5109/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5109/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5109/events | https://github.com/kubeflow/pipelines/issues/5109 | 802,745,682 | MDU6SXNzdWU4MDI3NDU2ODI= | 5,109 | How to specify the env values in ComponentSpec to build reusable components | {
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... | closed | false | null | [] | null | [
"/cc @chensun @Ark-kun \r\n\r\nHi @ShilpaGopal, mounting secrets are probably never reusable, because they depend on your cluster setup. Therefore, you can only override env vars after initiating a component as an instance. See https://github.com/kubeflow/pipelines/blob/cadcac08bd6e2712ce62d7eb59ff0b3f2ee1bbe2/samp... | 2021-02-06T17:14:31 | 2022-12-08T13:39:05 | 2022-03-03T04:05:34 | NONE | null | ### What steps did you take:
I am building a reusable component say alert-manager which will be used across every pipeline. The docker images used to build the component needs some environments variables to be set. As per the documentation (https://www.kubeflow.org/docs/pipelines/reference/component-spec/) , env can be set as maps. So I have added env params as shown below
```
name: dsw-alert
description: sends events to prometheus alert manager
inputs:
- {name: alert_name, type: String, description: 'alert name'}
- {name: severity, type: String, description: 'severity of the notification'}
implementation:
container:
image: dsw-alert-manager:1.0.0-10
args: [
"src/main.py",
"--alert_name", {inputValue: alert_name},
"--severity", {inputValue: severity}]
env:
- name: ALERT_MANAGER_HOST
value: 'http://alert-manager.fms-svc.svc.cluster.local'
```
### What happened:
When I am trying to load the component using load_component_from_file("data-loader-qa.yaml") dsl-compile of the piepline fails with below error (without the env section in reusable alert manager component, I am able to compile the pipeline component).
```
Error: ContainerSpec.from_dict(struct=OrderedDict([('image', 'dsw-alert-manager:1.0.0-10'), ('args', ['src/main.py', '--alert_name', OrderedDict([
('inputValue', 'alert_name')]), '--severity', OrderedDict([('inputValue', 'severity')])]), ('env', [OrderedDict([('name', 'ALERT_MANAGER_HOST'), ('value', 'http://alert-manager.fms-svc.sv
c.cluster.local')])])])) failed with exception:
Error: Structure "[OrderedDict([('name', 'ALERT_MANAGER_HOST'), ('value', 'http://alert-manager.fms-svc.svc.cluster.local')])]" is incompatible with type "typing.Mapping[str, str]" - it d
oes not have dict type.
Error: Structure "[OrderedDict([('name', 'ALERT_MANAGER_HOST'), ('value', 'http://alert-manager.fms-svc.svc.cluster.local')])]" is not None.
Error: Structure "[OrderedDict([('name', 'ALERT_MANAGER_HOST'), ('value', 'http://alert-manager.fms-svc.svc.cluster.local')])]" is incompatible with type "typing.Union[typing.Mapping[str,
str], NoneType]" - none of the types in Union are compatible.
Error: GraphImplementation.from_dict(struct=OrderedDict([('container', OrderedDict([('image', 'harbor-registry-mndc.uidai.gov.in/fms-qa/dsw-alert-manager:1.0.0-10'), ('args', ['src/main.p
y', '--alert_name', OrderedDict([('inputValue', 'alert_name')]), '--severity', OrderedDict([('inputValue', 'severity')])]), ('env', [OrderedDict([('name', 'ALERT_MANAGER_HOST'), ('value',
'http://alert-manager.fms-svc.svc.cluster.local')])])]))])) failed with exception:
__init__() got an unexpected keyword argument 'container'
Error: Structure "OrderedDict([('container', OrderedDict([('image', 'dsw-alert-manager:1.0.0-10'), ('args', ['src/main.py', '--alert_name', Ordere
dDict([('inputValue', 'alert_name')]), '--severity', OrderedDict([('inputValue', 'severity')])]), ('env', [OrderedDict([('name', 'ALERT_MANAGER_HOST'), ('value', 'http://alert-manager.fms
-svc.svc.cluster.local')])])]))])" is incompatible with type "typing.Union[kfp.components._structures.ContainerImplementation, kfp.components._structures.GraphImplementation, NoneType]" -
none of the types in Union are compatible.
```
### What did you expect to happen:
How to specify the environmental variables in reusable component spec? Any input on this is appreciated.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
Custom manifests deployment on on-prem kunernetes cluster with multi user support.
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. --> 1.2.0
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->1.1.1
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5106 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5106/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5106/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5106/events | https://github.com/kubeflow/pipelines/issues/5106 | 802,335,097 | MDU6SXNzdWU4MDIzMzUwOTc= | 5,106 | Collapsing For-loop components in the UI for large number of components | {
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"node_id": "MDU6TGFiZWwxMjg5NTg4MTQw... | closed | false | null | [] | null | [
"This is sth we are thinking about for KFP v2 UI",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. P... | 2021-02-05T17:14:58 | 2022-04-18T17:28:00 | 2022-04-18T17:28:00 | NONE | null | ### What steps did you take:
I ran a pipeline with a large fanout
### What happened:
### What did you expect to happen:
It would be better if the items that are generated from the same statement would stay collapsed until clicked on. Or some other form of ui system that would allow a good view of what is going on.
### Environment:
kubeflow pipelines 1.3 stand alone installation
How did you deploy Kubeflow Pipelines (KFP)?
Stand alone on AWS
1.3
### Anything else you would like to add:
I understand this is a feature request. Is there a way for our company to put a bounty on this?
/kind feature
/area frontend
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https://api.github.com/repos/kubeflow/pipelines/issues/5103 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5103/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5103/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5103/events | https://github.com/kubeflow/pipelines/issues/5103 | 801,725,126 | MDU6SXNzdWU4MDE3MjUxMjY= | 5,103 | [Bug] execution_caches table growing, no cached items ever deleted. | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-02-04T23:56:33 | 2022-04-28T18:00:30 | 2022-04-28T18:00:30 | CONTRIBUTOR | null | ### What steps did you take:
We are users of KFP caching feature and just noticed the `execution_caches` table is up to 450k rows 😅
### What happened:
The cache still works but likely using more space than desired.
### What did you expect to happen:
The cache somehow compacts itself, likely via expiry.
### Environment:
How did you deploy Kubeflow Pipelines (KFP)? Via the Kubeflow v1.1 manifests.
KFP version: v1.0.0
KFP SDK version: v1.0.4
### Anything else you would like to add:
Looks like there is a `DeleteExecutionCache` method, see [here](https://github.com/kubeflow/pipelines/blob/master/backend/src/cache/storage/execution_cache_store.go#L137) but I don't see an interface to it and don't see it used anywhere.
The original request was from a customer of ours about whether they could manually delete a cached item (perhaps via the UI or CLI) so I did a little digging and found the cache itself wasn't auto-expiring.
/kind bug
/area backend
cc @Ark-kun (at the request of @Bobgy from [this Slack thread](https://kubeflow.slack.com/archives/CE10KS9M4/p1612317365063400)). | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5101 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5101/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5101/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5101/events | https://github.com/kubeflow/pipelines/issues/5101 | 801,412,458 | MDU6SXNzdWU4MDE0MTI0NTg= | 5,101 | Installing KFP understand specific namespace causes controller-manager to raise many errors | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"@amitripshtos sorry for the late reply, can you still see this problem?",
"This issue has been automatically marked as stale because ... | 2021-02-04T15:54:35 | 2022-03-03T02:05:29 | null | NONE | null | ### What steps did you take:
Installed KFP standalone on namespace "dev" instead "kubeflow".
### What happened:
Everything works, but the controller-manager is spamming the following log:
```
github.com/kubernetes-sigs/application/vendor/sigs.k8s.io/controller-runtime/pkg/cache/internal/informers_map.go:126: Failed to list *v1beta1.Application: applications.app.k8s.io is forbidden: User "system:serviceaccount:development:application" cannot list resource "applications" in API group "app.k8s.io" in the namespace "kubeflow"
```
### What did you expect to happen:
The code in the controller-manager should get the namespace from the NAMESPACE environment variable and not use the default "kubeflow" namespace.
### Environment:
GKE
How did you deploy Kubeflow Pipelines (KFP)?
Standalone
KFP version: 1.3.0
/kind bug
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https://api.github.com/repos/kubeflow/pipelines/issues/5098 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5098/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5098/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5098/events | https://github.com/kubeflow/pipelines/issues/5098 | 801,095,043 | MDU6SXNzdWU4MDEwOTUwNDM= | 5,098 | TFX Trainer ModelRun output can't be visualized with TensorBoard | {
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"Thank you for reporting. I think that this is related to https://github.com/tensorflow/tfx/commit/18055491d38604934a25d49c53d456b96da04c47 . This should be fixed with the upcoming TFX version(0.28.0) (currently in RC)."
] | 2021-02-04T09:25:52 | 2021-03-12T01:14:22 | 2021-03-12T01:14:22 | NONE | null | ### What steps did you take:
* Run a TFX pipeline with Trainer component until successful completion
* Go to Run Output
* The Trainer component has 2 outputs, model and model_run:
```
model
Type: Model
Artifact: model
Properties:
uri: gs://<bucket>/pipelines/<pipeline_name>/Trainer/model/25
id: 31
span: None
type_id: 20
type_name: Model
state: published
split_names: None
producer_component: Trainer
model_run
Type: ModelRun
Artifact: model_run
Properties:
uri: gs://<bucket>/pipelines/<pipeline_name>/Trainer/model_run/25
id: 32
span: None
type_id: 35
type_name: ModelRun
state: published
split_names: None
producer_component: Trainer
```
* In the Trainer component section, create a TensorBoard then open it
### What happened:
TensorBoard shows INACTIVE and message `No dashboards are active for the current data set.`.
By switching SCALARS, under Runs, there is a single run:
```
gs://<bucket>/pipelines/<pipeline_name>/Trainer/model/25
```
The gs://.../model/ dir doesn't contain TensorBoard events.
The gs://.../model_run/ which contains the events for TensorBoard should be used as `logdir` instead.
```
gs://<bucket>/pipelines/<pipeline_name>/Trainer/model_run/25/serving_model_dir/events.out.tfevents.1612255967.model-train-d6wxv-2443271500
gs://<bucket>/pipelines/<pipeline_name>/Trainer/model_run/25/serving_model_dir/eval_model-eval/events.out.tfevents.1612256425.model-train-d6wxv-2443271500
```
The raw Trainer `mlpipeline-ui-metadata` output (names replaced):
```
{"outputs": [{"storage": "inline", "source": "# Execution properties:\n**custom\\_config**: null\n\n**eval\\_args**: {\n \"num\\_steps\": 500\n}\n\n**module\\_file**: None\n\n**run\\_fn**: None\n\n**train\\_args**: {\n \"num\\_steps\": 1000\n}\n\n**trainer\\_fn**: model.train.train.trainer\\_fn\n\n**kfp\\_pod\\_name**: <pipeline_name>-d6wxv-2443271500\n\n# Inputs:\n## examples\n\n**Type**: Examples\n\n**Artifact: transformed\\_examples**\n\n**Properties**:\n\n**uri**: gs://<bucket>/pipelines/<pipeline_name>/Transform/transformed\\_examples/23\n\n**id**: 25\n\n**span**: 0\n\n**type_id**: 10\n\n**type_name**: Examples\n\n**state**: published\n\n**split_names**: [\"train\", \"eval\"]\n\n**producer_component**: Transform\n\n## schema\n\n**Type**: Schema\n\n**Artifact: schema**\n\n**Properties**:\n\n**uri**: gs://<bucket>/pipelines/<pipeline_name>/SchemaGen/schema/19\n\n**id**: 20\n\n**span**: None\n\n**type_id**: 14\n\n**type_name**: Schema\n\n**state**: published\n\n**split_names**: None\n\n**producer_component**: SchemaGen\n\n## transform\\_graph\n\n**Type**: TransformGraph\n\n**Artifact: transform\\_graph**\n\n**Properties**:\n\n**uri**: gs://<bucket>/pipelines/<pipeline_name>/Transform/transform\\_graph/23\n\n**id**: 24\n\n**span**: None\n\n**type_id**: 18\n\n**type_name**: TransformGraph\n\n**state**: published\n\n**split_names**: None\n\n**producer_component**: Transform\n\n\n\n# Outputs:\n## model\n\n**Type**: Model\n\n**Artifact: model**\n\n**Properties**:\n\n**uri**: gs://<bucket>/pipelines/<pipeline_name>/Trainer/model/25\n\n**id**: 31\n\n**span**: None\n\n**type_id**: 20\n\n**type_name**: Model\n\n**state**: published\n\n**split_names**: None\n\n**producer_component**: Trainer\n\n## model\\_run\n\n**Type**: ModelRun\n\n**Artifact: model\\_run**\n\n**Properties**:\n\n**uri**: gs://<bucket>/pipelines/<pipeline_name>/Trainer/model\\_run/25\n\n**id**: 32\n\n**span**: None\n\n**type_id**: 35\n\n**type_name**: ModelRun\n\n**state**: published\n\n**split_names**: None\n\n**producer_component**: Trainer\n\n", "type": "markdown"}, {"type": "tensorboard", "source": "gs://<bucket>/pipelines/<pipeline_name>/Trainer/model/25"}]}
```
### What did you expect to happen:
TensorBoard should show events from model_run log dir.
### Environment:
How did you deploy Kubeflow Pipelines (KFP)?
Google Cloud AI Platform Pipelines
KFP version: 1.0.4
KFP SDK version: 1.3.0
### Anything else you would like to add:
TensorBoard 2.0.0
/kind bug
/area frontend
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https://api.github.com/repos/kubeflow/pipelines/issues/5095 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5095/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5095/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5095/events | https://github.com/kubeflow/pipelines/issues/5095 | 800,987,482 | MDU6SXNzdWU4MDA5ODc0ODI= | 5,095 | Add kube-api-qps/kube-api-burst for pipeline components | {
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"/assign",
"/area backend",
"/area perf",
"@Jeffwan @XXXXIEHF Welcome contribution on this!"
] | 2021-02-04T06:47:20 | 2021-03-16T18:14:15 | 2021-03-16T18:14:15 | NONE | null | In our internal clusters with KFP v1.0.0 release, we have over 20K workflow CRD in K8S Cluster.
The pipeline API Server/Cache Server has significant performance downgrade because of K8S client-go default QPS/Burst configuration. We can see logs from client-go below (the default QPS/Burst is 5/10 if unspecified.):
```
Throttling request took xxxxx, request: xxxxxxxx
```
In such scenarios, I think we need add two more parameters (like kube-api-qps/kube-api-burst) for all backend components using client-go. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5094 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5094/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5094/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5094/events | https://github.com/kubeflow/pipelines/issues/5094 | 800,987,448 | MDU6SXNzdWU4MDA5ODc0NDg= | 5,094 | Support `jobID` macro | {
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`http://pipeline.xxx.ingress.stg.xxx.com/#/runs/details/aba884eb-edcd-4828-aa18-887b4ab4abd2`
It's very handy for error tracking. e.g. we can put this macro in `ExitHandler` to send the owner the status together with the job URL for faster accessing. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5089 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5089/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5089/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5089/events | https://github.com/kubeflow/pipelines/issues/5089 | 800,823,448 | MDU6SXNzdWU4MDA4MjM0NDg= | 5,089 | The KFP preloaded XGboost sample is broken and out-dated. | {
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"> Util we have the sample working, I propose we remove it from the KFP preloaded pipelines and sample-tests.\r\n\r\nCompletely agree with this!\r\nThe sample is a nice to have, although it's more important get KFP releases healthy and running.",
"While the issue is mitigated by temporarily removing the sample fr... | 2021-02-04T00:36:45 | 2021-02-12T01:22:57 | 2021-02-12T01:22:57 | COLLABORATOR | null | **TL;DR: The preload XGBoost sample is currently broken.
Proposing we remove this sample from KFP preload and from sample test until we got a chance to refresh the sample.**
------------
The direct cause was that it used the Dataproc 1.2 image which is based on Python 2.7, and pip 21.0 dropped support for Python 2.7.
The symptom is that `dataproc_create_cluster` fails on initialization.
and the specific error is mentioned [here](https://github.com/kubeflow/pipelines/issues/5007#issuecomment-769637030).
https://github.com/kubeflow/pipelines/pull/5062 made an attempted fix by upgrading to Dataproc 1.5 image. It fixed the Dataproc cluster creation issue, but we hit [an error](https://github.com/kubeflow/pipelines/issues/5007#issuecomment-770182313) later at the Trainer step.
We were advised that newer versions of Dataproc images likely don't have XGBoost library preinstalled, as there's now [an initialization action that goes through extra steps to install XGBoost libraries](https://github.com/GoogleCloudDataproc/initialization-actions/tree/master/rapids).
Following that route, I tried installing the default XGBoost version using the rapids script, then hit the error as follows:
```
21/02/03 18:34:20 INFO org.spark_project.jetty.util.log: Logging initialized @3037ms
21/02/03 18:34:20 INFO org.spark_project.jetty.server.Server: jetty-9.3.z-SNAPSHOT, build timestamp: unknown, git hash: unknown
21/02/03 18:34:20 INFO org.spark_project.jetty.server.Server: Started @3169ms
21/02/03 18:34:20 INFO org.spark_project.jetty.server.AbstractConnector: Started ServerConnector@4159e81b{HTTP/1.1,[http/1.1]}{0.0.0.0:37489}
21/02/03 18:34:20 INFO org.apache.hadoop.yarn.client.RMProxy: Connecting to ResourceManager at xgb-bdd8f29b-fb13-4ec2-abcf-38b3699e7ca3-m/10.128.0.101:8032
21/02/03 18:34:21 INFO org.apache.hadoop.yarn.client.AHSProxy: Connecting to Application History server at xgb-bdd8f29b-fb13-4ec2-abcf-38b3699e7ca3-m/10.128.0.101:10200
21/02/03 18:34:21 INFO org.apache.hadoop.conf.Configuration: resource-types.xml not found
21/02/03 18:34:21 INFO org.apache.hadoop.yarn.util.resource.ResourceUtils: Unable to find 'resource-types.xml'.
21/02/03 18:34:21 INFO org.apache.hadoop.yarn.util.resource.ResourceUtils: Adding resource type - name = memory-mb, units = Mi, type = COUNTABLE
21/02/03 18:34:21 INFO org.apache.hadoop.yarn.util.resource.ResourceUtils: Adding resource type - name = vcores, units = , type = COUNTABLE
21/02/03 18:34:23 INFO org.apache.hadoop.yarn.client.api.impl.YarnClientImpl: Submitted application application_1612377093662_0003
21/02/03 18:34:30 INFO org.apache.hadoop.mapreduce.lib.input.FileInputFormat: Total input files to process : 1
21/02/03 18:34:30 INFO org.apache.hadoop.mapreduce.lib.input.FileInputFormat: Total input files to process : 1
21/02/03 18:34:30 INFO org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat: DEBUG: Terminated node allocation with : CompletedNodes: 1, size left: 0
21/02/03 18:34:36 INFO org.apache.hadoop.mapreduce.lib.input.FileInputFormat: Total input files to process : 1
21/02/03 18:34:36 INFO org.apache.hadoop.mapreduce.lib.input.FileInputFormat: Total input files to process : 1
21/02/03 18:34:36 INFO org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat: DEBUG: Terminated node allocation with : CompletedNodes: 1, size left: 0
Exception in thread "main" java.lang.NoSuchMethodError: ml.dmlc.xgboost4j.scala.spark.XGBoost$.trainWithDataFrame$default$5()Lml/dmlc/xgboost4j/scala/ObjectiveTrait;
at ml.dmlc.xgboost4j.scala.example.spark.XGBoostTrainer$.main(XGBoostTrainer.scala:120)
at ml.dmlc.xgboost4j.scala.example.spark.XGBoostTrainer.main(XGBoostTrainer.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:845)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:161)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:184)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:920)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:929)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
21/02/03 18:34:39 INFO org.spark_project.jetty.server.AbstractConnector: Stopped Spark@4159e81b{HTTP/1.1,[http/1.1]}{0.0.0.0:0}
Job output is complete
```
I then realized that the sample is based on the code from the deprecated component path, which was deleted by https://github.com/kubeflow/pipelines/pull/5045.
Specifically, the not found method from the above error was used here:
https://github.com/kubeflow/pipelines/blob/32ce8d8f90bfc8f89a2a3c347ad906f99ba776a8/components/deprecated/dataproc/train/src/XGBoostTrainer.scala#L121
And `trainWithDataFrame` only [exists in XGBoost 0.72](https://xgboost.readthedocs.io/en/release_0.72/jvm/scaladocs/xgboost4j-spark/index.html#ml.dmlc.xgboost4j.scala.spark.XGBoost$@trainWithDataFrame(trainingData:org.apache.spark.sql.Dataset[_],params:Map[String,Any],round:Int,nWorkers:Int,obj:ml.dmlc.xgboost4j.scala.ObjectiveTrait,eval:ml.dmlc.xgboost4j.scala.EvalTrait,useExternalMemory:Boolean,missing:Float,featureCol:String,labelCol:String):ml.dmlc.xgboost4j.scala.spark.XGBoostModel), but not seen from any versions beyond.
XGBoost 0.72 is too old and not even available from https://repo1.maven.org/maven2/com/nvidia/, which is [used by rapids to download XGBoost](https://github.com/GoogleCloudDataproc/initialization-actions/blob/8980d37d16ae580ad1d3eba7a40da59da52ff175/rapids/rapids.sh#L90).
At this point, I feel like we'd rather invest to rewrite the XGBoost sample using the latest XGBoost library than patching the existing one if we do think it's worth demoing running a XGBoost-on-Dataproc pipeline.
Util we have the sample working, I propose we remove it from the KFP preloaded pipelines and sample-tests. | {
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"/cc @chensun @neuromage ",
"The pkgutil-style namespace packages was introduced to turn the existing package into a namespace package, so that [kfp-pipeline-spec](https://github.com/kubeflow/pipelines/tree/master/api/v2alpha1/python/kfp/pipeline_spec) can share the same namespace `kfp` while it's published as a ... | 2021-02-03T16:56:22 | 2023-08-11T19:50:58 | null | NONE | null | ### What steps did you take:
I use Bazel to compile the pipeline. It has been working for `kfp==1.0.0` but failed for `kfp>=1.1.2`.
### Environment:
python==3.8.5
bazel==3.2.0
KFP SDK version: `kfp==1.1.2` as well as `kfp==1.3.0`
### Anything else you would like to add:
Traceback from `kfp==1.1.2`
```python
.../pypi_kfp/kfp/compiler/compiler.py", line 917, in compile
type_check_old_value = kfp.TYPE_CHECK
AttributeError: module 'kfp' has no attribute 'TYPE_CHECK'
```
I suspect that the pkgutil style namespace packaging breaks the import (at least in Bazel)
https://github.com/kubeflow/pipelines/blob/1.1.2/sdk/python/kfp/__init__.py#L17
/kind bug
/area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5085 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5085/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5085/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5085/events | https://github.com/kubeflow/pipelines/issues/5085 | 800,347,894 | MDU6SXNzdWU4MDAzNDc4OTQ= | 5,085 | BrokenPipe for ml engine train component | {
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"/cc @chensun @neuromage "
] | 2021-02-03T13:51:22 | 2021-04-08T04:10:08 | 2021-04-05T10:00:30 | CONTRIBUTOR | null | ### What steps did you take:
ML engine train component throws BrokenPipe Error after around 30 minutes of continuous training. This is not a one-off situation but jobs keep failing.
### What happened:
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
Kubeflow Pipelines was deployed using gcp "ai platform pipelines" tool.
Kubeflow Pipelines 1.0.4
Kubernetes: 1.17.14-gke.1600
```
ML_ENGINE_TRAIN_OP = comp.load_component_from_url(
"https://raw.githubusercontent.com/kubeflow/pipelines/1.3.0/components/gcp/ml_engine/train/component.yaml"
)
```
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: 1.0.4
KFP SDK version: 1.3.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"Part of https://github.com/kubeflow/pipelines/issues/4197",
"A summary of previous discussion,\r\n\r\nDo we all agree on key criteria for this feature?\r\n* p0 being backward-compatible (e.g. if user stays in non-separation mode for pipelines, they should still be able to do what they want).\r\n* p0 allow users ... | 2021-02-03T08:30:03 | 2023-02-20T08:43:41 | 2023-02-20T08:43:41 | MEMBER | null | Given the backend support of namespaced pipelines (https://github.com/kubeflow/pipelines/pull/4835), we can use this issue to discuss how to bring this feature to the UI as well. We (Arrikto) will start working on this soon and provide updates here for discussion.
cc @Bobgy @yanniszark @elikatsis @maganaluis | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5073 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5073/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5073/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5073/events | https://github.com/kubeflow/pipelines/issues/5073 | 798,932,648 | MDU6SXNzdWU3OTg5MzI2NDg= | 5,073 | Dataflow Launch Python Sample.ipynb is Unreadable | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reope... | 2021-02-02T05:07:04 | 2022-04-28T18:00:18 | 2022-04-28T18:00:18 | NONE | null | Unable to open components/gcp/dataflow/launch_python/sample.ipynb as the file is invalid because of invalid json. Please upload a working file. I tried opening it locally as well. But it's broken. Thanks.
https://github.com/kubeflow/pipelines/blob/master/components/gcp/dataflow/launch_python/sample.ipynb | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5068 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5068/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5068/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5068/events | https://github.com/kubeflow/pipelines/issues/5068 | 798,879,205 | MDU6SXNzdWU3OTg4NzkyMDU= | 5,068 | Dependency Dashboard | {
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"Part of https://github.com/kubeflow/pipelines/issues/4682\r\n\r\nThis is a dashboard provided by renovate-bot, maintainers can check the marks above to let the bot create certain update PRs.",
"@Bobgy Excuse me of mentioning.\r\n\r\n`robfig/cron` package now supports standard `CRON_TZ =` spec and this is importa... | 2021-02-02T02:55:43 | 2022-07-26T10:23:27 | null | NONE | null | This issue lists Renovate updates and detected dependencies. Read the [Dependency Dashboard](https://docs.renovatebot.com/key-concepts/dashboard/) docs to learn more.
## Pending Approval
These branches will be created by Renovate only once you click their checkbox below.
- [ ] <!-- approve-branch=renovate/gcr.io-inverting-proxy-agent-digest -->chore(deps): update gcr.io/inverting-proxy/agent digest to 762c500
- [ ] <!-- approve-branch=renovate/github.com-kubeflow-pipelines-api-digest -->fix(deps): update github.com/kubeflow/pipelines/api digest to b10440d
- [ ] <!-- approve-branch=renovate/github.com-kubeflow-pipelines-third_party-ml-metadata-digest -->fix(deps): update github.com/kubeflow/pipelines/third_party/ml-metadata digest to b10440d
- [ ] <!-- approve-branch=renovate/golang.org-x-net-digest -->fix(deps): update golang.org/x/net digest to 46097bf
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- [ ] <!-- approve-branch=renovate/patch-docker-updates -->chore(deps): update docker patch updates (patch) (`golang`, `node`, `tensorflow/tensorflow`)
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- [ ] <!-- approve-branch=renovate/alpine-3.x -->chore(deps): update dependency alpine to v3.16
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- [ ] <!-- approve-branch=renovate/node-12.x -->chore(deps): update node.js to v12.22.12
- [ ] <!-- approve-branch=renovate/node-14.x -->chore(deps): update node.js to v14.20.0
- [ ] <!-- approve-branch=renovate/go-mod-updates -->fix(deps): update go.mod dependencies (minor) (`github.com/aws/aws-sdk-go`, `github.com/eapache/go-resiliency`, `github.com/go-openapi/runtime`, `github.com/go-openapi/swag`, `github.com/go-openapi/validate`, `github.com/google/cel-go`, `github.com/mattn/go-sqlite3`, `github.com/sirupsen/logrus`, `github.com/spf13/viper`, `github.com/stretchr/testify`, `gocloud.dev`, `google.golang.org/grpc`, `google.golang.org/grpc/cmd/protoc-gen-go-grpc`, `google.golang.org/protobuf`, `k8s.io/api`, `k8s.io/apimachinery`, `k8s.io/client-go`, `k8s.io/code-generator`, `k8s.io/kubernetes`, `sigs.k8s.io/controller-runtime`)
- [ ] <!-- approve-branch=renovate/npm-updates -->fix(deps): update npm dependencies (minor) (`@craco/craco`, `@google-cloud/storage`, `@kubernetes/client-node`, `@storybook/addon-actions`, `@storybook/addon-essentials`, `@storybook/addon-links`, `@storybook/node-logger`, `@storybook/react`, `@testing-library/dom`, `@testing-library/user-event`, `@types/d3`, `@types/d3-dsv`, `@types/express`, `@types/google-protobuf`, `@types/http-proxy-middleware`, `@types/markdown-to-jsx`, `@types/node-fetch`, `@types/react`, `@types/react-test-renderer`, `@types/react-virtualized`, `axios`, `browserslist`, `coveralls`, `d3`, `d3-dsv`, `enzyme`, `enzyme-to-json`, `express`, `google-protobuf`, `grpc-web`, `http-proxy-middleware`, `jest`, `markdown-to-jsx`, `react`, `react-dom`, `react-flow-renderer`, `react-query`, `react-virtualized`, `runtypes`, `snapshot-diff`, `tailwindcss`, `tar-stream`, `ts-jest`, `ts-proto`, `tsconfig-paths`, `typescript`, `typestyle`, `wait-port`, `webpack-bundle-analyzer`, `yaml`)
- [ ] <!-- approve-branch=renovate/storybook-preset-create-react-app-4.x -->chore(deps): update dependency @storybook/preset-create-react-app to v4
- [ ] <!-- approve-branch=renovate/testing-library-react-13.x -->chore(deps): update dependency @testing-library/react to v13
- [ ] <!-- approve-branch=renovate/testing-library-user-event-14.x -->chore(deps): update dependency @testing-library/user-event to v14
- [ ] <!-- approve-branch=renovate/react-18.x -->chore(deps): update dependency @types/react to v18
- [ ] <!-- approve-branch=renovate/react-dom-18.x -->chore(deps): update dependency @types/react-dom to v18
- [ ] <!-- approve-branch=renovate/react-router-dom-5.x -->chore(deps): update dependency @types/react-router-dom to v5
- [ ] <!-- approve-branch=renovate/react-test-renderer-18.x -->chore(deps): update dependency @types/react-test-renderer to v18
- [ ] <!-- approve-branch=renovate/tar-6.x -->chore(deps): update dependency @types/tar to v6
- [ ] <!-- approve-branch=renovate/tar-stream-2.x -->chore(deps): update dependency @types/tar-stream to v2
- [ ] <!-- approve-branch=renovate/debian-11.x -->chore(deps): update dependency debian to v11
- [ ] <!-- approve-branch=renovate/google-cloud-sdk-394.x -->chore(deps): update dependency google/cloud-sdk to v394
- [ ] <!-- approve-branch=renovate/prettier-2.x -->chore(deps): update dependency prettier to v2 (`prettier`, `@types/prettier`)
- [ ] <!-- approve-branch=renovate/standard-version-9.x -->chore(deps): update dependency standard-version to v9
- [ ] <!-- approve-branch=renovate/supertest-6.x -->chore(deps): update dependency supertest to v6
- [ ] <!-- approve-branch=renovate/tensorflow-tensorflow-2.x -->chore(deps): update dependency tensorflow/tensorflow
- [ ] <!-- approve-branch=renovate/ts-node-10.x -->chore(deps): update dependency ts-node to v10
- [ ] <!-- approve-branch=renovate/ts-node-dev-2.x -->chore(deps): update dependency ts-node-dev to v2
- [ ] <!-- approve-branch=renovate/tsconfig-paths-4.x -->chore(deps): update dependency tsconfig-paths to v4
- [ ] <!-- approve-branch=renovate/typescript-4.x -->chore(deps): update dependency typescript to v4
- [ ] <!-- approve-branch=renovate/ubuntu-22.x -->chore(deps): update dependency ubuntu to v22
- [ ] <!-- approve-branch=renovate/webpack-bundle-analyzer-4.x -->chore(deps): update dependency webpack-bundle-analyzer to v4
- [ ] <!-- approve-branch=renovate/major-jest-monorepo -->chore(deps): update jest monorepo to v28 (major) (`@types/jest`, `jest`, `ts-jest`)
- [ ] <!-- approve-branch=renovate/node-16.x -->chore(deps): update node.js to v16 (`node`, `@types/node`)
- [ ] <!-- approve-branch=renovate/node-18.x -->chore(deps): update node.js to v18
- [ ] <!-- approve-branch=renovate/major-googleapis-packages -->fix(deps): update dependency @google-cloud/storage to v6
- [ ] <!-- approve-branch=renovate/pako-2.x -->fix(deps): update dependency @types/pako to v2
- [ ] <!-- approve-branch=renovate/crypto-js-4.x -->fix(deps): update dependency crypto-js to v4 (`crypto-js`, `@types/crypto-js`)
- [ ] <!-- approve-branch=renovate/d3-7.x -->fix(deps): update dependency d3 to v7 (`d3`, `@types/d3`)
- [ ] <!-- approve-branch=renovate/d3-dsv-3.x -->fix(deps): update dependency d3-dsv to v3 (`d3-dsv`, `@types/d3-dsv`)
- [ ] <!-- approve-branch=renovate/http-proxy-middleware-2.x -->fix(deps): update dependency http-proxy-middleware to v2
- [ ] <!-- approve-branch=renovate/js-yaml-4.x -->fix(deps): update dependency js-yaml to v4 (`js-yaml`, `@types/js-yaml`)
- [ ] <!-- approve-branch=renovate/markdown-to-jsx-7.x -->fix(deps): update dependency markdown-to-jsx to v7 (`markdown-to-jsx`, `@types/markdown-to-jsx`)
- [ ] <!-- approve-branch=renovate/mocha-10.x -->fix(deps): update dependency mocha to v10
- [ ] <!-- approve-branch=renovate/node-fetch-3.x -->fix(deps): update dependency node-fetch to v3
- [ ] <!-- approve-branch=renovate/proto3-json-serializer-1.x -->fix(deps): update dependency proto3-json-serializer to v1
- [ ] <!-- approve-branch=renovate/re-resizable-6.x -->fix(deps): update dependency re-resizable to v6
- [ ] <!-- approve-branch=renovate/react-ace-10.x -->fix(deps): update dependency react-ace to v10
- [ ] <!-- approve-branch=renovate/react-dropzone-14.x -->fix(deps): update dependency react-dropzone to v14
- [ ] <!-- approve-branch=renovate/react-flow-renderer-10.x -->fix(deps): update dependency react-flow-renderer to v10
- [ ] <!-- approve-branch=renovate/major-react-router-monorepo -->fix(deps): update dependency react-router-dom to v6
- [ ] <!-- approve-branch=renovate/major-webdriverio-monorepo -->fix(deps): update dependency webdriverio to v7
- [ ] <!-- approve-branch=renovate/major-material-ui-monorepo -->fix(deps): update material-ui monorepo to v4 (major) (`@material-ui/core`, `@material-ui/icons`)
- [ ] <!-- approve-branch=renovate/github.com-google-addlicense-1.x -->fix(deps): update module github.com/google/addlicense to v1
- [ ] <!-- approve-branch=renovate/github.com-masterminds-squirrel-1.x -->fix(deps): update module github.com/masterminds/squirrel to v1
- [ ] <!-- approve-branch=renovate/github.com-vividcortex-mysqlerr-1.x -->fix(deps): update module github.com/vividcortex/mysqlerr to v1
- [ ] <!-- approve-branch=renovate/major-kubernetes-go -->fix(deps): update module k8s.io/client-go to v1
- [ ] <!-- approve-branch=renovate/k8s.io-kubernetes-1.x -->fix(deps): update module k8s.io/kubernetes to v1
- [ ] <!-- approve-branch=renovate/major-react-monorepo -->fix(deps): update react monorepo to v18 (major) (`react`, `react-dom`, `react-test-renderer`)
## Open
These updates have all been created already. Click a checkbox below to force a retry/rebase of any.
- [ ] <!-- rebase-branch=renovate/npm-protobufjs-vulnerability -->[fix(deps): update dependency protobufjs to v6.11.3 [security]](../pull/7957)
- [ ] <!-- rebase-branch=renovate/patch-npm-updates -->[fix(deps): update npm dependencies (patch)](../pull/8000) (`@material-ui/icons`, `@storybook/addon-actions`, `@storybook/addon-essentials`, `@storybook/addon-links`, `@storybook/node-logger`, `@storybook/react`, `@testing-library/dom`, `@testing-library/react`, `@types/crypto-js`, `@types/d3-dsv`, `@types/dagre`, `@types/enzyme`, `@types/enzyme-adapter-react-16`, `@types/express`, `@types/google-protobuf`, `@types/jest`, `@types/js-yaml`, `@types/lodash`, `@types/lodash.groupby`, `@types/markdown-to-jsx`, `@types/minio`, `@types/node`, `@types/node-fetch`, `@types/pako`, `@types/prettier`, `@types/react`, `@types/react-dom`, `@types/react-router-dom`, `@types/react-virtualized`, `@types/supertest`, `@types/tar`, `@types/tar-stream`, `autoprefixer`, `axios`, `browserslist`, `coveralls`, `dagre`, `enzyme-adapter-react-16`, `express`, `gunzip-maybe`, `http-proxy-middleware`, `immer`, `minio`, `node-fetch`, `postcss`, `proto3-json-serializer`, `react-ace`, `react-flow-renderer`, `react-query`, `react-scripts`, `react-textarea-autosize`, `react-virtualized`, `react-vis`, `runtypes`, `snapshot-diff`, `standard-version`, `tailwindcss`, `tar-stream`, `ts-proto`, `typescript`, `wait-port`, `wdio-selenium-standalone-service`, `yaml`)
## Detected dependencies
<details><summary>cloudbuild</summary>
<blockquote>
<details><summary>components/google-cloud/google_cloud_pipeline_components/container/cloudbuild.yaml</summary>
- `gcr.io/kaniko-project/executor latest`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/cloudbuild.yaml</summary>
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `python 3.7-slim`
- `gcr.io/cloud-builders/gsutil no version found`
- `gcr.io/cloud-builders/kubectl no version found`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/mnist-ci-sample/cloudbuild.yaml</summary>
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `gcr.io/cloud-builders/docker no version found`
- `python 3.7-slim`
- `gcr.io/cloud-builders/gsutil no version found`
- `gcr.io/cloud-builders/kubectl no version found`
</details>
</blockquote>
</details>
<details><summary>docker-compose</summary>
<blockquote>
<details><summary>sdk/python/tests/compiler/testdata/compose.yaml</summary>
</details>
</blockquote>
</details>
<details><summary>dockerfile</summary>
<blockquote>
<details><summary>backend/Dockerfile</summary>
- `golang 1.17.6-stretch`
- `python 3.7`
- `debian stretch`
</details>
<details><summary>backend/Dockerfile.cacheserver</summary>
- `golang 1.17.6-alpine3.15`
- `alpine 3.8`
</details>
<details><summary>backend/Dockerfile.conformance</summary>
- `golang 1.17.6-alpine3.15`
- `alpine 3.8`
</details>
<details><summary>backend/Dockerfile.persistenceagent</summary>
- `golang 1.17.6-alpine3.15`
- `alpine 3.8`
</details>
<details><summary>backend/Dockerfile.scheduledworkflow</summary>
- `golang 1.17.6-alpine3.15`
- `alpine 3.8`
</details>
<details><summary>backend/Dockerfile.viewercontroller</summary>
- `golang 1.17.6-alpine3.15`
- `alpine no version found`
</details>
<details><summary>backend/Dockerfile.visualization</summary>
- `tensorflow/tensorflow 2.5.1`
</details>
<details><summary>backend/api/Dockerfile</summary>
- `golang 1.15.10`
</details>
<details><summary>backend/metadata_writer/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>backend/src/cache/deployer/Dockerfile</summary>
- `gcr.io/google.com/cloudsdktool/google-cloud-cli alpine`
</details>
<details><summary>backend/src/v2/test/Dockerfile</summary>
- `python 3.7-slim`
</details>
<details><summary>components/contrib/arena/docker/Dockerfile</summary>
- `golang 1.10-stretch`
- `python 3.7-alpine3.9`
</details>
<details><summary>components/contrib/presto/query/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>components/contrib/sample/keras/train_classifier/Dockerfile</summary>
- `tensorflow/tensorflow 1.12.0-py3`
</details>
<details><summary>components/gcp/container/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>components/google-cloud/google_cloud_pipeline_components/container/Dockerfile</summary>
- `gcr.io/google-appengine/python latest`
</details>
<details><summary>components/kserve/Dockerfile</summary>
- `python 3.6-slim`
</details>
<details><summary>components/kubeflow/deployer/Dockerfile</summary>
- `debian no version found`
</details>
<details><summary>components/kubeflow/dnntrainer/Dockerfile</summary>
- `undefined no version found`
</details>
<details><summary>components/kubeflow/katib-launcher/Dockerfile</summary>
- `python 3.6`
</details>
<details><summary>components/kubeflow/kfserving/Dockerfile</summary>
- `python 3.6-slim`
</details>
<details><summary>components/kubeflow/launcher/Dockerfile</summary>
- `python 3.6`
</details>
<details><summary>components/kubeflow/pytorch-launcher/Dockerfile</summary>
- `python 3.6`
</details>
<details><summary>components/local/base/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>components/local/confusion_matrix/Dockerfile</summary>
- `ml-pipeline-local-base no version found`
</details>
<details><summary>components/local/roc/Dockerfile</summary>
- `ml-pipeline-local-base no version found`
</details>
<details><summary>contrib/components/openvino/model_convert/containers/Dockerfile</summary>
- `ubuntu 16.04`
</details>
<details><summary>contrib/components/openvino/ovms-deployer/containers/Dockerfile</summary>
- `intelpython/intelpython3_core no version found`
</details>
<details><summary>contrib/components/openvino/predict/containers/Dockerfile</summary>
- `ubuntu 16.04`
- `ubuntu 16.04`
</details>
<details><summary>contrib/components/openvino/tf-slim/containers/Dockerfile</summary>
- `intelpython/intelpython3_core no version found`
- `intelpython/intelpython3_core no version found`
</details>
<details><summary>frontend/Dockerfile</summary>
- `node 14.18.2`
- `node 14.18.2-alpine`
</details>
<details><summary>manifests/gcp_marketplace/deployer/Dockerfile</summary>
- `gcr.io/cloud-marketplace-tools/k8s/deployer_helm/onbuild 0.11.3`
</details>
<details><summary>proxy/Dockerfile</summary>
- `gcr.io/inverting-proxy/agent sha256:c875588c1be53b1bc0c7183653347acf7887ab32a299a2e9b292bd6188a4e26b`
</details>
<details><summary>samples/contrib/image-captioning-gcp/src/Dockerfile</summary>
- `tensorflow/tensorflow 2.0.0b0-py3`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/inference_server_launcher/Dockerfile</summary>
- `ubuntu 16.04`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/preprocess/Dockerfile</summary>
- `nvcr.io/nvidia/tensorflow 19.03-py3`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/train/Dockerfile</summary>
- `nvcr.io/nvidia/tensorflow 19.03-py3`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/webapp/Dockerfile</summary>
- `base-trtis-client no version found`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/webapp_launcher/Dockerfile</summary>
- `ubuntu 16.04`
</details>
<details><summary>samples/contrib/nvidia-resnet/pipeline/Dockerfile</summary>
- `python 3.6`
</details>
<details><summary>samples/contrib/pytorch-samples/Dockerfile</summary>
- `pytorch/pytorch latest`
</details>
<details><summary>samples/contrib/pytorch-samples/common/tensorboard/Dockerfile</summary>
- `gcr.io/deeplearning-platform-release/tf2-cpu.2-2 latest`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/helloworld-ci-sample/helloworld/Dockerfile</summary>
- `python 3`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/download_dataset/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/submit_result/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/train_model/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/visualize_html/Dockerfile</summary>
- `tensorflow/tensorflow 2.0.0-py3`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/visualize_table/Dockerfile</summary>
- `python 3.7`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/mnist-ci-sample/tensorboard/Dockerfile</summary>
- `python 3.7-slim`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/mnist-ci-sample/train/Dockerfile</summary>
- `tensorflow/tensorflow 2.0.0-py3`
</details>
<details><summary>test/api-integration-test/Dockerfile</summary>
- `golang 1.17`
</details>
<details><summary>test/frontend-integration-test/Dockerfile</summary>
- `gcr.io/ml-pipeline-test/selenium-standalone-chrome-gcloud-nodejs v20200210-0.2.2-30-g05865480-e3b0c4`
</details>
<details><summary>test/frontend-integration-test/selenium-standalone-chrome-gcloud-nodejs.Docker/Dockerfile</summary>
- `selenium/standalone-chrome 3.141.59-oxygen`
</details>
<details><summary>test/imagebuilder/Dockerfile</summary>
- `google/cloud-sdk 279.0.0`
</details>
<details><summary>test/images/Dockerfile</summary>
- `gcr.io/k8s-testimages/kubekins-e2e v20200204-8eefa86-master`
</details>
<details><summary>test/initialization-test/Dockerfile</summary>
- `golang 1.17`
</details>
<details><summary>test/release/Dockerfile.release</summary>
- `gcr.io/ml-pipeline-test/api-generator latest`
</details>
<details><summary>test/sample-test/Dockerfile</summary>
- `google/cloud-sdk 352.0.0`
</details>
<details><summary>tools/bazel_builder/Dockerfile</summary>
- `gcr.io/cloud-marketplace/google/rbe-ubuntu16-04 sha256:69c9f1652941d64a46f6f7358a44c1718f25caa5cb1ced4a58ccc5281cd183b5`
</details>
</blockquote>
</details>
<details><summary>gomod</summary>
<blockquote>
<details><summary>api/go.mod</summary>
- `go 1.16`
- `google.golang.org/genproto v0.0.0-20211026145609-4688e4c4e024@4688e4c4e024`
- `google.golang.org/protobuf v1.27.1`
</details>
<details><summary>go.mod</summary>
- `github.com/Masterminds/squirrel v0.0.0-20190107164353-fa735ea14f09@fa735ea14f09`
- `github.com/VividCortex/mysqlerr v0.0.0-20170204212430-6c6b55f8796f@6c6b55f8796f`
- `github.com/argoproj/argo-workflows/v3 v3.3.8`
- `github.com/aws/aws-sdk-go v1.42.50`
- `github.com/cenkalti/backoff v2.2.1+incompatible`
- `github.com/eapache/go-resiliency v1.2.0`
- `github.com/fsnotify/fsnotify v1.5.1`
- `github.com/ghodss/yaml v1.0.1-0.20190212211648-25d852aebe32`
- `github.com/go-openapi/errors v0.20.2`
- `github.com/go-openapi/runtime v0.21.1`
- `github.com/go-openapi/strfmt v0.21.1`
- `github.com/go-openapi/swag v0.19.15`
- `github.com/go-openapi/validate v0.20.3`
- `github.com/go-sql-driver/mysql v1.6.0`
- `github.com/golang/glog v1.0.0`
- `github.com/golang/protobuf v1.5.2`
- `github.com/google/addlicense v0.0.0-20200906110928-a0294312aa76@a0294312aa76`
- `github.com/google/cel-go v0.9.0`
- `github.com/google/go-cmp v0.5.7`
- `github.com/google/uuid v1.3.0`
- `github.com/gorilla/mux v1.8.0`
- `github.com/grpc-ecosystem/go-grpc-middleware v1.3.0`
- `github.com/grpc-ecosystem/grpc-gateway v1.16.0`
- `github.com/jinzhu/gorm v1.9.1`
- `github.com/kubeflow/pipelines/api v0.0.0-20220311022801-11635101d944@11635101d944`
- `github.com/kubeflow/pipelines/third_party/ml-metadata v0.0.0-20220118175555-e78ed557ddcb@e78ed557ddcb`
- `github.com/lestrrat-go/strftime v1.0.4`
- `github.com/mattn/go-sqlite3 v1.9.0`
- `github.com/minio/minio-go/v6 v6.0.57`
- `github.com/peterhellberg/duration v0.0.0-20191119133758-ec6baeebcd10@ec6baeebcd10`
- `github.com/pkg/errors v0.9.1`
- `github.com/prometheus/client_golang v1.12.1`
- `github.com/robfig/cron v1.2.0`
- `github.com/sirupsen/logrus v1.8.1`
- `github.com/spf13/viper v1.10.1`
- `github.com/stretchr/testify v1.7.0`
- `gocloud.dev v0.22.0`
- `golang.org/x/net v0.0.0-20220127200216-cd36cc0744dd@cd36cc0744dd`
- `google.golang.org/genproto v0.0.0-20220310185008-1973136f34c6@1973136f34c6`
- `google.golang.org/grpc v1.44.0`
- `google.golang.org/grpc/cmd/protoc-gen-go-grpc v1.1.0`
- `google.golang.org/protobuf v1.27.1`
- `gopkg.in/yaml.v2 v2.4.0`
- `k8s.io/api v0.23.3`
- `k8s.io/apimachinery v0.23.3`
- `k8s.io/client-go v0.23.3`
- `k8s.io/code-generator v0.23.3`
- `k8s.io/kubernetes v0.17.9`
- `sigs.k8s.io/controller-runtime v0.11.1`
- `go 1.13`
</details>
<details><summary>test/tools/project-cleaner/go.mod</summary>
- `go 1.16`
</details>
</blockquote>
</details>
<details><summary>npm</summary>
<blockquote>
<details><summary>frontend/mock-backend/package.json</summary>
- `@types/express ^4.16.0`
- `express ^4.16.3`
</details>
<details><summary>frontend/package.json</summary>
- `@craco/craco ^6.2.0`
- `@material-ui/core ^3.9.1`
- `@material-ui/icons ^3.0.1`
- `@types/lodash.groupby ^4.6.6`
- `@types/pako ^1.0.3`
- `brace ^0.11.1`
- `d3 ^5.7.0`
- `d3-dsv ^1.0.10`
- `dagre ^0.8.2`
- `google-protobuf ^3.11.2`
- `grpc-web ^1.2.1`
- `http-proxy-middleware ^0.19.0`
- `immer ^9.0.6`
- `js-yaml ^3.14.1`
- `lodash ^4.17.21`
- `lodash.debounce ^4.0.8`
- `lodash.flatten ^4.4.0`
- `lodash.groupby ^4.6.0`
- `lodash.isfunction ^3.0.9`
- `markdown-to-jsx ^6.10.3`
- `pako ^2.0.4`
- `portable-fetch ^3.0.0`
- `proto3-json-serializer ^0.1.6`
- `protobufjs ~6.11.2`
- `re-resizable ^4.9.0`
- `react ^16.12.0`
- `react-ace ^7.0.2`
- `react-dom ^16.12.0`
- `react-dropzone ^5.1.0`
- `react-flow-renderer ^9.6.3`
- `react-query ^3.16.0`
- `react-router-dom ^4.3.1`
- `react-svg-line-chart ^2.0.2`
- `react-textarea-autosize ^8.3.3`
- `react-virtualized ^9.20.1`
- `react-vis ^1.11.2`
- `request ^2.88.2`
- `runtypes ^6.3.0`
- `ts-proto ^1.95.0`
- `typestyle ^2.0.4`
- `@google-cloud/storage ^4.1.3`
- `@storybook/addon-actions ^6.3.6`
- `@storybook/addon-essentials ^6.3.6`
- `@storybook/addon-links ^6.3.6`
- `@storybook/node-logger ^6.3.6`
- `@storybook/preset-create-react-app ^3.2.0`
- `@storybook/react ^6.3.6`
- `@testing-library/dom ^8.6.0`
- `@testing-library/react ^11.2.6`
- `@testing-library/user-event ^13.2.1`
- `@types/d3 ^5.0.0`
- `@types/d3-dsv ^1.0.33`
- `@types/dagre ^0.7.40`
- `@types/enzyme ^3.10.3`
- `@types/enzyme-adapter-react-16 ^1.0.5`
- `@types/express ^4.16.0`
- `@types/google-protobuf ^3.7.2`
- `@types/http-proxy-middleware ^0.17.5`
- `@types/jest ^27.5.1`
- `@types/js-yaml ^3.12.3`
- `@types/lodash >=4.14.117`
- `@types/markdown-to-jsx ^6.9.0`
- `@types/node ^10.17.60`
- `@types/prettier ^1.19.0`
- `@types/react ^16.9.22`
- `@types/react-dom ^16.9.5`
- `@types/react-router-dom ^4.3.1`
- `@types/react-test-renderer ^16.0.2`
- `@types/react-virtualized ^9.18.7`
- `autoprefixer ^10.4.1`
- `browserslist 4.16.5`
- `coveralls ^3.0.2`
- `enzyme ^3.10.0`
- `enzyme-adapter-react-16 ^1.15.1`
- `enzyme-to-json ^3.3.4`
- `fs 0.0.1-security`
- `jest-environment-jsdom-sixteen ^2.0.0`
- `license-checker ^25.0.1`
- `postcss ^8.4.5`
- `prettier 1.19.1`
- `react-router-test-context ^0.1.0`
- `react-scripts ^5.0.0`
- `react-test-renderer ^16.5.2`
- `snapshot-diff ^0.6.1`
- `swagger-ts-client ^0.9.6`
- `tailwindcss ^3.0.11`
- `ts-node ^7.0.1`
- `ts-node-dev ^1.1.8`
- `tsconfig-paths ^3.10.1`
- `tslint-config-prettier ^1.18.0`
- `typescript ^3.8.3`
- `webpack-bundle-analyzer ^3.6.1`
- `yaml ^2.0.0`
</details>
<details><summary>frontend/server/package.json</summary>
- `@google-cloud/storage ^2.5.0`
- `@kubernetes/client-node ^0.8.2`
- `axios >=0.21.1`
- `crypto-js ^3.1.8`
- `express ^4.16.3`
- `gunzip-maybe ^1.4.1`
- `http-proxy-middleware ^0.18.0`
- `lodash >=4.17.21`
- `minio ^7.0.0`
- `node-fetch ^2.6.1`
- `peek-stream ^1.1.3`
- `portable-fetch ^3.0.0`
- `tar-stream ^2.1.0`
- `@types/crypto-js ^3.1.43`
- `@types/express ^4.11.1`
- `@types/gunzip-maybe ^1.4.0`
- `@types/http-proxy-middleware ^0.19.3`
- `@types/jest ^24.9.1`
- `@types/minio ^7.0.3`
- `@types/node ^10.17.11`
- `@types/node-fetch ^2.1.2`
- `@types/supertest ^2.0.8`
- `@types/tar ^4.0.3`
- `@types/tar-stream ^1.6.1`
- `jest ^25.3.0`
- `prettier 1.19.1`
- `supertest ^4.0.2`
- `ts-jest ^25.2.1`
- `tslint ^5.20.1`
- `typescript ^3.6.4`
</details>
<details><summary>package.json</summary>
- `standard-version ^8.0.0`
</details>
<details><summary>samples/contrib/pytorch-samples/package.json</summary>
- `npm ^8.11.0`
- `yarn ^1.22.10`
</details>
<details><summary>test/frontend-integration-test/package.json</summary>
- `lodash >=4.17.21`
- `mocha ^5.2.0`
- `wait-port ^0.2.2`
- `wdio-junit-reporter ^0.4.4`
- `wdio-mocha-framework ^0.6.2`
- `wdio-selenium-standalone-service 0.0.10`
- `webdriverio ^4.14.1`
</details>
</blockquote>
</details>
<details><summary>nvm</summary>
<blockquote>
<details><summary>frontend/.nvmrc</summary>
- `node v12.14.1`
</details>
</blockquote>
</details>
<details><summary>pip_requirements</summary>
<blockquote>
<details><summary>backend/metadata_writer/requirements.txt</summary>
- `absl-py ==0.12.0`
- `attrs ==20.3.0`
- `cachetools ==5.0.0`
- `certifi ==2021.10.8`
- `charset-normalizer ==2.0.10`
- `google-auth ==2.4.1`
- `grpcio ==1.43.0`
- `idna ==3.3`
- `kubernetes ==10.1.0`
- `lru-dict ==1.1.7`
- `ml-metadata ==1.5.0`
- `oauthlib ==3.1.1`
- `protobuf ==3.19.3`
- `pyasn1-modules ==0.2.8`
- `pyasn1 ==0.4.8`
- `python-dateutil ==2.8.2`
- `pyyaml ==3.13`
- `requests-oauthlib ==1.3.0`
- `requests ==2.27.1`
- `rsa ==4.8`
- `six ==1.16.0`
- `urllib3 ==1.26.8`
- `websocket-client ==1.2.3`
</details>
<details><summary>backend/requirements.txt</summary>
- `absl-py ==0.11.0`
- `apache-beam ==2.34.0`
- `argcomplete ==1.12.3`
- `argon2-cffi-bindings ==21.2.0`
- `argon2-cffi ==21.3.0`
- `astunparse ==1.6.3`
- `attrs ==20.3.0`
- `avro-python3 ==1.9.2.1`
- `backcall ==0.2.0`
- `bleach ==4.1.0`
- `cached-property ==1.5.2`
- `cachetools ==4.2.4`
- `certifi ==2021.10.8`
- `cffi ==1.15.0`
- `charset-normalizer ==2.0.9`
- `clang ==5.0`
- `click ==7.1.2`
- `cloudpickle ==2.0.0`
- `crcmod ==1.7`
- `debugpy ==1.5.1`
- `decorator ==5.1.0`
- `defusedxml ==0.7.1`
- `deprecated ==1.2.13`
- `dill ==0.3.1.1`
- `docker ==4.4.4`
- `docopt ==0.6.2`
- `docstring-parser ==0.13`
- `entrypoints ==0.3`
- `fastavro ==1.4.7`
- `fasteners ==0.16.3`
- `fire ==0.4.0`
- `flatbuffers ==1.12`
- `future ==0.18.2`
- `gast ==0.4.0`
- `google-api-core ==1.31.5`
- `google-api-python-client ==1.12.8`
- `google-apitools ==0.5.31`
- `google-auth-httplib2 ==0.1.0`
- `google-auth-oauthlib ==0.4.6`
- `google-auth ==1.35.0`
- `google-cloud-aiplatform ==1.8.1`
- `google-cloud-bigquery-storage ==2.10.1`
- `google-cloud-bigquery ==2.31.0`
- `google-cloud-bigtable ==1.7.0`
- `google-cloud-core ==1.7.2`
- `google-cloud-datastore ==1.15.3`
- `google-cloud-dlp ==1.0.0`
- `google-cloud-language ==1.3.0`
- `google-cloud-pubsub ==1.7.0`
- `google-cloud-recommendations-ai ==0.2.0`
- `google-cloud-spanner ==1.19.1`
- `google-cloud-storage ==1.43.0`
- `google-cloud-videointelligence ==1.16.1`
- `google-cloud-vision ==1.0.0`
- `google-crc32c ==1.3.0`
- `google-pasta ==0.2.0`
- `google-resumable-media ==2.1.0`
- `googleapis-common-protos ==1.54.0`
- `grpc-google-iam-v1 ==0.12.3`
- `grpcio-gcp ==0.2.2`
- `grpcio ==1.43.0`
- `h5py ==3.1.0`
- `hdfs ==2.6.0`
- `httplib2 ==0.19.1`
- `idna ==3.3`
- `importlib-metadata ==4.10.0`
- `ipykernel ==6.6.0`
- `ipython-genutils ==0.2.0`
- `ipython ==7.30.1`
- `ipywidgets ==7.6.5`
- `jedi ==0.18.1`
- `jinja2 ==3.0.3`
- `joblib ==0.14.1`
- `jsonschema ==3.2.0`
- `jupyter-client ==7.1.0`
- `jupyter-core ==4.9.1`
- `jupyterlab-pygments ==0.1.2`
- `jupyterlab-widgets ==1.0.2`
- `keras-preprocessing ==1.1.2`
- `keras-tuner ==1.1.0`
- `keras ==2.6.0`
- `kfp-pipeline-spec ==0.1.13`
- `kfp-server-api ==1.7.1`
- `kfp ==1.8.10`
- `kt-legacy ==1.0.4`
- `kubernetes ==12.0.1`
- `libcst ==0.3.23`
- `markdown ==3.3.6`
- `markupsafe ==2.0.1`
- `matplotlib-inline ==0.1.3`
- `mistune ==0.8.4`
- `ml-metadata ==1.4.0`
- `ml-pipelines-sdk ==1.4.0`
- `mypy-extensions ==0.4.3`
- `nbclient ==0.5.9`
- `nbconvert ==6.3.0`
- `nbformat ==5.1.3`
- `nest-asyncio ==1.5.4`
- `notebook ==6.4.6`
- `numpy ==1.19.5`
- `oauth2client ==4.1.3`
- `oauthlib ==3.1.1`
- `opt-einsum ==3.3.0`
- `orjson ==3.6.5`
- `packaging ==20.9`
- `pandas ==1.3.5`
- `pandocfilters ==1.5.0`
- `parso ==0.8.3`
- `pexpect ==4.8.0`
- `pickleshare ==0.7.5`
- `portpicker ==1.5.0`
- `prometheus-client ==0.12.0`
- `prompt-toolkit ==3.0.24`
- `proto-plus ==1.19.8`
- `protobuf ==3.19.1`
- `psutil ==5.8.0`
- `ptyprocess ==0.7.0`
- `pyarrow ==5.0.0`
- `pyasn1-modules ==0.2.8`
- `pyasn1 ==0.4.8`
- `pycparser ==2.21`
- `pydantic ==1.8.2`
- `pydot ==1.4.2`
- `pygments ==2.10.0`
- `pymongo ==3.12.3`
- `pyparsing ==2.4.7`
- `pyrsistent ==0.18.0`
- `python-dateutil ==2.8.2`
- `pytz ==2021.3`
- `pyyaml ==5.4.1`
- `pyzmq ==22.3.0`
- `requests-oauthlib ==1.3.0`
- `requests-toolbelt ==0.9.1`
- `requests ==2.26.0`
- `rsa ==4.8`
- `scipy ==1.7.3`
- `send2trash ==1.8.0`
- `six ==1.15.0`
- `strip-hints ==0.1.10`
- `tabulate ==0.8.9`
- `tensorboard-data-server ==0.6.1`
- `tensorboard-plugin-wit ==1.8.0`
- `tensorboard ==2.6.0`
- `tensorflow-data-validation ==1.4.0`
- `tensorflow-estimator ==2.6.0`
- `tensorflow-hub ==0.12.0`
- `tensorflow-metadata ==1.4.0`
- `tensorflow-model-analysis ==0.35.0`
- `tensorflow-serving-api ==2.6.2`
- `tensorflow-transform ==1.4.0`
- `tensorflow ==2.6.2`
- `termcolor ==1.1.0`
- `terminado ==0.12.1`
- `testpath ==0.5.0`
- `tfx-bsl ==1.4.0`
- `tfx ==1.4.0`
- `tornado ==6.1`
- `traitlets ==5.1.1`
- `typer ==0.4.0`
- `typing-extensions ==3.7.4.3`
- `typing-inspect ==0.7.1`
- `uritemplate ==3.0.1`
- `urllib3 ==1.26.7`
- `wcwidth ==0.2.5`
- `webencodings ==0.5.1`
- `websocket-client ==1.2.3`
- `werkzeug ==2.0.2`
- `wheel ==0.37.1`
- `widgetsnbextension ==3.5.2`
- `wrapt ==1.12.1`
- `zipp ==3.6.0`
</details>
<details><summary>backend/src/apiserver/visualization/requirements.txt</summary>
- `absl-py ==0.12.0`
- `apache-beam ==2.34.0`
- `argon2-cffi-bindings ==21.2.0`
- `argon2-cffi ==21.3.0`
- `astunparse ==1.6.3`
- `attrs ==21.2.0`
- `avro-python3 ==1.9.2.1`
- `backcall ==0.2.0`
- `bleach ==4.1.0`
- `bokeh ==1.2.0`
- `cached-property ==1.5.2`
- `cachetools ==4.2.4`
- `certifi ==2021.10.8`
- `cffi ==1.15.0`
- `charset-normalizer ==2.0.9`
- `crcmod ==1.7`
- `dataclasses ==0.8`
- `decorator ==5.1.0`
- `defusedxml ==0.7.1`
- `dill ==0.3.1.1`
- `docopt ==0.6.2`
- `entrypoints ==0.3`
- `fastavro ==1.4.7`
- `fasteners ==0.16.3`
- `flatbuffers ==1.12`
- `future ==0.18.2`
- `gast ==0.4.0`
- `gcsfs ==0.2.3`
- `google-api-core ==1.31.5`
- `google-api-python-client ==1.7.12`
- `google-apitools ==0.5.31`
- `google-auth-httplib2 ==0.1.0`
- `google-auth-oauthlib ==0.4.6`
- `google-auth ==1.35.0`
- `google-cloud-bigquery-storage ==2.10.1`
- `google-cloud-bigquery ==2.20.0`
- `google-cloud-bigtable ==1.7.0`
- `google-cloud-core ==1.7.2`
- `google-cloud-datastore ==1.15.3`
- `google-cloud-dlp ==1.0.0`
- `google-cloud-language ==1.3.0`
- `google-cloud-pubsub ==1.7.0`
- `google-cloud-recommendations-ai ==0.2.0`
- `google-cloud-spanner ==1.19.1`
- `google-cloud-videointelligence ==1.16.1`
- `google-cloud-vision ==1.0.0`
- `google-crc32c ==1.3.0`
- `google-pasta ==0.2.0`
- `google-resumable-media ==1.3.3`
- `googleapis-common-protos ==1.54.0`
- `grpc-google-iam-v1 ==0.12.3`
- `grpcio-gcp ==0.2.2`
- `grpcio ==1.34.1`
- `h5py ==3.1.0`
- `hdfs ==2.6.0`
- `httplib2 ==0.19.1`
- `idna ==3.3`
- `importlib-metadata ==4.8.3`
- `ipykernel ==5.1.1`
- `ipython-genutils ==0.2.0`
- `ipython ==7.12.0`
- `ipywidgets ==7.6.5`
- `itables ==0.1.0`
- `jedi ==0.18.1`
- `jinja2 ==3.0.3`
- `joblib ==0.14.1`
- `jsonschema ==3.2.0`
- `jupyter-client ==5.3.5`
- `jupyter-core ==4.9.1`
- `jupyterlab-widgets ==1.0.2`
- `keras-nightly ==2.5.0.dev2021032900`
- `keras-preprocessing ==1.1.2`
- `libcst ==0.3.23`
- `markdown ==3.3.6`
- `markupsafe ==2.0.1`
- `mistune ==0.8.4`
- `mypy-extensions ==0.4.3`
- `nbconvert ==5.5.0`
- `nbformat ==4.4.0`
- `nest-asyncio ==1.5.4`
- `notebook ==6.4.6`
- `numpy ==1.19.5`
- `oauth2client ==4.1.3`
- `oauthlib ==3.1.1`
- `opt-einsum ==3.3.0`
- `orjson ==3.6.1`
- `packaging ==21.3`
- `pandas ==1.1.5`
- `pandocfilters ==1.5.0`
- `parso ==0.8.3`
- `pexpect ==4.8.0`
- `pickleshare ==0.7.5`
- `pillow ==8.4.0`
- `prometheus-client ==0.12.0`
- `prompt-toolkit ==3.0.24`
- `proto-plus ==1.19.8`
- `protobuf ==3.19.1`
- `ptyprocess ==0.7.0`
- `pyarrow ==2.0.0`
- `pyasn1-modules ==0.2.8`
- `pyasn1 ==0.4.8`
- `pycparser ==2.21`
- `pydot ==1.4.2`
- `pygments ==2.10.0`
- `pymongo ==3.12.3`
- `pyparsing ==2.4.7`
- `pyrsistent ==0.18.0`
- `python-dateutil ==2.8.2`
- `pytz ==2021.3`
- `pyyaml ==6.0`
- `pyzmq ==22.3.0`
- `requests-oauthlib ==1.3.0`
- `requests ==2.26.0`
- `rsa ==4.8`
- `scikit_learn ==0.21.2`
- `scipy ==1.5.4`
- `send2trash ==1.8.0`
- `six ==1.15.0`
- `tensorboard-data-server ==0.6.1`
- `tensorboard-plugin-wit ==1.8.0`
- `tensorboard ==2.7.0`
- `tensorflow-data-validation ==1.2.0`
- `tensorflow-estimator ==2.5.0`
- `tensorflow-metadata ==1.2.0`
- `tensorflow-model-analysis ==0.33.0`
- `tensorflow-serving-api ==2.5.1`
- `tensorflow ==2.5.1`
- `termcolor ==1.1.0`
- `terminado ==0.12.1`
- `testpath ==0.5.0`
- `tfx-bsl ==1.2.0`
- `tornado ==6.1`
- `traitlets ==4.3.3`
- `typing-extensions ==3.7.4.3`
- `typing-inspect ==0.7.1`
- `uritemplate ==3.0.1`
- `urllib3 ==1.26.7`
- `wcwidth ==0.2.5`
- `webencodings ==0.5.1`
- `werkzeug ==2.0.2`
- `wheel ==0.37.1`
- `widgetsnbextension ==3.5.2`
- `wrapt ==1.12.1`
- `zipp ==3.6.0`
</details>
<details><summary>backend/src/v2/test/requirements.txt</summary>
- `ml-metadata ==1.5.0`
- `minio ==7.0.4`
- `google-cloud-storage no version found`
</details>
<details><summary>components/contrib/arena/docker/requirements.txt</summary>
- `requests ==2.18.4`
- `six ==1.11.0`
- `pyyaml ==3.12`
</details>
<details><summary>components/contrib/sample/keras/train_classifier/requirements.txt</summary>
- `keras no version found`
</details>
<details><summary>components/gcp/container/component_sdk/python/test-requirements.txt</summary>
- `flake8 no version found`
- `pytest no version found`
- `mock no version found`
</details>
<details><summary>components/gcp/container/requirements.txt</summary>
- `pandas no version found`
</details>
<details><summary>components/kserve/requirements.txt</summary>
- `kubernetes ==19.15.0`
- `kserve ==0.7.0`
</details>
<details><summary>components/kubeflow/dnntrainer/requirements.txt</summary>
- `pyyaml ==3.12`
- `six ==1.11.0`
- `tensorflow-transform ==0.23.0`
- `tensorflow-model-analysis ==0.23.0`
</details>
<details><summary>components/kubeflow/katib-launcher/requirements.txt</summary>
- `kubernetes ==10.0.1`
- `kubeflow-katib ==0.10.1`
</details>
<details><summary>components/kubeflow/kfserving/requirements.txt</summary>
- `kubernetes ==12.0.0`
- `kfserving ==0.5.1`
</details>
<details><summary>components/kubeflow/launcher/requirements.txt</summary>
- `pyyaml no version found`
- `kubernetes no version found`
</details>
<details><summary>components/kubeflow/pytorch-launcher/requirements.txt</summary>
- `pyyaml no version found`
- `kubernetes no version found`
- `kubeflow-pytorchjob no version found`
- `retrying no version found`
</details>
<details><summary>components/local/base/requirements.txt</summary>
- `pandas ==0.24.2`
- `scikit-learn ==0.21.2`
- `scipy ==1.4.1`
- `tensorflow ==2.2.0`
</details>
<details><summary>contrib/components/openvino/ovms-deployer/containers/requirements.txt</summary>
- `jinja2 ==2.11.3`
- `futures ==3.1.1`
- `tensorflow-serving-api ==1.13.0`
</details>
<details><summary>contrib/components/openvino/predict/containers/requirements.txt</summary>
- `numpy no version found`
- `google-cloud-storage no version found`
</details>
<details><summary>docs/requirements.txt</summary>
- `sphinx ==5.0.2`
- `sphinx-click ==4.3.0`
- `sphinx-rtd-theme ==1.0.0`
- `m2r2 ==0.3.2`
</details>
<details><summary>proxy/requirements.txt</summary>
- `requests no version found`
</details>
<details><summary>requirements.txt</summary>
- `absl-py ==0.11.0`
- `ansiwrap ==0.8.4`
- `apache-beam ==2.31.0`
- `appdirs ==1.4.4`
- `argcomplete ==1.12.3`
- `argon2-cffi ==20.1.0`
- `astunparse ==1.6.3`
- `attrs ==20.3.0`
- `avro-python3 ==1.9.2.1`
- `backcall ==0.2.0`
- `black ==21.7b0`
- `bleach ==4.0.0`
- `cached-property ==1.5.2`
- `cachetools ==4.2.2`
- `certifi ==2021.5.30`
- `cffi ==1.14.6`
- `charset-normalizer ==2.0.4`
- `click ==7.1.2`
- `cloudpickle ==1.6.0`
- `colorama ==0.4.4`
- `crcmod ==1.7`
- `debugpy ==1.4.1`
- `decorator ==5.0.9`
- `defusedxml ==0.7.1`
- `deprecated ==1.2.12`
- `dill ==0.3.1.1`
- `docker ==4.4.4`
- `docopt ==0.6.2`
- `docstring-parser ==0.10`
- `entrypoints ==0.3`
- `fastavro ==1.4.4`
- `fasteners ==0.16.3`
- `fire ==0.4.0`
- `flatbuffers ==1.12`
- `future ==0.18.2`
- `gast ==0.4.0`
- `google-api-core ==1.31.2`
- `google-api-python-client ==1.12.8`
- `google-apitools ==0.5.31`
- `google-auth-httplib2 ==0.1.0`
- `google-auth-oauthlib ==0.4.5`
- `google-auth ==1.35.0`
- `google-cloud-aiplatform ==0.7.1`
- `google-cloud-bigquery ==1.28.0`
- `google-cloud-bigtable ==1.7.0`
- `google-cloud-core ==1.7.2`
- `google-cloud-datastore ==1.15.3`
- `google-cloud-dlp ==1.0.0`
- `google-cloud-language ==1.3.0`
- `google-cloud-profiler ==3.0.5`
- `google-cloud-pubsub ==1.7.0`
- `google-cloud-spanner ==1.19.1`
- `google-cloud-storage ==1.42.0`
- `google-cloud-videointelligence ==1.16.1`
- `google-cloud-vision ==1.0.0`
- `google-crc32c ==1.1.2`
- `google-pasta ==0.2.0`
- `google-resumable-media ==1.3.3`
- `googleapis-common-protos ==1.53.0`
- `grpc-google-iam-v1 ==0.12.3`
- `grpcio-gcp ==0.2.2`
- `grpcio ==1.34.1`
- `h5py ==3.1.0`
- `hdfs ==2.6.0`
- `httplib2 ==0.19.1`
- `idna ==3.2`
- `importlib-metadata ==4.6.4`
- `ipykernel ==6.2.0`
- `ipython-genutils ==0.2.0`
- `ipython ==7.26.0`
- `ipywidgets ==7.6.3`
- `jedi ==0.18.0`
- `jinja2 ==2.11.3`
- `joblib ==0.14.1`
- `jsonschema ==3.2.0`
- `junit-xml ==1.9`
- `jupyter-client ==6.1.12`
- `jupyter-core ==4.7.1`
- `jupyterlab-pygments ==0.1.2`
- `jupyterlab-widgets ==1.0.0`
- `keras-nightly ==2.5.0.dev2021032900`
- `keras-preprocessing ==1.1.2`
- `keras-tuner ==1.0.1`
- `kfp-pipeline-spec ==0.1.9`
- `kfp-server-api ==1.6.0`
- `kfp ==1.7.1`
- `kubernetes ==12.0.1`
- `markdown ==3.3.4`
- `markupsafe ==2.0.1`
- `matplotlib-inline ==0.1.2`
- `minio ==7.1.0`
- `mistune ==0.8.4`
- `ml-metadata ==1.2.0`
- `ml-pipelines-sdk ==1.2.0`
- `mypy-extensions ==0.4.3`
- `nbclient ==0.5.4`
- `nbconvert ==6.1.0`
- `nbformat ==5.1.3`
- `nest-asyncio ==1.5.1`
- `notebook ==6.4.3`
- `numpy ==1.19.5`
- `oauth2client ==4.1.3`
- `oauthlib ==3.1.1`
- `opt-einsum ==3.3.0`
- `packaging ==20.9`
- `pandas ==1.3.2`
- `pandocfilters ==1.4.3`
- `papermill ==2.3.3`
- `parso ==0.8.2`
- `pathspec ==0.9.0`
- `pexpect ==4.8.0`
- `pickleshare ==0.7.5`
- `portpicker ==1.4.0`
- `prometheus-client ==0.11.0`
- `prompt-toolkit ==3.0.19`
- `proto-plus ==1.19.0`
- `protobuf ==3.17.3`
- `ptyprocess ==0.7.0`
- `pyarrow ==2.0.0`
- `pyasn1-modules ==0.2.8`
- `pyasn1 ==0.4.8`
- `pycparser ==2.20`
- `pydot ==1.4.2`
- `pygments ==2.10.0`
- `pymongo ==3.12.0`
- `pyparsing ==2.4.7`
- `pyrsistent ==0.18.0`
- `python-dateutil ==2.8.2`
- `pytz ==2021.1`
- `pyyaml ==5.4.1`
- `pyzmq ==22.2.1`
- `regex ==2021.8.3`
- `requests-oauthlib ==1.3.0`
- `requests-toolbelt ==0.9.1`
- `requests ==2.26.0`
- `rsa ==4.7.2`
- `scikit-learn ==0.24.2`
- `scipy ==1.7.1`
- `send2trash ==1.8.0`
- `six ==1.15.0`
- `strip-hints ==0.1.10`
- `tabulate ==0.8.9`
- `tenacity ==8.0.1`
- `tensorboard-data-server ==0.6.1`
- `tensorboard-plugin-wit ==1.8.0`
- `tensorboard ==2.6.0`
- `tensorflow-data-validation ==1.2.0`
- `tensorflow-estimator ==2.5.0`
- `tensorflow-hub ==0.12.0`
- `tensorflow-metadata ==1.2.0`
- `tensorflow-model-analysis ==0.33.0`
- `tensorflow-serving-api ==2.5.1`
- `tensorflow-transform ==1.2.0`
- `tensorflow ==2.5.1`
- `termcolor ==1.1.0`
- `terminado ==0.11.0`
- `terminaltables ==3.1.0`
- `testpath ==0.5.0`
- `textwrap3 ==0.9.2`
- `tfx-bsl ==1.2.0`
- `tfx ==1.2.0`
- `threadpoolctl ==2.2.0`
- `tomli ==1.2.1`
- `tornado ==6.1`
- `tqdm ==4.62.1`
- `traitlets ==5.0.5`
- `typed-ast ==1.4.3`
- `typing-extensions ==3.7.4.3`
- `uritemplate ==3.0.1`
- `urllib3 ==1.26.6`
- `wcwidth ==0.2.5`
- `webencodings ==0.5.1`
- `websocket-client ==1.2.1`
- `werkzeug ==2.0.1`
- `wheel ==0.37.0`
- `widgetsnbextension ==3.5.1`
- `wrapt ==1.12.1`
- `yamale ==3.0.8`
- `zipp ==3.5.0`
</details>
<details><summary>samples/contrib/azure-samples/databricks-pipelines/requirements.txt</summary>
- `fire >=0.2.1`
</details>
<details><summary>samples/contrib/ibm-samples/ffdl-seldon/source/seldon-pytorch-serving-image/requirements.txt</summary>
- `torch ==1.0.0`
- `torchvision ==0.2.1`
- `boto3 ==1.9.83`
- `pandas no version found`
- `numpy no version found`
- `pyyaml no version found`
- `torchsummary no version found`
- `numpy no version found`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/preprocess/requirements.txt</summary>
- `keras no version found`
</details>
<details><summary>samples/contrib/nvidia-resnet/components/train/requirements.txt</summary>
- `keras no version found`
</details>
<details><summary>samples/contrib/pytorch-samples/bert/requirements.txt</summary>
- `pytorch-lightning no version found`
- `sklearn no version found`
- `captum no version found`
- `torchtext no version found`
</details>
<details><summary>samples/contrib/pytorch-samples/cifar10/requirements.txt</summary>
- `pytorch-lightning no version found`
- `sklearn no version found`
- `captum no version found`
- `torchtext no version found`
</details>
<details><summary>samples/contrib/pytorch-samples/requirements.txt</summary>
- `boto3 no version found`
- `image no version found`
- `matplotlib no version found`
- `pyarrow no version found`
- `sklearn no version found`
- `transformers no version found`
- `torchdata no version found`
- `webdataset no version found`
- `pandas no version found`
- `s3fs no version found`
- `wget no version found`
- `torch-model-archiver no version found`
- `minio no version found`
- `kfp no version found`
- `tensorboard no version found`
- `torchmetrics no version found`
- `pytorch-lightning no version found`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/download_dataset/requirements.txt</summary>
- `kaggle no version found`
- `google-cloud-storage no version found`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/submit_result/requirements.txt</summary>
- `kaggle no version found`
- `gcsfs no version found`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/train_model/requirements.txt</summary>
- `pandas ==0.25.1`
- `gcsfs no version found`
- `numpy no version found`
- `matplotlib no version found`
- `seaborn no version found`
- `sklearn no version found`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/visualize_html/requirements.txt</summary>
- `gcsfs no version found`
- `pandas no version found`
- `matplotlib no version found`
- `seaborn no version found`
</details>
<details><summary>samples/contrib/versioned-pipeline-ci-samples/kaggle-ci-sample/visualize_table/requirements.txt</summary>
- `gcsfs no version found`
- `pandas no version found`
</details>
<details><summary>sdk/python/requirements.txt</summary>
- `absl-py ==1.0.0`
- `attrs ==21.4.0`
- `cachetools ==5.0.0`
- `certifi ==2021.10.8`
- `charset-normalizer ==2.0.12`
- `click ==8.1.3`
- `cloudpickle ==2.0.0`
- `deprecated ==1.2.13`
- `docstring-parser ==0.14.1`
- `fire ==0.4.0`
- `google-api-core ==2.7.3`
- `google-auth ==2.6.6`
- `google-cloud-core ==2.3.0`
- `google-cloud-storage ==2.3.0`
- `google-crc32c ==1.3.0`
- `google-resumable-media ==2.3.2`
- `googleapis-common-protos ==1.56.0`
- `idna ==3.3`
- `importlib-metadata ==4.11.3`
- `jsonschema ==3.2.0`
- `kfp-pipeline-spec ==0.1.14`
- `kfp-server-api ==2.0.0a2`
- `kubernetes ==18.20.0`
- `oauthlib ==3.2.0`
- `protobuf ==3.20.1`
- `pyasn1 ==0.4.8`
- `pyasn1-modules ==0.2.8`
- `pyrsistent ==0.18.1`
- `python-dateutil ==2.8.2`
- `pyyaml ==5.4.1`
- `requests ==2.27.1`
- `requests-oauthlib ==1.3.1`
- `requests-toolbelt ==0.9.1`
- `rsa ==4.8`
- `six ==1.16.0`
- `strip-hints ==0.1.10`
- `tabulate ==0.8.9`
- `termcolor ==1.1.0`
- `typer ==0.4.1`
- `typing-extensions ==4.2.0`
- `uritemplate ==3.0.1`
- `urllib3 ==1.26.9`
- `websocket-client ==1.3.2`
- `wheel ==0.37.1`
- `wrapt ==1.14.1`
- `zipp ==3.8.0`
</details>
<details><summary>test/kfp-functional-test/requirements.txt</summary>
- `attrs ==20.3.0`
- `cachetools ==4.1.1`
- `certifi ==2020.12.5`
- `cffi ==1.14.4`
- `chardet ==3.0.4`
- `click ==7.1.2`
- `cloudpickle ==1.6.0`
- `deprecated ==1.2.10`
- `docstring-parser ==0.7.3`
- `google-api-core ==1.23.0`
- `google-auth ==1.23.0`
- `google-cloud-core ==1.4.4`
- `google-cloud-storage ==1.32.0`
- `google-crc32c ==1.0.0`
- `google-resumable-media ==1.1.0`
- `googleapis-common-protos ==1.52.0`
- `idna ==2.10`
- `importlib-metadata ==2.1.1`
- `jsonschema ==3.2.0`
- `kfp-pipeline-spec ==0.1.3.1`
- `kfp-server-api ==1.1.2rc1`
- `kfp ==1.1.2`
- `kubernetes ==11.0.0`
- `oauthlib ==3.1.0`
- `protobuf ==3.14.0`
- `pyasn1-modules ==0.2.8`
- `pyasn1 ==0.4.8`
- `pycparser ==2.20`
- `pyrsistent ==0.17.3`
- `python-dateutil ==2.8.1`
- `pytz ==2020.4`
- `pyyaml ==5.3.1`
- `requests-oauthlib ==1.3.0`
- `requests-toolbelt ==0.9.1`
- `requests ==2.25.0`
- `rsa ==4.6`
- `six ==1.15.0`
- `strip-hints ==0.1.9`
- `tabulate ==0.8.7`
- `urllib3 ==1.26.2`
- `websocket-client ==0.57.0`
- `wheel ==0.36.1`
- `wrapt ==1.12.1`
- `zipp ==1.2.0`
</details>
<details><summary>test/sample-test/requirements.txt</summary>
- `absl-py ==0.11.0`
- `ansiwrap ==0.8.4`
- `apache-beam ==2.34.0`
- `appdirs ==1.4.4`
- `argcomplete ==1.12.3`
- `argon2-cffi-bindings ==21.2.0`
- `argon2-cffi ==21.3.0`
- `astunparse ==1.6.3`
- `attrs ==20.3.0`
- `avro-python3 ==1.9.2.1`
- `backcall ==0.2.0`
- `black ==21.7b0`
- `bleach ==4.1.0`
- `cached-property ==1.5.2`
- `cachetools ==4.2.4`
- `certifi ==2021.10.8`
- `cffi ==1.15.0`
- `charset-normalizer ==2.0.9`
- `clang ==5.0`
- `click ==7.1.2`
- `cloudpickle ==2.0.0`
- `crcmod ==1.7`
- `debugpy ==1.5.1`
- `decorator ==5.1.0`
- `defusedxml ==0.7.1`
- `deprecated ==1.2.13`
- `dill ==0.3.1.1`
- `docker ==4.4.4`
- `docopt ==0.6.2`
- `docstring-parser ==0.13`
- `entrypoints ==0.3`
- `fastavro ==1.4.7`
- `fasteners ==0.16.3`
- `fire ==0.4.0`
- `flatbuffers ==1.12`
- `future ==0.18.2`
- `gast ==0.4.0`
- `google-api-core ==1.31.5`
- `google-api-python-client ==1.12.8`
- `google-apitools ==0.5.31`
- `google-auth-httplib2 ==0.1.0`
- `google-auth-oauthlib ==0.4.6`
- `google-auth ==1.35.0`
- `google-cloud-aiplatform ==1.8.1`
- `google-cloud-bigquery-storage ==2.10.1`
- `google-cloud-bigquery ==2.31.0`
- `google-cloud-bigtable ==1.7.0`
- `google-cloud-core ==1.7.2`
- `google-cloud-datastore ==1.15.3`
- `google-cloud-dlp ==1.0.0`
- `google-cloud-language ==1.3.0`
- `google-cloud-pubsub ==1.7.0`
- `google-cloud-recommendations-ai ==0.2.0`
- `google-cloud-spanner ==1.19.1`
- `google-cloud-storage ==1.43.0`
- `google-cloud-videointelligence ==1.16.1`
- `google-cloud-vision ==1.0.0`
- `google-crc32c ==1.3.0`
- `google-pasta ==0.2.0`
- `google-resumable-media ==2.1.0`
- `googleapis-common-protos ==1.54.0`
- `grpc-google-iam-v1 ==0.12.3`
- `grpcio-gcp ==0.2.2`
- `grpcio ==1.43.0`
- `h5py ==3.1.0`
- `hdfs ==2.6.0`
- `httplib2 ==0.19.1`
- `idna ==3.3`
- `importlib-metadata ==4.10.0`
- `ipykernel ==6.6.0`
- `ipython-genutils ==0.2.0`
- `ipython ==7.30.1`
- `ipywidgets ==7.6.5`
- `jedi ==0.18.1`
- `jinja2 ==3.0.3`
- `joblib ==0.14.1`
- `jsonschema ==3.2.0`
- `junit-xml ==1.9`
- `jupyter-client ==7.1.0`
- `jupyter-core ==4.9.1`
- `jupyterlab-pygments ==0.1.2`
- `jupyterlab-widgets ==1.0.2`
- `keras-preprocessing ==1.1.2`
- `keras-tuner ==1.1.0`
- `keras ==2.6.0`
- `kfp-pipeline-spec ==0.1.13`
- `kfp-server-api ==1.7.1`
- `kfp ==1.8.10`
- `kt-legacy ==1.0.4`
- `kubernetes ==12.0.1`
- `libcst ==0.3.23`
- `markdown ==3.3.6`
- `markupsafe ==2.0.1`
- `matplotlib-inline ==0.1.3`
- `minio ==7.1.2`
- `mistune ==0.8.4`
- `ml-metadata ==1.4.0`
- `ml-pipelines-sdk ==1.4.0`
- `mypy-extensions ==0.4.3`
- `nbclient ==0.5.9`
- `nbconvert ==6.3.0`
- `nbformat ==5.1.3`
- `nest-asyncio ==1.5.4`
- `notebook ==6.4.6`
- `numpy ==1.19.5`
- `oauth2client ==4.1.3`
- `oauthlib ==3.1.1`
- `opt-einsum ==3.3.0`
- `orjson ==3.6.5`
- `packaging ==20.9`
- `pandas ==1.3.5`
- `pandocfilters ==1.5.0`
- `papermill ==2.3.3`
- `parso ==0.8.3`
- `pathspec ==0.9.0`
- `pexpect ==4.8.0`
- `pickleshare ==0.7.5`
- `portpicker ==1.5.0`
- `prometheus-client ==0.12.0`
- `prompt-toolkit ==3.0.24`
- `proto-plus ==1.19.8`
- `protobuf ==3.19.1`
- `psutil ==5.8.0`
- `ptyprocess ==0.7.0`
- `pyarrow ==5.0.0`
- `pyasn1-modules ==0.2.8`
- `pyasn1 ==0.4.8`
- `pycparser ==2.21`
- `pydantic ==1.8.2`
- `pydot ==1.4.2`
- `pygments ==2.10.0`
- `pymongo ==3.12.3`
- `pyparsing ==2.4.7`
- `pyrsistent ==0.18.0`
- `python-dateutil ==2.8.2`
- `pytz ==2021.3`
- `pyyaml ==5.4.1`
- `pyzmq ==22.3.0`
- `regex ==2021.11.10`
- `requests-oauthlib ==1.3.0`
- `requests-toolbelt ==0.9.1`
- `requests ==2.26.0`
- `rsa ==4.8`
- `scipy ==1.7.3`
- `send2trash ==1.8.0`
- `six ==1.15.0`
- `strip-hints ==0.1.10`
- `tabulate ==0.8.9`
- `tenacity ==8.0.1`
- `tensorboard-data-server ==0.6.1`
- `tensorboard-plugin-wit ==1.8.0`
- `tensorboard ==2.6.0`
- `tensorflow-data-validation ==1.4.0`
- `tensorflow-estimator ==2.6.0`
- `tensorflow-hub ==0.12.0`
- `tensorflow-metadata ==1.4.0`
- `tensorflow-model-analysis ==0.35.0`
- `tensorflow-serving-api ==2.6.2`
- `tensorflow-transform ==1.4.0`
- `tensorflow ==2.6.2`
- `termcolor ==1.1.0`
- `terminado ==0.12.1`
- `testpath ==0.5.0`
- `textwrap3 ==0.9.2`
- `tfx-bsl ==1.4.0`
- `tfx ==1.4.0`
- `tomli ==1.2.3`
- `tornado ==6.1`
- `tqdm ==4.62.3`
- `traitlets ==5.1.1`
- `typed-ast ==1.5.1`
- `typer ==0.4.0`
- `typing-extensions ==3.7.4.3`
- `typing-inspect ==0.7.1`
- `uritemplate ==3.0.1`
- `urllib3 ==1.26.7`
- `wcwidth ==0.2.5`
- `webencodings ==0.5.1`
- `websocket-client ==1.2.3`
- `werkzeug ==2.0.2`
- `wheel ==0.37.1`
- `widgetsnbextension ==3.5.2`
- `wrapt ==1.12.1`
- `yamale ==4.0.2`
- `zipp ==3.6.0`
</details>
</blockquote>
</details>
<details><summary>pip_setup</summary>
<blockquote>
<details><summary>api/v2alpha1/python/setup.py</summary>
- `protobuf >=3.13.0,<4`
</details>
<details><summary>components/PyTorch/pytorch-kfp-components/setup.py</summary>
- `pytorch-lightning >=1.4.0`
- `torch >=1.7.1`
- `mock >=4.0.0`
- `flake8 >=3.0.0`
- `pytest >=6.0.0`
</details>
<details><summary>components/contrib/arena/python/setup.py</summary>
- `kfp >= 0.1`
</details>
<details><summary>components/gcp/container/component_sdk/python/setup.py</summary>
- `kubernetes >= 8.0.1`
- `urllib3 >=1.15,<1.25`
- `fire == 0.1.3`
- `google-api-python-client == 1.7.8`
- `google-cloud-storage == 1.14.0`
- `google-cloud-bigquery == 1.9.0`
</details>
<details><summary>components/kubeflow/dnntrainer/src/setup.py</summary>
- `tensorflow ==1.15.4`
</details>
<details><summary>samples/test/utils/setup.py</summary>
- `nbconvert ~=6.0`
</details>
</blockquote>
</details>
---
- [ ] <!-- manual job -->Check this box to trigger a request for Renovate to run again on this repository
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https://api.github.com/repos/kubeflow/pipelines/issues/5067 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5067/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5067/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5067/events | https://github.com/kubeflow/pipelines/issues/5067 | 798,279,201 | MDU6SXNzdWU3OTgyNzkyMDE= | 5,067 | Dataset location on BigQuery component | {
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"I think the location should be exposed in the https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/to_CSV/component.yaml",
"/cc @chensun @neuromage ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity o... | 2021-02-01T11:55:35 | 2022-04-28T18:00:19 | 2022-04-28T18:00:19 | NONE | null | I am using the [bigquery to_CSV component](https://raw.githubusercontent.com/kubeflow/pipelines/1.3.0/components/gcp/bigquery/query/to_CSV/component.yaml) like so:
```python
from kfp import dsl
from kfp.components import load_component_from_url
BQ_SPEC_URI = "https://raw.githubusercontent.com/kubeflow/pipelines/1.3.0/components/gcp/bigquery/query/to_CSV/component.yaml"
bq_to_csv_op = load_component_from_url(BQ_SPEC_URI)
...
@dsl.pipeline(name="My first pipeline", description="A hello world pipeline.")
def print_query_result_pipeline(
project_id=PROJECT_ID, query=QUERY, filename=FILENAME
):
bq_to_gcs_task = bq_to_csv_op(
query=query, project_id=project_id, output_filename=filename
)
```
The query tries to acces a dataset with location EU multiregional. My pipeline fails with `google.api_core.exceptions.NotFound: 404 Not found: Dataset XXXXX was not found in location US`.
I've noticed that, in the [component Python code](https://github.com/kubeflow/pipelines/blob/master/components/gcp/container/component_sdk/python/kfp_component/google/bigquery/_query.py), there is an `dataset_location` argument to the `_query()` function that is set 'US' by default. Nonetheless, that argument isn't "exposed" in the runtime arguments of the component's YAML. Should a new argument be added to this file or should the dataset_location be passed otherwise?
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https://api.github.com/repos/kubeflow/pipelines/issues/5061 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5061/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5061/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5061/events | https://github.com/kubeflow/pipelines/issues/5061 | 797,026,728 | MDU6SXNzdWU3OTcwMjY3Mjg= | 5,061 | present KFP at the Argo Workflows community meeting | {
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"Thank you for the invitation!\nI'm recently very busy with KFP v2 design I just shared with community. I'd love to sign up for a slot after a while when I get more time.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. T... | 2021-01-29T16:43:22 | 2022-04-29T03:59:43 | 2022-04-29T03:59:43 | NONE | null | We'd love you to come and present at our community meeting! http://bit.ly/argo-wf-cmty-mtng
Or what about writing a guest blog post? https://blog.argoproj.io/
I think our community would be super interested. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5055 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5055/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5055/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5055/events | https://github.com/kubeflow/pipelines/issues/5055 | 796,655,777 | MDU6SXNzdWU3OTY2NTU3Nzc= | 5,055 | Feature Request: kfp.dsl.ResourceOp should have a caching strategy option | {
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"ResourceOp is implemented using Argo. Fixing #4083 should provide this feature."
] | 2021-01-29T07:36:00 | 2021-01-29T07:44:17 | 2021-01-29T07:44:17 | CONTRIBUTOR | null | Currently only kfp.dsl.ContainerOp has caching strategy option, ResourceOp should have similar options, this Op also gets cached by minio.
REF: https://www.kubeflow.org/docs/pipelines/caching/#managing-caching-staleness
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https://api.github.com/repos/kubeflow/pipelines/issues/5053 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5053/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5053/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5053/events | https://github.com/kubeflow/pipelines/issues/5053 | 796,609,645 | MDU6SXNzdWU3OTY2MDk2NDU= | 5,053 | TypeErro occurs in gcp/automl/create_dataset_for_tables component | {
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"followers_url": "https://api.githu... | null | [] | 2021-01-29T06:09:04 | 2021-03-09T21:44:25 | 2021-01-29T07:33:02 | CONTRIBUTOR | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
[gcp/automl/create_dataset_for_tables component](https://github.com/kubeflow/pipelines/tree/master/components/gcp/automl/create_dataset_for_tables)'s `create_time` output is declared as a string:
https://github.com/kubeflow/pipelines/blob/ecb14f40bb819c0678589b6458892ece5369fa71/components/gcp/automl/create_dataset_for_tables/component.yaml#L15
however, `google.protobuf.timestamp_pb2.Timestamp` is returned in actual fact:
https://github.com/kubeflow/pipelines/blob/ecb14f40bb819c0678589b6458892ece5369fa71/components/gcp/automl/create_dataset_for_tables/component.py#L54
FYI: The `dataset` object is an instance of `google.cloud.automl_v1beta1.types.Dataset` class and its [document](https://googleapis.dev/python/automl/0.4.0/gapic/v1beta1/types.html#google.cloud.automl_v1beta1.types.Dataset.create_time) says:
> **create_time**
> Output only. Timestamp when this dataset was created.
### What happened:
`TypeError` occurs
### What did you expect to happen:
Work.
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)? AI Platform Pipelines
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: 1.0.4 <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: 1.3.0 <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5051 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5051/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5051/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5051/events | https://github.com/kubeflow/pipelines/issues/5051 | 796,312,193 | MDU6SXNzdWU3OTYzMTIxOTM= | 5,051 | Unpin pip version in presubmit-tests-tfx.sh | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-01-28T19:59:22 | 2022-04-28T18:00:28 | 2022-04-28T18:00:28 | COLLABORATOR | null | Unpin pip version once we figure out how to make the new dependency resolver in pip 20.3+ work in our case.
Related issues:
#5049
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https://api.github.com/repos/kubeflow/pipelines/issues/5049 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5049/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5049/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5049/events | https://github.com/kubeflow/pipelines/issues/5049 | 796,256,892 | MDU6SXNzdWU3OTYyNTY4OTI= | 5,049 | Presubmit test kubeflow-pipelines-tfx-python36 failing | {
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"followers_url": "https://api.githu... | null | [
"pip 21.0 came out on 1/23, and it still has the same dependency resolver issue as we hit with pip 20.3.*\r\n\r\nPrevious issue: #4853",
"This is due to a recent change from TFX side: https://github.com/tensorflow/tfx/issues/3157"
] | 2021-01-28T18:44:20 | 2021-01-29T23:16:02 | 2021-01-29T23:16:02 | COLLABORATOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5042/kubeflow-pipelines-tfx-python36/1354678589223604224
Times out while waiting for pip install to finish.
```
...
INFO: pip is looking at multiple versions of requests-oauthlib to determine which version is compatible with other requirements. This could take a while.
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run, you can press Ctrl + C to do so. To improve how pip performs, tell us what happened here: https://pip.pypa.io/surveys/backtracking
INFO: pip is looking at multiple versions of markdown to determine which version is compatible with other requirements. This could take a while.
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run, you can press Ctrl + C to do so. To improve how pip performs, tell us what happened here: https://pip.pypa.io/surveys/backtracking
INFO: pip is looking at multiple versions of google-auth-oauthlib to determine which version is compatible with other requirements. This could take a while.
Collecting google-auth-oauthlib<0.5,>=0.4.1
Downloading google_auth_oauthlib-0.4.1-py2.py3-none-any.whl (18 kB)
{"component":"entrypoint","file":"prow/entrypoint/run.go:165","func":"k8s.io/test-infra/prow/entrypoint.Options.ExecuteProcess","level":"error","msg":"Process did not finish before 2h0m0s timeout","severity":"error","time":"2021-01-28T08:32:17Z"}
{"component":"entrypoint","file":"prow/entrypoint/run.go:250","func":"k8s.io/test-infra/prow/entrypoint.gracefullyTerminate","level":"error","msg":"Process did not exit before 15s grace period","severity":"error","time":"2021-01-28T08:32:33Z"}
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5048 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5048/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5048/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5048/events | https://github.com/kubeflow/pipelines/issues/5048 | 796,074,939 | MDU6SXNzdWU3OTYwNzQ5Mzk= | 5,048 | How to make file_outputs dynamic in the KFP SDK? | {
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{
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"file_outputs is deprecated, please refer to `component` for the use case: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html",
"Thank you. Yes we ended up using outputs."
] | 2021-01-28T14:49:30 | 2021-03-07T08:24:01 | 2021-03-07T08:24:01 | NONE | null | /kind question
```
import json
import kfp
from kfp import components
from kfp import dsl
import os
import subprocess
from kfp.components import func_to_container_op, InputPath, OutputPath
def compute_test_features(
input_test_path,
output_path : OutputPath
):
return dsl.ContainerOp(
name='Compute Mesh Features',
image='gcr.io/brightclue-mlops/component-featurecomputation:v1',
arguments=[
'--input-test-path', input_test_path,
],
file_outputs={'output':output_path},
)
```
But when I try to compile the pipeline I get the below:
`TypeError: Object of type PipelineParam is not JSON serializable`
How can I have the file output be a parameter/dynamic? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5046 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5046/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5046/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5046/events | https://github.com/kubeflow/pipelines/issues/5046 | 795,754,203 | MDU6SXNzdWU3OTU3NTQyMDM= | 5,046 | Kubeflow 1.2 PVC volume is not getting deleted | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4",... | closed | false | null | [] | null | [
"In my case, I manually delete volume using ResourceOp with delete action in ExitHandler\r\n\r\nhttps://github.com/kubeflow/pipelines/issues/1779#issuecomment-674085829",
"This should be unrelated to KFP. It sounds like an issue/configuration of the storage class not deleting dynamically provisioned volumes on PV... | 2021-01-28T07:38:17 | 2021-03-05T00:45:21 | 2021-03-05T00:45:21 | NONE | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
### What happened:
I am able to train MNIST dataset and make predictions on Kubeflow using attached code:
[mnist.zip](https://github.com/kubeflow/pipelines/files/5885493/mnist.zip)
The execution of attached code on Kubeflow is as shown.
However even after deleting the volume, the actual volume on AWS ec2 is only getting detatched, its not getting deleted.
Detached volume on AWS ec2:
### What did you expect to happen:
I expected the volume to get deleted
How Do I properly delete the volume ?
### Environment:
kfp 1.3.0
kfp-pipeline-spec 0.1.4
kfp-server-api 1.2.0
/kind bug
/area sdk
/area testing
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5038 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5038/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5038/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5038/events | https://github.com/kubeflow/pipelines/issues/5038 | 794,335,060 | MDU6SXNzdWU3OTQzMzUwNjA= | 5,038 | ml_engine does not expose "labels" | {
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"color": "bbbbbb",
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"description": "The issue / pull request is stale, any activities remove this label.... | closed | false | null | [] | null | [
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n"
] | 2021-01-26T15:40:16 | 2022-04-28T18:00:22 | 2022-04-28T18:00:22 | CONTRIBUTOR | null | I am writing kubeflow pipeline with Ai platform train job, but can't add argument "labels" (e.g. tracking costs) into ML_ENGINE_TRAIN_OP since there is no such thing. I can add labels via gcloud, REST or even Airflow has one (https://github.com/apache/airflow/blob/7f4c88c0680b4fb98fe8b31800a93e1d0476c4db/airflow/providers/google/cloud/operators/mlengine.py#L1072)
```
ML_ENGINE_TRAIN_OP = comp.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/1.3.0/components/gcp/ml_engine/train/component.yaml')
``` | {
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"Has a fix been implemented yet and is there a working version with the model object that we can use in the meantime?",
"@devilmetal Thank you for the detailed report!\r\nThis hasn't been implemented, PRs welcomed.",
"This issue has been automatically marked as stale because it has not had recent activity. It w... | 2021-01-26T13:55:04 | 2022-04-18T17:27:40 | 2022-04-18T17:27:40 | NONE | null | ### What steps did you take:
After trying to use the "model" parameter of the ml_engine deploy component, we are now facing issue regarding deployment of model with the same names.
https://github.com/kubeflow/pipelines/tree/master/components/gcp/ml_engine/deploy
### What happened:
While using 'replace_existing_version' parameter, the component raison 409 error.
### What did you expect to happen:
While using 'replace_existing_version' parameter, the model version is replace by the one provided
"returned "Field: model.name Error: A model with the same name already exists."
### Environment:
Jupyter hub - Running on GCP
KubeFlow - Running on GCP
How did you deploy Kubeflow Pipelines (KFP)?
Using kubeflow components and python.
KFP version: 1.3.0
KFP SDK version: 1.3.0
/kind bug
Our main problem is regarding deployment. When using the "model" parameter with the following values, a 409 error raise and the model is actually not deployed. Without the "model" parameter, the behaviour is correct as expected.
model={
'name': 'some_name',
'regions': [
"europe-west1"
],
'onlinePredictionLogging': True,
'onlinePredictionConsoleLogging': True,
'labels': {
'some_label': 'some_value'
}
}
We spotted the faulty line here :
https://github.com/kubeflow/pipelines/blob/32ce8d8f90bfc8f89a2a3c347ad906f99ba776a8/components/gcp/container/component_sdk/python/kfp_component/google/ml_engine/_create_model.py#L69
It seems that the comparison between the existing model and the one passed by parameter is not matching.
While looking at the code we also spotted a few potential issues
At this line, the model name seems to be always overwritten, even if you specify it in the model object given as parameter.
https://github.com/kubeflow/pipelines/blob/32ce8d8f90bfc8f89a2a3c347ad906f99ba776a8/components/gcp/container/component_sdk/python/kfp_component/google/ml_engine/_create_model.py#L83
At this line, you might have omitted the field 'onlinePredictionConsoleLogging' and other new fields. https://github.com/kubeflow/pipelines/blob/32ce8d8f90bfc8f89a2a3c347ad906f99ba776a8/components/gcp/container/component_sdk/python/kfp_component/google/ml_engine/_create_model.py#L92
Here is the stacktrace
`Traceback (most recent call last):
File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/ml/kfp_component/launcher/__main__.py", line 45, in <module>
main()
File "/ml/kfp_component/launcher/__main__.py", line 42, in main
launch(args.file_or_module, args.args)
File "/ml/kfp_component/launcher/launcher.py", line 45, in launch
return fire.Fire(module, command=args, name=module.__name__)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 127, in Fire
component_trace = _Fire(component, args, context, name)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 366, in _Fire
component, remaining_args)
File "/usr/local/lib/python3.7/site-packages/fire/core.py", line 542, in _CallCallable
result = fn(*varargs, **kwargs)
File "/ml/kfp_component/google/ml_engine/_deploy.py", line 66, in deploy
model_name_output_path=model_name_output_path,
File "/ml/kfp_component/google/ml_engine/_create_model.py", line 39, in create_model
model_object_output_path=model_object_output_path,
File "/ml/kfp_component/google/ml_engine/_create_model.py", line 65, in execute
model = self._model)
File "/ml/kfp_component/google/ml_engine/_client.py", line 82, in create_model
body = model
File "/usr/local/lib/python3.7/site-packages/googleapiclient/_helpers.py", line 130, in positional_wrapper
return wrapped(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/googleapiclient/http.py", line 851, in execute
raise HttpError(resp, content, uri=self.uri)
googleapiclient.errors.HttpError: <HttpError 409 when requesting
https://ml.googleapis.com/v1/projects/xxxxxxxxx/models?alt=json
returned "Field: model.name Error: A model with the same name already exists.">` | {
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"The upgrade command is only supported by Kubeflow Pipelines standalone. You cannot use it to upgrade Kubeflow.",
"https://www.kubeflow.org/docs/pipelines/installation/overview/",
"We were trying to upgrade only pipelines - possibly the pasted results from above were from the other approach, that being a frest ... | 2021-01-25T19:49:00 | 2021-01-26T13:46:52 | 2021-01-26T07:41:36 | NONE | null | Referred to the upgrade website and ran the command:
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
with pipeline version being: 1.3
Here is the output:
Error from server (Invalid): error when applying patch:
{"metadata":{"annotations":{"kubectl.kubernetes.io/last-applied-configuration":"{\"apiVersion\":\"apps/v1\",\"kind\":\"Deployment\",\"metadata\":{\"annotations\":{},\"labels\":{\"application-crd-id\":\"kubeflow-pipelines\",\"component\":\"metadata-envoy\"},\"name\":\"metadata-envoy-deployment\",\"namespace\":\"kubeflow\"},\"spec\":{\"replicas\":1,\"selector\":{\"matchLabels\":{\"application-crd-id\":\"kubeflow-pipelines\",\"component\":\"metadata-envoy\"}},\"template\":{\"metadata\":{\"labels\":{\"application-crd-id\":\"kubeflow-pipelines\",\"component\":\"metadata-envoy\"}},\"spec\":{\"containers\":[{\"image\":\"gcr.io/ml-pipeline/metadata-envoy:1.3.0\",\"name\":\"container\",\"ports\":[{\"containerPort\":9090,\"name\":\"md-envoy\"},{\"containerPort\":9901,\"name\":\"envoy-admin\"}]}]}}}}\n"},"labels":{"app.kubernetes.io/component":null,"app.kubernetes.io/instance":null,"app.kubernetes.io/managed-by":null,"app.kubernetes.io/name":null,"app.kubernetes.io/part-of":null,"app.kubernetes.io/version":null,"application-crd-id":"kubeflow-pipelines","component":"metadata-envoy","kustomize.component":null}},"spec":{"selector":{"matchLabels":{"app.kubernetes.io/component":null,"app.kubernetes.io/instance":null,"app.kubernetes.io/managed-by":null,"app.kubernetes.io/name":null,"app.kubernetes.io/part-of":null,"app.kubernetes.io/version":null,"application-crd-id":"kubeflow-pipelines","component":"metadata-envoy","kustomize.component":null}},"template":{"metadata":{"labels":{"app.kubernetes.io/component":null,"app.kubernetes.io/instance":null,"app.kubernetes.io/managed-by":null,"app.kubernetes.io/name":null,"app.kubernetes.io/part-of":null,"app.kubernetes.io/version":null,"application-crd-id":"kubeflow-pipelines","component":"metadata-envoy","kustomize.component":null}},"spec":{"$setElementOrder/containers":[{"name":"container"}],"containers":[{"image":"gcr.io/ml-pipeline/metadata-envoy:1.3.0","name":"container"}]}}}}
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https://api.github.com/repos/kubeflow/pipelines/issues/5031 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5031/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5031/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5031/events | https://github.com/kubeflow/pipelines/issues/5031 | 793,256,323 | MDU6SXNzdWU3OTMyNTYzMjM= | 5,031 | `ml-pipeline-ui` cannot be configured for volume-support | {
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"Note, in multi user mode, there's an kfp artifact server in each user's namespace. You should be able to mount the volume there.",
"but in general, this feature in in Alpha stage, it might not work out. I'd love to hear your feedback if it could be configured to work for you, or do you have any suggestions to im... | 2021-01-25T10:30:45 | 2022-04-28T18:00:27 | 2022-04-28T18:00:27 | NONE | null | ### What steps did you take:
I'm starting a TFX-pipeline in a jupyter notebook in namespace `admin`. According to the [TFX kubeflow-on premise example](https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_kubeflow_local.py), the PV is mounted as follows:
```python
_persistent_volume_claim = 'pvc-tfx'
_persistent_volume = 'pv-tfx'
_persistent_volume_mount = '/mnt'
```
and used in
```python
...
kfp.onprem.mount_pvc(_persistent_volume_claim, _persistent_volume,
_persistent_volume_mount)
...
```
PV and PVC have been created in namespace `admin`.
The pipeline succeeds. Now I want to display the generated artifacts in the kubeflow UI. I'm following the instructions here to patch the `ml-pipeline-ui` in order to generate visualizations from volumes:
https://github.com/kubeflow/pipelines/blob/master/docs/config/volume-support.md
### What happened:
Since the PVC is in namespace `admin` and the `ml-pipeline-ui` is in namespace `kubeflow`, the PVC cannot be found and
the patch is unsuccessful:
```bash
$ kubectl rollout status deploy/ml-pipeline-ui -n kubeflow
Waiting for deployment "ml-pipeline-ui" rollout to finish: 1 old replicas are pending termination...
# analysis
$ kubectl describe pod ml-pipeline-ui-76fcd74b66-j47pp -n kubeflow
...
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Warning FailedScheduling 45s (x7 over 9m42s) default-scheduler persistentvolumeclaim "pvc-tfx," not found
```
How could this be solved?
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
Full Kubeflow 1.2 deployment on an on-premise K8s cluster
KFP version: build version v1beta1
KFP SDK version:
```python
kfp 1.3.0
kfp-pipeline-spec 0.1.3.1
kfp-server-api 1.3.0
```
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
/area frontend
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
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// /area backend
// /area sdk
// /area testing
// /area engprod
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https://api.github.com/repos/kubeflow/pipelines/issues/5029 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5029/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5029/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5029/events | https://github.com/kubeflow/pipelines/issues/5029 | 793,062,410 | MDU6SXNzdWU3OTMwNjI0MTA= | 5,029 | Can't access the endpoint generated at the end of the process | {
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"@Juggernaut1997 Do you use full Kubeflow or AI platform pipelines?\r\n\r\nYou may take a look at KFServing, I think they provide mechanism to expose generic container endpoint.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity oc... | 2021-01-25T06:08:00 | 2022-04-28T18:00:23 | 2022-04-28T18:00:23 | NONE | null | Hi team,
I am a ML Engineer and right now working on MLOps on GCP. So for that I developed my ML model and used `kfp functions` to deploy my model to Kubeflow and I successfully did that. Adding to that, I have also developed a simple UI to access my ML model using flask.
So for now, my application consist of 3 steps:
1. Pre-process
2. Train
3. Predict
In the end of predict, I had an endpoint generated, which is some localhost link but as this is generated inside kubeflow I went to workloads on GCP and tried to expose the pod to a LoadBalancing port but that is not working. It gets exposed to that port but I am not able to access that endpoint.
Can you guys help me with how can I access the endpoint?
Thank you so much in advance | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5026 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5026/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5026/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5026/events | https://github.com/kubeflow/pipelines/issues/5026 | 792,045,124 | MDU6SXNzdWU3OTIwNDUxMjQ= | 5,026 | Nvidia A100: GPU Slice definition not supported via Pipeline DSL | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"I'm having this problem too - we've got a 4 A100 cluster with heterogeneous GPU sizes. I might use the same workaround, but I'll let yo... | 2021-01-22T14:24:16 | 2022-03-02T21:05:11 | null | NONE | null | ### What steps did you take:
I created a simple test pipeline to check the Nvidia slicing definitions, using following code
```
train = dsl.ContainerOp(
name='Train Model',
image='tensorflow:v1',
).add_node_selector_constraint("nvidia.com/gpu.product", "A100-SXM4-40GB MIG 1g.5gb")
train.set_gpu_limit(1)
```
We have 3 node A100 setup on which the Kubeflow version 1.1 deployed and we are following [Mixed strategy ], (https://developer.nvidia.com/blog/getting-kubernetes-ready-for-the-a100-gpu-with-multi-instance-gpu/) basically the MiG mode is enabled.
### What happened:
Compiling and deploying the YAML definition, Pipeline run is showing an error, please see the attached screenshot
Changing the DSL definition
```
.add_node_selector_constraint("nvidia.com/gpu.product", "A100-SXM4-40GB MIG 1g.5gb")
trainOp.set_gpu_limit(1)
```
Creates following error, where the node is not able to be scheduled, (this may work if we dont create the MiG definition)
Then changing to last third option where the DSL , compilation error gets thrown, which basically complains about the vendor name to be Nvidia
```
.set_gpu_limit(1, "nvidia.com/mig-3g.20gb")
train.add_node_selector_constraint("nvidia.com/gpu.product","A100-SXM4-40GB")
File "D:\Apps\Anaconda3\lib\site-packages\kfp\dsl\_container_op.py", line 303, in set_gpu_limit
raise ValueError('vendor can only be nvidia or amd.')
ValueError: vendor can only be nvidia or amd.
```
When I changed the compiled YAML file to change I could run the pipeline successfully.
```
resources:
limits:
nvidia.com/mig-3g.20gb: 1
```
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. --> 1.0
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp --> 1.0.4
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
/area sdk
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https://api.github.com/repos/kubeflow/pipelines/issues/5022 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5022/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5022/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5022/events | https://github.com/kubeflow/pipelines/issues/5022 | 791,328,310 | MDU6SXNzdWU3OTEzMjgzMTA= | 5,022 | metadata-ui pod in CrashLoopBackOff status after making it to run as non root group | {
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"Metadata UI has been deprecated, the new metadata UI will be improved in KFP UI.\r\nDo you have the same problem with KFP UI?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"This... | 2021-01-21T16:55:01 | 2022-04-28T18:00:25 | 2022-04-28T18:00:25 | NONE | null | ### What steps did you take:
Added securityContext to metadata-ui to make it run as non root user and non root user group. Deployed metadata-ui using below spec. metadata-ui pod is stuck in CrashLoopBackOff status and below error is shown in the logs
` internal/modules/cjs/loader.js:626
throw err;
^
Error: Cannot find module 'express'
Require stack:
- /server/dist/server.js
at Function.Module._resolveFilename (internal/modules/cjs/loader.js:623:15)
at Function.Module._load (internal/modules/cjs/loader.js:527:27)
at Module.require (internal/modules/cjs/loader.js:681:19)
at require (internal/modules/cjs/helpers.js:16:16)
at Object.<anonymous> (/server/dist/server.js:52:15)
at Module._compile (internal/modules/cjs/loader.js:774:30)
at Object.Module._extensions..js (internal/modules/cjs/loader.js:785:10)
at Module.load (internal/modules/cjs/loader.js:641:32)
at Function.Module._load (internal/modules/cjs/loader.js:556:12)
at Function.Module.runMain (internal/modules/cjs/loader.js:837:10) {
code: 'MODULE_NOT_FOUND',
requireStack: [ '/server/dist/server.js' ]
}
stream closed`
### What happened:
metadata-ui pod in CrashLoopBackOff status
### What did you expect to happen:
metadata-ui should be running as non root user & non root user group
### Environment:
- Kubeflow version: (version number can be found at the bottom left corner of the Kubeflow dashboard): v1.1.0
- kfctl version: (use `kfctl version`): v1.0-0-g94c35cf
- Kubernetes platform: minikube
- Kubernetes version: (use `kubectl version`): 1.15.9
### Anything else you would like to add:
Removed runAsGroup from securityContext and metadata-ui pod is running as expected. Unable to understand why runAsGroup is causing the express node module to be missed.
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
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// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5021 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5021/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5021/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5021/events | https://github.com/kubeflow/pipelines/issues/5021 | 790,799,922 | MDU6SXNzdWU3OTA3OTk5MjI= | 5,021 | 2021-1-21: presubmit failure "Insufficient regional quota to satisfy request: resource "DISKS_TOTAL_GB" ..." | {
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"/cc @hilcj \r\n/assign @Bobgy \r\nI'll try to fix this",
"It's duplicate of #2523, which we should really prioritize",
"I'm going to first run the workaround script to clean up unused disks quickly.",
"Finished clean up"
] | 2021-01-21T08:02:11 | 2021-01-26T07:42:04 | 2021-01-26T07:42:04 | CONTRIBUTOR | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
### What happened:
test infra run out of quota: https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5019/kubeflow-pipeline-sample-v2/1352162018584432640#1:build-log.txt%3A285
### What did you expect to happen:
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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https://api.github.com/repos/kubeflow/pipelines/issues/5020 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5020/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5020/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5020/events | https://github.com/kubeflow/pipelines/issues/5020 | 790,736,869 | MDU6SXNzdWU3OTA3MzY4Njk= | 5,020 | NOTICE: "Context retired without replacement" during migration to google-oss-robot | {
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"Thanks for reporting! Hopefully we can migrate to google-oss-robot soon",
"Thanks, in fact google-oss-robot is still spamming, so we need to keep it for some time",
"We have migrated to @google-oss-robot, there was an issue during the migration that caused all presubmit tests in existing PRs to succeed with me... | 2021-01-21T06:21:15 | 2021-01-28T03:07:48 | 2021-01-26T07:45:01 | CONTRIBUTOR | null | ## UPDATE
We have migrated to @google-oss-robot, there was an issue during the migration that caused all presubmit tests in existing PRs to succeed with message "Context retired without replacement".
## Workaround
Please comment `/test all` on the PR to retry them.
============
original issues
For aws-kf-ci-bot: upstream issue - ~https://github.com/kubeflow/internal-acls/issues/354#issuecomment-764311315~ has already been resolved.
For google-oss-robot:
We are migrating to google-oss-prow, please ignore them during transition period. We'll disable k8s-ci-robot very soon.
ETA 1week
progress can be found on https://github.com/kubernetes/test-infra/issues/14343 | {
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https://api.github.com/repos/kubeflow/pipelines/issues/5014 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/5014/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/5014/comments | https://api.github.com/repos/kubeflow/pipelines/issues/5014/events | https://github.com/kubeflow/pipelines/issues/5014 | 789,897,955 | MDU6SXNzdWU3ODk4OTc5NTU= | 5,014 | Wrong cron format example | {
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"This is resolved by: https://github.com/kubeflow/pipelines/pull/5028 @hilcj ",
"from `godoc.org` to `pkg.go.dev`! Thanks a lot"
] | 2021-01-20T11:33:55 | 2021-01-25T01:45:59 | 2021-01-25T01:45:58 | CONTRIBUTOR | null | ### What steps did you take:
Creating recurring run on kubeflow central dashboard
### What happened:
there is link for cron format guide - `Allow editing cron expression. ( format is specified here)` on page for creating recurring run. and this link takes me to [godoc-cron](https://godoc.org/github.com/robfig/cron#hdr-CRON_Expression_Format)
and it says -
Field name | Mandatory? | Allowed values | Allowed special characters
---------- | ---------- | -------------- | --------------------------
Minutes | Yes | 0-59 | * / , -
Hours | Yes | 0-23 | * / , -
Day of month | Yes | 1-31 | * / , - ?
Month | Yes | 1-12 or JAN-DEC | * / , -
Day of week | Yes | 0-6 or SUN-SAT | * / , - ?
there is no `Seconds` part.
but recurring run's cron needs `Seconds` at first position.
so what I expected : `0 * * * *` -> run every hours (from what [godoc-cron](https://godoc.org/github.com/robfig/cron#hdr-CRON_Expression_Format) says)
but what happened : run every minutes
So I think this link needs to be updated.
### Environment:
<!-- Please fill in those that seem relevant. -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
1.0.4
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
1.2.0
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"Seems like something UI related. Does the container produce logs? Is it just this pipeline/pipeline step that does this?",
"Yes @parthmishra container produces logs using python logger. The previous step is able to log currently but the next step fails. \r\n\r\nI tried the sample flip coin example(https://github... | 2021-01-19T17:37:40 | 2022-04-28T18:00:26 | 2022-04-28T18:00:26 | NONE | null | ### What steps did you take:
Created a pipeline with two stages, On success of 1st stage status will be published in 2nd stage.
### What happened:
2nd stage always fails with exit code 255 and logs [object Object].
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
Custom build is deployed on on-prem cluster with Istio and Keycloak integration
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. --> 1.2.0
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->1.1.1
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"@Bobgy ",
"@hilcj do you need help from other members?\nIt's oncall's responsibility to do the initial investigations.\n\nBut feel free to delegate to us if you think it's out of your knowledge range",
"@Bobgy actually I just want to ask you if this is an known issue. Because the failure has started at least ... | 2021-01-19T16:15:51 | 2021-02-05T02:22:35 | 2021-02-05T02:22:35 | CONTRIBUTOR | null | KFP Oncall noticed [kubeflow-pipeline-postsubmit-integration-test](https://k8s-testgrid.appspot.com/sig-big-data#kubeflow-pipeline-postsubmit-integration-test) being failing.
/kind bug | {
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"I'm interested in something like this too, mostly to have a public namespace that everyone can see without having to manually add via contributors UI. Until support for RBAC groups in the Profile controller comes along, it's not easy. As for your use case, I haven't tried this out, but I'd suggest the following:\r... | 2021-01-19T14:00:47 | 2021-01-29T01:03:05 | 2021-01-29T01:02:56 | NONE | null | Question:
Please note this as advised by in question, I am reopening this question as per suggestions in # [5510](https://github.com/kubeflow/kubeflow/issues/5510)
We are trying to create a separate namespace where the different runs can be submitted, I understand from the documentation currently that pipelines have restrictions about namespaces, however our use-case is the runs where this is happening we want to allocate the GPU resources in that namespace, so users can submit their jobs to a queue and we deploy these jobs from a queue as and when the resources are available.
My Questions are:
1. What is the way we can ensure the users have read-only access to above namespace (e.g. training namespace )? Is this possible? so that they are able to view their runs, status of it, logs and metrics generated (I am aware as of 1.1 pipelines are not isolated from users, for which we are okay to live with)
2. At the sametime they should not be able to create resources in that specific namespaces - for example Notebook servers, running other pipelines or deploy a kf-serving model (for this forum - consider pipeline runs)
3. Managing above scenario via contributors is not great as it will allow the access of all resource in the common namespace
We are trying to ensure we utilize the GPUs (with all the sharing disabled in Kubernetes, its becoming all the more required) in best possible manner and resources are not stuck to a specific team/individual..
Please provide any updates/link or direction using which we can solve this. Unfortunately the documentation is not great on this aspect so I am not able to proceed (I have read through - [Issue # 3513](https://github.com/kubeflow/pipelines/issues/3513) it odes not look complete yet.)
KFP version: 1.0
Kubeflow version 1.1
KFP SDK version: NA
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
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// /area frontend
// /area backend
// /area sdk
// /area testing
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"/assign @Ark-kun \r\n\r\nUnderstood the issue, that's indeed a limitation of KFP caching.\r\nNote, the feature is in Alpha stage, you can also disable it via https://www.kubeflow.org/docs/pipelines/caching/#disabling-caching-in-your-kubeflow-pipelines-deployment\r\n(the doc is slightly outdated, the name of that c... | 2021-01-18T08:25:48 | 2022-04-28T18:00:25 | 2022-04-28T18:00:25 | NONE | null | ### What steps did you take:
installed kubeflow with kfp 1.0.4 manifest
### What happened:
pipeline step pod which come up with alpine image when cachedExecution is set don't run with restricted psp . Error: container has runAsNonRoot and image will run as root
Making the image name configurable through env var or workflow controller adding some default securitycontext to the pipeline step will be helpful .
[https://github.com/kubeflow/pipelines/blob/3b9fdff26be7d0f04563bbac8bc23807caa69ae2/backend/src/cache/server/mutation.go#L136](url) | {
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"@JoshZastrow Just to be clear, are you talking about Kubeflow as a whole or pipelines specifically? In regards to pipelines, it is possible to create python function based components rather than needing to create images (you do need to have a base image that contains the necessary dependencies such as pytorch for ... | 2021-01-15T19:56:24 | 2022-04-28T18:00:14 | 2022-04-28T18:00:14 | NONE | null | /kind feature
**Why you need this feature:**
Say I have a local package containing my application logic (i.e cleaning, feature generation, ML model training, etc..). This local package contains modules and functions used in my component.
I want to make changes to the application logic (i.e change a feature scaling method), then run my pipeline and 1) make sure the pipeline works or 2) see an improvement in my offline metrics.
My component image needs to have all the dependencies on the image, so this seems to mean that if I want to run my kubeflow pipeline with new code, I need to re-build and submit an image each time. This is a pretty slow process, and prevents us from wanting to make smaller components (better to develop pipelines in Python and run them as a bigger component via a CLI command).
I'm imagining one solution with a local Kubeflow instance, that has the component images pointing to locally built docker images that have the local application code mounted, you can get a much faster iteration cycle.
Is there a better way to develop faster with Kubeflow? It says it's experimentation friendly, but I haven't felt that from working with Kubeflow so far (it is nice that it has experiment management/tracking in the UI though!). I don't feel like I can swap my current experimentation workflow out for Kubeflow.
Maybe a user guide on developing locally could be a good solution? Something equivalent to `pip install -e .` for Kubeflow components would be great!
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"I solved this issue by changing the default region:\r\n\r\n`aws configure set default.region us-west-2`\r\n\r\nThe error is because kfctl completely ignores the region set in the .yaml file and just uses the aws cli default."
] | 2021-01-14T20:11:52 | 2021-01-15T05:14:04 | 2021-01-15T05:14:04 | NONE | null | ### What steps did you take:
[A clear and concise description of what the bug is.]
I setup cluster in me-south-1 region with 2xr5.xlarge instances. I follow docs here: https://www.kubeflow.org/docs/aws/deploy/install-kubeflow/
### What happened:
Error I get is:
```
Error: failed to apply: (kubeflow.error): Code 500 with message: coordinator Apply failed for aws: (kubeflow.error): Code 400 with message: IAM for Service Account is not supported on non-EKS cluster <nil>
kfctl exited with error: failed to apply: (kubeflow.error): Code 500 with message: coordinator Apply failed for aws: (kubeflow.error): Code 400 with message: IAM for Service Account is not supported on non-EKS cluster <nil>
```
### What did you expect to happen:
Kubeflow should have successfully installed
### Environment:
<!-- Please fill in those that seem relevant. -->
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
kfp 1.2.0
kfp-pipeline-spec 0.1.3.1
kfp-server-api 1.2.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
/area backend
/area testing
/area engprod
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
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"fyi @NikeNano @Bobgy ",
"There are several main areas that might need updating:\r\n\r\n* Python SDK deps\r\n* Backend Go deps\r\n* Kubernetes deps (e.g. Argo)\r\n* Deps of micro services (Metadata Writer, Visualization Server, etc.)\r\n\r\nFor python (SDK and Visualizations) we already have update scripts.",
"... | 2021-01-14T19:05:19 | 2021-01-19T19:36:13 | 2021-01-19T19:36:13 | CONTRIBUTOR | null | Currently, we do not usually update dependencies, so each time we update, they have already been pretty old and many things could be breaking after some dependencies are updated.
It's worth discussing the dependency update strategy of KFP.
Looks like there is no script or doc related to updating dependency. We may need to write a script like https://github.com/knative/eventing/blob/master/hack/update-deps.sh and write some docs related to updating dependency. | {
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"Hello @kd303, I need more information in order to reproduce this issue. Based on the description, this happens when Pods are getting initialized. But Pods should be up and running when triggering a pipeline run. Did you uninstall KFP instance and redeploy before the issue happens?\r\n\r\nTo further investigate thi... | 2021-01-14T13:38:00 | 2022-05-04T06:15:09 | 2022-04-29T03:59:40 | NONE | null | ### What steps did you take:
Pipeline runs disappears when executing pipeline using SDK. When we run the pipeline for a specific experiments, we are able to see the logs appearing in pods as well the runid (UUID) is also getting generated. I have done tracking in mysql/mlpipeline/run_details table and can also see run appearing there.
However in some cases after above observations the run gets disapper from mysql and UI and nothing appearing in the UI
### What happened:
### What did you expect to happen:
Usually this is observed when the Pods are getting initialized, and for same pipeline the run works sometime
### Environment:
<!-- Please fill in those that seem relevant. -->
KFP 1.1 on a local Kubernetes cluster
How did you deploy Kubeflow Pipelines (KFP)?
<!-- If you are not sure, here's [an introduction of all options](https://www.kubeflow.org/docs/pipelines/installation/overview/). -->
Full kubeflow deployment, on premise cluster
KFP version: 1.0 <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
KFP SDK version: 1. <!-- Please attach the output of this shell command: $pip list | grep kfp -->
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
/area backend
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// /area frontend
// /area sdk
// /area testing
// /area engprod
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"It seems all of the findings are warnings though, and the verification status is PASSED",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | 2021-01-14T02:19:58 | 2021-08-22T07:04:04 | 2021-08-22T07:04:04 | CONTRIBUTOR | null | https://storage.googleapis.com/kubernetes-jenkins/logs/kubeflow-pipeline-postsubmit-mkp-e2e-test/1346987369131151360/build-log.txt
```
Step #1 - "verify": === SCHEMA VALIDATION WARNING ===
Step #1 - "verify": WARNING: Schema is incompatible with the latest deployer, would fail with:
Step #1 - "verify":
Step #1 - "verify": Traceback (most recent call last):
Step #1 - "verify": File "/bin/validate_schema.py", line 35, in <module>
Step #1 - "verify": main()
Step #1 - "verify": File "/bin/validate_schema.py", line 31, in main
Step #1 - "verify": schema_values_common.load_schema_and_validate(args)
Step #1 - "verify": File "/bin/schema_values_common.py", line 65, in load_schema_and_validate
Step #1 - "verify": return load_schema(parsed_args).validate()
Step #1 - "verify": File "/bin/config_helper.py", line 170, in validate
Step #1 - "verify": self._x_google_marketplace._deployer_service_account.validate()
Step #1 - "verify": File "/bin/config_helper.py", line 1039, in validate
Step #1 - "verify": 'SERVICE_ACCOUNT must have a `description` '
Step #1 - "verify": config_helper.InvalidSchema: SERVICE_ACCOUNT must have a `description` explaining purpose and permission requirements. See docs: https://github.com/GoogleCloudPlatform/marketplace-k8s-app-tools/blob/master/docs/schema.md#type-service_account
Step #1 - "verify": ======== END OF WARNING =========
Step #1 - "verify": === ERROR SUMMARY ===
Step #1 - "verify":
Step #1 - "verify": Error events found in namespace "apptest-lf0xqn9s"
Step #1 - "verify": LAST SEEN TYPE REASON OBJECT MESSAGE
Step #1 - "verify": 4m26s Warning FailedMount pod/cache-server-d97bc8bdb-n9w2c MountVolume.SetUp failed for volume "webhook-tls-certs" : secret "webhook-server-tls" not found
Step #1 - "verify": 3m27s Warning FailedMount pod/cache-server-d97bc8bdb-n9w2c Unable to mount volumes for pod "cache-server-d97bc8bdb-n9w2c_apptest-lf0xqn9s(08120b68-63c6-48e3-b1c2-cda8cbe18f66)": timeout expired waiting for volumes to attach or mount for pod "apptest-lf0xqn9s"/"cache-server-d97bc8bdb-n9w2c". list of unmounted volumes=[webhook-tls-certs]. list of unattached volumes=[webhook-tls-certs kubeflow-pipelines-cache-token-njs8b]
Step #1 - "verify": 2m17s Warning BackOff pod/metadata-grpc-deployment-577c66c4dd-tvm5n Back-off restarting failed container
Step #1 - "verify": 5m29s Warning FailedMount pod/proxy-agent-795bfdc898-lt4g9 MountVolume.SetUp failed for volume "proxy-agent-runner-token-hphss" : couldn't propagate object cache: timed out waiting for the condition
Step #1 - "verify": 5m28s Warning FailedMount pod/workflow-controller-87f56f879-df6fj MountVolume.SetUp failed for volume "argo-token-v5l4z" : couldn't propagate object cache: timed out waiting for the condition
Step #1 - "verify": =====================
Step #1 - "verify":
Step #1 - "verify": === VERIFICATION STATUS ===
Step #1 - "verify": PASSED
Step #1 - "verify": ===========================
Finished Step #1 - "verify"
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/4987 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4987/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4987/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4987/events | https://github.com/kubeflow/pipelines/issues/4987 | 785,248,259 | MDU6SXNzdWU3ODUyNDgyNTk= | 4,987 | Add clear instructions for authorization code / token | {
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} | [] | closed | false | null | [] | null | [] | 2021-01-13T16:23:31 | 2021-01-13T18:30:40 | 2021-01-13T18:30:40 | CONTRIBUTOR | null | I was following [this official example](https://colab.research.google.com/github/kubeflow/website/blob/master/content/en/docs/pipelines/sdk/python-function-components.ipynb) to submit a pipeline via Jupyter or Python script
I filled in the client id and credentials [following these instructions](https://www.kubeflow.org/docs/gke/pipelines/authentication-sdk/#connecting-to-kubeflow-pipelines-in-a-full-kubeflow-deployment), but when I try to [compile the pipeline](https://www.kubeflow.org/docs/pipelines/sdk/build-component/#compile-the-pipeline) with
```
dsl-compile --py [path/to/python/file] --output [path/to/output/tar.gz]
```
The resulting prompt doesn't make it evident that the url can be copy pasted into the browser, to get the Authorization code, and I got stuck on it for a while. Looking back it's kind of an Occam's razor thing, but it could have saved a lot of time with even simple instructions "copy paste this url"
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https://api.github.com/repos/kubeflow/pipelines/issues/4986 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4986/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4986/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4986/events | https://github.com/kubeflow/pipelines/issues/4986 | 785,247,170 | MDU6SXNzdWU3ODUyNDcxNzA= | 4,986 | Add more detail to authorization step | {
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} | [] | closed | false | null | [] | null | [] | 2021-01-13T16:22:11 | 2021-01-13T16:33:19 | 2021-01-13T16:33:19 | CONTRIBUTOR | null | I was following [this official example](https://colab.research.google.com/github/kubeflow/website/blob/master/content/en/docs/pipelines/sdk/python-function-components.ipynb) to submit a pipeline via Jupyter or Python script
I filled in the client id and credentials [following these instructions](https://www.kubeflow.org/docs/gke/pipelines/authentication-sdk/#connecting-to-kubeflow-pipelines-in-a-full-kubeflow-deployment), but when I try to [compile the pipeline](https://www.kubeflow.org/docs/pipelines/sdk/build-component/#compile-the-pipeline) with
```
dsl-compile --py [path/to/python/file] --output [path/to/output/tar.gz]
```
The resulting prompt doesn't make it evident that the url can be copy pasted into the browser, and I got stuck on it for a while. Looking back it's kind of an Occam's razor thing, but it could have saved a lot of time with even simple instructions "copy paste this url"
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/4980 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4980/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4980/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4980/events | https://github.com/kubeflow/pipelines/issues/4980 | 784,640,292 | MDU6SXNzdWU3ODQ2NDAyOTI= | 4,980 | Error 1114: The table 'run_details' is full" | {
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"node_id": "MDU6TGFiZWwxMTE4ODk2OTA1... | closed | false | null | [] | null | [
"Which cloud platform do you deploy KFP? Is it part of a full Kubeflow installation?\r\n\r\nOur suggestion for productionizing a KFP instance is to use a cloud provider's managed SQL instance.\r\ne.g. in GCP we'd recommend using Cloud SQL, so that you'll be able to back up/resize/configure the SQL instance on your ... | 2021-01-12T22:28:05 | 2022-04-28T18:00:12 | 2022-04-28T18:00:12 | NONE | null | ### What steps did you take:
Kubeflow was already running ~50 pipelines at the time when new pipeline runs were submitted resulting in about ~500 pipeline runs running.
Volume size : 20 gigs, where `mysql` deployment was running.
### What happened:
Pipeline runs failed to run with error :
```
{"error":"Failed to create a new run.: InternalServerError: Failed to store run abc-xyz to table:
Error 1114: The table 'run_details' is full","message":"Failed to create a new run.:
InternalServerError: Failed to store run abc-xyz to table: Error 1114: The table 'run_details' is full",
"code":13,"details":[{"@type":"type.googleapis.com/api.Error","error_message":"Internal Server Error",
"error_details":"Failed to create a new run.: InternalServerError: Failed to store run abc-xyz to table:
Error 1114: The table 'run_details' is full"}]}
```
Kubeflow UI was also unresponsive when trying to check a pipeline run status.
### What did you expect to happen:
Kubeflow UI should return/display appropriate error.
Installation instructions should have this limitation documented. Also there should be a way to auto-clean this particular table or any other table which might be taking large amount of storage to keep KFP functional.
### Environment:
KFP version: 1.0.1
KFP SDK version: 1.0.1
### Anything else you would like to add:
checking `mlpipelines.run_details` table showed that table size has grown to 20 gigs.
/kind bug
/area frontend
/area backend
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https://api.github.com/repos/kubeflow/pipelines/issues/4976 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4976/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4976/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4976/events | https://github.com/kubeflow/pipelines/issues/4976 | 784,436,336 | MDU6SXNzdWU3ODQ0MzYzMzY= | 4,976 | HTTP response body: RBAC: access denied from kfp_server_api when creating a tfx pipeline | {
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"me too.\r\nwhen run from notebook server\r\nclient = kfp.Client()\r\nprint:\r\nERROR:root:Failed to get healthz info attempt 1 of 5.\r\nTraceback (most recent call last):\r\n File \"/home/jovyan/.local/lib/python3.6/site-packages/kfp/_client.py\", line 312, in get_kfp_healthz\r\n response = self._healthz_api.g... | 2021-01-12T17:28:51 | 2021-08-12T13:26:24 | 2021-03-05T00:50:15 | NONE | null | /kind question
### What steps did you take:
Ran the below code:
### What happened:
I got the below:
```
HTTP response body: RBAC: access denied
ERROR:root:Failed to get healthz info attempt 4 of 5.
Traceback (most recent call last):
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp/_client.py", line 312, in get_kfp_healthz
response = self._healthz_api.get_healthz()
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/api/healthz_service_api.py", line 77, in get_healthz
return self.get_healthz_with_http_info(**kwargs) # noqa: E501
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/api/healthz_service_api.py", line 162, in get_healthz_with_http_info
collection_formats=collection_formats)
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py", line 383, in call_api
_preload_content, _request_timeout, _host)
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py", line 202, in __call_api
raise e
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py", line 199, in __call_api
_request_timeout=_request_timeout)
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/api_client.py", line 407, in request
headers=headers)
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/rest.py", line 248, in GET
query_params=query_params)
File "/home/jovyan/.local/lib/python3.6/site-packages/kfp_server_api/rest.py", line 238, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (403)
Reason: Forbidden
HTTP response headers: HTTPHeaderDict({'content-length': '19', 'content-type': 'text/plain', 'date': 'Tue, 12 Jan 2021 17:25:02 GMT', 'server': 'envoy', 'x-envoy-upstream-service-time': '0'})
HTTP response body: RBAC: access denied
```
### What did you expect to happen:
Being able to create the pipeline
### Environment:
Kubeflow 1.2, Jupyter Notebook
How did you deploy Kubeflow Pipelines (KFP)?
KFP version:
build version v1beta1
KFP SDK version:
kfp 1.3.0
kfp-pipeline-spec 0.1.3.1
kfp-server-api 1.3.0
### Anything else you would like to add:
I'm using kubeflow in my username's namespace | {
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https://api.github.com/repos/kubeflow/pipelines/issues/4984 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4984/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4984/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4984/events | https://github.com/kubeflow/pipelines/issues/4984 | 784,823,544 | MDU6SXNzdWU3ODQ4MjM1NDQ= | 4,984 | Caching Documentation incorrect for 1.2 | {
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"followers_url": "https://api.githu... | null | [
"I think this issue belongs over at https://github.com/kubeflow/website/. ",
"/area pipelines\r\n/area docs\r\n/priority p1\r\n/kind bug",
"Moving to pipelines for better tracking",
"Yes, it will be `cache-webhook-{namespace}`, so for kubeflow, it's -kubeflow\r\n\r\n@Ark-kun can you work on the documentation ... | 2021-01-12T10:19:15 | 2021-02-19T11:21:59 | 2021-02-19T11:21:59 | NONE | null | As far as I can tell the documentation for Caching (https://www.kubeflow.org/docs/pipelines/caching/) is incorrect for 1.2. The mutatingwebhookconfiguration is not called cache-webhook, but **cache-webhook-kubeflow**. I.e. all commands need to be updated from
`kubectl patch mutatingwebhookconfiguration cache-webhook -n ${NAMESPACE} --type='json' -p='[{"op":"replace", "path": "/webhooks/0/rules/0/operations/0", "value": "DELETE"}]'
`
to
`kubectl patch mutatingwebhookconfiguration cache-webhook-kubeflow -n ${NAMESPACE} --type='json' -p='[{"op":"replace", "path": "/webhooks/0/rules/0/operations/0", "value": "DELETE"}]'
` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/4975 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4975/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4975/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4975/events | https://github.com/kubeflow/pipelines/issues/4975 | 783,805,330 | MDU6SXNzdWU3ODM4MDUzMzA= | 4,975 | Can't apply multiple filters on the same key for a given op | {
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"u... | closed | false | null | [] | null | [] | 2021-01-12T00:27:28 | 2021-01-20T23:57:04 | 2021-01-20T23:57:04 | CONTRIBUTOR | null | ### What steps did you take:
In a filter with more than one predicate using the same op **and** key, all but one of the predicates will be ignored. This is mostly relevant for the `NOT_EQUALS` and `IS_SUBSTRING` ops, although there might be unusual situations where someone wants multiple such predicates with other ops. At present, making a request with such a filter does not raise any errors or warnings despite this surprising behavior.
An example of such a filter, serialized to JSON, is the following:
```json
{ "predicates": [{
"op": "IS_SUBSTRING",
"key": "name",
"stringValue": "a"
}, {
"op": "IS_SUBSTRING",
"key": "name",
"stringValue": "b"
}]
}
```
### What happened:
Only results with name containing "b" were returned.
### What did you expect to happen:
All results containing both "a" and "b" should be returned.
### Environment:
How did you deploy Kubeflow Pipelines (KFP)?
I did not deploy it myself, but am using a full deployment in the cloud.
KFP version: Build [743746b](https://github.com/kubeflow/pipelines/tree/743746b96e6efc502c33e1e529f2fe89ce09481c)
KFP SDK version: 1.0.4 (don't think this is an SDK issue but I used the SDK to make the request)
### Anything else you would like to add:
This issue seems to stem from the [definition](https://github.com/kubeflow/pipelines/blob/c51c96a973f8b94d0f993b0b292d58008fe05607/backend/src/apiserver/filter/filter.go#L33-L46) of filter. I think I can fix this myself, if the KFP team thinks it's worth doing.
I think the best thing to do would be to start by changing the types of all fields of `Filter` from `map[string]interface{}` to `map[string][]interface{}`. Although most users would likely not apply multiple `EQUALS` (for example) filters, making this change would still result in more reasonable behavior (e.g. currently, if you have two filters that specify 'name' to be equal to different strings, you can get one result back, whereas you should get no results back).
/kind bug
/area backend | {
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https://api.github.com/repos/kubeflow/pipelines/issues/4973 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/4973/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/4973/comments | https://api.github.com/repos/kubeflow/pipelines/issues/4973/events | https://github.com/kubeflow/pipelines/issues/4973 | 783,269,675 | MDU6SXNzdWU3ODMyNjk2NzU= | 4,973 | "driver: bad connection" when using Managed storage via Google Cloud Marketplace deployment | {
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"@Svendegroote91 Sorry for the late reply, reading through your description, it sounds like\r\n> database username & password left empty to default to root user without password.\r\n\r\nthis part is wrong, you need to type `root` into database username, it's not the default when you leave it to empty.",
"I am hav... | 2021-01-11T10:58:53 | 2021-04-09T11:04:38 | 2021-04-09T11:04:38 | NONE | null | ### What steps did you take:
- Using GCP default VPC network settings
- Created multi-regional EU Google Cloud Storage bucket
- Create mySQL (v5.7) instance in europe-west1-d with only root user and no password (default settings: Public IP - I also tried enabling the private IP but that also did not resolve the issue)
- Configured AI Platform Pipelines (Google's KFP managed service) using Managed Storage
(params: storage bucket name: bucket name (without gs://), sql connection name: connection name copied from the mySQL UI, database prefix: 'db-test-sven', database username & password left empty to default to root user without password.
### What happened:
Deployment hangs on 'ml-pipeline' component that reports `driver: bad connection` as critical and a continuous stream of errors : `[mysql] 2021/01/11 10:52:23 packets.go:36: unexpected EOF`
Also the 'metadata-grpc-deployment' fails to start with following errors:
```
2021-01-11 10:52:07.160738: F ml_metadata/metadata_store/metadata_store_server_main.cc:220] Non-OK-status: status status: Internal: mysql_real_connect failed: errno: 2002, error: Can't connect to MySQL server on 'mysql' (115)MetadataStore cannot be created with the given connection config.
```
```
2021-01-11 10:52:08.781095: F ml_metadata/metadata_store/metadata_store_server_main.cc:220] Non-OK-status: status status: Internal: mysql_real_connect failed: errno: 2013, error: Lost connection to MySQL server at 'handshake: reading inital communication packet', system error: 11MetadataStore cannot be created with the given connection config.
```
It appears the mySQL connection cannot be instantiated, any help on how to resolve this?
### What did you expect to happen:
Deployment to successfully finish.
How did you deploy Kubeflow Pipelines (KFP)?
Google Cloud Marketplace
KFP version: 1.0.4
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
/area backend
// /area sdk
// /area testing
// /area engprod
-->
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"We can see the same issue with Kubeflow 1.2 with Dex on Kubernetes. Everything works fine after the first authentication attempt:\r\n\r\n\r\n\r\nI could even log out and log in again and it would still wo... | 2021-01-11T10:44:18 | 2021-03-19T05:39:06 | 2021-03-19T05:39:06 | NONE | null | ### What steps did you take:
1. I was installing kubeflow 1.2 with Dex on kubernetes cluster, following [this tutorial](https://www.kubeflow.org/docs/started/k8s/kfctl-istio-dex/).
2. I tested pipeline deployment using [this tutorial](https://github.com/kubeflow/pipelines/blob/b604c6171244cc1cd80bfdc46248eaebf5f985d6/samples/tutorials/Data%20passing%20in%20python%20components/Data%20passing%20in%20python%20components%20-%20Files.py).
### What happened:
The entire deployment was successful on the day when kubeflow was setup. All pipelines pods and kubeflow UI are behaved correctly.
However, in the following day (I suspect my SSO expired), even though I swapped a valid token, the experiment run UI always show the status is unknown. However, all pods are executed successfully when pipeline is deployed.
### What did you expect to happen:
The experiment run UI should display correctly
### Environment:
kubeflow 1.2
GKE: 1.18.12
How did you deploy Kubeflow Pipelines (KFP)?
I deploy it along with kubeflow installation.
KFP version: <!-- If you are not sure, build commit shows on bottom of KFP UI left sidenav. -->
Version: 1.0.4
KFP SDK version: <!-- Please attach the output of this shell command: $pip list | grep kfp -->
kfp 1.3.0
kfp-pipeline-spec 0.1.3.1
kfp-server-api 1.3.0
### Anything else you would like to add:
[Miscellaneous information that will assist in solving the issue.]
/kind bug
<!-- Please include labels by uncommenting them to help us better triage issues, choose from the following -->
<!--
// /area frontend
// /area backend
// /area sdk
// /area testing
// /area engprod
-->
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"I've realized this is, in fact, a bug (rather than a feature request) and opened a more detailed issue [here](https://github.com/kubeflow/pipelines/issues/4975)."
] | 2021-01-09T23:05:30 | 2021-01-12T00:27:35 | 2021-01-12T00:26:14 | CONTRIBUTOR | null | In a filter with more than one predicate using the same op, all but one of these predicates will be ignored. Making a request with such a filter does not raise any errors or warnings despite this surprising behavior. An example of such a filter, serialized to JSON, is the following:
```json
{ "predicates": [{
"op": "IS_SUBSTRING",
"key": "name",
"stringValue": "a"
}, {
"op": "IS_SUBSTRING",
"key": "name",
"stringValue": "b"
}]
}
```
In this case, only results with `name` containing "b" will be returned. It would nice be get all results containing "a" and "b".
This issue seems to stem from the backend [definition](https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/filter/filter.go#L33-L46) of filter. Until this is resolved, it would be good to at least attach a note about it in the documentation.
Update: I've started taking a closer look and might be able to extend this functionality if the KubeFlow team thinks it's a good idea! | {
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"The `pickled_object` is not a string, it's a binary blob. It might get corrupted when being written to a text file. This is probably the problem you're having.\r\n\r\nThe object is also likely pretty big.\r\n\r\nFor non-text or non-small data you should use file-based data passing.\r\nPlease check https://github.c... | 2021-01-08T15:33:11 | 2021-03-18T22:20:06 | 2021-03-18T22:20:05 | NONE | null | Hi,
We are trying to use the [Classification](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/classification.ipynb) method with kubflow pipelines, we've divided the code into multiple segments. In the data-investigation section, we have to return the object and use this in other sections so we used the `pickle.dumps` method for converting the object to a string and we are facing the below error.
> "tensorflow.python.framework.errors_impl.InternalError: Tensorflow type 21 not convertible to numpy dtype."
<img width="1219" alt="Screenshot 2021-01-08 at 8 49 09 PM" src="https://user-images.githubusercontent.com/7547493/104032405-220bc980-51f4-11eb-89f0-8ef74026061d.png">
Code-Snippet
```
`@func_to_container_op
def data_investigation()-> str:
....
....
....
d = {"train_ds":train_ds,"val_ds":val_ds,"class_names":class_names}
pickled_object = pickle.dumps(d)
return pickled_object`
....
....
....
@dsl.pipeline(name='Classification_pipeline',description='Classification')
def classification_pipeline():
data = data_investigation()
model_data = model_fitting(data.output)
train_data = training(model_data.output)
data_aug = data_augmentations_func(train_data.output)
``` | {
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