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https://api.github.com/repos/kubeflow/pipelines/issues/8873 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8873/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8873/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8873/events | https://github.com/kubeflow/pipelines/issues/8873 | 1,593,414,708 | I_kwDOB-71UM5e-ZQ0 | 8,873 | [sdk] Not Able to Run Sequential Steps with Optional Flags in KFP v2 | {
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"/assign @connor-mccarthy ",
"@rahuja23, I'm not sure I understand what you mean by optional flag. Can you explain? If possible, can you create a minimal reproducible example of the behavior you suspect is a bug?",
"This issue has been automatically marked as stale because it has not had recent activity. It wil... | 2023-02-21T12:42:50 | 2023-08-26T07:42:15 | null | NONE | null | ### Environment
* KFP version:
kfp 2.0.0b12
* KFP SDK version:
kfp-pipeline-spec 0.2.0
kfp-server-api 2.0.0a6
```
### Steps to reproduce
from kfp.components import load_component_from_file
@component(
base_image=base_image, packages_to_install=["kfp"]
)
def gold_ingredients_processor():
import logging
from datetime import datetime
from src import version
from src.constants.HyperParameters import GoldingredientsParameters
from src.constants.ApplicationConstants import ApplicationConstants
from src.shared.ReadConfiguration import ReadConfiguration
from src.shared.GenericUtil import GenericUtil
from src.goldingredientsprocessor.GoldIngredientsProcessorTrain import GoldIngredientsProcessorTrain
from src.goldingredientsprocessor import model_name, model_version
from kfp.v2 import dsl
def start_training(iso_code, enable_persist_results):
logging.info("Starting Training")
logging.info(f"Using Foodgraph version {version}")
logging.info(f"Using model {model_name} version: {model_version} for country {iso_code}")
gold_ingredient_processor = GoldIngredientsProcessorTrain(
enable_persist_results=enable_persist_results,
iso_code=iso_code
)
gold_ingredient_processor.fit()
logging.info("Completed the Gold Ingredients Images Processor Step")
# config = ReadConfiguration()
# gold_ingredient_processor_parameters = GenericUtil.get_module_parameters(
# parameters=GoldingredientsParameters, iso_code=iso_code
# )
# test_name = f"Gold-Ingredients-Processor-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"
# ModelUtil.upload_experiment(iso_code=iso_code, test_name=test_name,
# hyper_parameter=gold_ingredient_processor_parameters,
# metric=None)
logging.basicConfig(
format="%(asctime)s %(levelname)-8s %(message)s",
level=logging.INFO,
datefmt="%Y-%m-%d %H:%M:%S",
)
logging.getLogger().setLevel(logging.INFO)
op =start_training(ApplicationConstants.GERMANY_ISO_CODE, True)
return op
@component(
base_image=base_image
)
def data_preprocessor_train():
import logging
from datetime import datetime
from src import version
from src.datapreprocessor import model_name, model_version
from src.datapreprocessor.DataPreprocessorTrain import DataPreprocessorTrain
from src.constants.ApplicationConstants import ApplicationConstants
from src.shared.ReadConfiguration import ReadConfiguration
from src.constants.PipelineFiles import PipelineFiles
from src.shared.ModelUtil import ModelUtil
from src.shared.GenericUtil import GenericUtil
from src.constants.HyperParameters import DataPreprocessorParameters
def start_training(iso_code, enable_persist_results):
logging.info("Starting Training")
logging.info(f"Using Foodgraph version {version}")
logging.info(f"Using model {model_name} version: {model_version} for country {iso_code}")
config = ReadConfiguration()
data_preprocessor = DataPreprocessorTrain(
iso_code=iso_code,
enable_persist_results=enable_persist_results,
)
data_preprocessor.fit()
data_preprocessor_model_parameters = GenericUtil.get_module_parameters(
parameters=DataPreprocessorParameters, iso_code=iso_code
)
test_name = f"Data-Preprocessor-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"
ModelUtil.upload_experiment(iso_code=iso_code, test_name=test_name,
hyper_parameter=data_preprocessor_model_parameters,
metric=None)
if data_preprocessor.config.persist_data_preprocessor_stopwords:
ModelUtil.upload_model(
file_name=PipelineFiles.STOPWORDS_FILENAME,
model=data_preprocessor.stopwords_idf_df,
bucket_name=config.gcs_bucket,
version=version,
local_model_location=config.models_directory,
iso_code=iso_code,
experiment_run=None,
model_name=model_name,
)
if data_preprocessor.config.persist_short_ingredients:
ModelUtil.upload_model(
file_name=PipelineFiles.SHORT_INGREDIENTS_FILE,
model=data_preprocessor.short_ingredients,
bucket_name=config.gcs_bucket,
version=version,
local_model_location=config.models_directory,
iso_code=iso_code,
experiment_run=None,
model_name=model_name,
)
logging.basicConfig(
format="%(asctime)s %(levelname)-4s %(message)s",
level=logging.INFO,
datefmt="%Y-%m-%d %H:%M:%S",
)
logging.getLogger().setLevel(logging.INFO)
start_training(ApplicationConstants.GERMANY_ISO_CODE, True)
@kfp.v2.dsl.pipeline(name="foodgraph-training-pipeline", pipeline_root=PIPELINE_ROOT)
def my_pipeline_new( articles_to_ing_flag: bool = False,
data_preprocessor_flag: bool= True,
gold_ingredient_processor_flag: bool= False,
gap_analysis_flag: bool= False,
ingredient_normaliser_flag: bool= False,
menu_items_to_recipe_flag: bool= False,
recipes_to_ing_flag: bool= False):
with dsl.Condition(gold_ingredient_processor_flag == True) as C:
op1= gold_ingredients_processor().set_display_name("Gold-Ingredient-Processor").set_cpu_limit('50').set_memory_limit('75')
with dsl.Condition(data_preprocessor_flag == True):
op2 = data_preprocessor_train().set_display_name("Article-Ingredient").set_cpu_limit('50').set_memory_limit('75').after(C)
import os
from datetime import datetime
from pathlib import Path
from google.cloud import aiplatform
import json
if __name__ == "__main__":
kfp.v2.compiler.Compiler().compile(pipeline_func=my_pipeline_new)
run = aiplatform.PipelineJob(
project="hd-dwh-vertex-prod-17624",
enable_caching=False,
location="europe-west3",
display_name="food-training-pipeline",
template_path=filename,
job_id=f"test-pipeline-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}",
pipeline_root=f"gs://dev-vertex-foodgraph-model-artifacts",
parameter_values=json.dumps({'articles_to_ing_flag': True,
'data_preprocessor_flag': True,
'gold_ingredient_processor_flag': False}),
)
run.submit(service_account="vertex-ai@hd-dwh-vertex-prod-17624.iam.gserviceaccount.com")
```
### Expected result
The step "data_preprocessor_train()" should run skipping the first step i.e. gold_ingredients but none of the steps run if the first flag is set to false.
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8872 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8872/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8872/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8872/events | https://github.com/kubeflow/pipelines/issues/8872 | 1,591,976,149 | I_kwDOB-71UM5e46DV | 8,872 | [bug] Not able to change base_image for hyperparameter_tuning_job experimental components (GetTrialsOp, GetBestTrialOp, etc) | {
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"follower... | null | [
"@RansSelected, as a short term fix, I suggest you either (1) fork the component and change the code yourself or (2) modify the object in-memory to override the image tag.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. ... | 2023-02-20T14:31:57 | 2023-08-26T07:42:17 | null | NONE | null | * KFP version:
1.8.19 and earlier ones
### Steps to reproduce
- I'm constructing a Vertex AI pipeline;
- I'm using Hyperparameter tuning components in my pipeline;
- I have the very strict vpc-sc settings; if I want to use components such as GetBestTrialOp, etc from
https://github.com/kubeflow/pipelines/blob/37b3397aee374f5ea29665de7839e09e1462e794/components/google-cloud/google_cloud_pipeline_components/experimental/hyperparameter_tuning_job/utils.py
I only have the option to use predefined base_image and it also will try to install some packages; but because of vpc-sc I don;t have access to the public pypi;
- pipeline is failing because there is no way to modify the base image in experimental components from hyperparameter_tuning_job utils; also, there seems to be no way to specify to install packages that experimental component will try to install from the private pypi;
### Expected result
- I can specify image for the components in hyperparameter_tuning_job utils (GetBestTrialOp, GetBestHyperparametersOp, etc)
- I can specify private artifact registry for hyperparameter_tuning_job utils (GetBestTrialOp, GetBestHyperparametersOp, etc) component to load python packages from it;
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8870 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8870/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8870/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8870/events | https://github.com/kubeflow/pipelines/issues/8870 | 1,591,839,327 | I_kwDOB-71UM5e4Ypf | 8,870 | [sdk] kfp 2.0.0.b11 (and other betas) incompatible with kfp_server_api version 2.0.0.b | {
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"u... | closed | false | null | [] | null | [
"Hi @anifort!\r\nTo resolve it, please upgrade the `kfp` package to 2.0.0-b12"
] | 2023-02-20T13:04:36 | 2023-02-24T00:00:28 | 2023-02-24T00:00:28 | NONE | null | ### Description
KFP installation also install dependencies. this include kfp-server-api. It seems that kfp-server-api 2.0.0.b0 is now the required version for kfp 2.0.0.bX however the 2 are not compatible as kfp is attempting to use an attribute from kfp-server-api thad does not exist.
### Environment
* KFP version: 2.0.0.b11. (But other 2.0.0.bx also have the same issue)
* KFP SDK version: 2.0.0.b11
* All dependencies version:
kfp 2.0.0b11 \
kfp-pipeline-spec 0.2.0 \
kfp-server-api 2.0.0b0
### Steps to reproduce
pip install kfp==2.0.0.b11
python -c "import kfp"
*the import errors with:*
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/aniftos/Projects/propensity-modeling/.venv/lib/python3.10/site-packages/kfp/__init__.py", line 23, in <module>
from kfp.client import Client
File "/Users/aniftos/Projects/propensity-modeling/.venv/lib/python3.10/site-packages/kfp/client/__init__.py", line 20, in <module>
from kfp.client.client import Client
File "/Users/aniftos/Projects/propensity-modeling/.venv/lib/python3.10/site-packages/kfp/client/client.py", line 58, in <module>
class JobConfig:
File "/Users/aniftos/Projects/propensity-modeling/.venv/lib/python3.10/site-packages/kfp/client/client.py", line 61, in JobConfig
self, spec: kfp_server_api.ApiPipelineSpec,
AttributeError: module 'kfp_server_api' has no attribute 'ApiPipelineSpec'. Did you mean: 'ApiPipeline'?
```
### Expected result
to be able to import kfp without errors
### Materials and Reference
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8869 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8869/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8869/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8869/events | https://github.com/kubeflow/pipelines/issues/8869 | 1,591,301,025 | I_kwDOB-71UM5e2VOh | 8,869 | [Feature Request]Add metrics in experiments section of kubeflow for version 2 | {
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"Any updates on this ?\r\n",
"This is not planned for KFPv2. Instead of the above approach, you can use Scalar metrics https://www.kubeflow.org/docs/components/pipelines/v1/sdk/output-viewer/#scalar-metrics to achieve the same goal. You can define any amount of metrics with any value.",
"I would very much suppo... | 2023-02-20T07:23:45 | 2023-08-03T10:19:30 | null | NONE | null | How to get something like this (below image) in Kubeflow pipeline version 1.8.5 ( python kfp version 2 )?
Currently in the experiment dashboard , I don't see any metrics in the experiment section of the page like in the image above. Is this feature (metrics in column format) removed ?
<img width="1265" alt="image" src="https://user-images.githubusercontent.com/42741300/220033385-9294c251-4b59-4f3f-9dde-9bc9ce23f145.png">
Code snippet :
```
def produce_metrics(
# Note when the `create_component_from_func` method converts the function to a component, the function parameter "mlpipeline_metrics_path" becomes an output with name "mlpipeline_metrics" which is the correct name for metrics output.
mlpipeline_metrics_path: Output[Metrics],
):
import json
accuracy = 0.9
metrics = {
'metrics': [{
'name': 'accuracy-score', # The name of the metric. Visualized as the column name in the runs table.
'numberValue': accuracy, # The value of the metric. Must be a numeric value.
'format': "PERCENTAGE", # The optional format of the metric. Supported values are "RAW" (displayed in raw format) and "PERCENTAGE" (displayed in percentage format).
}]
}
with open(mlpipeline_metrics_path.path, 'w') as f:
json.dump(metrics, f)
```
I would love if we can filter and sort on metrics for a pipeline in an experiment.
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"@MatthewRalston: The label(s) `area/component` cannot be applied, because the repository doesn't have them.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/8868):\n\n>### Environment\r\n>\r\n>\r\n>Minikube v1.28.0\r\n>kubernetes 1.22.2\r\n>Kustomize v3.2.0\r\n>kubectl v1.25.5\r\n... | 2023-02-19T03:53:47 | 2023-08-26T07:42:19 | null | NONE | null | ### Environment
Minikube v1.28.0
kubernetes 1.22.2
Kustomize v3.2.0
kubectl v1.25.5
Manifests v1.6.1
KFP SDK version:
All dependencies version:
kfp 2.0.0b9
kfp-pipeline-spec 0.1.16
kfp-server-api 2.0.0a6
### Steps to reproduce
From arch Linux
```bash
yay -S minikube docker containerd minio
```
* `/etc/docker/daemon.json`
```json
{
"exec-opts": ["native.cgroupdriver=systemd"],
"log-driver": "json-file",
"log-opts": {
"max-size": "100m"
},
"data-root": "/var/lib/docker",
"storage-driver": "overlay2",
"rootless": false
}
```
* Default configurations otherwise, except Minio service paths/users
1. Launch minikube
2. Enable istio
3. `istioctl install --set revision=canary`
4. Use Kustomize to install Kubeflow 1.6 manifests
5. Port-fowarding
6. Use Kubeflow/pipelines dashboard to invoke a python kfp pipeline.
* `components/gunzip.yaml`
```yaml
name: gunzip
description: Gunzips an Input file to an Ouput filepath
inputs:
- {name: infile, type: String, description: 'Data for gzip decompression'}
outputs:
- {name: Output, type: String, description: 'Output decompressed plaintext'}
implementation:
container:
image: alpine:latest
# command is a list of strings (command-line arguments).
# The YAML language has two syntaxes for lists and you can use either of them.
# Here we use the "flow syntax" - comma-separated strings inside square brackets.
command: [
sh,
-c,
zcat,
{inputPath: infile},
'>',
{outputPath: Output}
]
```
* `pipeline.py`
```python
#!/bin/env python
import os
import sys
import argparse
from pathlib import Path
import gzip
import kfp.dsl as dsl
import kfp.components as comp
import kfp
components_dir = os.path.join(os.path.dirname(__file__), "components")
gunzip = comp.load_component_from_file(os.path.join(components_dir, "gunzip.yaml"))
echo = comp.load_component_from_file(os.path.join(components_dir, "echo.yaml"))
is_unzip_needed = comp.load_component_from_file(os.path.join(components_dir, "is_unzip_needed.yaml"))
@dsl.pipeline(
name='kubeflow-barebone-demo',
description='kubeflow demo with minimal setup'
)
def rnaseq_pipeline(fastq1:str):
#Path(str(decompressed_fastq1)).parent.mkdir(parents=True, exist_ok=True)
gunzip1 = gunzip(infile=fastq1).set_cpu_request('1').set_cpu_limit('1').set_memory_request('512Mi').set_memory_limit('512Mi')
if __name__ == "__main__":
logger = get_root_logger(2)
kfp.compiler.Compiler().compile(rnaseq_pipeline, 'pipeline.yaml')
```
### Expected result
Expected behavior should be that Kubeflow + manifests handles the creation of output `tmp` directories. I did not get an appropriate answer from [issue](https://github.com/kubeflow/pipelines/issues/8782). Either the requirement for users to create their own directories (as @chensun suggested) should be a documented *feature* of Kubeflow, or the correct way to handle file outputs with MinIO should be well documented. I'm a fairly tech savvy average user of Kubeflow, and with about 8+ hours in the documentation, I'm running into a crippling bug for my small business. Namely, the correct way to pass data from one step to antother, saving the output in the `/tmp/Output/output` directory. If that's where Kubeflow wants to put output datasets:
* why can't it also manage the creation of such a directory?
* Is this a kubeflow or manifests issue?
* What would be the proper way to produce/retrieve kfp outputs using 1.8 kfp syntax?
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
/area sdk
/area testing
<!-- /area samples -->
/area component
---
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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."
] | 2023-02-18T00:19:10 | 2023-08-26T07:42:21 | null | MEMBER | null | The majority of the backend code has test coverage under 90%, although pre-submit test-coverage is often expected to be above it and it is sometimes reasonable to expect >95% coverage. While enforcing high coverage is impractical, we have an opportunity to improve gradually improve the situation.
---
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"Any updates on this ?",
"/cc @connor-mccarthy to investigate more.",
"/assign @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-02-17T07:07:00 | 2023-08-26T07:42:23 | null | NONE | null | So I have a component where I need to save a dataframe as an output. So I used this code in my component
```
with open(f'{prediction_path.path}.csv', 'wb') as f:
df.to_csv(path_or_buf=f)
```
But I am getting this error
```
W0217 06:53:10.959325 73 launcher.go:560] Local filepath "/minio/mlpipeline/test/pipeline/product-recommend-5/b34809bb-e5e2-4a92-aa1c-e6622fef4bc8/test/prediction_path" does not exist
```
Pickling does work here if I use
```
with open(f'{prediction_path.path}', 'wb') as f:
pickle.dump(df, f)
```
But I want to save and read artifacts in original file format. How do I achieve this in Kubeflow cluster? On vertex I am able to do this but not working in kubeflow cluster
My component looks likes this
```
@component(
packages_to_install = package_list,
base_image="python:3.9",
output_component_file="test_model.yaml"
)
def test(
model_path: Input[Dataset],
test_dataset: Input[Dataset],
prediction_path: Output[Dataset]
):
import pandas as pd
import pickle
with open(f'{test_dataset.path}', 'rb') as f:
validation_df = pickle.load(f)
with open(f'{model_path.path}', 'rb') as f:
model = pickle.load(f)
# do something and generate a dataframe df
with open(f'{prediction_path.path}.csv', 'wb') as f:
df.to_csv(path_or_buf=f)
```
I am using Kubeflow pipelines installed on local mac os with minio storage.
Version: [1.8.5](https://www.github.com/kubeflow/pipelines/commit/8382cb027c49ac0f8981b20c70e50783a3a9dab1)
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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."
] | 2023-02-15T19:28:56 | 2023-08-26T07:42:26 | null | COLLABORATOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8854 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8854/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8854/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8854/events | https://github.com/kubeflow/pipelines/issues/8854 | 1,585,895,511 | I_kwDOB-71UM5ehthX | 8,854 | [backend] Permission issues with api-server:2.0.0-beta.0 | {
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"following_url": "https://api.github.com/users/b4sus/following{/other_user}",
"gists_url": "https://api.github.com/users/b4sus/gists{/gist_id}",
"starred_url": "https://api.github.com/users/b4sus/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/b4sus/subscriptions",
"organizations_url": "https://api.github.com/users/b4sus/orgs",
"repos_url": "https://api.github.com/users/b4sus/repos",
"events_url": "https://api.github.com/users/b4sus/events{/privacy}",
"received_events_url": "https://api.github.com/users/b4sus/received_events",
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"created_at": "2023-03-13T20:54:38",
"updated_at": "2023-06-06T17:36:22",
"due_on": null,
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} | [
"Same Issue on listing kubeflow pipelines thought UI. I was forced to update to 2.0.0-beta.0 because of AWS S3 IRSA support introduced in this update but now I'm totally blocked by this issue",
"I had the listing issue as well, I guess that for this the fix will come eventually from manifests, but I could fix it ... | 2023-02-15T13:50:33 | 2023-05-09T06:28:32 | 2023-05-04T23:53:27 | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Installed following kubeflow manifests installation - master version (currently identical to tag v1.7.0-rc.0) of manifests with beta.0 instead of alpha.6 version of kubeflow pipeline components.
* KFP version:
2.0.0-beta.0
* KFP SDK version:
2.0.0b12
### Steps to reproduce
Run pipeline like described in [pipelines documentation](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/).
The call to `create_run_from_pipeline_func` fails with following error:
```
HTTP response body: {"error":"Failed to create a new v1beta1 run: Failed to create a run due to authorization error. Check if you have write permissions to namespace kubeflow-user-example-com: PermissionDenied: User 'user@example.com' is not authorized with reason: (request: \u0026ResourceAttributes{Namespace:kubeflow-user-example-com,Verb:create,Group:,Version:,Resource:pipelines,Subresource:,Name:,}): Unauthorized access","code":7,"message":"Failed to create a new v1beta1 run: Failed to create a run due to authorization error. Check if you have write permissions to namespace kubeflow-user-example-com: PermissionDenied: User 'user@example.com' is not authorized with reason: (request: \u0026ResourceAttributes{Namespace:kubeflow-user-example-com,Verb:create,Group:,Version:,Resource:pipelines,Subresource:,Name:,}): Unauthorized access","details":[{"@type":"type.googleapis.com/google.rpc.Status","code":7,"message":"User 'user@example.com' is not authorized with reason: (request: \u0026ResourceAttributes{Namespace:kubeflow-user-example-com,Verb:create,Group:,Version:,Resource:pipelines,Subresource:,Name:,})"}]}
```
The roles and binding created from manifest look correct. After some digging I found that problem comes from [here](https://github.com/kubeflow/pipelines/blob/2.0.0-beta.0/backend/src/apiserver/server/run_server.go#L132) :
```go
resourceAttributes := &authorizationv1.ResourceAttributes{
Namespace: run.Namespace,
Verb: common.RbacResourceVerbCreate,
Resource: common.RbacResourceTypePipelines,
}
if err := s.resourceManager.IsAuthorized(ctx, resourceAttributes); err != nil {
return nil, util.Wrapf(err, "Failed to create a run due to authorization error. Check if you have write permissions to namespace %s", run.Namespace)
}
```
The `resourceAttributes` doesn't specify `Group`.
After adding `Group: common.RbacPipelinesGroup,` (`common.RbacPipelinesGroup` being `pipelines.kubeflow.org`) to attributes, building the docker image and using it instead of `gcr.io/ml-pipeline/api-server:2.0.0-beta.0` I run to exactly same problem [in other place](https://github.com/kubeflow/pipelines/blob/2.0.0-beta.0/backend/src/apiserver/server/pipeline_server.go#L122). Again, the group added, image built, pushed, used, the pipeline is running.
Problem could in other places as well.
### Expected result
Pipeline runs
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8853 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8853/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8853/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8853/events | https://github.com/kubeflow/pipelines/issues/8853 | 1,585,152,945 | I_kwDOB-71UM5ee4Ox | 8,853 | [backend] ReportWorkflow attempts to create duplicate entries in | {
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"node_id": "MDQ6VXNlcjM1MTU3MDk2",
"avatar_url": "https://avatars.githubusercontent.com/u/35157096?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/gkcalat",
"html_url": "https://github.com/gkcalat",
"followers_url": "https://api.github.com/users/gkcalat/followers",
"following_url": "https://api.github.com/users/gkcalat/following{/other_user}",
"gists_url": "https://api.github.com/users/gkcalat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/gkcalat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/gkcalat/subscriptions",
"organizations_url": "https://api.github.com/users/gkcalat/orgs",
"repos_url": "https://api.github.com/users/gkcalat/repos",
"events_url": "https://api.github.com/users/gkcalat/events{/privacy}",
"received_events_url": "https://api.github.com/users/gkcalat/received_events",
"type": "User",
"site_admin": false
} | [
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"html_url": "https://github.com/gkcalat",
"followers_url": "https://api.github.com/users/gkcalat/followers",
"following_url": "https://api.github.com/users/gkcalat/following{/other_user}",
"gists_url": "https://api.github.com/users/gkcalat/gists{/gist_id}",
"starred_url": "https://api.github.com/users/gkcalat/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/gkcalat/subscriptions",
"organizations_url": "https://api.github.com/users/gkcalat/orgs",
"repos_url": "https://api.github.com/users/gkcalat/repos",
"events_url": "https://api.github.com/users/gkcalat/events{/privacy}",
"received_events_url": "https://api.github.com/users/gkcalat/received_events",
"type": "User",
"site_admin": false
} | [
{
"login": "gkcalat",
"id": 35157096,
"node_id": "MDQ6VXNlcjM1MTU3MDk2",
"avatar_url": "https://avatars.githubusercontent.com/u/35157096?v=4",
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"url": "https://api.github.com/users/gkcalat",
"html_url": "https://github.com/gkcalat",
"followers_url": "https://api.git... | null | [
"This has been fixed by #8850 and #8926. Reopen it if you encounter the same issue.\r\n\r\n/CC @chensun "
] | 2023-02-15T03:30:46 | 2023-03-10T00:27:49 | 2023-03-10T00:27:49 | MEMBER | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
KFP Standalone from master
### Steps to reproduce
Logs:
```
api.ReportService/ReportWorkflowV1 handler starting
I0215 03:26:04.850861 7 error.go:270] InternalServerError: Failed to store resource references: Error 1062: Duplicate entry '2785d638-1136-4bd3-8fbe-6045636d147c-Run-Experiment' for key 'resource_references.PRIMARY'
InternalServerError: Failed to store resource references to table for run runoftutorialdatapassinginf6rz-91-408347388
github.com/kubeflow/pipelines/backend/src/common/util.NewInternalServerError
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:144
github.com/kubeflow/pipelines/backend/src/apiserver/storage.(*RunStore).CreateRun
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/storage/run_store.go:492
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).ReportWorkflowResource
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1346
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ReportServer).ReportWorkflowV1
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/report_server.go:40
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:338
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:340
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
Failed to report a workflow due to error creating run 2785d638-1136-4bd3-8fbe-6045636d147c
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:258
github.com/kubeflow/pipelines/backend/src/common/util.Wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:329
github.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).ReportWorkflowResource
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1348
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ReportServer).ReportWorkflowV1
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/report_server.go:40
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:338
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:340
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
Report workflow failed
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrap
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:263
github.com/kubeflow/pipelines/backend/src/common/util.Wrap
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:342
github.com/kubeflow/pipelines/backend/src/apiserver/server.(*ReportServer).ReportWorkflowV1
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/report_server.go:42
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler.func1
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:338
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:340
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
/api.ReportService/ReportWorkflowV1 call failed
github.com/kubeflow/pipelines/backend/src/common/util.(*UserError).wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:258
github.com/kubeflow/pipelines/backend/src/common/util.Wrapf
/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:329
main.apiServerInterceptor
/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:32
github.com/kubeflow/pipelines/backend/api/v1beta1/go_client._ReportService_ReportWorkflowV1_Handler
/go/src/github.com/kubeflow/pipelines/backend/api/v1beta1/go_client/report.pb.go:340
google.golang.org/grpc.(*Server).processUnaryRPC
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1282
google.golang.org/grpc.(*Server).handleStream
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:1616
google.golang.org/grpc.(*Server).serveStreams.func1.2
/go/pkg/mod/google.golang.org/grpc@v1.44.0/server.go:921
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1581
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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"type": "User",
"site_admin": false
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"Closed and see more info at #9859"
] | 2023-02-15T03:06:56 | 2023-08-28T20:57:30 | 2023-08-28T20:57:29 | MEMBER | null | Problem:
1. Backend's `storage` relies on `gorm` v1 package
2. `gorm` v1 is not actively developed and it will be difficult if we need to make more complex SQL queries
3. Lack of support of multiple database drivers
4. Bags and issues that will not be resolved in `gorm` v1
Solution:
1. Migrate to `gorm` v2 because it has:
- better support of multiple database drivers and dialects (including MySQL and PostgreSQL)
- simpler `gorm`-based SQL syntax
- built-in serializers
- smarter auto-migration
- support of `ROW_NUMBER` and other advances SQL
- support of `Upsert`
- performance improvements
- [other improvements](https://gorm.io/docs/v2_release_note.html)
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"followers_url": "https://api.git... | null | [] | 2023-02-15T02:42:34 | 2023-04-27T20:21:03 | 2023-04-27T20:21:03 | MEMBER | null | ### Feature Area
Problem:
- Backend testing uses SQLite as a fake DB instance
- Real DB is based on MySQL
- Two databases have slight differences, which makes the testing useless
Solution:
- Switch the fake DB to MySQL instances
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"fixed by #8860 "
] | 2023-02-13T19:22:46 | 2023-02-24T20:35:12 | 2023-02-24T20:35:12 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
standalone
* KFP version:
2.0.0-beta.0
* KFP SDK version:
N/A
### Steps to reproduce
Run https://github.com/kubeflow/pipelines/blob/master/sdk/python/test_data/pipelines/pipeline_with_condition.py
or find its run result in sdk execution test, e.g.: https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/8841/kubeflow-pipelines-sdk-execution-tests/1625208635669876736
Pipeline failed with error:
```
F0213 19:04:16.807068 19 main.go:74] KFP driver: failed to unmarshal component spec, error: unknown field "isOptional" in ml_pipelines.ComponentInputsSpec.ParameterSpec
componentSpec: 0xc00067b7e0
```
### Expected result
no error, pipeline succeed.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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* How did you deploy Kubeflow Pipelines (KFP)?
standalone
* KFP version:
2.0.0-beta.0
* KFP SDK version:
N/A
### Steps to reproduce
Run pipeline: https://github.com/kubeflow/pipelines/blob/master/samples/v2/pipeline_container_no_input.py
or find it run result from sdk-execution-test, e.g.: https://oss.gprow.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/8841/kubeflow-pipelines-sdk-execution-tests/1625208635669876736
Pipeline fails with pod error log:
```
I0213 19:00:39.519690 17 main.go:213] output ExecutorInput:{
"inputs": {},
"outputs": {
"outputFile": "/tmp/kfp_outputs/output_metadata.json"
}
}
F0213 19:00:39.519796 17 main.go:74] KFP driver: driver.Container(pipelineName=v2-container-component-no-input, runID=7cf802c0-d725-4eb2-b18d-ff22f8d4b112, task="hello-world-container", component="comp-hello-world-container", dagExecutionID=133285, componentSpec) failed: failure while getting executionCache: failed to list tasks: rpc error: code = Internal desc = List tasks failed: Failed to list tasks: InternalServerError: Failed to list tasks: sql: Scan error on column index 6, name "StartedTimestamp": converting NULL to int64 is unsupported: sql: Scan error on column index 6, name "StartedTimestamp": converting NULL to int64 is unsupported
```
### Expected result
No error, pipeline should succeed.
### Materials and Reference
---
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"Hi @ReggieCarey!\r\n\r\nCould you please check if you can retrieve logs from the `mysql` pod?",
"The same issue, mysql pod logs\r\n```\r\nmysql-55778745b6-lcl8q istio-proxy [2023-02-24T07:15:17.569Z] \"- - -\" 0 - - - \"-\" 10362 21332 29 - \"-\" \"-\" \"-\" \"-\" \"10.128.48.144:3306\" inbound|3306|| 127.0.0.6:... | 2023-02-13T19:05:03 | 2023-06-01T21:34:21 | 2023-06-01T20:43:01 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Deployed as part of Kubeflow 1.6.0 using all-in-one manifests.
* KFP version:
2.0.0.alpha.3
### Steps to reproduce
Login to Kubeflow
Get the dashboard visible
Click on "Artifacts" in left hand pane.
### Expected result
A list of my artifacts should appear, not an error at the top of the page.
### Materials and Reference
Found that metadata-grpc-deployment reports the error in its log:
For each press of the "Artifacts" button, the following logs are generated in metadata-grpc-deployment-<uuid>
```
W0213 18:50:37.803512 508 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.807418 519 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.808578 520 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.808811 517 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.808934 518 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.809177 512 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.809348 502 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.809360 494 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.809484 516 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.811550 506 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.811604 493 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.811686 497 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.811736 500 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.811801 498 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.811853 499 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.812080 510 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.812157 513 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
W0213 18:50:37.812229 515 metadata_store_service_impl.cc:432] Failed to connect to the database: mysql_real_connect failed: errno: , error:
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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"html_url": "https://github.com/connor-mccarthy",
"follower... | null | [
"Hi @ReggieCarey!\r\nThank you for reporting it. This component is not actively developed. If you wish, please create a PR adding the support for non-GCP distributions of kubernetes.",
"Thanks for the feedback! Great community. What was diagnose me supposed to do? I'll try to find the code and answer that myself.... | 2023-02-13T14:38:51 | 2023-08-26T07:42:27 | null | NONE | null | ### Environment
* KFP version:
KFP as installed by Kubeflow Manifests 1.6.0 onto private k8s.
* KFP SDK version:
Local install (MacOS) as installed via pip install kfp (as of Feb 13, 2023)
* All dependencies version:
kfp 1.8.18
kfp-pipeline-spec 0.1.16
kfp-server-api 1.8.5
### Steps to reproduce
Attempt to execute 'kfp diagnose me' on MacOS.
```
$ pip install kfp --upgrade
$ kfp diagnose_me
Google Cloud SDK is not installed, gcloud, gsutil and kubectl are required for this app to run. Please follow instructions at https://cloud.google.com/sdk/install to install the SDK.
```
### Expected result
I expected that an open source tool would not have a dependency on a commercial implementation of Kubernetes or be tied to Kubeflow Pipelines deployed to Googles infrastructure.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8842 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8842/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8842/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8842/events | https://github.com/kubeflow/pipelines/issues/8842 | 1,582,146,066 | I_kwDOB-71UM5eTaIS | 8,842 | This step is in Error state with this message: unexpected end of JSON input | {
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"description": ""
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | closed | false | null | [] | null | [
"Hi @AaronUST!\r\nCould you please check if you have specified a correct input for the component?",
"@AaronUST @gkcalat I found the cause of this issue, please see https://github.com/kubeflow/pipelines/issues/8942 for more information.\r\n\r\nI imagine you will be able to fix this by removing `WorkflowTaskResults... | 2023-02-13T11:09:52 | 2023-04-11T16:19:43 | 2023-04-11T16:19:43 | NONE | null | ### Environment
* KFP has been installed on a Google Cloud kubernetes cluster
* KFP version: 1.8.5
### Steps to reproduce
The error appears in the runs console when trying to perform a run. The first time I ran the same pipeline it worked fine, but the second and subsequent times I get this error.
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https://api.github.com/repos/kubeflow/pipelines/issues/8840 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8840/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8840/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8840/events | https://github.com/kubeflow/pipelines/issues/8840 | 1,580,530,196 | I_kwDOB-71UM5eNPoU | 8,840 | [backend] InternalServerError: Failed to convert a pipeline to API pipeline: Pipeline namespace cannot be empty | {
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"followers_url": "https://api.git... | null | [] | 2023-02-11T00:29:23 | 2023-02-16T07:31:11 | 2023-02-16T07:31:11 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
standalone
* KFP version:
2.0.0b0
* KFP SDK version:
dev
### Steps to reproduce
Calling [list_pipelines](https://github.com/kubeflow/pipelines/blob/a45d72d75dd99c9cbb8dc4706d380e4ceb9ec47e/sdk/python/kfp/client/client.py#L689-L722) API with or without passing namespace returns a list of pipeline that contains error message.
```
{'next_page_token': None,
'pipelines': [{'created_at': None,
'description': None,
'display_name': None,
'error': {'code': 13,
'details': [{'type_url': None, 'value': None}],
'message': 'InternalServerError: Failed to convert a '
'pipeline to API pipeline: Pipeline '
'namespace cannot be empty'},
'namespace': None,
'pipeline_id': '425faaa8-1dfb-4b8b-85f4-3c2cd5a83b75'},
{'created_at': None,
'description': None,
'display_name': None,
'error': {'code': 13,
'details': [{'type_url': None, 'value': None}],
'message': 'InternalServerError: Failed to convert a '
'pipeline to API pipeline: Pipeline '
'namespace cannot be empty'},
'namespace': None,
'pipeline_id': 'b5a9bd6b-b981-44a4-b8da-e7a23c101f17'},
{'created_at': None,
'description': None,
'display_name': None,
'error': {'code': 13,
'details': [{'type_url': None, 'value': None}],
'message': 'InternalServerError: Failed to convert a '
'pipeline to API pipeline: Pipeline '
'namespace cannot be empty'},
'namespace': None,
'pipeline_id': '68de8376-d386-409d-8e81-cf3cfc1a6366'},
{'created_at': None,
'description': None,
'display_name': None,
'error': {'code': 13,
'details': [{'type_url': None, 'value': None}],
'message': 'InternalServerError: Failed to convert a '
'pipeline to API pipeline: Pipeline '
'namespace cannot be empty'},
'namespace': None,
'pipeline_id': '39aabd86-627a-4616-a8e0-b90c8862e57d'}],
'total_size': 4}
```
### Expected result
expect no error
### Materials and Reference
---
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Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8837 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8837/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8837/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8837/events | https://github.com/kubeflow/pipelines/issues/8837 | 1,578,696,616 | I_kwDOB-71UM5eGP-o | 8,837 | [frontend] Cannot GET /apis/v2beta1/healthz | {
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"gists_url": "https://api.github.com/users/chensun/gists{/gist_id}",
"starred_url": "https://api.github.com/users/chensun/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/chensun/subscriptions",
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"organizations_url": "https://api.github.com/users/jlyaoyuli/orgs",
"repos_url": "https://api.github.com/users/jlyaoyuli/repos",
"events_url": "https://api.github.com/users/jlyaoyuli/events{/privacy}",
"received_events_url": "https://api.github.com/users/jlyaoyuli/received_events",
"type": "User",
"site_admin": false
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"login": "jlyaoyuli",
"id": 56132941,
"node_id": "MDQ6VXNlcjU2MTMyOTQx",
"avatar_url": "https://avatars.githubusercontent.com/u/56132941?v=4",
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"followers_url": "https://a... | null | [
"I was confused, the healthz response I saw was served by the frontend. So missing v2beta1/healthz is expected, as we haven't migrate frontend to connect to v2 APIs yet.\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/508b6a133ab7a0d8abefe7ac95a4de824461e30b/frontend/server/app.ts#L109-L116",
"This is resolved ... | 2023-02-09T22:19:09 | 2023-02-10T22:18:56 | 2023-02-10T22:18:56 | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? KFP standalone deployment
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 2.0.0-beta.0
* KFP SDK version: N/A
### Steps to reproduce
Hit healthz endpoint, e.g.: https://\<kfp endpoint\>/apis/v2beta1/healthz
### Expected result
Expect to get the same result for `v1beta1/healthz`, whic is something like
```
{"buildDate":"Tue Feb 7 23:42:37 UTC 2023","frontendCommitHash":"438b16597d4fcad18b7562cea5760c2b8374d6ef","frontendTagName":"2.0.0-beta.0","apiServerReady":true,"apiServerCommitHash":"438b16597d4fcad18b7562cea5760c2b8374d6ef","apiServerTagName":"2.0.0-beta.0","apiServerMultiUser":false,"multi_user":false}
```
Instead, for `v2beta1/healthz`, I got
```
404: Cannot GET /apis/v2beta1/healthz
```
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8833 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8833/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8833/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8833/events | https://github.com/kubeflow/pipelines/issues/8833 | 1,578,456,021 | I_kwDOB-71UM5eFVPV | 8,833 | feature(backend): Update healthz service in KFP v2 | {
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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."
] | 2023-02-09T19:20:38 | 2023-08-26T07:42:29 | null | MEMBER | null | Current state:
KFP v2 inherited HealthzSerivce from KFP v1:
```
service HealthzService {
// Get healthz data.
rpc GetHealthz(google.protobuf.Empty) returns (GetHealthzResponse) {
option (google.api.http) = {
get: "/apis/v2beta1/healthz"
};
}
}
message GetHealthzResponse {
// TODO(gkcalat): redesign this service to return status
// and move server configuration into a separate service
// TODO(gkcalat): rename or deprecate v1beta1 HealthzService
//
// Returns if KFP in multi-user mode
bool multi_user = 3;
}
```
We need to:
1. Make the service consistent with its purpose and compliant with AIP
2. Move `multi_user` and other server configuration-related information to a separate service
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8832 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8832/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8832/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8832/events | https://github.com/kubeflow/pipelines/issues/8832 | 1,578,449,945 | I_kwDOB-71UM5eFTwZ | 8,832 | chore(backend): Migrate backend API reference docs from kuberflow.org | {
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We have separate [v1](https://www.kubeflow.org/docs/components/pipelines/v1/reference/api/kubeflow-pipeline-api-spec/) and [v2](https://www.kubeflow.org/docs/components/pipelines/v2/reference/api/kubeflow-pipeline-api-spec/) API references on kubeflow.org. As kubeflow.org is versioned by full-fledged kubeflow releases, users can assume that the reference docs are pinned to Kubeflow version. Given that kubeflow.org is deployed from master branch, we can't store the latest API reference docs there before kubeflow makes a new release and include the latest KFP version.
Solution:
Host KFP API reference docs on an external website similar to [SDK reference docs](https://kubeflow-pipelines.readthedocs.io/).
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https://api.github.com/repos/kubeflow/pipelines/issues/8830 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8830/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8830/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8830/events | https://github.com/kubeflow/pipelines/issues/8830 | 1,577,582,214 | I_kwDOB-71UM5eB_6G | 8,830 | [backend] Argo workflow validation fails for kfp v2 pipeline with downstream tasks depending on tasks in `dsl.ParallelFor` (using `dsl.Collected`) | {
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"Not sure what was the reason adding a `-iterations` suffix to the loop task name [here](https://github.com/kubeflow/pipelines/blob/2.0.0-beta.0/backend/src/v2/compiler/argocompiler/dag.go#L297), but tested in my Kubeflow deployment, simply removing the suffix would make the compiled Argo workflow passing validatio... | 2023-02-09T09:59:36 | 2023-06-20T17:14:05 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Full Kubeflow deployment on GKE.
* KFP version:
2.0.0-beta.0
* KFP SDK version:
2.0.0-beta.11
### Steps to reproduce
```
# pipeline.py
from kfp import compiler, dsl
from components import components as test_components
@dsl.pipeline(
name='Churn model prediction pipeline',
description='Churn model prediction pipeline'
)
def bug_repro_pipeline(
loop_items: list = ['1', '2'],
) -> None:
with dsl.ParallelFor(
name='predict-loop',
items=loop_items,
parallelism=2,
) as item:
task_in_loop = test_components.op_in_loop(str_input_p1=item)
test_components.downstream_op(list_input_p1=dsl.Collected(task_in_loop.output))
if __name__ == '__main__':
compiler.Compiler().compile(bug_repro_pipeline, 'bug_repro_pipeline.yaml')
```
```
# components/components.py
from typing import List
from kfp import dsl
BASE_IMAGE = 'python:3.8'
TARGET_IMAGE = 'pipeline-bug-repro:v0.1'
@dsl.component(
base_image=BASE_IMAGE,
target_image=TARGET_IMAGE,
)
def op_in_loop(str_input_p1: str) -> str:
return str_input_p1
@dsl.component(
base_image=BASE_IMAGE,
target_image=TARGET_IMAGE,
)
def downstream_op(list_input_p1: List[str]) -> str:
return ' '.join(list_input_p1)
```
1. Compile the pipeline defined in `pipeline.py`.
2. Upload the compiled pipeline to KFP through the Kubeflow central dashboard.
3. Try to create a run of the uploaded pipeline through the Kubeflow central dashboard.
4. Got the following error message: `{"error":"Failed to create a new v1beta1 run: InternalServerError: Failed to validate workflow for (): templates.entrypoint.tasks.root templates.root sorting failed: invalid dependency for-loop-1","code":13,"message":"Failed to create a new v1beta1 run: InternalServerError: Failed to validate workflow for (): templates.entrypoint.tasks.root templates.root sorting failed: invalid dependency for-loop-1","details":[{"@type":"type.googleapis.com/google.rpc.Status","code":13,"message":"Internal Server Error"}]}`
### Expected result
Can start a run of the specified pipeline.
### Materials and Reference
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8827 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8827/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8827/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8827/events | https://github.com/kubeflow/pipelines/issues/8827 | 1,576,902,141 | I_kwDOB-71UM5d_Z39 | 8,827 | [sdk] Imports from kfp.dsl cause error AttributeError: module 'kfp_server_api' has no attribute 'ApiPipelineSpec | {
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"u... | closed | false | null | [] | null | [
"I've got the same error",
"In my environment, upgrading kfp to 2.0.0b12 resolved this issue.",
"I will close this issue. Feel free to reopen the issue if there's any more problems.",
"> In my environment, upgrading kfp to 2.0.0b12 resolved this issue.\r\n\r\nI tried upgrading to 2.0.0b12, but I have CSV file... | 2023-02-08T22:46:08 | 2023-06-05T19:58:13 | 2023-02-09T23:40:42 | NONE | null | ### Environment
* KFP version:
kfp==2.0.0b1
* All dependencies version:
kfp-pipeline-spec==0.1.16
kfp-server-api==2.0.0b0
### Steps to reproduce
1. `from kfp import dsl`
2. Use any part of dsl, such as pipeline or component decorators
```
from kfp import dsl, compiler
@dsl.pipeline(
name="test"
)
def pipeline_fn(
location: str,
project_id: str,
):
print("hello world")
compiler.Compiler().compile(
pipeline_func=pipeline_fn,
package_path='hello_world_pipeline.yaml')
```
4. Attempt to build component
5. Error results:
`Traceback (most recent call last):
File "/Users/viktorgarba/miniconda/envs/ml-env/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/Users/viktorgarba/miniconda/envs/ml-env/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/Users/viktorgarba/klover/attain-ai-ml/src/pipelines/bert_v1.py", line 1, in <module>
from kfp import dsl, compiler
File "/Users/viktorgarba/miniconda/envs/ml-env/lib/python3.9/site-packages/kfp/__init__.py", line 23, in <module>
from kfp.client import Client # pylint: disable=wrong-import-position
File "/Users/viktorgarba/miniconda/envs/ml-env/lib/python3.9/site-packages/kfp/client/__init__.py", line 15, in <module>
from kfp.client.client import Client
File "/Users/viktorgarba/miniconda/envs/ml-env/lib/python3.9/site-packages/kfp/client/client.py", line 56, in <module>
class JobConfig:
File "/Users/viktorgarba/miniconda/envs/ml-env/lib/python3.9/site-packages/kfp/client/client.py", line 59, in JobConfig
self, spec: kfp_server_api.ApiPipelineSpec,
AttributeError: module 'kfp_server_api' has no attribute 'ApiPipelineSpec'`
### Expected result
No error should occur. component yaml should be outout
### Materials and Reference
When downgrading `kfp-server-api` from `2.0.0b0` (which was deployed 4 hours ago) to `2.0.0a6` things work as expected
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"@JakeSummers, please see my comment here: https://github.com/kubeflow/pipelines/pull/7451#issuecomment-1423310652",
"The issue is lack of py.typed (+ setup.py to preserve py.typed in wheel). kfp uses types internally, but types are not exposed to users because of [pep 560](https://peps.python.org/pep-0560/). An ... | 2023-02-08T21:22:43 | 2023-07-28T08:16:49 | null | NONE | null | ### Environment
All dependencies version:
* kfp 1.8.12 and 1.8.18
* mypy 1.0.0
I am managing my dependencies with poetry.
### Steps to reproduce
```
import kfp.dsl as dsl
```
Run `mypy`
Produces error:
```
file.py:1: error: Skipping analyzing "kfp.dsl": module is installed, but missing library stubs or py.typed marker [import]
```
### Expected result
Type stubs are included in project.
### Materials and Reference
It looks like this issue was addressed in #7451 but it doesn't seem to work for me.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8825 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8825/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8825/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8825/events | https://github.com/kubeflow/pipelines/issues/8825 | 1,576,702,821 | I_kwDOB-71UM5d-pNl | 8,825 | [bug] Vertex AI and kfp v2 'ApiPipelineSpec' | {
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"I do not know if I should report this to the vertex-ai team, but I do not know how.",
"I think now with the last release of kfp `v2.0.12.0b` are now running fine."
] | 2023-02-08T19:42:10 | 2023-02-08T21:38:21 | 2023-02-08T21:38:21 | MEMBER | null | ### Environment
I am using Vertex AI from GCP
* How do you deploy Kubeflow Pipelines (KFP)?
Vertex
* KFP version:
Vertex
* KFP SDK version:
v2.0.12b and v2.0.11b
### Steps to reproduce
Any pipeline that was compiled to run with either of the last two versions are now failing as:
### Expected result
Kubeflow pipeline should finish sucessfully
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area backend -->
<!-- /area sdk -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8818 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8818/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8818/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8818/events | https://github.com/kubeflow/pipelines/issues/8818 | 1,574,621,559 | I_kwDOB-71UM5d2tF3 | 8,818 | chore(backend): Update release script due to proto generation changes in V2 | {
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"followers_url": "https://api.git... | null | [] | 2023-02-07T16:17:32 | 2023-03-10T00:55:41 | 2023-02-07T21:57:42 | MEMBER | null | We need to update one of the release scripts due to the changes in API generator script:
- Add `API_VERSION` variable
- Add proto compilation for v1 and v2
---
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"I came across the same issue when tried to convert an existing v1 pipeline implementation to v2, which had a downstream task depending on upstream tasks in a `ParallelFor` group. Then I came across this new [commit](https://github.com/kubeflow/pipelines/pull/8631) in kfp pre-release `2.0.0b12`. At least using `dsl... | 2023-02-07T14:29:56 | 2023-08-26T07:42:31 | null | NONE | null | ### Environment
* KFP SDK version: 2.0.0b11
* All dependencies version:
kfp 2.0.0b11
kfp-pipeline-spec 0.2.0
kfp-server-api 2.0.0a6
### Steps to reproduce
The following example fails to even create the graph component from the pipeline function with:
"RuntimeError: Tasks cannot depend on an upstream task inside a ParallelFor that is not a common ancestor of both tasks. Task outer-op depends on upstream task inner-op."
```python
@dsl.component
def inner_op(x: str):
print(x)
@dsl.component
def outer_op():
print("outer task")
@dsl.pipeline(name="test-pipeline")
def pipeline():
with dsl.ParallelFor(items=[{"a": "1"}, {"a": "2"}]) as x:
inner_task = inner_op(x=x.a)
outer_task = outer_op().after(inner_task)
```
### Expected result
should work, including compilation.
<!-- What should the correct behavior be? -->
I want to be able to wait for the loop to complete before executing a following task.
I would expect this example to work or have another way of referencing the loop task group in the "after" call.
---
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"Thanks, @b4sus. I'd be happy to review your contribution if you'd like to work on this!"
] | 2023-02-07T08:36:16 | 2023-03-01T17:33:03 | 2023-03-01T17:33:03 | CONTRIBUTOR | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
/area components
### What feature would you like to see?
So far the argument `pip_index_urls` is used only for lightweight python component. But when `target_image` is provided and therefore containerized python component can be generated with `kfp component build`, the argument is not considered - it is not a part of generated Dockerfile.
This is something that could be relatively easily (I think) implemented and I'd be happy to give it a try if there's interest.
### What is the use case or pain point?
Similarly as with lightweight python component, one might be using internal pypi index for internal libraries to be used in containerized components.
### Is there a workaround currently?
Yes. After running `kfp component build` modify generated Dockerfile (add --extra-index-url to the pip line), run docker build directly from commandline (not using `kfp` anymore) and then push the image.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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"This is a beta release blocker. \r\n\r\n@gkcalat for awareness."
] | 2023-02-07T05:50:06 | 2023-03-10T00:55:51 | 2023-02-07T21:57:32 | COLLABORATOR | null | null | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8803 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8803/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8803/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8803/events | https://github.com/kubeflow/pipelines/issues/8803 | 1,572,980,789 | I_kwDOB-71UM5dwcg1 | 8,803 | [frontend/backend] Invalid STORAGESTATE values are used in listing requests | {
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"This has been resolved together with #8703 "
] | 2023-02-06T17:27:43 | 2023-04-11T18:33:23 | 2023-04-11T18:33:23 | MEMBER | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
KFP standalone
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
KFP from master HEAD.
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. Deploy KFP
2. Create an experiment
3. Try to archive
4. Archived experiments are still listed as active
5. Archived experiment are missing from "Archived"
### Expected result
<!-- What should the correct behavior be? -->
4. Missing the archived experiments among the active ones
5. Showing archived experiments in "Achived"
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
This is caused by the latest change in API and how the values in backend:
[Old values](https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/experiment.proto#L177-L181):
```
STORAGESTATE_UNSPECIFIED = 0;
STORAGESTATE_AVAILABLE = 1;
STORAGESTATE_ARCHIVED = 2;
```
[New values](https://github.com/kubeflow/pipelines/blob/master/backend/api/v2beta1/experiment.proto#L89-L98):
```
STORAGE_STATE_UNSPECIFIED = 0;
AVAILABLE = 1;
ARCHIVED = 2;
```
As the old values are non-compliant to [AIP-216](https://google.aip.dev/216), I propose to update the frontend query.
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8802 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8802/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8802/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8802/events | https://github.com/kubeflow/pipelines/issues/8802 | 1,572,971,153 | I_kwDOB-71UM5dwaKR | 8,802 | [backend] Can't create a run from pipeline with empty PipelineSpec (stored as a file in OS) | {
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* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
KFP standalone
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
KFP from master branch
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
1. Deploy KFP.
2. Try to create a run from the sample pipelines.
3. Error message dialog will show the error validating the manifests.
### Expected result
<!-- What should the correct behavior be? -->
A run.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
We need to handle the case with PipelineSpec field is empty in the backend.
**Note**: a fix is in progress.
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8801 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8801/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8801/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8801/events | https://github.com/kubeflow/pipelines/issues/8801 | 1,572,278,407 | I_kwDOB-71UM5dtxCH | 8,801 | [sdk] Use the same component output twice fails | {
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"Thanks for raising this, @Roymprog. The Vertex AI Pipelines team is aware of this.\r\n\r\nFor posterity, here is a minimal reproducible example:\r\n\r\n```python\r\nfrom kfp import dsl\r\n\r\n@dsl.component\r\ndef c(a: dsl.Input[dsl.Artifact], b: dsl.Input[dsl.Artifact]):\r\n print(a)\r\n print(b)\r\n\r\n@ds... | 2023-02-06T10:09:13 | 2023-08-26T07:42:33 | null | NONE | null | ### Environment
* KFP version:
Running KFP (v2 pipelines) on Vertex AI.
* KFP SDK version:
1.8.18
* All dependencies version:
- kfp 1.8.18
- kfp-pipeline-spec 0.1.16
- kfp-server-api 1.8.5
### Steps to reproduce
```python
from kfp.v2.dsl import component, pipeline, Input, Output, Dataset
@component(base_image=TRAIN_IMAGE)
def output_step(
output_data: Output[Dataset],
):
import pandas as pd
train_df = pd.DataFrame(
[
{
"id": "A",
"value": 10
},
{
"id": "B",
"value": 5
}
]
)
output_data.path = f"{output_data.path}.csv"
train_df.to_csv(output_data.path, index=False)
@component(base_image=TRAIN_IMAGE)
def train_and_forecast_prophet(
train_data: Input[Dataset],
article_data: Input[Dataset],
output_data: Output[Dataset],
other_output_data: Output[Dataset]
):
import pandas as pd
df_train = pd.read_csv(train_data.path)
articles = pd.read_csv(article_data.path)
output_data.path = f"{output_data.path}.csv"
df_train.to_csv(output_data.path, index=False)
other_output_data.path = f"{other_output_data.path}.csv"
articles.to_csv(other_output_data.path, index=False)
@pipeline(name="test-pipeline")
def pipeline():
output = output_step()
train_and_forecast_prophet(
output.outputs["output_data"],
output.outputs["output_data"]
)
```
### Expected result
Expected the pipeline to run successfully, but it fails with:
```
articles = pd.read_csv(article_data.path)
AttributeError: 'NoneType' object has no attribute 'path'
```
It appears one can only pass an output parameter once to the same pipeline component. Not sure if this is intentional or a bug.
In my case, I have a component I reuse multiple times in a pipeline, once with the inputs mapping to the same output, another time they are different outputs.
### Materials and Reference
---
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8794 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8794/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8794/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8794/events | https://github.com/kubeflow/pipelines/issues/8794 | 1,570,443,409 | I_kwDOB-71UM5dmxCR | 8,794 | [feature] Add support for Tensorboard in KFP v2 | {
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"@zijianjoy Hello, I would like to hear your thoughts on this feature request, do you think it would be feasible in KFP v2?. \r\nThanks ",
"I think this feature is already supported for ROC curve, confusion matrix, and scalar metrics. Can you check if the followings match your need?\r\n\r\nCompare pipeline runs:... | 2023-02-03T21:01:53 | 2023-08-26T07:42:35 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area frontend
/area backend
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
As a Kubeflow Pipelines user, I would like to be able to use the SDK to log a list of numerical values and to be able to view it in Kubeflow UI and compare multiple runs to see how they compare.
It is very common when performing ML trainings to want to log some metrics over time eg: log a list of accuracy values over the epochs during training to get a learning curve or simply the `train_loss` and `val_loss` for instance.
This can also be used to compare how 2 models differ (eg: accuracy over time of model 1 vs accuracy over time of model 2).
Example:
<img src="https://user-images.githubusercontent.com/26939775/216708839-ad8a9135-af31-43fc-8ad9-6b66b48fc36f.png" height="300"/>
<img src="https://user-images.githubusercontent.com/26939775/216712850-9658c74b-f976-41c7-a7db-cfde32dc15a7.png" height="300"/>
### What is the use case or pain point?
The use case is to be able to easily compare runs for metrics that are more meaningful when represented as a list of numerical values instead of a single value.
Currently we can use `Output[Metrics]` to store a single key value pair metric (eg: using key=`train_accuracy` and value=`123`) or we can log a `dict`, the first approach does not allow to get all the values and the second approach is not suitable because it is super hard to reason about.
Here is an example of the two approaches (with random values just to illustrate the point):
<img src="https://user-images.githubusercontent.com/26939775/216711722-e789df50-59ed-4fcb-84c4-3df5f1281f67.png" height="100"/>
<img src="https://user-images.githubusercontent.com/26939775/216711611-9b86e58d-edde-4b2c-87a7-51702a34d3f1.png" height="300"/>
Adding this feature could allow the possibility to offer an option to overlap the 2 scatter plots of 2 runs for easier comparison (should remain an optional button because in some case it's easier to view the 2 plots separately).
### Is there a workaround currently?
Right now the workaround is to plot the values (eg: using matplotlib) during a pipeline step then to convert the matplotlib figure to an image (eg: jpg or png) then to convert the image to a base64 string that can be used in an `<img>` HTML tag that we can then log by using `Output[Markdown]` or `Output[HTML]`.
This is works but it has downsides :
- Does not support to dynamically scrub the values when viewing the visualisations after the execution of the pipeline
- Cannot be used to overlap 2+ runs and see how they compare for the same metric
- Requires some boilerplate code (plotting, converting to an image, image to base64, html etc)
Adding this feature would also keep Kubeflow competitive with other solutions which offer this kind of feature (eg: w&b, dvc pipelines, MLFlow etc)
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8792 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8792/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8792/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8792/events | https://github.com/kubeflow/pipelines/issues/8792 | 1,570,147,074 | I_kwDOB-71UM5dlosC | 8,792 | Cannot Import functions written in the same file | {
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"@vikashg, I suspect you are describing a component runtime error for a [Lightweight Python Component](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#1-lighweight-python-function-based-components).\r\n\r\nThis is expected behavior; the Lightweight Python Component function only ... | 2023-02-03T16:57:44 | 2023-02-24T03:45:16 | 2023-02-23T23:52:03 | NONE | null | Hello all,
I am trying to build a pipeline, the structure of which is like the following
`some_function.py`: This file contains 2 functions `func1` and `func2`. `func2` uses `func1` and gives an output
in my pipeline I will import the func2 as
`from some_function import func2`
due to this I get the following error
`NameError: name `func1` is not defined`
But if I copy the contents of `func1` inside `func2`, this works. Can someone shed some light on this? I am not able to figure this out.
Thanks in advance
| {
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"I've managed to find a workaround for this. The point is that the @component decorator is necessary for `kfp component build ...` and later when compiling/running the pipeline. But the target image and the code in it should not have it (at least not with current sdk version). Steps:\r\n1. run `kfp component build... | 2023-02-03T14:34:19 | 2023-02-28T00:57:12 | 2023-02-28T00:42:52 | CONTRIBUTOR | null | ### Environment
* KFP version:
2.0.0-alpha.5
* KFP SDK version:
2.0.0b9
* All dependencies version:
kfp 2.0.0b9
kfp-pipeline-spec 0.1.16
kfp-server-api 2.0.0a6
### Steps to reproduce
I am following these [instructions](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#2-containerized-python-components) to create containerized python component. I am able to build the component and push the image using kfp cli.
I am not quite sure how to use the component in pipeline, this is what I am doing:
Component file (components/mycomp.py):
```python
@component(
base_image='python:3.11-slim',
target_image='...',
packages_to_install=['pandas >=1.5.3']
)
def do_stuff(some_id: str, output_df: Output[Artifact]):
print(f'Hello {some_id} from containerized component')
df = pd.DataFrame(np.random.default_rng().random((4, 3)))
with open(output_df.path, 'w') as fp:
df.to_csv(fp, index=False)
```
Pipeline definition file
```python
import components.mycomp
@dsl.pipeline(name='cont-pipeline')
def cont_pipelinesome_id: str):
task1 = components.mycomp.do_stuff(some_id=some_id)
```
Then I can either run the pipeline or compile, upload and run. During run the image is downloaded and executed, but fails with following error:
```
[KFP Executor 2023-02-03 13:41:08,810 INFO]: --component_module_path is not specified. Looking for component `do_stuff` in config file `kfp_config.ini` instead
[KFP Executor 2023-02-03 13:41:08,810 INFO]: Loading KFP component "do_stuff" from mycomp.py (directory "." and module name "mycomp")
[KFP Executor 2023-02-03 13:41:09,067 INFO]: Got executor_input:
{
"inputs": {
"parameterValues": {
"some_id": "asd"
}
},
"outputs": {
"artifacts": {
"output_df": {
"artifacts": [
{
"type": {
"schemaTitle": "system.Artifact",
"schemaVersion": "0.0.1"
},
"uri": "minio://mlpipeline/v2/artifacts/pipeline/cont-pipeline/88d0c5ea-b5c0-4fc2-b461-6aa2d4233c5c/do_stuff/output_df"
}
]
}
},
"outputFile": "/tmp/kfp_outputs/output_metadata.json"
}
}
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/usr/local/lib/python3.11/site-packages/kfp/components/executor_main.py", line 105, in <module>
executor_main()
File "/usr/local/lib/python3.11/site-packages/kfp/components/executor_main.py", line 98, in executor_main
executor = component_executor.Executor(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/kfp/components/executor.py", line 63, in __init__
output_artifact = self.make_artifact(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/kfp/components/executor.py", line 81, in make_artifact
annotation = func.__annotations__.get(name)
^^^^^^^^^^^^^^^^^^^^
AttributeError: 'PythonComponent' object has no attribute '__annotations__'
F0203 13:41:09.239538 36 main.go:49] failed to execute component: exit status 1
Error: exit status 1
```
Why I think it is an sdk problem - looking at executor_main.py, this is how the function is loaded:
```
function_to_execute = getattr(module, func_name)
```
and later in [Executor.__init__](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/components/executor.py#L81) the __annotations__ are accessed and the error is raised.
The `func` (or `function_to_execute` above) is not the actual function `do_stuff` (as the code seems to expect), but it is the PythonComponent because `do_stuff `is decorated with @component, that's why the annotations are not there.
Am I creating the pipeline incorrectly (and what would be the correct way?) perhaps or is it a bug?
### Expected result
Pipeline runs successfully.
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8790 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8790/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8790/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8790/events | https://github.com/kubeflow/pipelines/issues/8790 | 1,569,633,373 | I_kwDOB-71UM5djrRd | 8,790 | [bug] How to restart Kubeflow pipeline starting from a failed step/component? | {
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"@jayaprasadt Thanks for reaching out. Would you mind sharing what type of issue you're trying to fix? If the change is related to pipeline template, it won't start from the failed step by design.\r\n\r\nAt the meantime, we suggest you to try the KFP v2 to see whether the problem exists.",
"@jlyaoyuli Thanks for ... | 2023-02-03T10:41:41 | 2023-08-26T07:42:37 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How can I restart the Kubeflow pipeline after fixing error in a failed step, retaining the data/model in previous steps?
* I'm running a pipeline that has 4 steps/components
1. Download Data
2. Load data as frame
3. Train and save model
4. Predict
If the 'Predict' step/component fails because of a syntax error, is there a way to restart the run only for the 'Predict' step after fixing the error?
This should retain the data and model info till the 'Train and save model' step, and rerun only the modified 'Predict' component.
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.6.3
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
> kfp 1.6.3
> kfp-pipeline-spec 0.1.16
> kfp-server-api 1.6.0
### Steps to reproduce
Create a pipeline with more than one component / step.
Run the pipeline with a syntax error in any step other than the first step.
Fix the error in the failed step/component and retry the pipeline.
Pipeline starts from the first step onward losing all the data and models from previous run.
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
The pipeline should retain the data and model info from previous run and start rerunning from the failed step.
<!-- What should the correct behavior be? -->
### Materials and reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
<!-- /area components -->
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8788 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8788/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8788/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8788/events | https://github.com/kubeflow/pipelines/issues/8788 | 1,568,977,209 | I_kwDOB-71UM5dhLE5 | 8,788 | which version of gcr.io/ml-pipeline/api-server has driver task in argo workflow? | {
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"anyone knows this issue?",
"@elim19 Thanks for reaching out! The driver task is supported in KFP v2 SDK. https://www.kubeflow.org/docs/components/pipelines/v2/run-a-pipeline/"
] | 2023-02-03T00:29:57 | 2023-02-17T00:00:50 | 2023-02-17T00:00:50 | NONE | null | I see in the pipeline code(tag 1.8.5), when pipeline is transformed to argo workflow, it will create a driver task to make podSpecPatch to the pod, but when I deployed 1.8.5 gcr.io/ml-pipeline/api-server, I don't see this feature, which version have this driver task? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8786 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8786/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8786/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8786/events | https://github.com/kubeflow/pipelines/issues/8786 | 1,568,590,002 | I_kwDOB-71UM5dfsiy | 8,786 | [bug] ml-pipeline-api-server: net/http vulnerability (CVE-2022-27664) | {
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"We've upgraded golang to 1.20: https://github.com/kubeflow/pipelines/blob/c0640d7a033b6fa2133b92c2a459d9eb3dabee32/backend/Dockerfile#L16\r\n\r\nAlso I can confirm we no longer see this CVE in our latest api-server image (2.0.0-rc.1)"
] | 2023-02-02T18:45:15 | 2023-06-07T20:08:26 | 2023-06-07T20:08:25 | NONE | null | ### Environment
* How do you deploy Kubeflow Pipelines (KFP)?
KFP is deployed via a 'full kubeflow deployment' on GKE
* KFP version:
Kubeflow v1.5.0
### Steps to reproduce
A security scanning tool running against our kubeflow cluster (Orca Security) detected that the ml-pipeline-api-server pod is vulnerable to denial of service attacks "because an HTTP/2 connection can hang during closing if shutdown were preempted by a fatal error" (https://nvd.nist.gov/vuln/detail/CVE-2022-27664). This vulnerability has been patched in Go versions 1.19.1 and beyond.
Is this a known issue? If so, do plans exist to upgrade the version of Go used to build the image for this service, or are there any recommended steps for intermediate remediation?
### Expected result
If necessary, patching the CVE-2022-27664 security vulnerability
### Materials and reference
https://nvd.nist.gov/vuln/detail/CVE-2022-27664
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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"Does it work for 1.8.5 but not for 1.8.9? Not sure which local deployment methods are you using, and did you get any specific error? \r\nMaybe you've already seen this, but here's our local deployment instruction: https://www.kubeflow.org/docs/components/pipelines/v1/installation/localcluster-deployment/",
"Yes ... | 2023-02-02T17:19:07 | 2023-02-17T00:05:02 | 2023-02-17T00:05:01 | NONE | null | Changing pipeline version doesn't work during local deployment
The following commands does not work for PIPELINE_VERSION=1.8.9
```
export PIPELINE_VERSION=1.8.5
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION"
``` | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8783 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8783/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8783/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8783/events | https://github.com/kubeflow/pipelines/issues/8783 | 1,568,279,897 | I_kwDOB-71UM5deg1Z | 8,783 | Use multiple components in the ExitHandler | {
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"Hi, @AlbertCintas. If you're using KFP v2 (BE nearly in beta), the following example should demonstrate the usage you're looking for:\r\n\r\n```python\r\nfrom kfp import compiler\r\nfrom kfp import dsl\r\nfrom kfp.dsl import component\r\n\r\n\r\n@component\r\ndef print_op(message: str):\r\n \"\"\"Prints a messa... | 2023-02-02T15:38:42 | 2023-08-26T07:42:39 | null | NONE | null | Hello!
Is it possible to run two steps with two components in the Exit Handler? Maybe some workaround to perform a similar task?
I've been trying to do it in many different ways but all attempts have been unsuccessful.
Thank you! | {
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"Hi @MatthewRalston , you need to create parent directories for the output path, before you can write the data. See example: https://github.com/kubeflow/pipelines/blob/61a6172e7d88e21ee90af8b3ecf63b0224b8172c/sdk/python/test_data/pipelines/pipeline_with_outputs.py#L36",
"@chensun that's a nice example, and thank ... | 2023-02-02T14:33:55 | 2023-02-10T01:12:03 | 2023-02-02T23:56:28 | NONE | null | @chensun I spun off this new issue related to `/tmp/outputs/Output/data: no such file or directory`
_Originally posted by @MatthewRalston in https://github.com/kubeflow/pipelines/issues/8683#issuecomment-1401289909_
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"@JakeSummers, I think this is a good idea. If you're interested in contributing this (including the [docs](https://github.com/kubeflow/website) fix) I would happy to review your PR.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activi... | 2023-02-01T16:42:34 | 2023-08-26T07:42:41 | null | NONE | null | ### Feature Area
/area sdk
/area samples
### What feature would you like to see?
#6748 added support for filters.
The suggested workflow, is to build the predicates like this ([doc](https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/sdk-examples/#example-2-listing-pipelines-with-a-filter)).:
```
filter = json.dumps(
{
'predicates': [
{
'key': 'name',
'op': 1, # <-- NOTE: 1 means "EQUALS" - very hard to read.
'string_value': '{}'.format(pipeline_name)
}
]
})
```
It would be much nicer, if the sdk provided a dataclass that would help to build these predicates. Something like this:
```
from dataclasses import dataclass
from enum import Enum
from typing import List
# https://www.kubeflow.org/docs/components/pipelines/v1/tutorials/sdk-examples/#example-2-listing-pipelines-with-a-filter
# https://github.com/kubeflow/pipelines/blob/64bf95333ccc3a0411793c4a11325b71595ab150/backend/api/filter.proto#L25-L63
class Operation(Enum):
# Operators on scalar values. Only applies to one of |int_value|,
# |long_value|, |string_value| or |timestamp_value|.
EQUALS = 1
NOT_EQUALS = 2
GREATER_THAN = 3
GREATER_THAN_EQUALS = 5
LESS_THAN = 6
LESS_THAN_EQUALS = 7
# Checks if the value is a member of a given array, which should be one of
# |int_values|, |long_values| or |string_values|.
IN = 8
# Checks if the value contains |string_value| as a substring match. Only
# applies to |string_value|.
IS_SUBSTRING = 9
@dataclass()
class Predicate:
key: str
op: Operation
string_value: str
@dataclass
class Filter:
predicates: List[Predicate]
```
This could then be used like this:
```
my_filter = Filter([
Predicate(
key="name",
op=Operation.EQUALS,
string_value="my-experiment"
)
])
```
As an aside, the inline doc is currently, broken:
### What is the use case or pain point?
Needing to build json, particularly json that uses `'op': 1` where `1` means `Equals` is not very user friendly.
### Is there a workaround currently?
Yes.
---
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Love this idea? Give it a 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/8777 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8777/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8777/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8777/events | https://github.com/kubeflow/pipelines/issues/8777 | 1,566,399,321 | I_kwDOB-71UM5dXVtZ | 8,777 | [sdk] `list_recurring_runs` needs a namespace filter even-though the kfpClient was instantiated with a namespace | {
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"Looking at this a bit further, it seems like it is not asking for the `namespace` but for the `experiment`. \r\n\r\nChanging this: \r\n\r\n```\r\n current_recurring_runs: ApiListJobsResponse = client.list_recurring_runs(page_token=None)\r\n```\r\n\r\nTo this: \r\n\r\n```\r\n current_recurring_runs: ApiListJ... | 2023-02-01T16:11:57 | 2023-08-26T07:42:43 | null | NONE | null | ### Environment
* KFP version: kubeflow 1.6 and kubeflow pipelines 1.8.3
* KFP SDK version: 1.8.12
### Steps to reproduce
```
# Create a kfp client with a namespace
client = kfp.Client(kubeflow_host.host,
namespace="NamespaceJake", # NOTE: Namespace is provided here!
existing_token=token,
)
# List recurring runs:
current_recurring_runs: ApiListJobsResponse = client.list_recurring_runs(page_token=None)
```
I get the following error:
```
Traceback (most recent call last):
...
File "code.py", line 66, in _find_recurring_run
current_recurring_runs: ApiListJobsResponse = client.list_recurring_runs(page_token=None)
File "/.../site-packages/kfp/_client.py", line 1200, in list_recurring_runs
response = self._job_api.list_jobs(
File "/.../site-packages/kfp_server_api/api/job_service_api.py", line 689, in list_jobs
return self.list_jobs_with_http_info(**kwargs) # noqa: E501
File "/.../site-packages/kfp_server_api/api/job_service_api.py", line 790, in list_jobs_with_http_info
return self.api_client.call_api(
File "/.../site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/.../site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/.../site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/.../site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/.../site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/.../site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'x-powered-by': 'Express', 'content-type': 'application/json', 'date': 'Wed, 01 Feb 2023 15:52:17 GMT', 'content-length': '437', ...)
HTTP response body: {"error":"Invalid input error: ListJobs must filter by resource reference in multi-user mode.","code":3,"message":"Invalid input error: ListJobs must filter by resource reference in multi-user mode.","details":[{"@type":"type.googleapis.com/api.Error","error_message":"ListJobs must filter by resource reference in multi-user mode.","error_details":"Invalid input error: ListJobs must filter by resource reference in multi-user mode."}]}
```
### Expected result
When I instantiated the kfp client, I provided my namespace. I should not need to provide the namespace to the `list_recurring_runs`.
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8776 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8776/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8776/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8776/events | https://github.com/kubeflow/pipelines/issues/8776 | 1,566,381,082 | I_kwDOB-71UM5dXRQa | 8,776 | `list_recurring_runs` needs a namespace filter even-though the kfpClient was instantiated with a namespace | {
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"node_id": "MDU6TGFiZWwxMDczMTUzOTA4... | closed | false | null | [] | null | [
"I logged this under the wrong area. Relogging as #8777"
] | 2023-02-01T16:02:25 | 2023-02-01T16:13:11 | 2023-02-01T16:13:10 | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? _Not sure - hosted by another team_
* KFP version: kubeflow 1.6 and kubeflow pipelines 1.8.3
* KFP Client version: 1.8.12
### Steps to reproduce
```
# Create a kfp client with a namespace
client = kfp.Client(kubeflow_host.host,
namespace="NamespaceJake", # NOTE: Namespace is provided here!
existing_token=token,
)
# List recurring runs:
current_recurring_runs: ApiListJobsResponse = client.list_recurring_runs(page_token=None)
```
I get the following error:
```
Traceback (most recent call last):
...
File "code.py", line 66, in _find_recurring_run
current_recurring_runs: ApiListJobsResponse = client.list_recurring_runs(page_token=None)
File "/.../site-packages/kfp/_client.py", line 1200, in list_recurring_runs
response = self._job_api.list_jobs(
File "/.../site-packages/kfp_server_api/api/job_service_api.py", line 689, in list_jobs
return self.list_jobs_with_http_info(**kwargs) # noqa: E501
File "/.../site-packages/kfp_server_api/api/job_service_api.py", line 790, in list_jobs_with_http_info
return self.api_client.call_api(
File "/.../site-packages/kfp_server_api/api_client.py", line 364, in call_api
return self.__call_api(resource_path, method,
File "/.../site-packages/kfp_server_api/api_client.py", line 188, in __call_api
raise e
File "/.../site-packages/kfp_server_api/api_client.py", line 181, in __call_api
response_data = self.request(
File "/.../site-packages/kfp_server_api/api_client.py", line 389, in request
return self.rest_client.GET(url,
File "/.../site-packages/kfp_server_api/rest.py", line 230, in GET
return self.request("GET", url,
File "/.../site-packages/kfp_server_api/rest.py", line 224, in request
raise ApiException(http_resp=r)
kfp_server_api.exceptions.ApiException: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'x-powered-by': 'Express', 'content-type': 'application/json', 'date': 'Wed, 01 Feb 2023 15:52:17 GMT', 'content-length': '437', ...)
HTTP response body: {"error":"Invalid input error: ListJobs must filter by resource reference in multi-user mode.","code":3,"message":"Invalid input error: ListJobs must filter by resource reference in multi-user mode.","details":[{"@type":"type.googleapis.com/api.Error","error_message":"ListJobs must filter by resource reference in multi-user mode.","error_details":"Invalid input error: ListJobs must filter by resource reference in multi-user mode."}]}
```
### Expected result
When I instantiated the kfp client, I provided my namespace. I should not need to provide the namespace to the `list_recurring_runs`.
Impacted by this bug? Give it a 👍. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8775 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8775/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8775/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8775/events | https://github.com/kubeflow/pipelines/issues/8775 | 1,565,729,846 | I_kwDOB-71UM5dUyQ2 | 8,775 | Use a custom python function in two different components of kubeflow pipelines | {
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"Hi @Rstar1998 , you can achieve that using \"Containerized Python components\": https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#2-containerized-python-components"
] | 2023-02-01T09:14:21 | 2023-02-02T23:50:56 | 2023-02-02T23:50:55 | NONE | null | For example I have a custom function
```
def do_something(a,b):
//doing something
return something
@component(
packages_to_install=["xyz"],
base_image="python:3.9",
output_component_file="component_1.yaml"
)
def component_1(
query: str,
df_path: Output[Dataset]
):
import xyz
do_something(a,b);
@component(
packages_to_install=["xyz"],
base_image="python:3.9",
output_component_file="component_2.yaml"
)
def component_2(
query: str,
df_path: Output[Dataset]
):
import mnc
do_something(m,c);
```
How Use a custom python function in two different components of kubeflow pipelines ? | {
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https://api.github.com/repos/kubeflow/pipelines/issues/8768 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8768/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8768/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8768/events | https://github.com/kubeflow/pipelines/issues/8768 | 1,563,993,479 | I_kwDOB-71UM5dOKWH | 8,768 | is there any doc about kfp-launcher, I just upgrade to pipeline v2, found kfp use this binary to start the pod command | {
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"Hi @elim19, you may find more info about launcher in the design doc we shared with the community: [bit.ly/kfp-v2](https://bit.ly/kfp-v2)\r\nTo gain access to the doc, please join https://groups.google.com/g/kubeflow-discuss google group.\r\n"
] | 2023-01-31T09:57:17 | 2023-02-03T00:28:56 | 2023-02-02T23:46:35 | NONE | null | is there any doc about kfp-launcher, I just upgrade to pipeline v2, found kfp use this binary to start the pod command | {
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"/assign @chensun ",
"We also experience the very same issue, once our cluster Auto-Scales. All logs are gone and NOT read from our mirror in MINIO (interface of KFP to access S3) / AWS S3 (physical location).",
"This issue has been automatically marked as stale because it has not had recent activity. It will b... | 2023-01-26T06:52:18 | 2023-08-26T07:42:45 | null | NONE | null | I have set up logging.info () at various places in my code and the UI Log console is retrieving only a few of them. I have also noticed that it stops logging in the middle and the last log I see is Tearing down program. Although my pipeline doesn't seem to fail anywhere, I can see this happening across most of them.
I have used this as to set up my logs.
# Set up logging
root = logging.getLogger()
root.setLevel(logging.INFO)
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.INFO)
This has worked well previously and hasn't been working fine since last 4-5 days.
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https://api.github.com/repos/kubeflow/pipelines/issues/8757 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8757/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8757/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8757/events | https://github.com/kubeflow/pipelines/issues/8757 | 1,556,035,938 | I_kwDOB-71UM5cvzli | 8,757 | [backend] Performance issue: ScheduledWorkflow is taking significant amount of etcd storage | {
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"/assign @gkcalat ",
"Hi @deepk2u!\r\nIt may be due to insufficient resource provisioning or the lack of etc maintenance (see [here](https://etcd.io/docs/v3.3/op-guide/maintenance/)). How longs did it take for you to reach these numbers?",
"It's an eks cluster. We connected with AWS support and they are maintai... | 2023-01-25T04:25:41 | 2023-08-26T07:42:47 | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Full Kuebflow deployment using manifests
* KFP version: 2.0.0b6
* KFP SDK version: 2.0.0b6
### Steps to reproduce
We have around 125 recurring runs within a single namespace. After a few months of historical runs, we have started seeing performance issues in the k8s cluster.
After digging deeper, we found that we are seeing timeouts in the calls to etcd. When we checked the etcd database for objects we found that one particular namespace which has 125 recurring runs is taking 996MB of etcd space
some data to look at:
```
Entries by 'KEY GROUP' (total 1.6 GB):
+----------------------------------------------------------------------------------------------------------+--------------------------------+--------+
| KEY GROUP | KIND | SIZE |
+----------------------------------------------------------------------------------------------------------+--------------------------------+--------+
| /registry/kubeflow.org/scheduledworkflows/<namespace1> | ScheduledWorkflow | 996 MB |
| /registry/kubeflow.org/scheduledworkflows/<namespace2> | ScheduledWorkflow | 211 MB |
| /registry/kubeflow.org/scheduledworkflows/<namespace3> | ScheduledWorkflow | 118 MB |
.....
```
namespace1 has 123 recurring runs
namespace2 has 40 recurring runs
namespace3 has 63 recurring runs
### Expected result
Looks like we are storing a lot of unnecessary information in the ScheduledWorkflow object, which eventually is taking space in etcd database resulting in all the performance issues
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
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"/assign @chensun ",
"I'm seeing something similar when trying to test with a local k3s install. I installed kfp 2.0.0b12 backend and sdk, and then used the example [from the new 2.0.0b12 docs](https://kubeflow-pipelines.readthedocs.io/en/2.0.0b12/#getting-started), but I get errors. Oddly enough, I was able to r... | 2023-01-24T15:55:20 | 2023-08-26T07:42:49 | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
/area backend
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
We would like to run a KFP v2 pipeline in a multi-user environment.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
We deployed KF in a multi user environment with `kfp==2.0.0b5`, which supports KPF v2. We tried to run a simple KFP v2 [pipeline](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/#compile-a-pipeline) and a [pipeline](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#special-case-importer-components) with an importer component. Running the first example resulted in this error:
```
F1014 10:08:22.858076 19 main.go:49] failed to execute component: failed to pre-publish Pod info to ML Metadata:
get execution ID=169: rpc error: code = Unavailable desc = connection error: desc = "transport: Error while dialing
dial tcp: address tcp/$(METADATA_GRPC_SERVICE_PORT): unknown port"
```
while running the second one in this error:
```
F0118 11:29:01.454870 16 main.go:49] failed to execute importer component: Failed
GetContextByTypeAndName(type="system.Pipeline", name="pipeline-with-importer")
```
The pain point in our use case is that the MLMD server `address` and `port`, are passed in the launcher via environment variables
```
--mlmd_server_address
$(METADATA_GRPC_SERVICE_HOST)
--mlmd_server_port
$(METADATA_GRPC_SERVICE_PORT)
```
set either in the `podSpecPatch` the driver creates ([source](https://github.com/kubeflow/pipelines/blob/78d31dc8a3600a0907d1a7604552e9f8197809d0/backend/src/v2/driver/driver.go#L334)), or in the importer code ([source](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/compiler/argocompiler/importer.go#L79)). Taking into consideration that in our use case, the KFP v2 pipelines will run on a multi-user deployment, these environment variables are not set by the kubelet on the pipeline steps' pods, because the service is in a different namespace.
In our deployment, the MLMD service is in the `kubeflow` namespace, so the pods created in the user's namespace (e.g., `kubeflow-user`) don't get the service's `port` and `address` environment variables.
We've thought of the following: since the driver speaks to a certain MLMD client, registers the step in MLMD (context, execution, etc.), and creates the `podSpecPatch` for the launcher, it should forward this MLMD endpoint to the launcher, so that they don't end up speaking to different MLMD services. What do you think?
/cc @chensun @zijianjoy @Linchin @elikatsis @StefanoFioravanzo
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Unfortunately, there isn't any at the moment. Everything is hardcoded.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8747 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8747/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8747/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8747/events | https://github.com/kubeflow/pipelines/issues/8747 | 1,552,184,183 | I_kwDOB-71UM5chHN3 | 8,747 | KFP building component with local custom utils.py throws "No module named 'utils'" | {
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"@samcaetano, I recommend you try KFP SDK v2 (`pip install kfp --pre`). It is in beta phase, but stable.",
"Any update on this?",
"@LuigiCerone, no fix for this yet. This is captured by https://github.com/kubeflow/pipelines/issues/8385. Let's treat that one as the canonical bug and close this one, since the oth... | 2023-01-22T16:26:21 | 2023-03-17T20:37:45 | 2023-03-17T20:37:45 | NONE | null | https://github.com/kubeflow/pipelines/blob/8f472d98723b446a1eecca2db48664fc08cfcde7/sdk/python/kfp/components/utils.py#L47
I have found this issue while trying to build a component from my main.py script, which imports my local custom utils1.py script. But when I run `kfp components build my_component/ `, all I get is:
```
INFO: Building component using KFP package path: kfp==1.8.18
main
No module named 'utils1'
```
I have follow the kfp components source script, while debugging, till I found this line (which throws the above error). It appears that kubeflow will try to import the main.py as a module for the environment, but it fails to recognize local import since those files are not mapped yet.
My main.py is a component that imports utils1.py in the preamble of the file. And my dir is structured as follows:
my_component/
|- main.py
|- utils1.py | {
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"/assign @chensun",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-01-20T15:18:50 | 2023-08-26T07:42:51 | null | NONE | null | ### Feature Area
/area backend
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
Make the driver and launcher images configurable, at deployment time via environment variables, or a kustomize overlay.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
There are two use cases that suggest the images to be configurable.
1. Testing the deployment end-to-end
2. Special deployments/distributions
**Testing the deployment end-to-end**
Right now, the driver and launcher images are strictly tied to the apiserver image. This means that if the admin changes the code of either the driver or the launcher, they will have to:
1. build the driver and launcher images
4. modify the apiserver code to change the hardcoded images
5. build the apiserver image
6. use the new apiserver image to the deployment
7. test the new image
8. repeat this process for every bug found in the driver or launcher code
This takes more time and effort, since the admin has to build the apiserver image, although they have only changed the hardcoded values of the driver and launcher images.
**Special deployments/distributions**
A use case we've seen in production is airgapped clusters. Airgapped environments have no access to the internet, which means that they cannot pull images from GCR. Changing the apiserver code should be avoided when someone wants to deploy a KFP release. Otherwise, the admin would have to maintain a fork of the KFP project in order to use a private image registry to pull the driver and launcher images.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
As regards to testing, unfortunately, only modifying the corresponding images in the apiserver code and rebuilding the apiserver image. This is not an ideal scenario. It needs extra effort and time to maintain it.
For the airgapped environment on the other hand, there is not a workaround at the moment.
/cc @chensun @zijianjoy @Linchin @elikatsis @StefanoFioravanzo
---
Love this idea? Give it a 👍. | {
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"/assign @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions."
] | 2023-01-20T05:45:14 | 2023-08-27T07:41:43 | null | NONE | null | ### Environment
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
I deployed Kubeflow by docker driver on minikube
```
minikube start --driver=docker --kubernetes-version=1.23.14 --memory=12g --cpus=4
```
* KFP version:
1.6.0
* KFP SDK version:
2.0.0b2
### Steps to reproduce
<img width="573" alt="image" src="https://user-images.githubusercontent.com/63439911/213625117-2e7e4d32-6f92-4cbd-8982-ac4e3b5a98ef.png">
### Expected result
There should not be error because there is a model.h5 in the directory.
<img width="1097" alt="image" src="https://user-images.githubusercontent.com/63439911/213625626-1ce3758a-7a0e-43cd-a41c-5656eab1ba49.png">
Since no model outputs detected, the further task cannot be completed like this.
<img width="630" alt="image" src="https://user-images.githubusercontent.com/63439911/213625932-62c91689-0744-43b1-b167-8a9fa3d09d5c.png">
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. | {
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"`kfp==2.0.0b10` and `kfp-pipeline==0.1.17` are yanked. This is no longer be an issue."
] | 2023-01-20T00:35:32 | 2023-01-20T21:46:19 | 2023-01-20T21:46:19 | MEMBER | null | If you are getting `kfp_server_api.exceptions.ApiException: (400)` errors when trying to run a pipeline and have `kfp-pipeline-spec==0.1.17` installed in your environment, you must `pip install kfp<=2.0.0b9 kfp-pipeline-spec==0.1.16` and pin these dependencies in your KFP SDK environment. | {
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"/cc @gkcalat ",
"This has been fixed in #8633"
] | 2023-01-19T22:38:17 | 2023-03-10T00:54:43 | 2023-02-07T00:27:12 | COLLABORATOR | null | null | {
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"Hi @fstetic!\r\nThank you for reporting this. Could you please share more details on this: error message, logs, yaml pipeline spec?",
"@fstetic, if you `pip install kfp-pipeline-spec==0.1.16` is the error resolved?",
"@gkcalat sure!\r\n\r\nHere is the full error message:\r\n`{\"error_message\":\"Error creating... | 2023-01-19T10:45:25 | 2023-06-06T07:20:38 | 2023-06-06T07:20:38 | NONE | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
Local Canonical Kubeflow using [this guide](https://charmed-kubeflow.io/docs/quickstart)
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
Bottom of KFP UI left sidenav says build version dev_local and the guide states 1.6
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp 2.0.0b10
kfp-pipeline-spec 0.1.17
kfp-server-api 2.0.0a6
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
Try to upload a pipeline with default values in pipeline function definition.
E.g.
```
@dsl.pipeline(name='addition-pipeline')
def my_pipeline(a: int, b: int, c: int = 10):
```
### Expected result
<!-- What should the correct behavior be? -->
Pipeline should be uploaded or, if this is the expected behaviour because there exist the option `pipeline_parameters` in compiling, then the [documentation](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/#compile-a-pipeline) should be updated.
<!-- Don't delete message below to encourage users to support your issue! -->
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https://api.github.com/repos/kubeflow/pipelines/issues/8733 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8733/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8733/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8733/events | https://github.com/kubeflow/pipelines/issues/8733 | 1,548,888,123 | I_kwDOB-71UM5cUig7 | 8,733 | [backend] Cannot get MLMD objects from Metadata store when running v2 pipeline | {
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"Hi @fstetic!\r\nThank you for reporting this. Could you confirm whether the problem is persistent or if it goes away after the run completes?",
"Hi @gkcalat! Thanks for the quick response.\r\n\r\nThe run doesn't complete. That error happens at the start of the run.\r\n\r\nI tried a tutorial pipeline with v1 YAML... | 2023-01-19T10:30:18 | 2023-09-06T02:12:32 | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
Local Canonical Kubeflow using [this guide](https://charmed-kubeflow.io/docs/quickstart)
* KFP version:
Bottom of KFP UI left sidenav says `build version dev_local` and the guide states 1.6
* KFP SDK version:
kfp 2.0.0b10
kfp-pipeline-spec 0.1.17
kfp-server-api 2.0.0a6
### Steps to reproduce
Install Kubeflow using aforementioned guide. Copy addition pipeline and compile it and either run it after uploading through UI or run it from code. Both doesn't work.
### Expected result
Pipeline shouldn't fail.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
In details it says `Cannot find context with {"typeName":"system.PipelineRun","contextName":"a5e7085e-ef10-48b2-a0a5-1ced3b93e2e5"}: Unknown Content-type received.`
Addition pipeline [from documentation](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline/#compile-a-pipeline)
```
from kfp import compiler
from kfp import dsl
@dsl.component
def addition_component(num1: int, num2: int) -> int:
return num1 + num2
@dsl.pipeline(name="addition-pipeline")
def my_pipeline(a: int, b: int, c: int):
add_task_1 = addition_component(num1=a, num2=b)
add_task_2 = addition_component(num1=add_task_1.output, num2=c)
cmplr = compiler.Compiler()
cmplr.compile(my_pipeline, package_path="my_pipeline.yaml")
```
---
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https://api.github.com/repos/kubeflow/pipelines/issues/8732 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8732/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8732/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8732/events | https://github.com/kubeflow/pipelines/issues/8732 | 1,548,511,796 | I_kwDOB-71UM5cTGo0 | 8,732 | [backend] API Server on startup attempts to drop indices that don't exist. | {
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"Hi @HumairAK!\r\nThank you for reporting thins. Could you please share if you encounter this problem with `MySQL` DB?",
"@gkcalat Percona xtradb is a drop in replacement for mysql db so I expect the same queries are made against mysql db, they may not show up in stdout logs though, you may need to check the quer... | 2023-01-19T04:52:31 | 2023-08-28T20:58:38 | null | NONE | null | When running a fresh deployment of pipelines (from kfp-tekton repo) using Percona Xtradb as a replacement db, I noticed some errors during start up in the logs where apiserver attempts to drop indexes named `Name` that don't exist.
```
{"log":"2023-01-18T19:01:09.808652Z 1 [ERROR] [MY-010584] [Repl] Slave SQL: Error 'Can't DROP 'Name'; check that column/key exists' on query. Default database: 'mlpipeline'. Query: 'DROP INDEX Name ON `experiments`', Error_code: MY-001091\n","file":"/var/lib/mysql/mysqld-error.log"}
```
I checked these tables out, and indeed it seems like no such index exists, though indexes on the columns "Name,Namespace" exist, but is called "name_namespace_index":
```
show indexes from pipelines;
+-----------+------------+----------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-----------+------------+----------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| pipelines | 0 | PRIMARY | 1 | UUID | A | 0 | NULL | NULL | | BTREE | | |
| pipelines | 0 | name_namespace_index | 1 | Name | A | 0 | NULL | NULL | | BTREE | | |
| pipelines | 0 | name_namespace_index | 2 | Namespace | A | 0 | NULL | NULL | YES | BTREE | | |
+-----------+------------+----------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
```
Seems to originate from [here](https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/client_manager.go#L256-L264).
I'm guessing this may be a leftover from some migration procedure, but it seems strange to see these errors in my logs, maybe a check to see if these indexes exist before attempting to remove them would make more sense?
### Environment
Deploying kfp-tekton in OCP, but this seems like a general pipelines concern.
### Steps to reproduce
Any fresh deployment of kubeflow pipelines should result in these queries against the mlpipelines databse. Though some mysql/mysql-like db's may not display these errors in the console, you can find these queries being made in the query logs.
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https://api.github.com/repos/kubeflow/pipelines/issues/8731 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8731/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8731/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8731/events | https://github.com/kubeflow/pipelines/issues/8731 | 1,548,450,355 | I_kwDOB-71UM5cS3oz | 8,731 | [sdk] registry client delete_version KeyError: 'done' | {
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"issue also present in kfp 2.0.0b11",
"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."
] | 2023-01-19T03:11:55 | 2023-08-27T07:41:47 | null | NONE | null | ### Environment
Python version 3.8.6 Local Jupyter Notebook
MacBook Pro 2019
* KFP version: kfp 2.0.0b10
* KFP SDK version: kfp 2.0.0b10
* All dependencies version:
kfp 2.0.0b10
kfp-pipeline-spec 0.1.17
kfp-server-api 2.0.0a6
### Steps to reproduce
1. Create a repository in Artifact Registry in Google Cloud with kubeflow pipelines format. Replace the host with the repository created and upload a test component yaml file(i.e. pipeline with one component) to this repository. Example code as below:
```
from kfp.registry import RegistryClient
client = RegistryClient(host="https://us-east1-kfp.pkg.dev/<project_id>/component-test")
component_name, version = client.upload_pipeline("compoent.yaml", tags=["0.0.1"])
```
2. Use a **registryClient** and **component_name, version** variables from the previous step and run the code below
```
client.delete_version(component_name,version)
client.delete_package(component_name)
```
The given version is successfully deleted in Artifact Registry in GCP but raises the following error:
<img width="1025" alt="image" src="https://user-images.githubusercontent.com/105866640/213487957-fae2d435-63b6-40bf-aee0-31083bfaa3ee.png">
When no such version exists in the artifact registry repository, **delete_version** function will return True instead shown as below:
<img width="613" alt="image" src="https://user-images.githubusercontent.com/105866640/213488636-1401ff53-0c16-46f5-888e-5761fb4ada2a.png">
delete_package function raises the error regardless of the existence of the package.
<img width="1021" alt="image" src="https://user-images.githubusercontent.com/105866640/213486899-981040e9-dd49-4777-a3e0-7f9f0f354bef.png">
### Expected result
The given version is successfully deleted without raising any errors. The given package is deleted from Artifact Registry repository without any errors.
### Materials and Reference
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https://api.github.com/repos/kubeflow/pipelines/issues/8730 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/8730/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/8730/comments | https://api.github.com/repos/kubeflow/pipelines/issues/8730/events | https://github.com/kubeflow/pipelines/issues/8730 | 1,548,243,623 | I_kwDOB-71UM5cSFKn | 8,730 | feat(frontend): Run details panel: Show Artifact Info | {
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"I was able to reproduce it. A possible root cause for this behavior is a failed MLMD update at runtime. As UI calls MLMD to get states of individual node (tasks) in a DAG, we can observe inconsistent states between run' state and its underlying tasks' states. This is already implemented in the backend in #8850.\r\... | 2023-01-18T21:47:24 | 2023-05-04T16:53:11 | 2023-05-04T16:53:10 | COLLABORATOR | null | null | {
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"Is there a workaround for this issue in the meantime?"
] | 2023-01-18T21:28:10 | 2023-05-03T00:58:16 | 2023-05-03T00:58:15 | COLLABORATOR | null | Importer no longer works
```
F0118 21:12:37.782413 14 main.go:49] failed to execute importer component: failed to create MLMD artifact from ImporterSpec: failed to get or insert artifact type with schema title: system.Dataset: getOrInsertArtifactType(schema="title: system.Dataset") failed: rpc error: code = NotFound desc = No type found for query, name: `system.Dataset`, version: `nullopt`
Error: exit status 1
```
Likely due to it expects instanceSchema ([ref](https://github.com/kubeflow/pipelines/blob/88a9a31d86d34e61ac0cce9ed22f3455d91c5427/backend/src/v2/component/importer_launcher.go#L140)), while we only pass schemaTitle. | {
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* [x] #8726
* [x] #8727
* [x] #8729
* [x] #8728
* [ ] #9019
* [x] #9049
* [ ] #9537
* [ ] #9538
* [ ] #9539
* [ ] #9540 | {
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"2.0.0-beta.0 is released: https://github.com/kubeflow/pipelines/releases/tag/2.0.0-beta.0"
] | 2023-01-18T09:29:36 | 2023-02-15T19:28:05 | 2023-02-08T18:25:08 | COLLABORATOR | null | null | {
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] | 2023-01-18T08:33:14 | 2023-08-27T07:41:59 | null | COLLABORATOR | null | Up to debate/design on whether volume should be included in caching key.
(Open the issue to track an earlier thought). | {
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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."
] | 2023-01-17T23:58:28 | 2023-08-27T07:42:00 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Migrate KFP SDK tests to use [`pytest`](https://docs.pytest.org/en/7.2.x/) only.
Currently we use [`absl.testing`](https://abseil.io/docs/python/guides/testing), [`unittest`](https://docs.python.org/3/library/unittest.html), and [`pytest`](https://docs.pytest.org/en/7.2.x/) together.
This includes at least the following steps:
* Convert unittest assertions to [pytest assertions](https://docs.pytest.org/en/7.1.x/how-to/assert.html)
* Drop remove subclassing (unittest.TestCase, absl.testing.parameterized.TestCase)
* Convert absl parameterization to pytest.mark.parametrize
* Possibly need to modify a small number of mocks?
* Move setup/teardown code into fixtures
---
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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."
] | 2023-01-17T22:28:11 | 2023-08-27T07:42:02 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
Refactor [kfp.components.type_utils](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/components/types/type_utils.py) and [kfp.components.type_annotations](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/components/types/type_annotations.py)
Currently:
* Naming is inconsistent (particularly for mappings in `kfp.components.type_utils`)
* `type_utils` functions are confusing
* Unclear what the delineation is between `type_utils` and `type_annotations`. (Presumably everything that isn’t an annotation should go in `type_utils`.)
---
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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."
] | 2023-01-17T22:24:17 | 2023-08-27T07:42:03 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Comprehensive test support for [Custom Container Components](https://www.kubeflow.org/docs/components/pipelines/v2/author-a-pipeline/components/#3-custom-container-components). Specifically, testing the behavior when you run the `kfp component build` command.
This [code](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/cli/component.py) handles `kfp component build`. Some existing relevant tests are [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/cli/component_test.py).
---
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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."
] | 2023-01-17T22:17:59 | 2023-08-27T07:42:05 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Provide better error messages (i.e., not an "internal" error) to users, possibly with correct suggestions. Specifically, it can be hard to known how users should change their code in response to unknown/incorrect component interface annotations discovered in [kfp.components.component_factory.extract_component_interface](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/components/component_factory.py#L146).
---
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] | 2023-01-17T22:13:47 | 2023-08-27T07:42:07 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
Per [Python docs](https://docs.python.org/3/howto/logging.html#when-to-use-logging), use `warnings.warn` when warning is avoidable via user code changes. There are some instances of `warnings.warn` that should probably be `logging.warning`.
This work item is ensure the warnings in the KFP SDK are used correctly.
---
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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."
] | 2023-01-17T22:08:05 | 2023-08-27T07:42:08 | null | MEMBER | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
/area sdk
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
Support Python 3.11.
Unit tests for 3.11 are set up, but [failing](https://oss.gprow.dev/job-history/gs/oss-prow/pr-logs/directory/kubeflow-pipelines-sdk-python311). There may be code changes necessary to make the SDK work on 3.11. Tests can be [run](https://github.com/kubeflow/pipelines/blob/master/sdk/CONTRIBUTING.md#testing) locally in a 3.11 environment to reproduce failures.
In the PR that implements 3.11 support, we should upper bound the Python version in [setup.py](https://github.com/kubeflow/pipelines/blob/master/sdk/python/setup.py#L96).
Once tests are passing in CI/CD, we should remove the `optional` and `skip_report` fields in the [presubmit configuration](https://github.com/GoogleCloudPlatform/oss-test-infra/blob/c3fd9605af955e34c00647d1ef1f8e78c8a922aa/prow/prowjobs/kubeflow/pipelines/kubeflow-pipelines-presubmits.yaml#L117-L118).
---
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