Merge branch 'kubeflow:master' into master

RELEASE.md

@@ -169,7 +169,7 @@ Do the following things before a release:
 
 1. Verify cloudbuild and postsubmit tests are passing: visit <https://github.com/kubeflow/pipelines/commits/master> for master branch.
 
-![How to very cloudbuild and postsubmit status](release-status-check.png)
+![How to very cloudbuild and postsubmit status](images/release-status-check.png)
 
 If not, contact the KFP team to determine if the failure(s) would block the release. You can also retry the failed job by opening the detail page of prow job, and click the refresh button next ot the job title.
 
@@ -235,7 +235,7 @@ in gcr.io/ml-pipeline-test succeeded. If it fails, please click "View more detai
 on Google Cloud Build" and then "Retry".
 
     NOTE: you can find your latest release commit in <https://github.com/kubeflow/pipelines/commits/master> and select your release branch.
-    ![How to very cloudbuild and postsubmit status](release-status-check.png)
+    ![How to very cloudbuild and postsubmit status](images/release-status-check.png)
 
 1. Select the `release-on-tag` cloudbuild job that copies built images and artifacts to
 public image registry and gcs bucket. This job should have already failed because
@@ -246,9 +246,9 @@ and then "Retry", because after waiting for previous step, artifacts are now rea
     It will create a build with "Branch: $BRANCH" instead of "TAG: $VERSION".
     Open "View more details on Google Cloud Build", and rerun from there.
 
-    ![Retry release-on-tag from the Google Cloud Build details page](retry-release-on-tag.png)
+    ![Retry release-on-tag from the Google Cloud Build details page](images/retry-release-on-tag.png)
 
-    ![Verify that you're retrying the right build](verify-retry-the-right-build.png)
+    ![Verify that you're retrying the right build](images/verify-retry-the-right-build.png)
 
     TODO: we should have an automation KFP cluster, and the waiting and submiting
     `release-on-tag` cloudbuild task should happen automatically.

components/google-cloud/Dockerfile

@@ -44,7 +44,7 @@ RUN pip3 install -U "fsspec>=0.7.4" "gcsfs>=0.6.0" "pandas<=1.3.5" "scikit-learn
 RUN pip3 install -U google-cloud-notebooks
 
 # Install main package
-RUN pip3 install "git+https://github.com/kubeflow/pipelines.git@google-cloud-pipeline-components-2.16.1#egg=google-cloud-pipeline-components&subdirectory=components/google-cloud"
+RUN pip3 install "git+https://github.com/kubeflow/pipelines.git@google-cloud-pipeline-components-2.17.0#egg=google-cloud-pipeline-components&subdirectory=components/google-cloud"
 
 # Note that components can override the container entry ponint.
 ENTRYPOINT ["python3","-m","google_cloud_pipeline_components.container.v1.aiplatform.remote_runner"]

components/google-cloud/RELEASE.md

@@ -1,5 +1,13 @@
 ## Upcoming release
+
+## Release 2.17.0
 * Fix Gemini batch prediction support to `v1.model_evaluation.autosxs_pipeline` after output schema change.
+* Modify the code to support latest bp result format
+* Update the StarryNet package metadata.
+* Use instance.target_field_name format for text-bison models only, use target_field_name for gemini models.
+* Pass model name to eval_runner to process batch prediction's output as per the output schema of model used.
+* Use LLM Model Evaluation image version v0.7
+* Update AutoSxS and RLHF image tags
 
 ## Release 2.16.1
 * Fix to model batch explanation component for Structured Data pipelines; image bump.

components/google-cloud/docs/source/versions.json

@@ -1,8 +1,13 @@
 [
   {
-  "version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.16.1",
-  "title": "2.16.1",
-  "aliases": []
+    "version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.17.0",
+    "title": "2.17.0",
+    "aliases": []
+  },
+  {
+    "version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.16.1",
+    "title": "2.16.1",
+    "aliases": []
   },
   {
     "version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.16.0",

components/google-cloud/google_cloud_pipeline_components/__init__.py

@@ -20,9 +20,9 @@
 if sys.version_info < (3, 9):
   warnings.warn(
       (
-          ' Google Cloud Pipeline Components will drop support for Python 3.9'
-          ' on Oct 1, 2025. To use new versions of the GCPC SDK after that'
-          ' date, you will need to upgrade to Python >= 3.10. See'
+          ' Google Cloud Pipeline Components will drop support for Python 3.8'
+          ' on Oct 1, 2024. To use new versions of the GCPC SDK after that'
+          ' date, you will need to upgrade to Python >= 3.9. See'
           ' https://devguide.python.org/versions/ for more details.'
       ),
       FutureWarning,

components/google-cloud/google_cloud_pipeline_components/version.py

@@ -13,4 +13,4 @@
 # limitations under the License.
 """Google Cloud Pipeline Components version."""
 
-__version__ = "2.16.1"
+__version__ = "2.17.0"

frontend/src/pages/NewRunSwitcher.tsx

@@ -162,7 +162,7 @@ function NewRunSwitcher(props: PageProps) {
     recurringRunIsFetching ||
     pipelineIsFetching ||
     pipelineVersionIsFetching ||
-    v1TemplateStrIsFetching ||
+    (!isTemplateV2(templateString) && v1TemplateStrIsFetching) ||
     experimentIsFetching
   ) {
     return <div>Currently loading pipeline information</div>;

frontend/src/pages/RunDetails.tsx

@@ -101,6 +101,7 @@ interface SelectedNodeDetails {
 
 // exported only for testing
 export interface RunDetailsInternalProps {
+  isLoading?: boolean;
   runId?: string;
   gkeMetadata: GkeMetadata;
 }
@@ -237,6 +238,10 @@ class RunDetails extends Page<RunDetailsInternalProps, RunDetailsState> {
   }
 
   public render(): JSX.Element {
+    if (this.props.isLoading) {
+      return <div>Currently loading run information</div>;
+    }
+
     const {
       allArtifactConfigs,
       allowCustomVisualizations,

frontend/src/pages/RunDetailsRouter.tsx

@@ -70,9 +70,5 @@ export default function RunDetailsRouter(props: RunDetailsProps) {
     }
   }
 
-  if (runIsFetching || templateStrIsFetching) {
-    return <div>Currently loading run information</div>;
-  }
-
-  return <EnhancedRunDetails {...props} />;
+  return <EnhancedRunDetails {...props} isLoading={runIsFetching || templateStrIsFetching} />;
 }

kubernetes_platform/python/README.md

@@ -1,27 +1,33 @@
-# Kubernetes Platform-specific Features
+# Kubeflow Pipelines SDK kfp-kubernetes API Reference
 
-The `kfp-kubernetes` Python library enables authoring [Kubeflow pipelines](https://www.kubeflow.org/docs/components/pipelines/v2/) with Kubernetes-specific features. These features are supported by the [default KFP open source BE](https://github.com/kubeflow/pipelines/tree/master/backend). Specifically, the `kfp-kubernetes` library supports authoring pipelines that use:
+The Kubeflow Pipelines SDK kfp-kubernetes python library (part of the [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/) project) is an addon to the [Kubeflow Pipelines SDK](https://kubeflow-pipelines.readthedocs.io/) that enables authoring Kubeflow pipelines with Kubernetes-specific features and concepts, such as:
 
 * [Secrets](https://kubernetes.io/docs/concepts/configuration/secret/)
 * [PersistentVolumeClaims](https://kubernetes.io/docs/concepts/storage/persistent-volumes/#persistentvolumeclaims)
-* [ImagePullPolicy](https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy)
+* [ImagePullPolicies](https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy)
+* [Ephemeral volumes](https://kubernetes.io/docs/concepts/storage/ephemeral-volumes/)
+* [Node selectors](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector)
+* [Tolerations](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/)
+* [Labels and annotations](https://kubernetes.io/docs/reference/kubernetes-api/common-definitions/object-meta/#ObjectMeta)
+* and more
 
-See the [`kfp-kubernetes` reference documentation](https://kfp-kubernetes.readthedocs.io/).
+Be sure to check out the full [API Reference](https://kfp-kubernetes.readthedocs.io/) for more details.
 
 ## Installation
-The `kfp-kubernetes` package can be installed as a `kfp` SDK extra dependency with `kfp==2.x.x`:
-<!-- TODO: remove --pre when kfp v2 goes to GA -->
+The `kfp-kubernetes` package can be installed as a KFP SDK extra dependency.
 ```sh
-pip install kfp[kubernetes] --pre
+pip install kfp[kubernetes]
 ```
 
 Or installed independently:
 ```sh
 pip install kfp-kubernetes
 ```
 
-## Example usage
-<!-- TODO: test these examples once the BE implementation exists -->
+## Getting started
+
+The following is an example of a simple pipeline that uses the kfp-kubernetes library to mount a pre-existing secret as an environment variable available in the task's container.
+
 ### Secret: As environment variable
 ```python
 from kfp import dsl
@@ -40,6 +46,10 @@ def pipeline():
                                  secret_key_to_env={'password': 'SECRET_VAR'})
 ```
 
+## Other examples
+
+Here is a non-exhaustive list of some other examples of how to use the kfp-kubernetes library. Be sure to check out the full [API Reference](https://kfp-kubernetes.readthedocs.io/) for more details.
+
 ### Secret: As mounted volume
 ```python
 from kfp import dsl

kubernetes_platform/python/docs/conf.py

@@ -77,7 +77,7 @@ def decorator(func):
 }
 
 html_theme = 'sphinx_immaterial'
-html_title = 'kfp-kubernetes Reference Documentation'
+html_title = 'KFP SDK kfp-kubernetes API Reference'
 html_static_path = ['_static']
 html_css_files = ['custom.css']
 html_logo = '_static/kubeflow.png'