Migrate Elyra to KFP v2 (cont.)

Makefile

@@ -165,7 +165,7 @@ uninstall-server-package:
 	@$(PYTHON_PIP) uninstall elyra -y
 
 install-server-package: uninstall-server-package
-	$(PYTHON_PIP) install --upgrade --upgrade-strategy $(UPGRADE_STRATEGY) "$(shell find dist -name "elyra-*-py3-none-any.whl")[kfp-tekton]"
+	$(PYTHON_PIP) install --upgrade --upgrade-strategy $(UPGRADE_STRATEGY) "$(shell find dist -name "elyra-*-py3-none-any.whl")"
 
 install-server: build-dependencies lint-server build-server install-server-package ## Build and install backend
 

docs/source/getting_started/installation.md

@@ -39,7 +39,6 @@ You can install elyra with all optional dependencies or with specific dependenci
 - `elyra[airflow]` - install the Elyra core features and support for [Apache Airflow pipelines](https://github.com/apache/airflow)
 - `elyra[airflow-gitlab]` - install the Elyra core features and GitLab support for [Apache Airflow pipelines](https://github.com/apache/airflow)
 - `elyra[kfp]` - install the Elyra core features and support for [Kubeflow Pipelines](https://github.com/kubeflow/pipelines)
-- `elyra[kfp-tekton]` - install the Elyra core features and support for [Kubeflow Pipelines on Tekton](https://github.com/kubeflow/kfp-tekton)
 - `elyra[kfp-examples]` - install the Elyra core features and [Kubeflow Pipelines custom component examples](https://github.com/elyra-ai/examples/tree/main/component-catalog-connectors/kfp-example-components-connector)
 
 
@@ -92,7 +91,7 @@ conda install -c conda-forge "elyra[all]"
 ```
 
 **NOTE:**
-The Elyra packaging process was changed in version 4.0. The [Apache Airflow pipelines](https://github.com/apache/airflow) or [Kubeflow Pipelines on Tekton](https://github.com/kubeflow/kfp-tekton) dependencies are no longer installed by default. To install this dependency, you must specify `elyra[all]`, `elyra[kfp]` or `elyra[kfp-tekton]`.
+The Elyra packaging process was changed in version 4.0. The [Apache Airflow pipelines](https://github.com/apache/airflow) or [Kubeflow Pipelines](https://github.com/kubeflow/pipelines) dependencies are no longer installed by default. To install this dependency, you must specify `elyra[all]` or `elyra[kfp]`.
 
 You can also install the Pipeline editor, Code Snippet, Code Viewer, or Script editor extensions individually:
 

docs/source/user_guide/pipelines.md

@@ -36,7 +36,7 @@ Please review the [_Best practices for file-based pipeline nodes_ topic in the _
 
 Elyra pipelines support three runtime platforms:
 - Local/JupyterLab
-- [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/) (with Argo or [Tekton](https://github.com/kubeflow/kfp-tekton/) workflow engines)
+- [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/) (with Argo workflow engine)
 - [Apache Airflow](https://airflow.apache.org/)
 
 #### Generic pipelines
@@ -373,7 +373,7 @@ Use the [`elyra-pipeline`](command-line-interface.html#working-with-pipelines) `
 
 ```bash
 $ elyra-pipeline submit elyra-pipelines/a-kubeflow.pipeline \
-      --runtime-config kfp-shared-tekton
+      --runtime-config kfp
 ```
 
 For Kubeflow Pipelines the `--monitor` option is supported. If specified, the pipeline execution status is monitored for up to `--monitor-timeout` minutes (default: 60) and the `elyra-pipeline submit` command terminates as follows:

docs/source/user_guide/runtime-conf.md

@@ -259,14 +259,11 @@ A password or token is required for most authentication types. Refer to the [Kub
 Example: `mypassword`
 
 ##### Kubeflow Pipelines engine (engine)
-The engine being used by Kubeflow Pipelines to run pipelines: `Argo` or `Tekton`. If you have access to the Kubernetes cluster where Kubeflow Pipelines is deployed, run these commands in a terminal window to determine the engine type.
+The engine being used by Kubeflow Pipelines to run pipelines: `Argo`. If you have access to the Kubernetes cluster where Kubeflow Pipelines is deployed, run these commands in a terminal window to determine the engine type.
 
 ```
 # If this command completes successfully, the engine type is Argo.
 kubectl describe configmap -n kubeflow workflow-controller-configmap
-
-# If this command completes successfully, the engine type is Tekton.
-kubectl describe configmap -n kubeflow kfp-tekton-config
 ```
 
 The default is `Argo`.

elyra/cli/pipeline_app.py

@@ -428,8 +428,7 @@ def _monitor_kfp_submission(runtime_config: dict, runtime_config_name: str, run_
         raise click.ClickException(f"Kubeflow authentication failed: {ae}")
 
     try:
-        # Create a Kubeflow Pipelines client. There is no need to use a Tekton client,
-        # because the monitoring API is agnostic.
+        # Create a Kubeflow Pipelines client.
         client = ArgoClient(
             host=runtime_config.metadata["api_endpoint"].rstrip("/"),
             cookies=auth_info.get("cookies", None),

elyra/metadata/schemas/kfp.json

@@ -77,7 +77,7 @@
           "title": "Kubeflow Pipelines engine",
           "description": "The Kubeflow Pipelines engine in use",
           "type": "string",
-          "enum": ["Argo", "Tekton"],
+          "enum": ["Argo"],
           "default": "Argo",
           "uihints": {
             "category": "Kubeflow Pipelines"

elyra/metadata/schemasproviders.py

@@ -23,12 +23,6 @@
 import entrypoints
 from traitlets import log  # noqa H306
 
-try:
-    from kfp_tekton import TektonClient
-except ImportError:
-    # We may not have kfp-tekton available and that's okay, for example when only using airflow!
-    TektonClient = None
-
 from elyra.metadata.schema import SchemasProvider
 from elyra.metadata.schemaspaces import CodeSnippets
 from elyra.metadata.schemaspaces import ComponentCatalogs
@@ -98,17 +92,6 @@ def get_schemas(self) -> List[Dict]:
                 )
 
         if kfp_schema_present:  # Update the kfp engine enum to reflect current packages...
-            # If TektonClient package is missing, navigate to the engine property
-            # and remove 'tekton' entry if present and return updated result.
-            if not TektonClient:
-                # locate the schema and update the enum
-                for schema in runtime_schemas:
-                    if schema["name"] == "kfp":
-                        engine_enum: list = schema["properties"]["metadata"]["properties"]["engine"]["enum"]
-                        if "Tekton" in engine_enum:
-                            engine_enum.remove("Tekton")
-                            schema["properties"]["metadata"]["properties"]["engine"]["enum"] = engine_enum
-
             # For KFP schemas replace placeholders:
             # - properties.metadata.properties.auth_type.enum ({AUTH_PROVIDER_PLACEHOLDERS})
             # - properties.metadata.properties.auth_type.default ({DEFAULT_AUTH_PROVIDER_PLACEHOLDER})

elyra/pipeline/kfp/PipelineConf.py

@@ -1,3 +1,18 @@
+#
+# Copyright 2018-2025 Elyra Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 from typing import Union
 
 from kubernetes.client.models import V1PodDNSConfig
@@ -20,7 +35,6 @@ def __init__(self):
 
     def set_image_pull_secrets(self, image_pull_secrets):
         """Configures the pipeline level imagepullsecret.
-
         Args:
           image_pull_secrets: a list of Kubernetes V1LocalObjectReference For
             detailed description, check Kubernetes V1LocalObjectReference definition
@@ -31,7 +45,6 @@ def set_image_pull_secrets(self, image_pull_secrets):
 
     def set_timeout(self, seconds: int):
         """Configures the pipeline level timeout.
-
         Args:
           seconds: number of seconds for timeout
         """
@@ -41,7 +54,6 @@ def set_timeout(self, seconds: int):
     def set_parallelism(self, max_num_pods: int):
         """Configures the max number of total parallel pods that can execute at
         the same time in a workflow.
-
         Args:
           max_num_pods: max number of total parallel pods.
         """
@@ -53,7 +65,6 @@ def set_parallelism(self, max_num_pods: int):
 
     def set_ttl_seconds_after_finished(self, seconds: int):
         """Configures the ttl after the pipeline has finished.
-
         Args:
           seconds: number of seconds for the workflow to be garbage collected after
             it is finished.
@@ -64,7 +75,6 @@ def set_ttl_seconds_after_finished(self, seconds: int):
     def set_pod_disruption_budget(self, min_available: Union[int, str]):
         """PodDisruptionBudget holds the number of concurrent disruptions that
         you allow for pipeline Pods.
-
         Args:
           min_available (Union[int, str]):  An eviction is allowed if at least
             "minAvailable" pods selected by "selector" will still be available after
@@ -77,12 +87,9 @@ def set_pod_disruption_budget(self, min_available: Union[int, str]):
 
     def set_default_pod_node_selector(self, label_name: str, value: str):
         """Add a constraint for nodeSelector for a pipeline.
-
         Each constraint is a key-value pair label.
-
         For the container to be eligible to run on a node, the node must have each
         of the constraints appeared as labels.
-
         Args:
           label_name: The name of the constraint label.
           value: The value of the constraint label.
@@ -92,7 +99,6 @@ def set_default_pod_node_selector(self, label_name: str, value: str):
 
     def set_image_pull_policy(self, policy: str):
         """Configures the default image pull policy.
-
         Args:
           policy: the pull policy, has to be one of: Always, Never, IfNotPresent.
             For more info:
@@ -104,23 +110,19 @@ def set_image_pull_policy(self, policy: str):
     def add_op_transformer(self, transformer):
         """Configures the op_transformers which will be applied to all ops in
         the pipeline. The ops can be ResourceOp, VolumeOp, or ContainerOp.
-
         Args:
           transformer: A function that takes a kfp Op as input and returns a kfp Op
         """
         self.op_transformers.append(transformer)
 
     def set_dns_config(self, dns_config: V1PodDNSConfig):
         """Set the dnsConfig to be given to each pod.
-
         Args:
           dns_config: Kubernetes V1PodDNSConfig For detailed description, check
             Kubernetes V1PodDNSConfig definition
             https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1PodDNSConfig.md
-
         Example:
           ::
-
             import kfp
             from kubernetes.client.models import V1PodDNSConfig, V1PodDNSConfigOption
             pipeline_conf = kfp.dsl.PipelineConf()
@@ -139,10 +141,8 @@ def data_passing_method(self):
     def data_passing_method(self, value):
         """Sets the object representing the method used for intermediate data
         passing.
-
         Example:
           ::
-
             from kfp.dsl import PipelineConf, data_passing_methods
             from kubernetes.client.models import V1Volume, V1PersistentVolumeClaimVolumeSource
             pipeline_conf = PipelineConf()

elyra/pipeline/kfp/kfp_authentication.py

@@ -27,9 +27,9 @@
 from typing import Tuple
 from urllib.parse import urlsplit
 
-from kfp.auth import KF_PIPELINES_SA_TOKEN_ENV
-from kfp.auth import KF_PIPELINES_SA_TOKEN_PATH
-from kfp.auth import ServiceAccountTokenVolumeCredentials
+from kfp.client import KF_PIPELINES_SA_TOKEN_ENV
+from kfp.client import KF_PIPELINES_SA_TOKEN_PATH
+from kfp.client import ServiceAccountTokenVolumeCredentials
 import requests