port: kfp2 (#2304)

* port: kfp2

* fix: import

* fix: params

* fix: json/yaml

notebooks/official/pipelines/automl_tabular_classification_beans.ipynb

@@ -155,8 +155,8 @@
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
         "                                 google-cloud-storage \\\n",
-        "                                 'kfp<2' \\\n",
-        "                                 'google-cloud-pipeline-components<2'"
+        "                                 kfp \\\n",
+        "                                 google-cloud-pipeline-components"
       ]
     },
     {
@@ -344,10 +344,8 @@
       },
       "outputs": [],
       "source": [
-        "IS_COLAB = False\n",
         "# from google.colab import auth\n",
-        "# auth.authenticate_user()\n",
-        "# IS_COLAB = True"
+        "# auth.authenticate_user()"
       ]
     },
     {
@@ -401,7 +399,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -434,6 +432,9 @@
       },
       "outputs": [],
       "source": [
+        "import sys\n",
+        "\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
         "if (\n",
         "    SERVICE_ACCOUNT == \"\"\n",
         "    or SERVICE_ACCOUNT is None\n",
@@ -497,9 +498,9 @@
         "\n",
         "import kfp\n",
         "from google.cloud import aiplatform\n",
-        "from kfp.v2 import dsl\n",
-        "from kfp.v2.dsl import (Artifact, ClassificationMetrics, Input, Metrics,\n",
-        "                        Output, component)"
+        "from kfp import compiler, dsl\n",
+        "from kfp.dsl import (Artifact, ClassificationMetrics, Input, Metrics, Output,\n",
+        "                     component)"
       ]
     },
     {
@@ -585,7 +586,6 @@
       "source": [
         "@component(\n",
         "    base_image=\"gcr.io/deeplearning-platform-release/tf2-cpu.2-6:latest\",\n",
-        "    output_component_file=\"tabular_eval_component.yaml\",\n",
         "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
         ")\n",
         "def classification_model_eval_metrics(\n",
@@ -695,7 +695,12 @@
         "        dep_decision = \"false\"\n",
         "    logging.info(\"deployment decision is %s\", dep_decision)\n",
         "\n",
-        "    return (dep_decision,)"
+        "    return (dep_decision,)\n",
+        "\n",
+        "\n",
+        "compiler.Compiler().compile(\n",
+        "    classification_model_eval_metrics, \"tabular_eval_component.yaml\"\n",
+        ")"
       ]
     },
     {
@@ -729,13 +734,20 @@
         "    gcp_region: str,\n",
         "    thresholds_dict_str: str,\n",
         "):\n",
-        "\n",
-        "    from google_cloud_pipeline_components.aiplatform import (\n",
-        "        AutoMLTabularTrainingJobRunOp, EndpointCreateOp, ModelDeployOp,\n",
-        "        TabularDatasetCreateOp)\n",
+        "    from google_cloud_pipeline_components.v1.automl.training_job import \\\n",
+        "        AutoMLTabularTrainingJobRunOp\n",
+        "    from google_cloud_pipeline_components.v1.dataset.create_tabular_dataset.component import \\\n",
+        "        tabular_dataset_create as TabularDatasetCreateOp\n",
+        "    from google_cloud_pipeline_components.v1.endpoint.create_endpoint.component import \\\n",
+        "        endpoint_create as EndpointCreateOp\n",
+        "    from google_cloud_pipeline_components.v1.endpoint.deploy_model.component import \\\n",
+        "        model_deploy as ModelDeployOp\n",
         "\n",
         "    dataset_create_op = TabularDatasetCreateOp(\n",
-        "        project=project, location=gcp_region, display_name=DATASET_DISPLAY_NAME, bq_source=bq_source\n",
+        "        project=project,\n",
+        "        location=gcp_region,\n",
+        "        display_name=DATASET_DISPLAY_NAME,\n",
+        "        bq_source=bq_source,\n",
         "    )\n",
         "\n",
         "    training_op = AutoMLTabularTrainingJobRunOp(\n",
@@ -770,10 +782,10 @@
         "    )\n",
         "\n",
         "    model_eval_task = classification_model_eval_metrics(\n",
-        "        project,\n",
-        "        gcp_region,\n",
-        "        thresholds_dict_str,\n",
-        "        training_op.outputs[\"model\"],\n",
+        "        project=project,\n",
+        "        location=gcp_region,\n",
+        "        thresholds_dict_str=thresholds_dict_str,\n",
+        "        model=training_op.outputs[\"model\"],\n",
         "    )\n",
         "\n",
         "    with dsl.Condition(\n",
@@ -804,7 +816,7 @@
       "source": [
         "## Compile the pipeline\n",
         "\n",
-        "Next, compile the pipeline to the specified json file."
+        "Next, compile the pipeline to the specified yaml file."
       ]
     },
     {
@@ -815,11 +827,9 @@
       },
       "outputs": [],
       "source": [
-        "from kfp.v2 import compiler\n",
-        "\n",
         "compiler.Compiler().compile(\n",
         "    pipeline_func=pipeline,\n",
-        "    package_path=\"tabular_classification_pipeline.json\",\n",
+        "    package_path=\"tabular_classification_pipeline.yaml\",\n",
         ")"
       ]
     },
@@ -899,12 +909,14 @@
         "    assert bq_region in REGION\n",
         "    print(f\"Region validated: {REGION}\")\n",
         "except AssertionError:\n",
-        "    print(\"Please make sure the region of BigQuery (source) and that of the pipeline are the same.\")\n",
+        "    print(\n",
+        "        \"Please make sure the region of BigQuery (source) and that of the pipeline are the same.\"\n",
+        "    )\n",
         "\n",
         "# Configure the pipeline\n",
         "job = aiplatform.PipelineJob(\n",
         "    display_name=PIPELINE_DISPLAY_NAME,\n",
-        "    template_path=\"tabular_classification_pipeline.json\",\n",
+        "    template_path=\"tabular_classification_pipeline.yaml\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         "    parameter_values={\n",
         "        \"project\": PROJECT_ID,\n",