port: kfp2

notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb

@@ -150,8 +150,8 @@
       "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"
       ]
     },
     {
@@ -530,18 +530,20 @@
         "def pipeline(\n",
         "    project: str = PROJECT_ID, region: str = REGION, import_file: str = IMPORT_FILE\n",
         "):\n",
-        "    from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
+        "    from google_cloud_pipeline_components.v1.automl.training_job import \\\n",
+        "        AutoMLTextTrainingJobRunOp\n",
+        "    from google_cloud_pipeline_components.v1.dataset import TextDatasetCreateOp\n",
         "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
         "                                                              ModelDeployOp)\n",
         "\n",
-        "    dataset_create_task = gcc_aip.TextDatasetCreateOp(\n",
+        "    dataset_create_task = TextDatasetCreateOp(\n",
         "        display_name=\"train-automl-happydb\",\n",
         "        gcs_source=import_file,\n",
         "        import_schema_uri=aip.schema.dataset.ioformat.text.multi_label_classification,\n",
         "        project=project,\n",
         "    )\n",
         "\n",
-        "    training_run_task = gcc_aip.AutoMLTextTrainingJobRunOp(\n",
+        "    training_run_task = AutoMLTextTrainingJobRunOp(\n",
         "        dataset=dataset_create_task.outputs[\"dataset\"],\n",
         "        display_name=\"train-automl-happydb\",\n",
         "        prediction_type=\"classification\",\n",
@@ -590,7 +592,7 @@
         "\n",
         "compiler.Compiler().compile(\n",
         "    pipeline_func=pipeline,\n",
-        "    package_path=\"text_classification_pipeline.json\",\n",
+        "    package_path=\"text_classification_pipeline.yaml\",\n",
         ")"
       ]
     },
@@ -617,14 +619,14 @@
         "\n",
         "job = aip.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
-        "    template_path=\"text_classification_pipeline.json\",\n",
+        "    template_path=\"text_classification_pipeline.yaml\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         "    enable_caching=False,\n",
         ")\n",
         "\n",
         "job.run()\n",
         "\n",
-        "! rm text_classification_pipeline.json"
+        "! rm text_classification_pipeline.yaml"
       ]
     },
     {
@@ -655,16 +657,7 @@
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
         "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n",
-        "\n",
-        "- Dataset\n",
-        "- Pipeline\n",
-        "- Model\n",
-        "- Endpoint\n",
-        "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
-        "- Cloud Storage Bucket"
+        "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial."
       ]
     },
     {
@@ -675,17 +668,10 @@
       },
       "outputs": [],
       "source": [
-        "delete_dataset = True\n",
-        "delete_pipeline = True\n",
-        "delete_model = True\n",
-        "delete_endpoint = True\n",
-        "delete_batchjob = True\n",
-        "delete_customjob = True\n",
-        "delete_hptjob = True\n",
-        "delete_bucket = True\n",
+        "delete_bucket = False\n",
         "\n",
         "try:\n",
-        "    if delete_model and \"DISPLAY_NAME\" in globals():\n",
+        "    if \"DISPLAY_NAME\" in globals():\n",
         "        models = aip.Model.list(\n",
         "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
         "        )\n",
@@ -707,53 +693,20 @@
         "except Exception as e:\n",
         "    print(e)\n",
         "\n",
-        "if delete_dataset and \"DISPLAY_NAME\" in globals():\n",
-        "    if \"text\" == \"tabular\":\n",
-        "        try:\n",
-        "            datasets = aip.TabularDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.TabularDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
+        "if \"DISPLAY_NAME\" in globals():\n",
         "\n",
-        "    if \"text\" == \"image\":\n",
-        "        try:\n",
-        "            datasets = aip.ImageDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.ImageDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "    if \"text\" == \"text\":\n",
-        "        try:\n",
-        "            datasets = aip.TextDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.TextDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "    if \"text\" == \"video\":\n",
-        "        try:\n",
-        "            datasets = aip.VideoDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.VideoDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
+        "    try:\n",
+        "        datasets = aip.TextDataset.list(\n",
+        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        dataset = datasets[0]\n",
+        "        aip.TextDataset.delete(dataset.resource_name)\n",
+        "        print(\"Deleted dataset:\", dataset)\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
         "\n",
         "try:\n",
-        "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",
+        "    if \"DISPLAY_NAME\" in globals():\n",
         "        pipelines = aip.PipelineJob.list(\n",
         "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
         "        )\n",