port: kfp2

notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb

@@ -149,8 +149,8 @@
       "outputs": [],
       "source": [
         "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 'kfp<2' \\\n",
-        "                                 'google-cloud-pipeline-components<2' \\\n",
+        "                                 kfp \\\n",
+        "                                 google-cloud-pipeline-components \\\n",
         "                                 google-cloud-storage"
       ]
     },
@@ -349,6 +349,17 @@
         "Create a storage bucket to store intermediate artifacts such as datasets."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MzGDU7TWdts_"
+      },
+      "outputs": [],
+      "source": [
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -362,11 +373,11 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "MzGDU7TWdts_"
+        "id": "create_bucket"
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+        "! gsutil mb -l {REGION} {BUCKET_URI}"
       ]
     },
     {
@@ -399,6 +410,9 @@
       },
       "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",
@@ -463,7 +477,7 @@
         "\n",
         "import google.cloud.aiplatform as aip\n",
         "import kfp\n",
-        "from kfp.v2 import compiler"
+        "from kfp import compiler"
       ]
     },
     {
@@ -485,7 +499,7 @@
       },
       "outputs": [],
       "source": [
-        "PIPELINE_ROOT = \"{}/pipeline_root/flowers\".format(BUCKET_URI)"
+        "PIPELINE_ROOT = f\"{BUCKET_URI}/pipeline_root/flowers\""
       ]
     },
     {
@@ -533,18 +547,21 @@
       "source": [
         "@kfp.dsl.pipeline(name=\"automl-image-training-v2\")\n",
         "def pipeline(project: str = PROJECT_ID, region: str = REGION):\n",
-        "    from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
+        "    from google_cloud_pipeline_components.v1.automl.training_job import \\\n",
+        "        AutoMLImageTrainingJobRunOp\n",
+        "    from google_cloud_pipeline_components.v1.dataset import \\\n",
+        "        ImageDatasetCreateOp\n",
         "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
         "                                                              ModelDeployOp)\n",
         "\n",
-        "    ds_op = gcc_aip.ImageDatasetCreateOp(\n",
+        "    ds_op = ImageDatasetCreateOp(\n",
         "        project=project,\n",
         "        display_name=\"flowers\",\n",
         "        gcs_source=\"gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv\",\n",
         "        import_schema_uri=aip.schema.dataset.ioformat.image.single_label_classification,\n",
         "    )\n",
         "\n",
-        "    training_job_run_op = gcc_aip.AutoMLImageTrainingJobRunOp(\n",
+        "    training_job_run_op = AutoMLImageTrainingJobRunOp(\n",
         "        project=project,\n",
         "        display_name=\"train-automl-flowers\",\n",
         "        prediction_type=\"classification\",\n",
@@ -591,8 +608,7 @@
       "outputs": [],
       "source": [
         "compiler.Compiler().compile(\n",
-        "    pipeline_func=pipeline,\n",
-        "    package_path=\"image classification_pipeline.json\".replace(\" \", \"_\"),\n",
+        "    pipeline_func=pipeline, package_path=\"image_classification_pipeline.yaml\"\n",
         ")"
       ]
     },
@@ -619,14 +635,14 @@
         "\n",
         "job = aip.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
-        "    template_path=\"image classification_pipeline.json\".replace(\" \", \"_\"),\n",
+        "    template_path=\"image_classification_pipeline.yaml\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         "    enable_caching=False,\n",
         ")\n",
         "\n",
         "job.run()\n",
         "\n",
-        "! rm image_classification_pipeline.json"
+        "! rm image_classification_pipeline.yaml"
       ]
     },
     {