Merge branch 'main' into imkc--tabular-regression

notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb

@@ -277,7 +277,7 @@
       },
       "source": [
         "**4. Service account or other**\n",
-        "* See all authentication options here: [Google Cloud Platform Jupyter Notebook Authentication Guide](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_authentication_guide.ipynb)"
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
       ]
     },
     {
@@ -479,7 +479,7 @@
       "outputs": [],
       "source": [
         "dataset = aiplatform.TabularDataset.create(\n",
-        "    display_name=\"NOAA historical weather data\" + \"_\" + \"unique\",\n",
+        "    display_name=\"NOAA historical weather data_unique\",\n",
         "    bq_source=[f\"bq://{TRAINING_INPUT_TABLE_ID}\"],\n",
         ")\n",
         "\n",
@@ -550,7 +550,151 @@
       "outputs": [],
       "source": [
         "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
-        "    display_name=\"gsod_\" + \"unique\",\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
+        "    optimization_prediction_type=\"regression\",\n",
+        "    optimization_objective=\"minimize-rmse\",\n",
+        "    column_specs=COLUMN_SPECS,\n",
+        ")\n",
+        "\n",
+        "print(training_job)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_automl_pipeline:tabular,lrg,transformations"
+      },
+      "outputs": [],
+      "source": [
+        "training_job = aiplatform.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"job_unique\",\n",
         "    optimization_prediction_type=\"regression\",\n",
         "    optimization_objective=\"minimize-rmse\",\n",
         "    column_specs=COLUMN_SPECS,\n",
@@ -593,7 +737,7 @@
       "source": [
         "model = training_job.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"gsod_\" + \"unique\",\n",
+        "    model_display_name=\"model_unique\",\n",
         "    training_fraction_split=0.6,\n",
         "    validation_fraction_split=0.2,\n",
         "    test_fraction_split=0.2,\n",

notebooks/official/custom/sdk-custom-image-classification-online.ipynb

@@ -283,8 +283,15 @@
       },
       "source": [
         "**4. Service account or other**\n",
-        "* See all authentication options here: [Google Cloud Platform Jupyter Notebook Authentication Guide](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_authentication_guide.ipynb)\n",
-        "\n",
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "70a42f1033a3"
+      },
+      "source": [
         "### Create a Cloud Storage bucket\n",
         "\n",
         "Create a storage bucket to store intermediate artifacts such as datasets."