fix: review

migration: experiments
2026-09-30 18:31:58 +00:00 · 2023-01-24 17:13:41 +00:00 · 2023-01-23 21:44:23 +00:00
11 changed files with 52 additions and 5030 deletions
@@ -5,6 +5,6 @@ nbconvert
 black==22.10.0
 pyupgrade==2.38.4
 isort==5.10.1
-flake8==6.0.0
+flake8==4.0.1
 nbqa==1.5.3

@@ -40,4 +40,4 @@
 /notebooks/community/pipelines/google_cloud_pipeline_components_bqml_pipeline_anomaly_detection.ipynb @inardini
 /notebooks/community/pipelines/google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb @Narwhalprime
 /notebooks/community/pipelines/google_cloud_pipeline_components_ready_to_go_text_classification_pipeline.ipynb @Narwhalprime
-/notebooks/community/feature_store/get_started_vertex_feature_store.ipynb @junkourata
+
@@ -54,20 +54,18 @@
    {
      "cell_type": "markdown",
      "metadata": {
-        "id": "239ba71252d3"
+        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
-        "This notebook shows how to use `Vertex AI Pipelines` and `BigQuery ML pipeline components` to train and evaluate a demand forecasting model."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "25c28706c23e"
-      },
-      "source": [
+        "This notebook shows how to use `Vertex AI Pipelines` and `BigQuery ML pipeline components` to train and evaluate a demand forecasting model.\n",
+        "\n",
+        "### Dataset\n",
+        "\n",
+        "The dataset is a modified version of the dataset in [Build and visualize demand forecast predictions using Datastream, Dataflow, BigQuery ML, and Looker\n",
+        "](https://cloud.google.com/architecture/build-visualize-demand-forecast-prediction-datastream-dataflow-bigqueryml-looker) solution architecture\n",
+        "\n",
        "### Objective\n",
        "\n",
        "In this tutorial, you learn how to train and evaluate a BigQuery ML model using Vertex AI Pipelines and BigQuery ML pipeline components. \n",
@@ -89,27 +87,8 @@
        "  - Generate the ARIMA Plus forecasts\n",
        "  - Generate the ARIMA PLUS forecast explainations\n",
        "- Compile the pipeline.\n",
-        "- Execute the pipeline."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "586acfa9b502"
-      },
-      "source": [
-        "### Dataset\n",
+        "- Execute the pipeline.\n",
        "\n",
-        "The dataset is a modified version of the dataset in [Build and visualize demand forecast predictions using Datastream, Dataflow, BigQuery ML, and Looker\n",
-        "](https://cloud.google.com/architecture/build-visualize-demand-forecast-prediction-datastream-dataflow-bigqueryml-looker) solution architecture\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tvgnzT1CKxrO"
-      },
-      "source": [
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
@@ -373,8 +352,9 @@
        "id": "06571eb4063b"
      },
      "source": [
-        "#### UUID\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+        "#### Timestamp\n",
+        "\n",
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
@@ -385,16 +365,9 @@
      },
      "outputs": [],
      "source": [
-        "import random\n",
-        "import string\n",
+        "from datetime import datetime\n",
        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
+        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
@@ -512,7 +485,7 @@
      "outputs": [],
      "source": [
        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"-aip-\" + UUID\n",
+        "    BUCKET_NAME = PROJECT_ID + \"-aip-\" + TIMESTAMP\n",
        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
      ]
    },
@@ -733,7 +706,6 @@
        "KFP_COMPONENTS_PATH = \"components\"\n",
        "PIPELINES_PATH = \"pipelines\"\n",
        "\n",
-        "! mkdir -m 777 -p {DATA_PATH}\n",
        "! mkdir -m 777 -p {KFP_COMPONENTS_PATH}\n",
        "! mkdir -m 777 -p {PIPELINES_PATH}"
      ]
@@ -799,7 +771,7 @@
        "  --location={LOCATION} \\\n",
        "  --source_format=CSV \\\n",
        "  --skip_leading_rows=1\\\n",
-        "  fast_fresh.orders_{UUID} \\\n",
+        "  fast_fresh.orders_{TIMESTAMP} \\\n",
        "  {RAW_DATA_URI} \\\n",
        "  time_of_sale:DATETIME,order_id:INTEGER,product_name:STRING,price:NUMERIC,quantity:NUMERIC,payment_method:STRING,store_id:INTEGER,user_id:INTEGER"
      ]
@@ -810,7 +782,7 @@
        "id": "ZrgOD30o7HcL"
      },
      "source": [
-        "## BigQuery ML Training Formalization\n",
+        "## BQML Training Formalization\n",
        "\n",
        "In the next cells, you build the components and pipeline to train and evaluate the BQML demand forecasting model."
      ]
@@ -848,13 +820,13 @@
        "BQ_EVALUATE_MODEL_TABLE_PREFIX = \"orders_arima_model_evaluate\"\n",
        "BQ_FORECAST_TABLE_PREFIX = \"orders_arima_forecast\"\n",
        "BQ_EXPLAIN_FORECAST_TABLE_PREFIX = \"orders_arima_explain_forecast\"\n",
-        "BQ_ORDERS_TABLE = f\"{BQ_ORDERS_TABLE_PREFIX}_{UUID}\"\n",
-        "BQ_TRAINING_TABLE = f\"{BQ_TRAINING_TABLE_PREFIX}_{UUID}\"\n",
-        "BQ_MODEL_TABLE = f\"{BQ_MODEL_TABLE_PREFIX}_{UUID}\"\n",
-        "BQ_EVALUATE_TS_TABLE = f\"{BQ_EVALUATE_TS_TABLE_PREFIX}_{UUID}\"\n",
-        "BQ_EVALUATE_MODEL_TABLE = f\"{BQ_EVALUATE_MODEL_TABLE_PREFIX}_{UUID}\"\n",
-        "BQ_FORECAST_TABLE = f\"{BQ_FORECAST_TABLE_PREFIX}_{UUID}\"\n",
-        "BQ_EXPLAIN_FORECAST_TABLE = f\"{BQ_EXPLAIN_FORECAST_TABLE_PREFIX}_{UUID}\"\n",
+        "BQ_ORDERS_TABLE = f\"{BQ_ORDERS_TABLE_PREFIX}_{TIMESTAMP}\"\n",
+        "BQ_TRAINING_TABLE = f\"{BQ_TRAINING_TABLE_PREFIX}_{TIMESTAMP}\"\n",
+        "BQ_MODEL_TABLE = f\"{BQ_MODEL_TABLE_PREFIX}_{TIMESTAMP}\"\n",
+        "BQ_EVALUATE_TS_TABLE = f\"{BQ_EVALUATE_TS_TABLE_PREFIX}_{TIMESTAMP}\"\n",
+        "BQ_EVALUATE_MODEL_TABLE = f\"{BQ_EVALUATE_MODEL_TABLE_PREFIX}_{TIMESTAMP}\"\n",
+        "BQ_FORECAST_TABLE = f\"{BQ_FORECAST_TABLE_PREFIX}_{TIMESTAMP}\"\n",
+        "BQ_EXPLAIN_FORECAST_TABLE = f\"{BQ_EXPLAIN_FORECAST_TABLE_PREFIX}_{TIMESTAMP}\"\n",
        "\n",
        "BQ_TRAIN_CONFIGURATION = {\n",
        "    \"destinationTable\": {\n",
@@ -1050,7 +1022,7 @@
        "id": "pcSL1FHk69KT"
      },
      "source": [
-        "### Build the BigQuery ML training pipeline\n",
+        "### Build the BQML training pipeline\n",
        "\n",
        "Define your workflow using Kubeflow Pipelines DSL package. \n",
        "\n",
@@ -1122,8 +1094,8 @@
        "        location=location,\n",
        "    ).set_display_name(\"get train data\")\n",
        "\n",
-        "    # Run an ARIMA PLUS experiment\n",
-        "    bq_arima_model_exp_op = (\n",
+        "    # Train the ARIMA PLUS model\n",
+        "    bq_arima_model_op = (\n",
        "        BigqueryCreateModelJobOp(\n",
        "            query=f\"\"\"\n",
        "        -- create model table\n",
@@ -1132,7 +1104,10 @@
        "        MODEL_TYPE = \\'ARIMA_PLUS\\',\n",
        "        TIME_SERIES_TIMESTAMP_COL = \\'hourly_timestamp\\',\n",
        "        TIME_SERIES_DATA_COL = \\'total_sold\\',\n",
-        "        TIME_SERIES_ID_COL = [\\'product_name\\']\n",
+        "        TIME_SERIES_ID_COL = [\\'product_name\\'],\n",
+        "        MODEL_REGISTRY = \\'vertex_ai\\',\n",
+        "        VERTEX_AI_MODEL_ID = \\'order_demand_forecasting\\',\n",
+        "        VERTEX_AI_MODEL_VERSION_ALIASES = [\\'staging\\']\n",
        "        ) AS\n",
        "        SELECT\n",
        "          hourly_timestamp,\n",
@@ -1144,7 +1119,7 @@
        "            project=project,\n",
        "            location=location,\n",
        "        )\n",
-        "        .set_display_name(\"run arima+ model experiment\")\n",
+        "        .set_display_name(\"train arima plus model\")\n",
        "        .after(create_training_dataset_op)\n",
        "    )\n",
        "\n",
@@ -1153,12 +1128,12 @@
        "        BigqueryMLArimaEvaluateJobOp(\n",
        "            project=project,\n",
        "            location=location,\n",
-        "            model=bq_arima_model_exp_op.outputs[\"model\"],\n",
+        "            model=bq_arima_model_op.outputs[\"model\"],\n",
        "            show_all_candidate_models=False,\n",
        "            job_configuration_query=bq_evaluate_time_series_configuration,\n",
        "        )\n",
        "        .set_display_name(\"evaluate arima plus time series\")\n",
-        "        .after(bq_arima_model_exp_op)\n",
+        "        .after(bq_arima_model_op)\n",
        "    )\n",
        "\n",
        "    # Evaluate ARIMA Plus model\n",
@@ -1166,12 +1141,12 @@
        "        BigqueryEvaluateModelJobOp(\n",
        "            project=project,\n",
        "            location=location,\n",
-        "            model=bq_arima_model_exp_op.outputs[\"model\"],\n",
+        "            model=bq_arima_model_op.outputs[\"model\"],\n",
        "            query_statement=f\"\"\"SELECT * FROM `{project}.{bq_dataset}.{bq_training_table}` WHERE split='TEST'\"\"\",\n",
        "            job_configuration_query=bq_evaluate_model_configuration,\n",
        "        )\n",
        "        .set_display_name(\"evaluate arima plus model\")\n",
-        "        .after(bq_arima_model_exp_op)\n",
+        "        .after(bq_arima_model_op)\n",
        "    )\n",
        "\n",
        "    # Plot model metrics\n",
@@ -1189,34 +1164,6 @@
        "        < PERF_THRESHOLD,\n",
        "        name=\"avg. mae good\",\n",
        "    ):\n",
-        "        # Train the ARIMA PLUS model\n",
-        "        bq_arima_model_op = (\n",
-        "            BigqueryCreateModelJobOp(\n",
-        "                query=f\"\"\"\n",
-        "        -- create model table\n",
-        "        CREATE OR REPLACE MODEL `{project}.{bq_dataset}.{bq_model_table}`\n",
-        "        OPTIONS(\n",
-        "        MODEL_TYPE = \\'ARIMA_PLUS\\',\n",
-        "        TIME_SERIES_TIMESTAMP_COL = \\'hourly_timestamp\\',\n",
-        "        TIME_SERIES_DATA_COL = \\'total_sold\\',\n",
-        "        TIME_SERIES_ID_COL = [\\'product_name\\'],\n",
-        "        MODEL_REGISTRY = \\'vertex_ai\\',\n",
-        "        VERTEX_AI_MODEL_ID = \\'order_demand_forecasting\\',\n",
-        "        VERTEX_AI_MODEL_VERSION_ALIASES = [\\'staging\\']\n",
-        "        ) AS\n",
-        "        SELECT\n",
-        "          DATETIME_TRUNC(time_of_sale, HOUR) as hourly_timestamp,\n",
-        "          product_name,\n",
-        "          SUM(quantity) AS total_sold,\n",
-        "          FROM `{project}.{bq_dataset}.{bq_orders_table}`\n",
-        "        GROUP BY hourly_timestamp, product_name;\n",
-        "        \"\"\",\n",
-        "                project=project,\n",
-        "                location=location,\n",
-        "            )\n",
-        "            .set_display_name(\"train arima+ model\")\n",
-        "            .after(get_evaluation_model_metrics_op)\n",
-        "        )\n",
        "\n",
        "        # Generate the ARIMA PLUS forecasts\n",
        "        bq_arima_forecast_op = (\n",
@@ -1277,7 +1224,7 @@
      "source": [
        "### Execute your pipeline\n",
        "\n",
-        "Next, we execute the pipeline. It takes the following parameters which we set as default:\n",
+        "Next, you execute the pipeline. It takes the following parameters which we set as default:\n",
        "\n",
        "- `bq_dataset`: The BigQuery dataset to train on.\n",
        "- `bq_orders_table` : The BigQuery table of raw data.\n",
@@ -1319,7 +1266,7 @@
      "source": [
        "### View BigQuery ML training pipeline results\n",
        "\n",
-        "Finally, you view the artifact outputs of each task in the pipeline."
+        "Finally, you will view the artifact outputs of each task in the pipeline."
      ]
    },
    {
@@ -1395,8 +1342,8 @@
        "print(\"bigquery-ml-arima-evaluate-job\")\n",
        "artifacts = print_pipeline_output(bqml_pipeline, \"bigquery-ml-arima-evaluate-job\")\n",
        "print(\"\\n\\n\")\n",
-        "print(\"bigquery-evaluate-model-job\")\n",
-        "artifacts = print_pipeline_output(bqml_pipeline, \"bigquery-evaluate-model-job\")\n",
+        "print(\"get-model-evaluation-metrics\")\n",
+        "artifacts = print_pipeline_output(bqml_pipeline, \"get-model-evaluation-metrics\")\n",
        "print(\"\\n\\n\")\n",
        "print(\"bigquery-forecast-model-job\")\n",
        "artifacts = print_pipeline_output(bqml_pipeline, \"bigquery-forecast-model-job\")\n",
@@ -1460,8 +1407,7 @@
        "\n",
        "# Remove local resorces\n",
        "! rm -rf {KFP_COMPONENTS_PATH}\n",
-        "! rm -rf {PIPELINES_PATH}\n",
-        "! rm -rf {DATA_PATH}"
+        "! rm -rf {PIPELINES_PATH}"
      ]
    }
  ],
@@ -42,12 +42,12 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/pipelines/google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/natural_language/cloud_natural_language_pipeline.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/pipelines/google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/natural_language/cloud_natural_language_pipeline.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
@@ -328,7 +328,7 @@
      },
      "outputs": [],
      "source": [
-        "PROJECT_ID = \"your-project-id\"  # @param {type:\"string\"}\n",
+        "PROJECT_ID = \"cloud-ml-language-test\"  # @param {type:\"string\"}\n",
        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
        "    # Get your GCP project id from gcloud\n",
        "    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
@@ -368,7 +368,7 @@
      "source": [
        "REGION = \"us\"  # @param {type:\"string\"}\n",
        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
-        "TRAINING_DATA_LOCATION = \"gs://your-training-data-location\"  # @param {type:\"string\"}\n",
+        "TRAINING_DATA_LOCATION = \"gs://dougchen-20221130-pipeline-colab-test/data-00001-of-00001.jsonl\"  # @param {type:\"string\"}\n",
        "TASK_TYPE = \"CLASSIFICATION\"  # @param [\"CLASSIFICATION\", \"MULTILABEL_CLASSIFICATION\"]"
      ]
    },
@@ -740,7 +740,7 @@
        "\n",
        "This sends a create pipeline job request to Vertex Pipelines. Note that this  task run synchronously and may take a while to complete.\n",
        "\n",
-        "You may view the progress of the job at any time by clicking on the generated links (after \"View Pipeline Job\" in the console output of the cell below). Once the pipeline finishes, you may examine the artifacts produced from this pipeline."
+        "You may view the progress of the job at any time by clicking on the generated links (after \"View Pipeline Job\" in the console output of the cell below). Once the pipeline finishes, you may examine the artifacts produced from this pipeline. See "
      ]
    },
    {
@@ -42,12 +42,12 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/pipelines/google_cloud_pipeline_components_ready_to_go_text_classification_pipeline.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/community-content/ready_to_go_text_classification_pipeline/ready_to_go_text_classification_pipeline.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/pipelines/google_cloud_pipeline_components_ready_to_go_text_classification_pipeline.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/community-content/ready_to_go_text_classification_pipeline/ready_to_go_text_classification_pipeline.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
@@ -706,7 +706,7 @@
        ")\n",
        "\n",
        "# The GCS directory for keeping staging files for model evaluation.\n",
-        "ROOT_DIR = \"'f\\\"{BASE_OUTPUT_DIR}/root\\\"'\"  # @param {type:\"string\"}"
+        "ROOT_DIR = 'f\"{BASE_OUTPUT_DIR}/root\"'  # @param {type:\"string\"}"
      ]
    },
    {
@@ -30,7 +30,6 @@
 /automl/automl_forecasting_bqml_arima_plus_comparison.ipynb @TheMichaelHu
 /automl/automl_tabular_on_vertex_pipelines.ipynb @helinwang
 /custom/custom_training_tensorboard_profiler.ipynb @itseric
-/custom/get_started_with_vertex_endpoint_and_shared_vm.ipynb @andrewferlitsch @xingziye
 /workbench/spark/spark_sample_notebook.ipynb @bradmiro
 /workbench/spark/spark_ml.ipynb @bradmiro
 /model_registry/bqml_vertexai_model_registry.ipynb @soheilazangeneh
@@ -352,10 +352,7 @@
      },
      "outputs": [],
      "source": [
-        "EMAIL = \"[your-email-address]\"  # @param {type: \"string\"}\n",
-        "\n",
-        "if os.getenv(\"IS_TESTING\"):\n",
-        "    EMAIL = \"noreply@google.com\""
+        "EMAIL = \"[your-email-address]\"  # @param {type: \"string\"}"
      ]
    },
    {
@@ -1,18 +0,0 @@
-
-[Training, tuning and deploying a PyTorch stable diffusion model on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/stable_diffusion/dreambooth_stablediffusion.ipynb)
-
-```
-Learn to build, train, tune and deploy a stable diffusion 2.0 model using HuggingFace and PyTorch on
-[Vertex AI](https://cloud.google.com/vertex/).
-
-The steps performed include:
-
- Fine tune the stable diffusion 2.0 model with your own images.
- Upload the model artifacts to Google Cloud Storage.
- Check the created model artifacts.
- Upload the model in the PyTorch prebuilt container.
- Create an endpoint on Vertex AI.
- Deploy the model to the endpoint.
- Generate a new image.
-
-```
Author	SHA1	Message	Date
Andrew Ferlitsch	98277d0497	fix: review	2023-01-24 17:13:41 +00:00
Andrew Ferlitsch	cb5dbbb055	migration: experiments	2023-01-23 21:44:23 +00:00