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Reduces the max_steps parameter + Textual content updates and fixes (#2168)

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* fix: issue 2125

* removes duplicate parameters, reduces max_steps to 100, fixes grammar and updates realted to the writing guidelines

* removes f from the string parameter

* ran linter test

* sets max_steps to 20 and adds lines in the cleanup step to remove the pipeline jobs

* ran linter test

---------

Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>
This commit is contained in:
Krishna Chaithanya Movva
2023-08-10 20:49:01 +00:00
committed by GitHub
co-authored by Andrew Ferlitsch
parent 06926f8318
commit 07ec84687e
1 changed files with 122 additions and 115 deletions
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notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb
+122 -115
View File
@@ -53,6 +53,17 @@
"<br/><br/><br/>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "962e636b5cee"
},
"source": [
"**_NOTE_**: This notebook has been tested in the following environment:\n",
"\n",
"* Python version = 3.9"
]
},
{
"cell_type": "markdown",
"metadata": {
@@ -74,7 +85,7 @@
"source": [
"### Objective\n",
"\n",
"In this tutorial, you learn how to create two classification models using Vertex AI TabNet Tabular Workflows. Each workflow is a managed instance of [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).\n",
"In this tutorial, you learn how to create classification models on tabular data using two of the Vertex AI TabNet Tabular Workflows. Each workflow is a managed instance of [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).\n",
"\n",
"This tutorial uses the following Google Cloud ML services and resources:\n",
"\n",
@@ -98,8 +109,10 @@
"source": [
"### Dataset\n",
"\n",
"The dataset you will be using is [Bank Marketing](https://archive.ics.uci.edu/ml/datasets/bank+marketing).\n",
"The data is for direct marketing campaigns (phone calls) of a Portuguese banking institution. The binary classification goal is to predict if a client subscribe a term deposit. For this notebook, you randomly selected 90% of the rows in the original dataset and saved them in a train.csv file hosted on Cloud Storage. To download the file, click [here](https://storage.googleapis.com/cloud-samples-data-us-central1/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv)."
"The dataset you use in this notebook is the [Bank Marketing](https://archive.ics.uci.edu/ml/datasets/bank+marketing) dataset.\n",
"It consists of data related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The objective of the binary classification task in this notebook is to predict if a client subscribes to a term deposit or not. \n",
"\n",
"For this notebook, a subset of randomly selected rows that makes 90% of the original dataset was saved to `train.csv` file and hosted on Cloud Storage. To download the file, click [here](https://storage.googleapis.com/cloud-samples-data-us-central1/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv)."
]
},
{
@@ -141,7 +154,8 @@
},
"outputs": [],
"source": [
"! pip3 install --upgrade --quiet google-cloud-aiplatform google-cloud-pipeline-components"
"! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
" google-cloud-pipeline-components"
]
},
{
@@ -239,37 +253,6 @@
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"source": [
"#### UUID\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 uuid for each instance session, and append it onto the name of resources you create in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "84Vdv7R-QEH6"
},
"outputs": [],
"source": [
"import random\n",
"import string\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()"
]
},
{
"cell_type": "markdown",
"metadata": {
@@ -353,7 +336,7 @@
},
"outputs": [],
"source": [
"BUCKET_URI = \"gs://your-bucket-name-unique\" # @param {type:\"string\"}"
"BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\" # @param {type:\"string\"}"
]
},
{
@@ -373,7 +356,7 @@
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
"! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
]
},
{
@@ -382,7 +365,7 @@
"id": "zebLBGXOky2A"
},
"source": [
"## Notes about service account and permission\n",
"### Notes about service account and permission\n",
"\n",
"**By default no configuration is required**, if you run into any permission related issue, please make sure the service accounts have the required roles listed in the [Service accounts for Tabular Workflow for TabNet, and Tabular Workflow for Wide & Deep, and Prophet documentation](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/service-accounts#fte-workflow)."
]
@@ -467,7 +450,7 @@
"id": "fbbc3479a1da"
},
"source": [
"## Import libraries and define constants"
"### Import libraries"
]
},
{
@@ -480,11 +463,10 @@
"source": [
"# Import required modules\n",
"import os\n",
"import uuid\n",
"from typing import Any, Dict, List\n",
"\n",
"from google.cloud import aiplatform, storage\n",
"from google_cloud_pipeline_components.experimental.automl.tabular import \\\n",
"from google_cloud_pipeline_components.preview.automl.tabular import \\\n",
" utils as automl_tabular_utils"
]
},
@@ -494,7 +476,7 @@
"id": "c0423f260423"
},
"source": [
"## Initialize Vertex AI SDK for Python\n",
"### Initialize Vertex AI SDK for Python\n",
"\n",
"Initialize the Vertex AI SDK for Python for your project."
]
@@ -516,17 +498,17 @@
"id": "3LWH3PRF5o2v"
},
"source": [
"### Define helper functions\n",
"## Define helper functions\n",
"Define the following helper functions:\n",
"\n",
"- `get_model_artifacts_path`: Get the model artifacts path from task details.\n",
"- `get_model_uri`: Get the model uri from the task details..\n",
"- `get_bucket_name_and_path`: Get the bucket name and path.\n",
"- `download_from_gcs`: Download the content from the bucket.\n",
"- `write_to_gcs`: Upload content into the bucket.\n",
"- `get_task_detail`: Get the task details by using task name.\n",
"- `get_model_name`: Get the model name from pipeline job ID.\n",
"- `get_evaluation_metrics`: Get the evaluation metrics from pipeline task details.\n"
"- `get_model_artifacts_path`: Gets the model artifacts path from task details.\n",
"- `get_model_uri`: Gets the model uri from the task details.\n",
"- `get_bucket_name_and_path`: Gets the bucket name and path.\n",
"- `download_from_gcs`: Downloads the content from the bucket.\n",
"- `write_to_gcs`: Uploads content into the bucket.\n",
"- `get_task_detail`: Gets the task details by using task name.\n",
"- `get_model_name`: Gets the model name from pipeline job ID.\n",
"- `get_evaluation_metrics`: Gets the evaluation metrics from pipeline task details.\n"
]
},
{
@@ -538,6 +520,8 @@
"outputs": [],
"source": [
"# Get the model artifacts path from task details.\n",
"\n",
"\n",
"def get_model_artifacts_path(task_details: List[Dict[str, Any]], task_name: str) -> str:\n",
" task = get_task_detail(task_details, task_name)\n",
" return task.outputs[\"unmanaged_container_model\"].artifacts[0].uri\n",
@@ -604,27 +588,27 @@
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gvNFMRmBegZq"
},
"source": [
"## Define the training specification"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7a7332a3f8e2"
},
"source": [
"## Define training specifications\n",
"\n",
"Before creating the training job, you create the below steps in this section:\n",
"\n",
"1. Configure the source dataset.\n",
"2. Configure the feature transformation process.\n",
"3. Configure the feature selection process.\n",
"4. Set up the parameters needed for running the training process.\n",
"\n",
"### Configure the dataset\n",
"\n",
"You define either of the following parameters:\n",
"\n",
"- `data_source_csv_filenames`: The CSV data source.\n",
"- `data_source_bigquery_table_path`: The BigQuery data source.\n",
"- `data_source_csv_filenames`: The CSV data source. You specify the Cloud Storage path to the `train.csv` file described in the dataset section.\n",
"- `data_source_bigquery_table_path`: The BigQuery data source. As you use the Cloud Storage source, this is kept as none.\n",
"\n",
"***Notes***: Please note that the dataset's location has to be the same as the same as the service location (i.e., `REGION`) set for launching the training pipeline.\n"
]
@@ -653,18 +637,18 @@
"\n",
"Transformations can be specified using Feature Transform Engine (FTE) specific configurations. FTE supports both TensorFlow-based row-level and BigQuery-based dataset-level transformations.\n",
"\n",
"* TensorFlow-based row-level transformations:\n",
"* **TensorFlow-based row-level transformations**:\n",
" * Full automatic transformations: FTE automatically configures a set of built-in transformations for each input column based on its data statistics. This can be set via `tf_auto_transform_features` in the training pipeline.\n",
" * Fully specified transformations: All transformations on input columns are explicitly specified with FTE's built-in transformations. Chaining of multiple transformations on a single column is also supported. These transformations can be saved to JSON configuration file and specified via `tf_transformations_path` argument of the training pipeline.\n",
" * Custom transformations: Custom, bring-your-own transform function, where you can define and import your own transform function and use it with other FTE's built-in transformations. You can specify custom transformations as an array of JSON object and pass through the `tf_custom_transformation_definitions` argument of the training pipeline.\n",
"\n",
"* BigQuery-based dataset-level transformations:\n",
"* **BigQuery-based dataset-level transformations**:\n",
" * Fully specified transformations: All transformations on input columns are explicitly specified with FTE's built-in transformations. These transformations can be specified as an array of JSON objects via `dataset_level_transformations` argument of the training pipeline.\n",
" * Custom transformations: Custom, bring-your-own transform function, where you can define and import your own transform function and use it with other FTE's built-in transformations. You can specify custom transformations as an array of JSON object and pass through the `dataset_level_custom_transformation_definitions` argument of the training pipeline.\n",
"\n",
"Below, you configure full automatic transformations by specifying a list of input features to pass to the `tf_auto_transform_features` argument of the training pipeline.\n",
"\n",
"For a complete list of supported feature transformation configurations and examples, please go [here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.31/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.FeatureTransformEngineOp)."
"Learn more about [feature transformation configurations](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.31/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.FeatureTransformEngineOp)."
]
},
{
@@ -707,9 +691,9 @@
"\n",
"To enable it, you need to set `run_feature_selection` to True.\n",
"\n",
"To configure the algorihtm to use, and number of features to be selected, you need to configure both `feature_selection_algorithm` and `max_selected_features` parameter.\n",
"To configure the algorihtm to use, and number of features to be selected, you need to configure both `feature_selection_algorithm` and `max_selected_features` parameters.\n",
"\n",
"For a complete list of supported feature selection algorithms and configurations, please go [here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.31/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.FeatureTransformEngineOp)."
"Learn more about [feature selection algorithms and configurations](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.31/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.FeatureTransformEngineOp)."
]
},
{
@@ -735,7 +719,7 @@
"source": [
"### Setup training configuration\n",
"\n",
"You define the following:\n",
"Now, you define the following parameters for training:\n",
"\n",
"- `target_column`: The target column name.\n",
"- `prediction_type`: The type of prediction the model is to produce.\n",
@@ -769,9 +753,6 @@
"\n",
"timestamp_split_key = None # timestamp column name when using timestamp split\n",
"stratified_split_key = None # target column name when using stratified split\n",
"training_fraction = 0.8\n",
"validation_fraction = 0.1\n",
"test_fraction = 0.1\n",
"\n",
"predefined_split_key = None\n",
"if predefined_split_key:\n",
@@ -788,26 +769,26 @@
"id": "zyWGg2s09xOk"
},
"source": [
"## VPC related config\n",
"## Setup VPC configuration for Dataflow\n",
"\n",
"You define the following:\n",
"In this section, you define the following parameters:\n",
"\n",
"- `dataflow_subnetwork`: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example:\n",
"https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications\n",
"- `dataflow_subnetwork`: Dataflow's fully qualified subnetwork name, when empty the default subnetwork is used. See an [example](\n",
"https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications).\n",
"- `dataflow_use_public_ips`: Specifies whether Dataflow workers use public IP\n",
" addresses.\n",
"\n",
"If you need to use a custom Dataflow subnetwork, you can set it through the `dataflow_subnetwork` parameter. The requirements are:\n",
"1. `dataflow_subnetwork` must be fully qualified subnetwork name.\n",
"1. `dataflow_subnetwork` must be a fully qualified subnetwork name.\n",
" [[reference](https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications)]\n",
"1. The following service accounts must have [Compute Network User role](https://cloud.google.com/compute/docs/access/iam#compute.networkUser) assigned on the specified dataflow subnetwork [[reference](https://cloud.google.com/dataflow/docs/guides/specifying-networks#shared)]:\n",
" 1. Compute Engine default service account: PROJECT_NUMBER-compute@developer.gserviceaccount.com\n",
" 1. Dataflow service account: service-PROJECT_NUMBER@dataflow-service-producer-prod.iam.gserviceaccount.com\n",
"\n",
"If your project has VPC-SC enabled, please make sure:\n",
"If your project has VPC-SC enabled, please make sure of the following:\n",
"\n",
"1. The dataflow subnetwork used in VPC-SC is configured properly for Dataflow.\n",
" [[reference](https://cloud.google.com/dataflow/docs/guides/routes-firewall)]\n",
" See [reference](https://cloud.google.com/dataflow/docs/guides/routes-firewall).\n",
"1. `dataflow_use_public_ips` is set to False.\n"
]
},
@@ -831,17 +812,19 @@
"source": [
"## Customize TabNet CustomJob configuration and create pipeline\n",
"\n",
"This is best choice if you know exactly which hyperparameter values to use for model training. It uses fewer training resources than a HyperparameterTuningJob.\n",
"Creating a TabNet CustomJob is the best choice if you know exactly which hyperparameter values to use for model training. It uses fewer training resources than a HyperparameterTuningJob.\n",
"\n",
"In the example below, you configure the following:\n",
"In the example below, you configure the following key parameters:\n",
"\n",
"- `root_dir`: The root GCS directory for the pipeline components.\n",
"- `worker_pool_specs_override`: The dictionary for overriding training and evaluation worker pool specs. The dictionary should be of [this format]( https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172). TabNet supports both CPU and GPU training.\n",
"- `worker_pool_specs_override`: The dictionary for overriding training and evaluation worker pool specs. The dictionary should follow a [particular format]( https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172). TabNet supports training using both CPUs and GPUs.\n",
"- `learning_rate`: The learning rate used by the linear optimizer.\n",
"- `max_steps`: Number of steps to run the trainer for.\n",
"- `max_train_secs`: Amount of time in seconds to run the trainer for.\n",
"\n",
"A complete list of pipeline inputs and model hyperparameters is available [here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.23/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.utils.get_tabnet_trainer_pipeline_and_parameters)."
"Learn more about [pipeline inputs and model hyperparameters](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.23/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.utils.get_tabnet_trainer_pipeline_and_parameters).\n",
"\n",
"Learn more about the parameters needed for [creating a pipeline job](https://cloud.google.com/vertex-ai/docs/pipelines/run-pipeline#create_a_pipeline_run)."
]
},
{
@@ -852,20 +835,22 @@
},
"outputs": [],
"source": [
"# set a unique display name for your pipeline\n",
"pipeline_job_id = \"tabnet-unique\" # @param {type: \"string\"}\n",
"# set the root dir\n",
"pipeline_job_root_dir = os.path.join(BUCKET_URI, \"tabnet_custom_job\")\n",
"\n",
"# max_steps and/or max_train_secs must be set. If both are\n",
"# specified, training stop after either condition is met.\n",
"# By default, max_train_secs is set to -1.\n",
"\n",
"max_steps = 1000\n",
"max_train_secs = -1\n",
"\n",
"learning_rate = 0.01\n",
"\n",
"# set the worker pool specs\n",
"worker_pool_specs_override = [\n",
" {\"machine_spec\": {\"machine_type\": \"c2-standard-16\"}} # Override for TF chief node\n",
"]\n",
"# set the learning rate\n",
"learning_rate = 0.01\n",
"# max_steps and/or max_train_secs must be set. If both are\n",
"# specified, training stop after either condition is met.\n",
"# By default, max_train_secs is set to -1.\n",
"max_steps = 20\n",
"\n",
"max_train_secs = -1\n",
"\n",
"# To test GPU training, the worker_pool_specs_override can be specified like this.\n",
"# worker_pool_specs_override = [\n",
@@ -877,6 +862,7 @@
"# }\n",
"# ]\n",
"\n",
"# define the pipeline\n",
"# If your system does not use Python, you can save the JSON file (`template_path`),\n",
"# and use another programming language to submit the pipeline.\n",
"(\n",
@@ -906,10 +892,8 @@
" run_evaluation=run_evaluation,\n",
")\n",
"\n",
"pipeline_job_id = f\"tabnet-{uuid.uuid4()}\"\n",
"# More info on parameters PipelineJob accepts:\n",
"# https://cloud.google.com/vertex-ai/docs/pipelines/run-pipeline#create_a_pipeline_run\n",
"pipeline_job = aiplatform.PipelineJob(\n",
"# create the pipeline job\n",
"training_pipeline_job = aiplatform.PipelineJob(\n",
" display_name=pipeline_job_id,\n",
" template_path=template_path,\n",
" job_id=pipeline_job_id,\n",
@@ -918,7 +902,8 @@
" enable_caching=False,\n",
")\n",
"\n",
"pipeline_job.run(service_account=SERVICE_ACCOUNT)"
"# run the pipeline\n",
"training_pipeline_job.run(service_account=SERVICE_ACCOUNT)"
]
},
{
@@ -928,7 +913,8 @@
},
"source": [
"### Go to the Vertex Model UI\n",
"From the link below, you can deploy the model and test online prediction or run batch prediction."
"\n",
"Through the link generated from the below cell, you can deploy the model and run online prediction or batch prediction."
]
},
{
@@ -958,14 +944,20 @@
"source": [
"## Customize TabNet HyperparameterTuningJob configuration and create pipeline\n",
"\n",
"To get the best set of hyperparameters for your dataset, it is recommended to run a HyperparameterTuningJob.\n",
"To get the best set of hyperparameters on your dataset, it is recommended to run a HyperparameterTuningJob.\n",
"\n",
"Hyperparameters that can be tuned are set in the optional `study_spec_parameters_override` parameter. you provide a helper function called `get_tabnet_study_spec_parameters_override` to get these hyperparameters. You provide `dataset_size_bucket` (one of 'small' (< 1M rows), 'medium' (1M - 100M rows), or 'large' (> 100M rows)), `training_budget_bucket` (one of 'small' (< \\\\$600), 'medium' (\\\\$600 - \\\\$2400), or 'large' (> \\\\$2400)), and `prediction_type` and Vertex AI returns a list of hyperparameters and ranges. `study_spec_parameters_override` can be empty or one or more of these hyperparameters can be specified. For hyperparameters not specified in `study_spec_parameters_override`, you set ranges in the pipeline. For a full list of hyperparameters available for tuning, see [here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.23/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.utils.get_tabnet_trainer_pipeline_and_parameters).\n",
"Hyperparameters that can be tuned are set with the optional `study_spec_parameters_override` parameter. You provide a helper function named `get_tabnet_study_spec_parameters_override` to get these hyperparameters. To this helper function, you provide:\n",
"\n",
"In addition to hyperparameters, HyperparameterTuningJob takes the following values in the example below:\n",
"- `dataset_size_bucket`: one of 'small' (< 1M rows), 'medium' (1M - 100M rows), or 'large' (> 100M rows)).\n",
"- `training_budget_bucket`: one of 'small' (< \\\\$600), 'medium' (\\\\$600 - \\\\$2400), or 'large' (> \\\\$2400)).\n",
"- `prediction_type`: The type of prediction the model is to produce. “classification” or “regression”.\n",
"\n",
"Then, you get the list of hyperparameters and ranges. `study_spec_parameters_override` can be empty or one or more of the above hyperparameters can be specified. For hyperparameters not specified, you can set their ranges in the pipeline. Learn more about the [hyperparameters available for tuning](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.23/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.utils.get_tabnet_trainer_pipeline_and_parameters).\n",
"\n",
"In addition to hyperparameters, HyperparameterTuningJob takes the following values:\n",
"\n",
"- `root_dir`: The root GCS directory for the pipeline components.\n",
"- `worker_pool_specs_override`: The dictionary for overriding training and evaluation worker pool specs. The dictionary should be of [this format]( https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172). TabNet supports both CPU and GPU training.\n",
"- `worker_pool_specs_override`: The dictionary for overriding training and evaluation worker pool specs. The dictionary should follow a [particular format]( https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172). TabNet supports training using both CPUs and GPUs.\n",
"- `study_spec_metric_id`: Metric to optimize, possible values: ['loss', 'average_loss', 'rmse', 'mae', 'mql', 'accuracy', 'auc', 'precision', 'recall'].\n",
"- `study_spec_metric_goal`: Optimization goal of the metric, possible values: \"MAXIMIZE\", \"MINIMIZE\".\n",
"- `max_trial_count`: The desired total number of trials.\n",
@@ -974,9 +966,9 @@
"- `study_spec_algorithm`: The search algorithm specified for the study. One of\n",
"'ALGORITHM_UNSPECIFIED', 'GRID_SEARCH', or 'RANDOM_SEARCH'.\n",
"\n",
"For a full list of HyperparameterTuningJob parameters, see [here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.23/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.utils.get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters).\n",
"Learno more about the [HyperparameterTuningJob parameters](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.23/google_cloud_pipeline_components.experimental.automl.tabular.html#google_cloud_pipeline_components.experimental.automl.tabular.utils.get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters).\n",
"\n",
"Multiple trials can be configured. The pipeline returns the best trial based on the metric configured in `study_spec_metrics`. In the example below, you return the trial with the lowest loss value."
"Multiple trials can be configured. The pipeline returns the best trial based on the metric specified in `study_spec_metrics`. In the example below, you return the trial with the lowest loss value."
]
},
{
@@ -987,11 +979,18 @@
},
"outputs": [],
"source": [
"# set a unique display name for pipeline\n",
"pipeline_job_id = \"tabnet-hpt-unique\" # @param {type: \"string\"}\n",
"# set the root dir\n",
"pipeline_job_root_dir = os.path.join(BUCKET_URI, \"tabnet_hyperparameter_tuning_job\")\n",
"\n",
"# set the worker pool specs\n",
"worker_pool_specs_override = [\n",
" {\"machine_spec\": {\"machine_type\": \"c2-standard-16\"}} # Override for TF chief node\n",
"]\n",
"# set the metric\n",
"study_spec_metric_id = \"loss\"\n",
"# set the objective for metric\n",
"study_spec_metric_goal = \"MINIMIZE\"\n",
"\n",
"# To test GPU training, the worker_pool_specs_override can be specified like this.\n",
"# worker_pool_specs_override = [\n",
@@ -1004,9 +1003,8 @@
"# }\n",
"# ]\n",
"\n",
"study_spec_metric_id = \"loss\"\n",
"study_spec_metric_goal = \"MINIMIZE\"\n",
"\n",
"# define the component to get the hyperparameters\n",
"# max_steps and/or max_train_secs must be set. If both are\n",
"# specified, training stop after either condition is met.\n",
"# By default, max_train_secs is set to -1 and max_steps is set to\n",
@@ -1019,6 +1017,7 @@
" )\n",
")\n",
"\n",
"# define the hyperparameter tuning pipeline\n",
"# If your system does not use Python, you can save the JSON file (`template_path`),\n",
"# and use another programming language to submit the pipeline.\n",
"(\n",
@@ -1051,10 +1050,8 @@
" run_evaluation=True,\n",
")\n",
"\n",
"pipeline_job_id = f\"tabnet-hpt-{uuid.uuid4()}\"\n",
"# More info on parameters PipelineJob accepts:\n",
"# https://cloud.google.com/vertex-ai/docs/pipelines/run-pipeline#create_a_pipeline_run\n",
"pipeline_job = aiplatform.PipelineJob(\n",
"# create the pipeline job\n",
"tuning_pipeline_job = aiplatform.PipelineJob(\n",
" display_name=pipeline_job_id,\n",
" template_path=template_path,\n",
" job_id=pipeline_job_id,\n",
@@ -1063,7 +1060,8 @@
" enable_caching=False,\n",
")\n",
"\n",
"pipeline_job.run(service_account=SERVICE_ACCOUNT)"
"# run the pipeline job\n",
"tuning_pipeline_job.run(service_account=SERVICE_ACCOUNT)"
]
},
{
@@ -1073,7 +1071,8 @@
},
"source": [
"### Go to the Vertex Model UI\n",
"From the link below, you can deploy the model and test online prediction or run batch prediction."
"\n",
"Through the link generated from the below cell, you can deploy the model and run online prediction or batch prediction."
]
},
{
@@ -1111,9 +1110,11 @@
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"- Cloud Storage Bucket\n",
"- Pipeline from CustomJob pipeline\n",
"- Pipeline from HyperparameterTuningJob pipeline\n",
"- Model from CustomJob pipeline\n",
"- Model from HyperparameterTuningJob pipeline"
"- Model from HyperparameterTuningJob pipeline\n",
"- Cloud Storage Bucket (set `delete_bucket` to True to delete the bucket)"
]
},
{
@@ -1124,6 +1125,12 @@
},
"outputs": [],
"source": [
"# Delete the training pipeline job\n",
"training_pipeline_job.delete()\n",
"\n",
"# Delete the tuning pipeline job\n",
"tuning_pipeline_job.delete()\n",
"\n",
"# Delete model resources\n",
"custom_job_model = aiplatform.Model(CUSTOM_JOB_MODEL)\n",
"hpt_job_model = aiplatform.Model(HPT_JOB_MODEL)\n",