model_garden/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb

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Training a TensorFlow model on BigQuery data

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Overview

This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom tabular classification model for online prediction.

Learn more about Vertex AI Training.

Objective

In this notebook, you learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then get a prediction from the deployed model by sending data. Alternatively, you can create custom-trained models using gcloud command-line tool, or online using the Cloud Console.

This tutorial uses the following Google Cloud ML services and resources:

• BigQuery
• Cloud Storage
• Vertex AI managed Datasets
• Vertex AI Training
• Vertex AI Endpoints

The steps performed include:

• Create a Vertex AI custom TrainingPipeline for training a model.
• Train a TensorFlow model.
• Deploy the Model resource to a serving Endpoint resource.
• Make a prediction.
• Undeploy the Model resource.

Dataset

The dataset used for this tutorial is the penguins dataset from BigQuery public datasets. For this tutorial, you use only the fields culmen_length_mm, culmen_depth_mm, flipper_length_mm, body_mass_g from the dataset to predict the penguins species (species).

Costs

This tutorial uses billable components of Google Cloud:

• Vertex AI
• Cloud Storage
• BigQuery

Learn about Vertex AI pricing, Cloud Storage pricing, BigQuery pricing and use the Pricing Calculator to generate a cost estimate based on your projected usage.

Get Started

Install Vertex AI SDK for Python and other required packages

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# Install the packages
! pip3 install --upgrade --quiet google-cloud-aiplatform \
                        google-cloud-storage \
                        'google-cloud-bigquery[pandas]'

Restart runtime (Colab only)

To use the newly installed packages, you must restart the runtime on Google Colab.

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import sys

if "google.colab" in sys.modules:

    import IPython

    app = IPython.Application.instance()
    app.kernel.do_shutdown(True)

⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️

Authenticate your notebook environment (Colab only)

Authenticate your environment on Google Colab.

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import sys

if "google.colab" in sys.modules:

    from google.colab import auth

    auth.authenticate_user()

Set Google Cloud project information and initialize Vertex AI SDK for Python

To get started using Vertex AI, you must have an existing Google Cloud project and enable the Vertex AI API. Learn more about setting up a project and a development environment.

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PROJECT_ID = "[your-project-id]"  # @param {type:"string"}
LOCATION = "us-central1"  # @param {type:"string"}

Create a Cloud Storage bucket

Create a storage bucket to store intermediate artifacts such as datasets.

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BUCKET_URI = "gs://your-bucket-name-unique"  # @param {type:"string"}

Only if your bucket doesn't already exist: Run the following cell to create your Cloud Storage bucket.

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! gcloud storage buckets create --location=$LOCATION --project=$PROJECT_ID $BUCKET_URI

Import libraries

Import the Vertex AI Python SDK and other required Python libraries.

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import numpy as np
import pandas as pd
from google.cloud import aiplatform, bigquery

Initialize Vertex AI SDK for Python

Initialize the Vertex AI SDK for Python for your project and corresponding bucket.

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# Initialize the Vertex AI SDK for Python
aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)

Initialize BigQuery Client

Initialize the BigQuery Python client for your project.

To use BigQuery, make sure your account has the "BigQuery User" role.

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# Set up BigQuery client
bq_client = bigquery.Client(project=PROJECT_ID)

Preprocess data and split data

First you should download and preprocess your data for training and testing.

• Convert categorical features to numeric
• Remove unused columns
• Remove unusable rows
• Split train and test data

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LABEL_COLUMN = "species"

# Define the BigQuery source dataset
BQ_SOURCE = "bigquery-public-data.ml_datasets.penguins"

# Define NA values
NA_VALUES = ["NA", "."]

# Download a table
table = bq_client.get_table(BQ_SOURCE)
df = bq_client.list_rows(table).to_dataframe()

# Drop unusable rows
df = df.replace(to_replace=NA_VALUES, value=np.nan).dropna()

# Convert categorical columns to numeric
df["island"], _ = pd.factorize(df["island"])
df["species"], _ = pd.factorize(df["species"])
df["sex"], _ = pd.factorize(df["sex"])

# Split into a training and holdout dataset
df_train = df.sample(frac=0.8, random_state=100)
df_holdout = df[~df.index.isin(df_train.index)]

Create a Vertex AI Tabular dataset from BigQuery dataset

Create a Vertex AI tabular dataset resource from your BigQuery training data.

See more info here: https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets

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# Create BigQuery dataset
bq_dataset_id = f"{PROJECT_ID}.dataset_id_unique"
bq_dataset = bigquery.Dataset(bq_dataset_id)
bq_client.create_dataset(bq_dataset, exists_ok=True)

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dataset = aiplatform.TabularDataset.create_from_dataframe(
    df_source=df_train,
    staging_path=f"bq://{bq_dataset_id}.table-unique",
    display_name="sample-penguins",
)

Train a model

There are two ways you can train a model using a container image:

• Use a Vertex AI pre-built container. If you use a pre-built training container, you must additionally specify a Python package to install into the container image. This Python package contains your training code.

• Use your own custom container image. If you use your own container, the container image must contain your training code.

You will use a pre-built container for this demo.

Define the command args for the training script

Prepare the command-line arguments to pass to your training script.

• args: The command line arguments to pass to the corresponding Python module. In this example, they are:
  • label_column: The label column in your data to predict.
  • epochs: The number of epochs for training.
  • batch_size: The number of batch size for training.

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JOB_NAME = "custom_job_unique"

EPOCHS = 20
BATCH_SIZE = 10

CMDARGS = [
    "--label_column=" + LABEL_COLUMN,
    "--epochs=" + str(EPOCHS),
    "--batch_size=" + str(BATCH_SIZE),
]

Training script

In the next cell, write the contents of the training script, task.py. In summary, the script does the following:

• Loads the data from the BigQuery table using the BigQuery Python client library.
• Builds a model using TF.Keras model API.
• Compiles the model (compile()).
• Sets a training distribution strategy according to the argument args.distribute.
• Trains the model (fit()) with epochs and batch size according to the arguments args.epochs and args.batch_size
• Gets the directory where to save the model artifacts from the environment variable AIP_MODEL_DIR. This variable is set by the training service.
• Saves the trained model to the model directory.

NOTE: To improve model performance, it's recommended to normalize your inputs to the model before training. See the TensorFlow tutorial at https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers#numerical_columns for details.

NOTE: The following training code requires you to grant the training account the "BigQuery Read Session User" role. See "https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents for details on how to find this account.

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%%writefile task.py

import argparse
import numpy as np
import os

import pandas as pd
import tensorflow as tf

from google.cloud import bigquery
from google.cloud import storage

# Read environmental variables
training_data_uri = os.getenv("AIP_TRAINING_DATA_URI")
validation_data_uri = os.getenv("AIP_VALIDATION_DATA_URI")
test_data_uri = os.getenv("AIP_TEST_DATA_URI")

# Read args
parser = argparse.ArgumentParser()
parser.add_argument('--label_column', required=True, type=str)
parser.add_argument('--epochs', default=10, type=int)
parser.add_argument('--batch_size', default=10, type=int)
args = parser.parse_args()

# Set up training variables
LABEL_COLUMN = args.label_column

# See https://cloud.google.com/vertex-ai/docs/workbench/managed/executor#explicit-project-selection for issues regarding permissions.
PROJECT_NUMBER = os.environ["CLOUD_ML_PROJECT_ID"]
bq_client = bigquery.Client(project=PROJECT_NUMBER)


# Download a table
def download_table(bq_table_uri: str):
    # Remove bq:// prefix if present
    prefix = "bq://"
    if bq_table_uri.startswith(prefix):
        bq_table_uri = bq_table_uri[len(prefix) :]
        
    # Download the BigQuery table as a dataframe
    # This requires the "BigQuery Read Session User" role on the custom training service account.
    table = bq_client.get_table(bq_table_uri)
    return bq_client.list_rows(table).to_dataframe()

# Download dataset splits
df_train = download_table(training_data_uri)
df_validation = download_table(validation_data_uri)
df_test = download_table(test_data_uri)

def convert_dataframe_to_dataset(
    df_train: pd.DataFrame,
    df_validation: pd.DataFrame,
):
    df_train_x, df_train_y = df_train, df_train.pop(LABEL_COLUMN)
    df_validation_x, df_validation_y = df_validation, df_validation.pop(LABEL_COLUMN)

    y_train = tf.convert_to_tensor(np.asarray(df_train_y).astype("float32"))
    y_validation = tf.convert_to_tensor(np.asarray(df_validation_y).astype("float32"))

    # Convert to numpy representation
    x_train = tf.convert_to_tensor(np.asarray(df_train_x).astype("float32"))
    x_test = tf.convert_to_tensor(np.asarray(df_validation_x).astype("float32"))

    # Convert to one-hot representation
    num_species = len(df_train_y.unique())
    y_train = tf.keras.utils.to_categorical(y_train, num_classes=num_species)
    y_validation = tf.keras.utils.to_categorical(y_validation, num_classes=num_species)

    dataset_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset_validation = tf.data.Dataset.from_tensor_slices((x_test, y_validation))
    return (dataset_train, dataset_validation)

# Create datasets
dataset_train, dataset_validation = convert_dataframe_to_dataset(df_train, df_validation)

# Shuffle train set
dataset_train = dataset_train.shuffle(len(df_train))

def create_model(num_features):
    # Create model
    Dense = tf.keras.layers.Dense
    model = tf.keras.Sequential(
        [
            Dense(
                100,
                activation=tf.nn.relu,
                kernel_initializer="uniform",
                input_dim=num_features,
            ),
            Dense(75, activation=tf.nn.relu),
            Dense(50, activation=tf.nn.relu),            
            Dense(25, activation=tf.nn.relu),
            Dense(3, activation=tf.nn.softmax),
        ]
    )
    
    # Compile Keras model
    optimizer = tf.keras.optimizers.RMSprop(lr=0.001)
    model.compile(
        loss="categorical_crossentropy", metrics=["accuracy"], optimizer=optimizer
    )
    
    return model

# Create the model
model = create_model(num_features=dataset_train._flat_shapes[0].dims[0].value)

# Set up datasets
dataset_train = dataset_train.batch(args.batch_size)
dataset_validation = dataset_validation.batch(args.batch_size)

# Train the model
model.fit(dataset_train, epochs=args.epochs, validation_data=dataset_validation)

tf.saved_model.save(model, os.getenv("AIP_MODEL_DIR"))

Train the model

Define your custom TrainingPipeline on Vertex AI.

Use the CustomTrainingJob class to define the TrainingPipeline. The class takes the following parameters:

• display_name: The user-defined name of this training pipeline.
• script_path: The local path to the training script.
• container_uri: The URI of the training container image.
• requirements: The list of Python package dependencies of the script.
• model_serving_container_image_uri: The URI of a container that can serve predictions for your model — either a pre-built container or a custom container.

Use the run function to start training. The function takes the following parameters:

• args: The command line arguments to be passed to the Python script.
• replica_count: The number of worker replicas.
• model_display_name: The display name of the Model if the script produces a managed Model.
• machine_type: The type of machine to use for training.
• accelerator_type: The hardware accelerator type.
• accelerator_count: The number of accelerators to attach to a worker replica.

The run function creates a training pipeline that trains and creates a Model object. After the training pipeline completes, the run function returns the Model object.

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job = aiplatform.CustomTrainingJob(
    display_name=JOB_NAME,
    script_path="task.py",
    container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest",
    requirements=["google-cloud-bigquery[pandas]", "protobuf<3.20.0"],
    model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
)

MODEL_DISPLAY_NAME = "penguins_model_unique"

# Start the training
model = job.run(
    dataset=dataset,
    model_display_name=MODEL_DISPLAY_NAME,
    bigquery_destination=f"bq://{PROJECT_ID}",
    args=CMDARGS,
)

Deploy the model

Before you use your model to make predictions, you must deploy it to an Endpoint. You can do this by calling the deploy function on the Model resource. This does two things:

1. Create an Endpoint resource for deploying the Model resource to.
2. Deploy the Model resource to the Endpoint resource.

The function takes the following parameters:

• deployed_model_display_name: A human readable name for the deployed model.
• traffic_split: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.
  • If only one model, then specify { "0": 100 }, where "0" refers to this model being uploaded and 100 means 100% of the traffic.
  • If there are existing models on the endpoint, for which the traffic is split, then use model_id to specify { "0": percent, model_id: percent, ... }, where model_id is the ID of an existing DeployedModel on the endpoint. The percentages must add up to 100.
• machine_type: The type of machine to use for training.
• accelerator_type: The hardware accelerator type.
• accelerator_count: The number of accelerators to attach to a worker replica.
• starting_replica_count: The number of compute instances to initially provision.
• max_replica_count: The maximum number of compute instances to scale to. In this tutorial, only one instance is provisioned.

Traffic split

The traffic_split parameter is specified as a Python dictionary. You can deploy more than one instance of your model to an endpoint, and then set the percentage of traffic that goes to each instance.

You can use a traffic split to introduce a new model gradually into production. For example, if you had one existing model in production with 100% of the traffic, you could deploy a new model to the same endpoint, direct 10% of traffic to it, and reduce the original model's traffic to 90%. This allows you to monitor the new model's performance while minimizing the distruption to the majority of users.

Compute instance scaling

You can specify a single instance (or node) to serve your online prediction requests. This tutorial uses a single node, so the variables MIN_NODES and MAX_NODES are both set to 1.

If you want to use multiple nodes to serve your online prediction requests, set MAX_NODES to the maximum number of nodes you want to use. Vertex AI autoscales the number of nodes used to serve your predictions, up to the maximum number you set. Refer to the pricing page to understand the costs of autoscaling with multiple nodes.

Endpoint

The deploy method waits until the model is deployed and eventually returns an Endpoint object. If this is the first time a model is deployed to the endpoint, it may take a few additional minutes to complete provisioning of resources.

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DEPLOYED_NAME = "penguins_deployed_unique"

endpoint = model.deploy(deployed_model_display_name=DEPLOYED_NAME)

Make an online prediction request

Send an online prediction request to your deployed model.

Prepare test data

Prepare test data by convert it to a Python list

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df_holdout_y = df_holdout.pop(LABEL_COLUMN)
df_holdout_x = df_holdout

# Convert to list representation
holdout_x = np.array(df_holdout_x).tolist()
holdout_y = np.array(df_holdout_y).astype("float32").tolist()

Send the prediction request

Now that you have test data, you can use it to send a prediction request. Use the Endpoint object's predict function, which takes the following parameters:

• instances: A list of penguin measurement instances. According to your custom model, each instance should be an array of numbers. You prepared this list in the previous step.

The predict function returns a list, where each element in the list corresponds to the an instance in the request. In the output for each prediction, you see the following:

• Confidence level for the prediction (predictions), between 0 and 1, for each of the ten classes.

You can then run a quick evaluation on the prediction results:

1. np.argmax: Convert each list of confidence levels to a label
2. Print predictions

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predictions = endpoint.predict(instances=holdout_x)
y_predicted = np.argmax(predictions.predictions, axis=1)

y_predicted

Undeploy models

To undeploy all Model resources from the serving Endpoint resource, use the endpoint's undeploy_all method.

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endpoint.undeploy_all()

Cleaning up

To clean up all Google Cloud resources used in this project, you can delete the Google Cloud project you used for the tutorial.

Otherwise, you can delete the individual resources you created in this tutorial:

• Training Job
• Model
• Endpoint
• Cloud Storage Bucket

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# Delete the training job
job.delete()

# Delete the model
model.delete()

# Delete the endpoint
endpoint.delete()

# Warning: Setting this to true deletes everything in your bucket
delete_bucket = True

if delete_bucket:
    ! gcloud storage rm --recursive $BUCKET_URI