Overview

In this lab, you learn how to use Vertex AI to train and serve a TensorFlow model using code in a custom container.

While you're using TensorFlow for the model code here, you could easily replace it with another framework.

Learning objectives

• Build and containerize model training code in Vertex Notebooks.
• Submit a custom model training job to Vertex AI.
• Deploy your trained model to an endpoint, and use that endpoint to get predictions.

Introduction to Vertex AI

This lab uses the newest AI product offering available on Google Cloud. Vertex AI integrates the ML offerings across Google Cloud into a seamless development experience. Previously, models trained with AutoML and custom models were accessible via separate services. The new offering combines both into a single API, along with other new products. You can also migrate existing projects to Vertex AI. If you have any feedback, please see the support page.

Vertex AI includes many different products to support end-to-end ML workflows. This lab will focus on the products highlighted below: Training, Prediction, and Notebooks.

Task 1. Set up your environment

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Enable the Compute Engine API

Navigate to Compute Engine API and select Enable if it isn't already enabled. You'll need this to create your Workbench instance.

Enable the Container Registry API

Navigate to the Container Registry and select Enable if it isn't already enabled. You'll use this to create a container for your custom training job.

Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click OPEN JUPYTERLAB next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 2. Containerize training code

You are going to submit this training job to Vertex by putting your training code in a Docker container and pushing this container to Google Container Registry. Using this approach, you can train a model built with any framework.

To start, from the Launcher menu, open a terminal window in your Workbench instance.

Create a new directory called mpg and cd into it:

mkdir mpg cd mpg

Create a Dockerfile

Your first step in containerizing your code is to create a Dockerfile. In your Dockerfile you'll include all the commands needed to run your image. It'll install all the libraries you're using and set up the entry point for your training code. From your terminal, create an empty Dockerfile:

touch Dockerfile

Open the Dockerfile by navigating to mpg > Dockerfile and copy the following into it:

FROM gcr.io/deeplearning-platform-release/tf2-cpu.2-3 WORKDIR /root WORKDIR / # Copies the trainer code to the docker image. COPY trainer /trainer # Sets up the entry point to invoke the trainer. ENTRYPOINT ["python", "-m", "trainer.train"]

This Dockerfile uses the Deep Learning Container TensorFlow Enterprise 2.3 Docker image. The Deep Learning Containers on Google Cloud come with many common ML and data science frameworks pre-installed. The one you're using includes TF Enterprise 2.3, Pandas, Scikit-learn, and others. After downloading that image, this Dockerfile sets up the entrypoint for your training code. You haven't created these files yet--in the next step, you'll add the code for training and exporting your model.

Create a Cloud Storage bucket

In your training job, you'll export your trained TensorFlow model to a Cloud Storage Bucket. Vertex will use this to read your exported model assets and deploy the model. From your terminal, run the following to define an env variable for your project, making sure to replace your-cloud-project with the ID of your project:

Note: You can get your project ID by running gcloud config list --format 'value(core.project)' in your terminal. PROJECT_ID='your-cloud-project'

Next, run the following in your terminal to create a new bucket in your project. The -l (location) flag is important since this needs to be in the same region where you'll deploy a model endpoint later in the tutorial:

BUCKET_NAME="gs://${PROJECT_ID}-bucket" gsutil mb -l {{{ project_0.default_region | "REGION" }}} $BUCKET_NAME

Add model training code

From your terminal, run the following to create a directory for your training code and a Python file where you'll add the code:

mkdir trainer touch trainer/train.py

You should now have the following in your mpg/ directory:

+ Dockerfile + trainer/ + train.py

Next, open the train.py file you just created by navigating to mpg > trainer > train.py and copy the code below (this is adapted from the tutorial in the TensorFlow docs).

At the beginning of the file, update the BUCKET variable with the name of the Storage Bucket you created in the previous step:

import numpy as np import pandas as pd import pathlib import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers print(tf.__version__) """## The Auto MPG dataset The dataset is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/). ### Get the data First download the dataset. """ dataset_path = keras.utils.get_file("auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data") dataset_path """Import it using pandas""" column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight', 'Acceleration', 'Model Year', 'Origin'] dataset = pd.read_csv(dataset_path, names=column_names, na_values = "?", comment='\t', sep=" ", skipinitialspace=True) dataset.tail() # TODO: replace `your-gcs-bucket` with the name of the Storage bucket you created earlier BUCKET = 'gs://your-gcs-bucket' """### Clean the data The dataset contains a few unknown values. """ dataset.isna().sum() """To keep this initial tutorial simple, drop those rows.""" dataset = dataset.dropna() """The `"Origin"` column is really categorical, not numeric. So convert that to a one-hot:""" dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'}) dataset = pd.get_dummies(dataset, prefix='', prefix_sep='') dataset.tail() """### Split the data into train and test Now split the dataset into a training set and a test set. You will use the test set in the final evaluation of your model. """ train_dataset = dataset.sample(frac=0.8,random_state=0) test_dataset = dataset.drop(train_dataset.index) """### Inspect the data Have a quick look at the joint distribution of a few pairs of columns from the training set. Also look at the overall statistics: """ train_stats = train_dataset.describe() train_stats.pop("MPG") train_stats = train_stats.transpose() train_stats """### Split features from labels Separate the target value, or "label", from the features. This label is the value that you will train the model to predict. """ train_labels = train_dataset.pop('MPG') test_labels = test_dataset.pop('MPG') """### Normalize the data Look again at the `train_stats` block above and note how different the ranges of each feature are. It is good practice to normalize features that use different scales and ranges. Although the model *might* converge without feature normalization, it makes training more difficult, and it makes the resulting model dependent on the choice of units used in the input. Note: Although we intentionally generate these statistics from only the training dataset, these statistics will also be used to normalize the test dataset. We need to do that to project the test dataset into the same distribution that the model has been trained on. """ def norm(x): return (x - train_stats['mean']) / train_stats['std'] normed_train_data = norm(train_dataset) normed_test_data = norm(test_dataset) """This normalized data is what we will use to train the model. Caution: The statistics used to normalize the inputs here (mean and standard deviation) need to be applied to any other data that is fed to the model, along with the one-hot encoding that we did earlier. That includes the test set as well as live data when the model is used in production. ## The model ### Build the model Let's build our model. Here, we'll use a `Sequential` model with two densely connected hidden layers, and an output layer that returns a single, continuous value. The model building steps are wrapped in a function, `build_model`, since we'll create a second model later on. """ def build_model(): model = keras.Sequential([ layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]), layers.Dense(64, activation='relu'), layers.Dense(1) ]) optimizer = tf.keras.optimizers.RMSprop(0.001) model.compile(loss='mse', optimizer=optimizer, metrics=['mae', 'mse']) return model model = build_model() """### Inspect the model Use the `.summary` method to print a simple description of the model """ model.summary() """Now try out the model. Take a batch of `10` examples from the training data and call `model.predict` on it. It seems to be working, and it produces a result of the expected shape and type. ### Train the model Train the model for 1000 epochs, and record the training and validation accuracy in the `history` object. Visualize the model's training progress using the stats stored in the `history` object. This graph shows little improvement, or even degradation, in the validation error after about 100 epochs. Let's update the `model.fit` call to automatically stop training when the validation score doesn't improve. We'll use an *EarlyStopping callback* that tests a training condition for every epoch. If a set amount of epochs elapses without showing improvement, then it will automatically stop the training. You can learn more about this callback [here](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping). """ model = build_model() EPOCHS = 1000 # The patience parameter is the amount of epochs to check for improvement early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10) early_history = model.fit(normed_train_data, train_labels, epochs=EPOCHS, validation_split = 0.2, callbacks=[early_stop]) # Export model and save to GCS model.save(BUCKET + '/mpg/model')

Build and test the container locally

From your terminal, define a variable with the URI of your container image in Google Container Registry:

IMAGE_URI="gcr.io/$PROJECT_ID/mpg:v1"

Then, build the container by running the following from the root of your mpg directory:

docker build ./ -t $IMAGE_URI

Run the container within your Workbench instance to ensure it's working correctly:

docker run $IMAGE_URI

The model should finish training in 1-2 minutes with a validation accuracy around 72% (exact accuracy may vary). When you've finished running the container locally, push it to Google Container Registry:

docker push $IMAGE_URI

With our container pushed to Container Registry, you're now ready to kick off a custom model training job.

Task 3. Run a training job on Vertex AI

Vertex AI gives you two options for training models:

• AutoML: Train high-quality models with minimal effort and ML expertise.
• Custom training: Run your custom training applications in the cloud using one of Google Cloud's pre-built containers, or use your own.

In this lab, you're using custom training via our own custom container on Google Container Registry. To start, navigate to the Model Registry section in the Vertex section of your Cloud console:

Kick off the training job

Click Create to enter the parameters for your training job and deployed model:

• For Dataset, select No managed dataset.
• Then select Custom training (advanced) as your training method and click Continue.
• Select Train new model and Enter mpg (or whatever you'd like to call your model) for Model name.
• Click Continue.

In the Container settings step, select Custom container:

In the first box (Container image on GCR), enter the value of your IMAGE_URI variable above. It should be: gcr.io/your-cloud-project/mpg:v1, with your own project name. Leave the rest of the fields blank and click Continue.

You won't use hyperparameter tuning in this tutorial, so leave the Enable hyperparameter tuning box unchecked and click Continue.

In Compute and pricing, select the Region and select Deploy to new worker pool and for machine type select e2-standard-4 and click Continue.

Because the model in this demo trains quickly, you're using a smaller machine type.

Note: You are welcome to experiment with larger machine types and GPUs if you'd like. If you use GPUs, you'll need to use a GPU-enabled base container image.

For the Prediction container step, select Pre-built container and select 2.11 as the Model framework version.

Leave the default settings for the pre-built container as is. For model directory, browse your GCS bucket with the mpg subdirectory. This is the path in your model training script where you export your trained model. It should look like this:

Vertex will look in this location when it deploys your model. Now you're ready for training! Click Start training to kick off the training job. In the Training section of your console, select Region and you'll see something like this:

Note: The training job will take about 15-20 minutes to complete.

Task 4. Deploy a model endpoint

When you set up your training job, you specified where Vertex AI should look for your exported model assets. As part of our training pipeline, Vertex will create a model resource based on this asset path. The model resource itself isn't a deployed model, but once you have a model you're ready to deploy it to an endpoint. To learn more about models and endpoints in Vertex AI, check out the Get started documentation for Vertex AI.

In this step you'll create an endpoint for our trained model. You can use this to get predictions on our model via the Vertex AI API.

Deploy endpoint

When your training job completes, you should see a model named mpg (or whatever you named it) in the Model Registry section of your console:

When your training job ran, Vertex created a model resource for you. In order to use this model, you need to deploy an endpoint. You can have many endpoints per model. Click on the model and select Deploy & Test tab and then click Deploy to endpoint.

Select Create new endpoint and give it a name, like v1, and click Continue. Leave Traffic split at 100 and enter 1 for Minimum number of compute nodes. Under Machine type, select e2-standard-2 (or any machine type you'd like). Then click Done and then Deploy.

Deploying the endpoint will take 10-15 minutes. When the endpoint has finished deploying, you'll see the following, which shows one endpoint deployed under your model resource:

Note: Bucket region and the region used to deploy the model endpoint need to be the same.

Get predictions on the deployed model

You'll get predictions on our trained model from a Python notebook, using the Vertex Python API. Go back to your Workbench instance, and create a Python 3 notebook from the Launcher:

In your notebook, run the following in a cell to install the Vertex AI SDK:

!pip3 install google-cloud-aiplatform --upgrade --user

Then add a cell in your notebook to import the SDK and create a reference to the endpoint you just deployed:

from google.cloud import aiplatform endpoint = aiplatform.Endpoint( endpoint_name="projects/YOUR-PROJECT-NUMBER/locations/{{{ project_0.default_region | REGION }}}/endpoints/YOUR-ENDPOINT-ID" )

You'll need to replace two values in the endpoint_name string above with your project number and endpoint. You can find your project number by navigating to your project dashboard and getting the Project Number value.

You can find your endpoint ID in the endpoints section of the console here:

Finally, make a prediction to your endpoint by copying and running the code below in a new cell:

test_mpg = [1.4838871833555929, 1.8659883497083019, 2.234620276849616, 1.0187816540094903, -2.530890710602246, -1.6046416850441676, -0.4651483719733302, -0.4952254087173721, 0.7746763768735953] response = endpoint.predict([test_mpg]) print('API response: ', response) print('Predicted MPG: ', response.predictions[0][0])

This example already has normalized values, which is the format our model is expecting.

Run this cell, and you should see a prediction output around 16 miles per gallon.

Task 5. Cleanup

1. Do one of the following:

   • To continue using the notebook you created in this lab, it is recommended that you turn it off when not in use. From the Notebooks UI in the Cloud Console, select the notebook and then click Stop.
   • To delete the notebook entirely, click Delete.

2. To delete the endpoint you deployed, in the Online Prediction section of your Vertex AI console, select Region from the drop down and click v1. Now, click on three dots icon next to your endpoint and select Undeploy model from endpoint. Finally, click Undeploy.

3. To remove the endpoint, navigate back to Endpoints section and click the Actions icon , and then click Delete endpoint.

4. To delete the Cloud Storage bucket, on the Cloud Storage page, select your bucket, and then click Delete.

Congratulations!

You've learned how to use Vertex AI to:

• Train a model by providing the training code in a custom container. You used a TensorFlow model in this example, but you can train a model built with any framework using custom containers.
• Deploy a TensorFlow model using a pre-built container as part of the same workflow you used for training.
• Create a model endpoint and generate a prediction.

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