model_garden/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb

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Custom training and batch prediction

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Overview

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

Learn more about Custom training and Vertex AI Batch Prediction.

Objective

In this tutorial, you learn to use Vertex AI Training to create a custom trained model and use Vertex AI Batch Prediction to do a batch prediction on the trained model.

Create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then do a prediction on 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:

• Vertex AI Training
• Vertex AI Batch Prediction
• Vertex AI Model resource

The steps performed include:

• Create a Vertex AI custom job for training a TensorFlow model.
• Upload the trained model artifacts as a model resource.
• Make a batch prediction.

Dataset

The dataset used for this tutorial is the cifar10 dataset from TensorFlow Datasets. The version of the dataset you will use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.

Costs

This tutorial uses billable components of Google Cloud (GCP):

• Vertex AI
• Cloud Storage

Learn about Vertex AI pricing and Cloud Storage 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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! pip3 install --upgrade google-cloud-aiplatform \
                        google-cloud-storage \
                        pillow  \
                        numpy    

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

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

Initialize Vertex AI SDK for Python

Initialize the Vertex AI SDK for Python for your project.

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from google.cloud import aiplatform

aiplatform.init(project=PROJECT_ID, location=LOCATION, staging_bucket=BUCKET_URI)

Import libraries and define constants

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import os

Set pre-built containers

Vertex AI provides pre-built containers to run training and prediction.

For the latest list, see Pre-built containers for training and Pre-built containers for prediction

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TRAIN_VERSION = "tf-cpu.2-9"
DEPLOY_VERSION = "tf2-cpu.2-9"

TRAIN_IMAGE = "us-docker.pkg.dev/vertex-ai/training/{}:latest".format(TRAIN_VERSION)
DEPLOY_IMAGE = "us-docker.pkg.dev/vertex-ai/prediction/{}:latest".format(DEPLOY_VERSION)

Tutorial

Now you are ready to start creating your own custom-trained model with CIFAR10.

Train a model

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

• Use a Google Cloud prebuilt container. If you use a prebuilt container, you will additionally specify a Python package to install into the container image. This Python package contains your code for training a custom model.

• Use your own custom container image. If you use your own container, the container needs to contain your code for training a custom model.

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 will be:
  • "--epochs=" + EPOCHS: The number of epochs for training.
  • "--steps=" + STEPS: The number of steps (batches) per epoch.
  • "--distribute=" + TRAIN_STRATEGY" : The training distribution strategy to use for single or distributed training.
    • "single": single device.
    • "mirror": all GPU devices on a single compute instance.
    • "multi": all GPU devices on all compute instances.

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JOB_NAME = "custom_job_unique"
MODEL_DIR = "{}/{}".format(BUCKET_URI, JOB_NAME)


TRAIN_STRATEGY = "single"

EPOCHS = 20
STEPS = 100

CMDARGS = [
    "--epochs=" + str(EPOCHS),
    "--steps=" + str(STEPS),
    "--distribute=" + TRAIN_STRATEGY,
]

Training script

In the next cell, you will write the contents of the training script, task.py. In summary:

• Get the directory where to save the model artifacts from the environment variable AIP_MODEL_DIR. This variable is set by the training service.
• Loads CIFAR10 dataset from TF Datasets (tfds).
• 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 steps according to the arguments args.epochs and args.steps
• Saves the trained model (save(MODEL_DIR)) to the specified model directory.

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%%writefile task.py
# Single, Mirror and Multi-Machine Distributed Training for CIFAR-10

import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow.python.client import device_lib
import argparse
import os
import sys
tfds.disable_progress_bar()

parser = argparse.ArgumentParser()
parser.add_argument('--lr', dest='lr',
                    default=0.01, type=float,
                    help='Learning rate.')
parser.add_argument('--epochs', dest='epochs',
                    default=10, type=int,
                    help='Number of epochs.')
parser.add_argument('--steps', dest='steps',
                    default=200, type=int,
                    help='Number of steps per epoch.')
parser.add_argument('--distribute', dest='distribute', type=str, default='single',
                    help='distributed training strategy')
args = parser.parse_args()

print('Python Version = {}'.format(sys.version))
print('TensorFlow Version = {}'.format(tf.__version__))
print('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))
print('DEVICES', device_lib.list_local_devices())

# Single Machine, single compute device
if args.distribute == 'single':
    if tf.test.is_gpu_available():
        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
    else:
        strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
# Single Machine, multiple compute device
elif args.distribute == 'mirror':
    strategy = tf.distribute.MirroredStrategy()
# Multiple Machine, multiple compute device
elif args.distribute == 'multi':
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()

# Multi-worker configuration
print('num_replicas_in_sync = {}'.format(strategy.num_replicas_in_sync))

# Preparing dataset
BUFFER_SIZE = 10000
BATCH_SIZE = 64

def make_datasets_unbatched():
  # Scaling CIFAR10 data from (0, 255] to (0., 1.]
  def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255.0
    return image, label

  datasets, info = tfds.load(name='cifar10',
                            with_info=True,
                            as_supervised=True)
  return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE).repeat()


# Build the Keras model
def build_and_compile_cnn_model():
  model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(32, 32, 3)),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Conv2D(32, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(10, activation='softmax')
  ])
  model.compile(
      loss=tf.keras.losses.sparse_categorical_crossentropy,
      optimizer=tf.keras.optimizers.SGD(learning_rate=args.lr),
      metrics=['accuracy'])
  return model

# Train the model
NUM_WORKERS = strategy.num_replicas_in_sync
# Here the batch size scales up by number of workers since
# `tf.data.Dataset.batch` expects the global batch size.
GLOBAL_BATCH_SIZE = BATCH_SIZE * NUM_WORKERS
MODEL_DIR = os.getenv("AIP_MODEL_DIR")

train_dataset = make_datasets_unbatched().batch(GLOBAL_BATCH_SIZE)

with strategy.scope():
  # Creation of dataset, and model building/compiling need to be within
  # `strategy.scope()`.
  model = build_and_compile_cnn_model()

model.fit(x=train_dataset, epochs=args.epochs, steps_per_epoch=args.steps)
model.save(MODEL_DIR)

Train the model

Define your custom training job on Vertex AI.

Use the CustomTrainingJob class to define the job, which 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 prebuilt container or a custom container.

Use the run function to start training, which 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=TRAIN_IMAGE,
    requirements=["tensorflow_datasets==1.3.0"],
    model_serving_container_image_uri=DEPLOY_IMAGE,
)

MODEL_DISPLAY_NAME = "model_unique"

# Start the training

model = job.run(
    model_display_name=MODEL_DISPLAY_NAME,
    args=CMDARGS,
    replica_count=1,
)

Make a batch prediction request

Send a batch prediction request to your deployed model.

Get test data

Download images from the CIFAR dataset and preprocess them.

Download the test images

Download the provided set of images from the CIFAR dataset:

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# Download the images
! gcloud storage cp --recursive gs://cloud-samples-data/ai-platform-unified/cifar_test_images .

Preprocess the images

Before you can run the data through the endpoint, you need to preprocess it to match the format that your custom model defined in task.py expects.

x_test: Normalize (rescale) the pixel data by dividing each pixel by 255. This replaces each single byte integer pixel with a 32-bit floating point number between 0 and 1.

y_test: You can extract the labels from the image filenames. Each image's filename format is "image_{LABEL}_{IMAGE_NUMBER}.jpg"

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import numpy as np
from PIL import Image

# Load image data
IMAGE_DIRECTORY = "cifar_test_images"

image_files = [file for file in os.listdir(IMAGE_DIRECTORY) if file.endswith(".jpg")]

# Decode JPEG images into numpy arrays
image_data = [
    np.asarray(Image.open(os.path.join(IMAGE_DIRECTORY, file))) for file in image_files
]

# Scale and convert to expected format
x_test = [(image / 255.0).astype(np.float32).tolist() for image in image_data]

# Extract labels from image name
y_test = [int(file.split("_")[1]) for file in image_files]

Prepare data for batch prediction

Before you can run the data through batch prediction, you need to save the data into one of a few possible formats.

For this tutorial, use JSONL as it's compatible with the 3-dimensional list that each image is currently represented in. To do this:

1. In a file, write each instance as JSON on its own line.
2. Upload this file to Cloud Storage.

For more details on batch prediction input formats: https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions#batch_request_input

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import json

BATCH_PREDICTION_INSTANCES_FILE = "batch_prediction_instances.jsonl"

BATCH_PREDICTION_GCS_SOURCE = (
    BUCKET_URI + "/batch_prediction_instances/" + BATCH_PREDICTION_INSTANCES_FILE
)

# Write instances at JSONL
with open(BATCH_PREDICTION_INSTANCES_FILE, "w") as f:
    for x in x_test:
        f.write(json.dumps(x) + "\n")

# Upload to Cloud Storage bucket
! gcloud storage cp $BATCH_PREDICTION_INSTANCES_FILE $BATCH_PREDICTION_GCS_SOURCE

print("Uploaded instances to: ", BATCH_PREDICTION_GCS_SOURCE)

Send the prediction request

To make a batch prediction request, call the model object's batch_predict method with the following parameters:

• instances_format: The format of the batch prediction request file: "jsonl", "csv", "bigquery", "tf-record", "tf-record-gzip" or "file-list"
• prediction_format: The format of the batch prediction response file: "jsonl", "csv", "bigquery", "tf-record", "tf-record-gzip" or "file-list"
• job_display_name: The human readable name for the prediction job.
• gcs_source: A list of one or more Cloud Storage paths to your batch prediction requests.
• gcs_destination_prefix: The Cloud Storage path that the service will write the predictions to.
• model_parameters: Additional filtering parameters for serving prediction results.
• 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.

Compute instance scaling

You can specify a single instance (or node) to process your batch prediction request. 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 process your batch prediction request, 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.

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MIN_NODES = 1
MAX_NODES = 1

# The name of the job
BATCH_PREDICTION_JOB_NAME = "cifar10_batch_prediction_unique"

# Folder in the bucket to write results to
DESTINATION_FOLDER = "batch_prediction_results"

# The Cloud Storage bucket to upload results to
BATCH_PREDICTION_GCS_DEST_PREFIX = BUCKET_URI + "/" + DESTINATION_FOLDER

# Make SDK batch_predict method call
batch_prediction_job = model.batch_predict(
    instances_format="jsonl",
    predictions_format="jsonl",
    job_display_name=BATCH_PREDICTION_JOB_NAME,
    gcs_source=BATCH_PREDICTION_GCS_SOURCE,
    gcs_destination_prefix=BATCH_PREDICTION_GCS_DEST_PREFIX,
    model_parameters=None,
    starting_replica_count=MIN_NODES,
    max_replica_count=MAX_NODES,
    machine_type="n1-standard-4",
    sync=True,
)

Retrieve batch prediction results

When the batch prediction is done processing, you can finally view the predictions stored at the Cloud Storage path you set as output. The predictions will be in a JSONL format, which you indicated when you created the batch prediction job. The predictions are located in a subdirectory starting with the name prediction. Within that directory, there is a file named prediction.results-xxxx-of-xxxx.

Let's display the contents. You will get a row for each prediction. The row is the softmax probability distribution for the corresponding CIFAR10 classes.

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RESULTS_DIRECTORY = "prediction_results"
RESULTS_DIRECTORY_FULL = RESULTS_DIRECTORY + "/" + DESTINATION_FOLDER

# Create missing directories
os.makedirs(RESULTS_DIRECTORY, exist_ok=True)

# Get the Cloud Storage paths for each result
! gcloud storage cp --recursive $BATCH_PREDICTION_GCS_DEST_PREFIX $RESULTS_DIRECTORY

# Get most recently modified directory
latest_directory = max(
    (
        os.path.join(RESULTS_DIRECTORY_FULL, d)
        for d in os.listdir(RESULTS_DIRECTORY_FULL)
    ),
    key=os.path.getmtime,
)

# Get downloaded results in directory
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
    for file in files:
        if file.startswith("prediction.results"):
            results_files.append(os.path.join(dirpath, file))

# Consolidate all the results into a list
results = []
for results_file in results_files:
    # Download each result
    with open(results_file) as file:
        results.extend([json.loads(line) for line in file.readlines()])

Evaluate results

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

1. np.argmax: Convert each list of confidence levels to a label
2. Compare the predicted labels to the actual labels
3. Calculate accuracy as correct/total

To improve the accuracy, try training for a higher number of epochs.

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y_predicted = [np.argmax(result["prediction"]) for result in results]

correct = sum(y_predicted == np.array(y_test))
accuracy = len(y_predicted)
print(
    f"Correct predictions = {correct}, Total predictions = {accuracy}, Accuracy = {correct/accuracy}"
)

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
• Cloud Storage Bucket

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# Warning: Setting this to true will delete everything in your bucket
delete_bucket = False

# Delete the training job
job.delete()

# Delete the model
model.delete()

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