model_garden/notebooks/official/automl/get_started_automl_training.ipynb

In [ ]:

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Get started with AutoML training

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Overview

This tutorial demonstrates how to use AutoML in production. This tutorial covers get started with AutoML training.

Learn more about AutoML training.

Objective

In this tutorial, you learn how to use AutoML for training with Vertex AI.

This tutorial uses the following Google Cloud ML services:

• AutoML training
• Vertex AI Datasets

The steps performed include:

• Train an image model
• Export the image model as an edge model
• Train a tabular model
• Export the tabular model as a cloud model
• Train a text model
• Train a video model

Recommendations

When doing E2E MLOps on Google Cloud, the following are best practices for when to use AutoML:

• You have a limited amount of training data

• You want to establish a baseline metric before experimenting with a custom model

Datasets

Image

The image dataset used for this tutorial is the Flowers dataset from TensorFlow Datasets. The version of the dataset in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower in a given image from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip.

Tabular

The tabular dataset used for this tutorial is the GSOD dataset from BigQuery public datasets. The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp).

Text

The text dataset used for this tutorial is the Happy Moments dataset from Kaggle Datasets. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket.

Video

The video dataset used for this tutorial is the golf swing recognition portion of the Human Motion dataset from MIT. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the start frame where a golf swing begins.

Costs

This tutorial uses billable components of Google Cloud:

• 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

In [ ]:

import os

# Install the packages

! pip3 install --upgrade --quiet google-cloud-aiplatform \
                                 google-cloud-storage 

Restart runtime (Colab only)

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

In [ ]:

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.

In [ ]:

import sys

if "google.colab" in sys.modules:

    from google.colab import auth

    auth.authenticate_user()

Set Google Cloud project information

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

In [ ]:

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"}

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 $BUCKET_URI

Set up variables

Next, set up some variables used throughout the tutorial.

Import libraries and define constants

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import sys

import google.cloud.aiplatform as aiplatform

Initialize Vertex AI SDK for Python

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

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

AutoML training job

AutoML can be used to automatically train a wide variety of image model types. AutoML automates the following:

• Dataset preprocessing
• Feature Engineering
• Data feeding
• Model Architecture selection
• Hyperparameter tuning
• Training the model

Learn more about Vertex AI for AutoML users

AutoML image models

AutoML can train the following types of image models:

• classification
• objection detection
• segmentation

A model can be trained for either deployment to the cloud or exported to the edge.

Learn more about AutoML Model Types

Data preparation

The Vertex Dataset resource for images has some requirements for your data:

• Images must be stored in a Cloud Storage bucket.
• Each image file must be in an image format (PNG, JPEG, BMP, ...).
• There must be an index file stored in your Cloud Storage bucket that contains the path and label for each image.
• The index file must be either CSV or JSONL.

Learn more about Preparing image data

CSV

For image classification, the CSV index file has the requirements:

• No heading.
• First column is the Cloud Storage path to the image.
• Second column is the label.
• Any remaining columns are additional labels for multi-label image classification.

For image object detection, the CSV index file has the requirements:

• No heading.
• First column is the Cloud Storage path to the image.
• Second column is the label.
• Third/Fourth columns are the upper left corner of bounding box. Coordinates are normalized, between 0 and 1.
• Fifth/Sixth/Seventh columns are not used and should be 0.
• Eighth/Ninth columns are the lower right corner of the bounding box.

ML_USE

Each row may additionally specify which split to assign the data item to when the dataset is split for training; otherwise, the dataset will be randomly split: 80/10/10.

The ml_use assignment is specified by prepending a column for specifying the assignment -- as the first column. The value may be one of: training, test, or validation.

JSONL

For image classification, the JSONL index file has the requirements:

• Each data item is a separate JSON object, on a separate line.

• The key/value pair image_gcs_uri is the Cloud Storage path to the image.

• The key/value pair display_name is the label for the image.

  { 'image_gcs_uri': image, 'classification_annotations': { 'display_name': label } }

For multi-label, the labels are specified as a list of display_name key/value pairs:

{ 'image_gcs_uri': image, 
  'classification_annotations': [
      { 'display_name': label1
      },
      { 'display_name': labelN
      },
   ]
}

For object detection, the JSONL index file has the requirements:

• Each data item is a separate JSON object, on a separate line.
• The key/value pair image_gcs_uri is the Cloud Storage path to the image.
• The key/value pair bounding_box_annotations is a list of:
  • display_name: The label of the object
  • x_min, y_min, x_max, y_max: The coordinates for the bounding box

{ "image_gcs_uri": image, "bounding_box_annotations": [ { "display name": label, "x_min": "X_MIN", "y_min": "Y_MIN", "x_max": "X_MAX", "y_max": "Y_MAX" } }, { "displayName": "OBJECT2_LABEL", "x_min": "X_MIN", "y_min": "Y_MIN", "x_max": "X_MAX", "y_max": "Y_MAX" } ] }

For image segmentation, the JSONL index file has the requirements:

• Each data item is a separate JSON object, on a separate line.
• The key/value pair image_gcs_uri is the Cloud Storage path to the image.
• The key/value pair category_mask_uri is the Cloud Storage path to the mask image in PNG format.
• The key/value pair 'annotation_spec_colors' is a list mapping mask colors to a label.

  • The key/value pair pair display_name is the label for the pixel color mask.

  • The key/value pair pair color are the RGB normalized pixel values (between 0 and 1) of the mask for the corresponding label.

    { 'image_gcs_uri': image, 'segmentation_annotations': { 'category_mask_uri': mask_image, 'annotation_spec_colors' : [ { 'display_name': label, 'color': {"red": value, "blue", value, "green": value} }, ... ] }

ML_USE

Each JSONL object may additionally specify which split to assign the data item to when the dataset is split for training; otherwise, the dataset will be randomly split: 80/10/10.

"data_item_resource_labels": { "aiplatform.googleapis.com/ml_use": "training|test|validation" }

Note: The dictionary key fields may alternatively be in camelCase. For example, 'image_gcs_uri' can also be 'imageGcsUri'.

Location of Cloud Storage training data.

Now set the variable IMPORT_FILE to the location of the CSV index file in Cloud Storage.

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IMPORT_FILE = "gs://cloud-samples-data/ai-platform/flowers/flowers.csv"

Quick peek at your data

This tutorial uses a version of the Happy Moments dataset that is stored in a public Cloud Storage bucket, using a CSV index file.

Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file (wc -l) and then peek at the first few rows.

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FILE = IMPORT_FILE

count = ! gcloud storage cat $FILE | wc -l
print("Number of Examples", int(count[0]))

print("First 10 rows")
! gcloud storage cat $FILE | head

Create the Dataset

Next, create the Dataset resource using the create method for the ImageDataset class, which takes the following parameters:

• display_name: The human readable name for the Dataset resource.
• gcs_source: A list of one or more dataset index files to import the data items into the Dataset resource.
• import_schema_uri: The data labeling schema for the data items:
  • single_label: Binary and multi-class classification
  • multi_label: Multi-label multi-class classification
  • bounding_box: Object detection
  • image_segmentation: Segmentation

Learn more about ImageDataset.

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dataset = aiplatform.ImageDataset.create(
    display_name="flowers",
    gcs_source=[IMPORT_FILE],
    import_schema_uri=aiplatform.schema.dataset.ioformat.image.single_label_classification,
)

print(dataset.resource_name)

Create and run training pipeline

To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.

Create training pipeline

An AutoML training pipeline is created with the AutoMLImageTrainingJob class, with the following parameters:

• display_name: The human readable name for the TrainingJob resource.
• prediction_type: The type task to train the model for.
  • classification: An image classification model.
  • object_detection: An image object detection model.
• multi_label: If a classification task, whether single (False) or multi-labeled (True).
• model_type: The type of model for deployment.
  • CLOUD: Deployment on Google Cloud
  • CLOUD_HIGH_ACCURACY_1: Optimized for accuracy over latency for deployment on Google Cloud.
  • CLOUD_LOW_LATENCY_: Optimized for latency over accuracy for deployment on Google Cloud.
  • MOBILE_TF_VERSATILE_1: Deployment on an edge device.
  • MOBILE_TF_HIGH_ACCURACY_1:Optimized for accuracy over latency for deployment on an edge device.
  • MOBILE_TF_LOW_LATENCY_1: Optimized for latency over accuracy for deployment on an edge device.
• base_model: (optional) Transfer learning from existing Model resource -- supported for image classification only.

The instantiated object is the DAG (directed acyclic graph) for the training job.

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dag = aiplatform.AutoMLImageTrainingJob(
    display_name="flowers",
    prediction_type="classification",
    multi_label=False,
    model_type="MOBILE_TF_LOW_LATENCY_1",
    base_model=None,
)

print(dag)

Run the training pipeline

Next, you run the created DAG to start the training job by invoking the method run, with the following parameters:

• dataset: The Dataset resource to train the model.
• model_display_name: The human readable name for the trained model.
• training_fraction_split: The percentage of the dataset to use for training.
• test_fraction_split: The percentage of the dataset to use for test (holdout data).
• validation_fraction_split: The percentage of the dataset to use for validation.
• budget_milli_node_hours: (optional) Maximum training time specified in unit of milli node-hours (1000 = node-hour).
• disable_early_stopping: If True, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.

The run method when completed returns the Model resource.

The execution of the training pipeline will take upto > 30 minutes.

In [ ]:

import os

if os.getenv("IS_TESTING"):
    sys.exit(0)

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model = dag.run(
    dataset=dataset,
    model_display_name="flowers",
    training_fraction_split=0.8,
    validation_fraction_split=0.1,
    test_fraction_split=0.1,
    budget_milli_node_hours=8000,
    disable_early_stopping=False,
)

Review model evaluation scores

After your model training has finished, you can review the evaluation scores for it using the list_model_evaluations() method. This method will return an iterator for each evaluation slice.

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model_evaluations = model.list_model_evaluations()

for model_evaluation in model_evaluations:
    print(model_evaluation.to_dict())

Deploy the model

Next, deploy your model for online prediction. To deploy the model, you invoke the deploy method.

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endpoint = model.deploy()

Send a online prediction request

Send a online prediction to your deployed model.

Get test item

You use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model. You're just looking at how to make a prediction.

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test_item = !gcloud storage cat $IMPORT_FILE | head -n1
if len(str(test_item[0]).split(",")) == 3:
    _, test_item, test_label = str(test_item[0]).split(",")
else:
    test_item, test_label = str(test_item[0]).split(",")

print(test_item, test_label)

Make the prediction

Now that your Model resource is deployed to an Endpoint resource, you can do online predictions by sending prediction requests to the Endpoint resource.

Request

Since your test item is in a public Cloud Storage bucket in this example, you copy it to your bucket and read the contents of the image using Cloud Storage SDK. To pass the test data to the prediction service, you encode the bytes into base64 which makes the content safe from modification while transmitting binary data over the network.

The format of each instance is:

{ 'content': { 'b64': base64_encoded_bytes } }

Since the predict() method can take multiple items (instances), send your single test item as a list of one test item.

Response

The response from the predict() call is a Python dictionary with the following entries:

• ids: The internal assigned unique identifiers for each prediction request.
• displayNames: The class names for each class label.
• confidences: The predicted confidence, between 0 and 1, per class label.
• deployed_model_id: The Vertex AI identifier for the deployed Model resource which did the predictions.

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import base64

from google.cloud import storage

# Copy the test image to the Cloud storage bucket as "test.jpg"
test_image_local = "{}/test.jpg".format(BUCKET_URI)
! gcloud storage cp $test_item $test_image_local

# Download the test image in bytes format
storage_client = storage.Client(project=PROJECT_ID)
bucket = storage_client.bucket(bucket_name=BUCKET_URI[5:])
test_content = bucket.get_blob("test.jpg").download_as_bytes()

# The format of each instance should conform to the deployed model's prediction input schema.
instances = [{"content": base64.b64encode(test_content).decode("utf-8")}]

prediction = endpoint.predict(instances=instances)

print(prediction)

Alternate method using GFile

Alternatively, GFile method from tensorflow-io library can be used to read the data from Cloud storage directly. The following code snippet does the same :

import base64
import tensorflow as tf

# Read the test file using GFile
with tf.io.gfile.GFile(test_item, "rb") as f:
    content = f.read()

# The format of each instance should conform to the deployed model's prediction input schema.
instances = [{"content": base64.b64encode(content).decode("utf-8")}]

prediction = endpoint.predict(instances=instances)

print(prediction)

Nevertheless, tf.io.gfile.GFile supports multiple file system implementations, including local files, Google Cloud Storage (using a gs:// prefix), and HDFS (using an hdfs:// prefix).

Undeploy the model

When you're done doing predictions, undeploy the model from the Endpoint resource. This deprovisions all compute resources and ends billing for the deployed model.

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endpoint.undeploy_all()

Export as Edge model

You can export an AutoML cloud model as a Edge model which you can then custom deploy to an edge device or download locally. Use the method export_model() to export the model to Cloud Storage, which takes the following parameters:

• artifact_destination: The Cloud Storage location to store the SavedFormat model artifacts to.

• export_format_id: The format to save the model format as. For AutoML cloud there is just one option:

  • tf-saved-model: TensorFlow SavedFormat for deployment to a container.
  • tflite: TensorFlow Lite for deployment to an edge or mobile device.
  • edgetpu-tflite: TensorFlow Lite for TPU
  • tf-js: TensorFlow for web client
  • coral-ml: for Coral devices

• sync: Whether to perform operational sychronously or asynchronously.

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response = model.export_model(
    artifact_destination=BUCKET_URI, export_format_id="tflite", sync=True
)

model_package = response["artifactOutputUri"]

Delete the model

The method 'delete()' will delete the model.

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model.delete()

Delete the dataset

The method 'delete()' will delete the dataset.

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dataset.delete()

Delete the endpoint

The method 'delete()' will delete the endpoint.

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endpoint.delete()

AutoML tabular models

AutoML can train the following types of tabular models:

• classification
• regression
• forecasting

A model can be trained for either automatic deployment to the cloud or exported for manual deployment to the cloud.

Learn more about AutoML Model Types

Data preparation

The Vertex AI Dataset resource for tabular has a couple of requirements for your tabular data.

• Must be in a CSV file or a BigQuery table.

Learn more about Preparing tabular data

CSV

For tabular models, the CSV file has a few requirements:

• The first row must be the heading -- note how this is different from Image, Text and Video where the requirement is no heading.
• All but one column are features.
• One column is the label, which you will specify when you subsequently create the training pipeline.

ML_USE

Each row may additionally specify which split to assign the data item to when the dataset is split for training; otherwise, the dataset will be randomly split: 80/10/10.

The ml_use assignment is specified by prepending a column for specifying the assignment -- as the first column. The value may be one of: training, test, or validation.

Location of BigQuery training data.

Now set the variable IMPORT_FILE to the location of the data table in BigQuery.

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IMPORT_FILE = "bq://bigquery-public-data.samples.gsod"
BQ_TABLE = "bigquery-public-data.samples.gsod"

Create the Dataset

BigQuery input data

Next, create the Dataset resource using the create method for the TabularDataset class, which takes the following parameters:

• display_name: The human readable name for the Dataset resource.
• bq_source: Import data items from a BigQuery table into the Dataset resource.
• labels: User defined metadata. In this example, you store the location of the Cloud Storage bucket containing the user defined data.

Learn more about TabularDataset from BigQuery table.

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dataset = aiplatform.TabularDataset.create(
    display_name="gsod",
    bq_source=[IMPORT_FILE],
    labels={"user_metadata": BUCKET_URI[5:]},
)

label_column = "mean_temp"

print(dataset.resource_name)

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TRANSFORMATIONS = [
    {"auto": {"column_name": "year"}},
    {"auto": {"column_name": "month"}},
    {"auto": {"column_name": "day"}},
]

label_column = "mean_temp"

Create and run training pipeline

To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.

Create training pipeline

An AutoML training pipeline is created with the AutoMLTabularTrainingJob class, with the following parameters:

• display_name: The human readable name for the TrainingJob resource.
• optimization_prediction_type: The type task to train the model for.
  • classification: A tabuar classification model.
  • regression: A tabular regression model.
• column_transformations: (Optional): Transformations to apply to the input columns
• optimization_objective: The optimization objective to minimize or maximize.
  • binary classification:
    • minimize-log-loss
    • maximize-au-roc
    • maximize-au-prc
    • maximize-precision-at-recall
    • maximize-recall-at-precision
  • multi-class classification:
    • minimize-log-loss
  • regression:
    • minimize-rmse
    • minimize-mae
    • minimize-rmsle

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dag = aiplatform.AutoMLTabularTrainingJob(
    display_name="gsod",
    optimization_prediction_type="regression",
    optimization_objective="minimize-rmse",
    column_transformations=TRANSFORMATIONS,
)

print(dag)

Run the training pipeline

Next, you run the created DAG to start the training job by invoking the method run, with the following parameters:

• dataset: The Dataset resource to train the model.
• model_display_name: The human readable name for the trained model.
• training_fraction_split: The percentage of the dataset to use for training.
• test_fraction_split: The percentage of the dataset to use for test (holdout data).
• validation_fraction_split: The percentage of the dataset to use for validation.
• target_column: The name of the column to train as the label.
• budget_milli_node_hours: (optional) Maximum training time specified in unit of millihours (1000 = hour).
• disable_early_stopping: If True, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.

The run method when completed returns the Model resource.

The execution of the training pipeline will take upto > 30 minutes.

In [ ]:

model = dag.run(
    dataset=dataset,
    model_display_name="gsod",
    training_fraction_split=0.8,
    validation_fraction_split=0.1,
    test_fraction_split=0.1,
    budget_milli_node_hours=8000,
    disable_early_stopping=False,
    target_column="mean_temp",
)

Review model evaluation scores

After your model training has finished, you can review the evaluation scores for it using the list_model_evaluations() method. This method will return an iterator for each evaluation slice.

In [ ]:

model_evaluations = model.list_model_evaluations()

for model_evaluation in model_evaluations:
    print(model_evaluation.to_dict())

Deploy the model

Next, deploy your model for online prediction. To deploy the model, you invoke the deploy method, with the following parameters:

• machine_type: The type of compute machine.

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endpoint = model.deploy(machine_type="n1-standard-4")

Undeploy the model

When you're done doing predictions, undeploy the model from the Endpoint resource. This deprovisions all compute resources and ends billing for the deployed model.

In [ ]:

endpoint.undeploy_all()

Export as cloud model

You can export an AutoML cloud model as a TensorFlow SavedFormat model which you can then custom deploy to Cloud Storage or download locally. Use the method export_model() to export the model to Cloud Storage, which takes the following parameters:

• artifact_destination: The Cloud Storage location to store the SavedFormat model artifacts to.
• export_format_id: The format to save the model format as. For AutoML cloud there is just one option:
  • tf-saved-model: TensorFlow SavedFormat
• sync: Whether to perform operational sychronously or asynchronously.

In [ ]:

response = model.export_model(
    artifact_destination=BUCKET_URI, export_format_id="tf-saved-model", sync=True
)

model_package = response["artifactOutputUri"]

Delete the model

The method 'delete()' will delete the model.

In [ ]:

model.delete()

Delete the dataset

The method 'delete()' will delete the dataset.

In [ ]:

dataset.delete()

Delete the endpoint

The method 'delete()' will delete the endpoint.

In [ ]:

endpoint.delete()

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.

In [ ]:

# Delete the Cloud Storage bucket
delete_bucket = False  # Set True for deletion
if delete_bucket:
    ! gcloud storage rm --recursive $BUCKET_URI