1. Hyperparameter Optimization with SageMaker Tuning Jobs


    When you want to optimize the hyperparameters of a machine learning model in AWS, you can use Amazon SageMaker's Hyperparameter Tuning Jobs. SageMaker offers a way to automatically adjust hyperparameters to find the model version that performs the best, based on a defined metric, like accuracy or F1 score.

    Here's how you can set up a SageMaker Hyperparameter Tuning Job using Pulumi in Python:

    1. Define data sources: These are S3 locations where SageMaker expects to find the training data and where to store the output.
    2. Set up the training job definition: This includes the Docker image for the training code, the type/quantity of hardware to use, and parameters like learning rate, number of trees (for forest-based models), etc.
    3. Configure the hyperparameter tuner: This involves defining the parameters to tune, the range of values for each, the objective to optimize, and the strategy for tuning.

    The following Pulumi program creates a SageMaker Hyperparameter Tuning Job:

    import pulumi import pulumi_aws as aws from pulumi_aws import sagemaker # Define the role for SageMaker to assume sagemaker_execution_role = aws.iam.Role("SageMakerExecutionRole", assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "sagemaker.amazonaws.com" }, }] }) sagemaker_role_policy_attachment = aws.iam.RolePolicyAttachment("SageMakerRolePolicyAttachment", role=sagemaker_execution_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGEMAKER_FULL_ACCESS) # Define the training job training_job = sagemaker.TrainingJob("MyTrainingJob", role_arn=sagemaker_execution_role.arn, algorithm_specification={ "training_image": "training-image-uri", # Specify your training image URI "training_input_mode": "File", }, # The hyperparameters that you want to use for model training hyperparameters={ "batch-size": "256", # ... include other static hyperparameters here ... }, input_data_config=[{ "channel_name": "train", "data_source": { "s3_data_source": { "s3_data_type": "S3Prefix", "s3_uri": "s3://my-bucket/train", "s3_data_distribution_type": "FullyReplicated", } }, }], output_data_config={ "s3_output_path": "s3://my-bucket/output", }, resource_config={ "instance_type": "ml.m5.large", "instance_count": 1, "volume_size_in_gb": 50, }) # Create a hyperparameter tuner hyperparameter_tuner = sagemaker.HyperParameterTuningJob("MyHyperparameterTuner", hyper_parameter_tuning_job_config={ "strategy": "Bayesian", "hyper_parameter_tuning_job_objective": { "type": "Maximize", "metric_name": "validation:accuracy", # Define the metric to optimize }, # Set the range of hyperparameters to tune "parameter_ranges": { "continuous_parameter_ranges": [{ "name": "learning-rate", "min_value": "0.01", "max_value": "0.2", "scaling_type": "Auto", }], # ... you can add more ranges for other hyperparameters ... }, "resource_limits": { "max_number_of_training_jobs": 20, "max_parallel_training_jobs": 3, }, }, training_job_definition=training_job, role_arn=sagemaker_execution_role.arn) # Export the name of the tuning job pulumi.export("hyperparameter_tuning_job_name", hyperparameter_tuner.hyper_parameter_tuning_job_name)

    Let me explain some key elements of this program:

    • Role Creation: To allow SageMaker to access AWS resources, we create an IAM role and attach the AmazonSageMakerFullAccess policy to it with aws.iam.Role and aws.iam.RolePolicyAttachment.
    • Training Job: sagemaker.TrainingJob creates a SageMaker training job definition. It specifies the compute resources to use, the input and output data locations, and the static hyperparameters.
    • HyperParameter Tuning Job: sagemaker.HyperParameterTuningJob is the heart of hyperparameter optimization, specifying the tuning strategy, objective metric, and the hyperparameter ranges to search.

    A specific URI for the training image (training-image-uri) and your S3 bucket paths (s3://my-bucket/...) need to be replaced with your actual project details. The values for the hyperparameters' ranges, objective metric name, and other configuration parameters should be chosen based on the machine learning model and problem specifics.

    By running this Pulumi program, it would set up and start the hyperparameter tuning job. Once complete, SageMaker will have found the best combination of the hyperparameters you defined within the search ranges that optimize the specified objective metric.