1. Hyperparameter Tuning with AWS SSM Parameter Store


    When conducting machine learning experiments, hyperparameter tuning is an essential process that involves finding the most optimal set of hyperparameters for your model. These hyperparameters are often stored and managed so that they can be tweaked and reused across different models and experiments.

    AWS Systems Manager (SSM) Parameter Store provides a centralized service to manage your application's configuration data and secrets. You can use it to store values needed for hyperparameter tuning, such as learning rate, number of epochs, batch size, etc., securely and scalably.

    Below, I'll guide you through using Pulumi to create parameters in AWS SSM Parameter Store. These parameters will represent the hyperparameters you wish to tune.

    We will be using aws.ssm.Parameter resource from Pulumi's AWS provider (pulumi_aws). You can store each hyperparameter as a separate parameter or use a single parameter to store a JSON object with all your hyperparameters.

    Here's the program that sets up several hyperparameters in the AWS SSM Parameter Store:

    import pulumi import pulumi_aws as aws # Create a single SSM Parameter storing a JSON object with hyperparameters learning_rate_param = aws.ssm.Parameter("learningRateParam", name="learning-rate", type="String", # AWS parameter types can be String, StringList, or SecureString value="0.01" # example learning rate value ) batch_size_param = aws.ssm.Parameter("batchSizeParam", name="batch-size", type="String", value="64" # example batch size value ) num_epochs_param = aws.ssm.Parameter("numEpochsParam", name="num-epochs", type="String", value="10" # example number of epochs ) # Example of storing a more complex object, such as the entire hyperparameter set model_hyperparameters = { "learning_rate": 0.01, "batch_size": 64, "epochs": 10, # Add other hyperparameters as needed } hyperparameters_param = aws.ssm.Parameter("hyperparametersParam", name="model-hyperparameters", type="String", value=pulumi.Output.from_input(model_hyperparameters).apply(pulumi.json.encode) # Convert the Python dictionary to a JSON string ) # Export the names of the parameters pulumi.export("learning_rate_param_name", learning_rate_param.name) pulumi.export("batch_size_param_name", batch_size_param.name) pulumi.export("num_epochs_param_name", num_epochs_param.name) pulumi.export("hyperparameters_param_name", hyperparameters_param.name)

    Each aws.ssm.Parameter resource is created with a name, type, and value. The name is the identifier used to fetch the parameter later. The type can be String, StringList, or SecureString. In most cases, when storing hyperparameters, String is sufficient unless you are storing sensitive data, in which case you’d choose SecureString. The value is the actual data for the hyperparameter. Here, I've created separate parameters for learning_rate, batch_size, and number_of_epochs.

    If you wish to use a single SSM parameter to store multiple hyperparameters, you can serialize a dictionary into a JSON string and store it. For example, model_hyperparameters is a dictionary that contains all the hyperparameters necessary for your model. It's encoded into a JSON string using Pulumi's apply(pulumi.json.encode). This is a common pattern when you want to store a structured object as a configuration.

    The pulumi.export lines at the end of the script will print out the names of the SSM Parameters when you deploy this Pulumi stack, which will be useful for retrieving them in your application or model training scripts.

    You can then retrieve these values within your application using AWS SDKs or the AWS CLI. For example, in Python, you would use the boto3 library to retrieve the parameter value like this:

    import boto3 # Initialize a session using your AWS credentials session = boto3.Session( aws_access_key_id='ACCESS_KEY', aws_secret_access_key='SECRET_KEY', region_name='us-west-2' # For example ) # Create a SSM client ssm = session.client('ssm') # Fetch the parameter parameter = ssm.get_parameter(Name='learning-rate') learning_rate = parameter['Parameter']['Value']

    Be sure to replace 'ACCESS_KEY', 'SECRET_KEY', and 'region_name' with your actual AWS credentials and region. This example assumes that you have valid AWS credentials configured, which is a requirement to run Pulumi programs interacting with AWS resources.