1. Batch Inference on Azure for Large Language Models


    Batch inference on Azure for large language models involves running predictions on large datasets by sending them in batches to the trained model hosted on Azure. This process typically leverages Azure Machine Learning services which can orchestrate the batch scoring process over scalable compute resources, making it efficient to process large volumes of data.

    Below is a Pulumi program that sets up a batch inference operation. It involves creating a Batch Endpoint within Azure Machine Learning Workspace, where a Batch Deployment organizes and manages the inference batch jobs. We use the BatchDeployment resource to define the specifics of the batch scoring job, including the model asset reference, compute requirements, environment, and output configurations. This deployment is bound to an Azure Machine Learning workspace, which acts as a centralized hub for all machine learning activities within Azure.

    For the purpose of this demonstration, we're not setting up the entire infrastructure from scratch, like an Azure Machine Learning Workspace or the Model itself, we're assuming these exist and we are only setting up the batch inference process within them. Also note that we will be using default values for some of the properties to keep the program simple while providing placeholders where custom values might be necessary.

    Let's see how this can be done with Pulumi in Python:

    import pulumi import pulumi_azure_native.machinelearningservices as ml # Set up the Batch Deployment for Azure Machine Learning inference. # This assumes you have an existing Azure Machine Learning Workspace and a model asset. # Parameters (these would ideally come from configuration or be dynamically determined) resource_group_name = 'your-resource-group' workspace_name = 'your-ml-workspace' deployment_name = 'your-batch-deployment' compute_name = 'your-compute-instance' # This is the compute resource name where the batch job will run # You would replace 'your_workspace_resource_id' with the Azure Resource ID of the machine learning workspace # and 'your_model_asset_id' with the Azure Resource ID of the model asset you are using for batch inference batch_deployment = ml.BatchDeployment("batchDeployment", resource_group_name=resource_group_name, workspace_name=workspace_name, deployment_name=deployment_name, properties=ml.BatchDeploymentResourcePropertiesArgs( compute=compute_name, # Example environment and model asset reference, these would be specific to your setup environment_id="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/environments/{environment-name}", model="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/models/{model-name}", # Batch scoring process properties batch_deployment_properties=ml.DeploymentPropertiesArgs( # Code configuration for the scoring script code_configuration=ml.CodeConfigurationArgs( code_id="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/codes/{code-id}", scoring_script="score.py" # This is the script that gets run for each batch ), # Details about the compute resource to use resources=ml.BatchDeploymentResourcesArgs( instance_count=1, # Number of instances to run the job on instance_type="Standard_D3_v2" # The type of the Azure VM to use for each instance ), # Output configuration defines how the outputs of the batch job are handled output_configuration=ml.OutputConfigurationArgs( output_data_store="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/datastores/{datastore-name}", path="outputs" # Path within the datastore to store outputs ), # Other properties include error threshold, retry settings, logging level, etc. error_threshold=10, retry_settings=ml.BatchRetrySettingsArgs( max_retries=3, # Number of retries on task failure timeout="PT30S" # Timeout for each try ), logging_level="Info" ) ) ) # Export the id of the batch deployment so it can be referenced as needed. pulumi.export('batch_deployment_id', batch_deployment.id)

    This Pulumi program sets up a BatchDeployment for inference in Azure Machine Learning service. The BatchDeploymentResourcePropertiesArgs specifies the necessary compute resources, the model to use, environment configurations, and output data handling for the batch inference jobs.

    You should replace placeholders like {subscription-id}, {resource-group}, and {workspace} with your actual Azure subscription ID, resource group name, and workspace name respectively, and similar placeholders like {environment-name}, {model-name}, {datastore-name}, and {code-id} with your specific environment, model, datastore, and code asset names or identifiers.

    The compute field is the compute resource name on which the batch job will run. The environment_id and model fields are the Azure Resource IDs indicating where the model and environment are located. CodeConfigurationArgs includes the ID of the code resource and the name of the script to use for batch processing (scoring_script). The resources detail (BatchDeploymentResourcesArgs) defines the number and type of Azure Virtual Machines to run the batch jobs on. OutputConfigurationArgs specifies the datastore and path where job outputs will be saved.

    Once deployed, this setup allows you to perform batch inference jobs at scale by leveraging Azure's infrastructure to efficiently process large amounts of data and output predictions based on the provided model.