1. Distributed Deep Learning Training on Azure Databricks


    Distributed deep learning training involves leveraging multiple compute nodes to process large datasets and complex neural network models in parallel. This can significantly reduce training time and is facilitated by frameworks like Apache Spark on Databricks. Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform.

    For distributed deep learning training on Azure, we'll provision an Azure Databricks workspace, which provides an integrated environment for data preparation, machine learning training, and collaborative analysis. Once we have the workspace, we can set up clusters for running our distributed training jobs, using the Databricks runtime for machine learning that includes pre-installed deep learning frameworks like TensorFlow.

    Here's a Pulumi program in Python that sets up an Azure Databricks workspace suitable for distributed deep learning training. The program uses the azure-native provider for Azure resources, which means it uses resources provided directly by Azure rather than through a Pulumi abstraction.

    import pulumi import pulumi_azure_native as azure_native # Create a resource group if one doesn't already exist. # All resources will be created within this group. resource_group = azure_native.resources.ResourceGroup("my-resource-group") # Create an Azure Databricks workspace. This workspace will be the # foundation for all subsequent operations, like creating clusters. databricks_workspace = azure_native.databricks.Workspace( "my-databricks-workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.databricks.SkuArgs( name="standard" # Choose "premium" for greater capabilities ) ) # Outputs pulumi.export('databricks_workspace_name', databricks_workspace.name) pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url)

    In the code above:

    • We import the required modules, including pulumi and pulumi_azure_native.
    • A resource group is created. Azure encourages the use of resource groups to organize resources.
    • We then create an Azure Databricks workspace within the resource group. The workspace is where we'll conduct our distributed deep learning training.
    • We use the standard SKU, but for more capabilities, you could choose the premium SKU instead.
    • The workspace name and URL are exported. You would use the URL to access the Databricks workspace via a browser.

    After deploying this Pulumi program, you would typically use the Databricks workspace to create Databricks clusters and execute your distributed deep learning jobs. This is done through the Databricks UI or API, which you would access using the exported workspace URL.

    To run jobs, you would upload your deep learning scripts or notebooks to the Databricks workspace and configure a job to execute the script/notebook on a new or existing cluster. The Databricks runtime includes the necessary machine learning libraries such as TensorFlow, Keras, and PyTorch, which you can use to define and train your models.

    Please note that this program assumes you have already set up Azure credentials for Pulumi and does not include setting up the networking or data storage, which may be required for a full production setup. In a typical application, you would also need to configure secure access to your data sources, such as Azure Blob Storage or Azure Data Lake, and consider configuring VNet peering for secure and reliable networking.