1. High-Throughput Datasets for Machine Learning on Azure Managed Disks


    In order to handle high-throughput datasets for machine learning on Azure, you would typically need to set up Azure Machine Learning workspaces, attach managed disks to compute resources, and make sure your datasets are accessible to your machine learning models. Below, I will guide you through a Pulumi program which sets up these components.

    Azure Managed Disks are block-level storage volumes that are managed by Azure and used with Azure Virtual Machines. Managed Disks provide high durability, availability, and security for your data, and they are suitable for high I/O (Input/Output) intensive workloads that are typical in machine learning scenarios.

    In the context of Pulumi and Azure, here's how you might structure a Python program to set up high-throughput datasets for machine learning:

    1. Create an Azure Machine Learning Workspace, which is a foundational service in Azure providing a space where you can operate across the machine learning lifecycle from data preparation to model training and deployment.
    2. Set up an Azure Compute resource, such as an Azure VM or an Azure Kubernetes Service, where your machine learning models will run.
    3. Attach Managed Disks to the compute resource to handle high-throughput datasets.
    4. Prepare your datasets for machine learning tasks by either creating or using existing Machine Learning Datasets.

    Before running this program, ensure that your Pulumi CLI is configured with the appropriate Azure credentials.

    Let's start with the Pulumi code to accomplish the above tasks. Remember to install the required Pulumi Azure Native package by running pip install pulumi-azure-native.

    import pulumi import pulumi_azure_native as azure_native # Define the organization location location = "East US" # Create an Azure Machine Learning Workspace workspace = azure_native.machinelearningservices.Workspace("myWorkspace", location=location, resource_group_name=azure_native.machinelearningservices.ResourceGroupNameArgs( name="myResourceGroup" # Replace with your resource group name ), workspace_name="myMachineLearningWorkspace", # Choose a name for your workspace sku=azure_native.machinelearningservices.SkuArgs( name="Basic" # You can choose between Basic, Enterprise, etc. ), description="My machine learning workspace for high-throughput datasets" ) # Creating a compute instance (VM) for machine learning tasks compute_instance = azure_native.machinelearningservices.Compute("myComputeInstance", location=location, resource_group_name=workspace.name, workspace_name=workspace.name, compute_name="myMachineLearningCompute", properties=azure_native.machinelearningservices.ComputeInstanceArgs( # In this case, provide the required configuration for the Compute Instance # such as VM size, disk size, and image reference. # Refer to Azure documentation to configure these settings as per your requirements. ) ) # Attaching a managed disk to the create compute instance # This managed disk will be used for high-throughput data storage needed for machine learning tasks managed_disk = azure_native.compute.Disk("myManagedDisk", location=location, resource_group_name=workspace.name, disk_size_gb=1024, # Size of the disk in GB, adjust per data requirements creation_data=azure_native.compute.CreationDataArgs( create_option='Empty' # Options include Copy, FromImage, Import, etc. ), ) # Associate the managed disk with the compute instance managed_disk_attachment = azure_native.compute.VirtualMachineDataDiskAttachment("myDiskAttachment", managed_disk_id=managed_disk.id, virtual_machine_id=compute_instance.id, lun=0, # Logical Unit Number for the disk, should be unique for each disk attached create_option='Attach', # We are attaching an existing managed disk ) # To utilize the datasets in your machine learning tasks, make sure # they are provisioned or prepared using Azure Machine Learning Dataset features, # and ensure they are accessible from your compute instance. # Here we would include steps to prepare or provision datasets using Azure Machine Learning Datasets, # which is beyond the scope of this example and varies based on your data. # It's possible to export details so they can be used in another Pulumi program. pulumi.export("workspace_name", workspace.name) pulumi.export("compute_instance_id", compute_instance.id) pulumi.export("managed_disk_id", managed_disk.id)

    In the code above, we're creating an Azure Machine Learning Workspace, setting up a compute instance (which acts as our machine learning environment where models can be trained), and attaching a managed disk to it. This managed disk is designated for high-throughput data, which is essential for training machine learning models with large datasets. Adjust this example to your specific scenario, like choosing the appropriate VM size, disk size, or image for compute instance based on your machine learning workload requirements.

    Remember to replace placeholders like "myResourceGroup", "myMachineLearningWorkspace", and "myMachineLearningCompute" with actual names that fit your Azure environment setup. The workspace, compute instance, and managed disk are tagged with pulumi exports, which allows you to easily retrieve their identifiers for use in other parts of your infrastructure or in other Pulumi programs.