1. Cost-Effective AI Batch Jobs with Azure-native Elastic Compute


    Creating a cost-effective AI batch job solution on Azure involves several steps. You'll use Azure Machine Learning and Azure Batch services together to achieve this, using Pulumi's azure-native infrastructure as code approach to define and deploy these resources.

    Azure Machine Learning (MachineLearningServices) provides a cloud service to train, deploy, automate, manage, and track ML models. When used alongside Azure Batch (Batch), you can create flexible and fine-grained compute resources for running your AI batch jobs efficiently at scale.

    To set up a cost-effective AI batch job system, you would typically do the following:

    1. Define an Azure Machine Learning workspace where your ML models and experiments will reside.
    2. Define a compute resource within this workspace to run your AI models. Here you select which kind of compute - like GPU or CPU-intensive compute instances - is most suitable for your job.
    3. Define an Azure Batch account, which provides the job scheduling and compute management functionality required to run parallel batch processing tasks.
    4. Create a Batch pool within the Batch account. A pool is a collection of compute nodes (VMs) where your batch jobs run.
    5. Finally, create a Batch job that uses the pool. The Batch job is a logical grouping for one or more tasks.

    In Pulumi, all this can be codified in Python as follows:

    import pulumi import pulumi_azure_native.machinelearningservices as ml import pulumi_azure_native.batch as batch # Set up an Azure Machine Learning workspace ml_workspace = ml.Workspace( resource_name="mlworkspace", # Replace with your desired resource name # The remaining required resource properties for the workspace are assumed, # such as location, sku, etc. They should be specified per your requirements. ) # Define the compute instance within the ML workspace # The type of compute targeted depends on the type of AI jobs you plan to run ml_compute = ml.Compute( resource_name="mlcompute", # Replace with your desired resource name # Here you would specify the compute size, type (CPU/GPU), etc. per your need. # These specifications heavily depend on the nature of the ML workload planned. # The compute instance also requires details like the workspace name. workspace_name=ml_workspace.name, ) # Create an Azure Batch account batch_account = batch.BatchAccount( resource_name="batchaccount", # Replace with your desired resource name # Again, you need to fill in additional needed property values such as the location, # pool allocation mode, etc. ) # Create a pool within the Azure Batch Account batch_pool = batch.Pool( resource_name="batchpool", # Replace with your desired resource name # The pool should specify things like the VM size, image, and number of nodes. # Important considerations include whether to use low-priority (preemptible) VMs to lower costs. account_name=batch_account.name, # Additional properties would go here. ) # Define the job that will run in the Batch pool batch_job = batch.Job( resource_name="batchjob", # Replace with your desired resource name pool_name=batch_pool.name, # Properties like the priority of the job can be set here. ) # Export the IDs of the resources created, which can be used in further commands or scripts pulumi.export('ml_workspace_id', ml_workspace.id) pulumi.export('ml_compute_id', ml_compute.id) pulumi.export('batch_account_id', batch_account.id) pulumi.export('batch_pool_id', batch_pool.id) pulumi.export('batch_job_id', batch_job.id)

    In this program:

    • We created a Machine Learning workspace using the MachineLearningServices.Workspace Pulumi resource. The workspace is a fundamental resource for running ML jobs in Azure.
    • We defined a Machine Learning Compute instance within the workspace. This is a VM or a cluster of VMs that run your AI models.
    • We then created an Azure Batch account using the Batch.BatchAccount resource, which is where your Batch resources will be managed.
    • We created a Batch pool using the Batch.Pool resource, allowing us to define a collection of compute nodes to run our jobs.
    • Lastly, we defined a Batch job with the Batch.Job resource. The job will reference the created pool and will be the container for our ML tasks.

    Please note that the above code is a template and must be adapted with the required properties based on your specific scenario. Each Pulumi resource creation should be provided with required parameters, like location, sku, and properties, which have been omitted for brevity.

    You would deploy your Pulumi stack to Azure, and these resources would be provisioned. After that, you would use Azure ML SDKs or the Azure Portal to manage your ML tasks and experiments further, and use Azure Batch to manage job scheduling and task execution details.

    With these Pulumi resources, you build a robust, scalable, and cost-effective infrastructure that can handle complex AI batch processing tasks on Azure.