1. Job Scheduling for Deep Learning Pipelines on AWS Batch


    AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It manages the provisioning of computing resources, thus allowing you to focus on your business and research tasks without having to worry about the underlying infrastructure.

    AWS Batch will dynamically launch Amazon EC2 instances or utilize Spot Fleets for compute resources based on the volume and specific resource requirements of the submitted batch jobs.

    Here's how you can set up job scheduling for deep learning pipelines with AWS Batch using Pulumi:

    1. Compute Environment: This is where your jobs are run. You set up a managed or unmanaged environment that provides computing resources.

    2. Job Queue: This queue holds and dispatches jobs to your compute environments. You define job priorities within this queue.

    3. Job Definition: This is a blueprint for your batch jobs. You define memory, vCPUs, the Docker image, and command that need to be executed.

    4. Scheduling Policy (optional): You can optionally define a scheduling policy if you need advanced job scheduling options.

    Below is the Python program using Pulumi that sets up a basic job scheduling on AWS Batch for running deep learning pipelines:

    import pulumi import pulumi_aws as aws # First, we define a compute environment where batch jobs can be executed. # This example creates a managed environment using EC2 on-demand instances. compute_environment = aws.batch.ComputeEnvironment("deep_learning_env", compute_resources={ "instance_type": "c5.large", # Appropriate for computing tasks "min_vcpus": 0, # Start with no instances "max_vcpus": 100, # Scale up to 100 instances "type": "EC2", # EC2 service "allocation_strategy": "BEST_FIT_PROGRESSIVE", # Allocation strategy }, service_role=aws.iam.Role("batch_service_role", # IAM role for AWS Batch service assume_role_policy=YOUR_AMAZON_EC2_CONTAINER_SERVICE_ROLE_ASSUME_ROLE_POLICY ).name, state="ENABLED", ) # Next, we set up a job queue that will hold submitted job to be processed. job_queue = aws.batch.JobQueue("deep_learning_queue", compute_environments=[{ "order": 1, "compute_environment": compute_environment.arn, }], priority=1, state="ENABLED", ) # A job definition that specifies the how jobs should be run. job_definition = aws.batch.JobDefinition("deep_learning_job_definition", platform_capabilities=["FARGATE"], container_properties="{\"image\": \"YOUR-DOCKER-IMAGE\", \"vcpus\": 4, \"memory\": 8192, \"command\": [\"python\", \"your_script.py\"]}", type="container", ) # Optionally, define a scheduling policy if you need more control over job scheduling. scheduling_policy = aws.batch.SchedulingPolicy("deep_learning_scheduling_policy", fair_share_policy={ "compute_reservation": 1, "share_decay_seconds": 3600, "share_distribution": [{"share_identifier": "HIPRI", "weight_factor": 0.5}], }, ) # Exports pulumi.export('compute_environment_arn', compute_environment.arn) pulumi.export('job_queue_arn', job_queue.arn) pulumi.export('job_definition_arn', job_definition.arn) pulumi.export('scheduling_policy_arn', scheduling_policy.arn)


    • Compute Environment: aws.batch.ComputeEnvironment sets up the environment where AWS Batch jobs will run. In this example, we've defined a managed environment using EC2 instances for cost-effective compute capacity.

      You need to supply your own IAM role trust policy (YOUR_AMAZON_EC2_CONTAINER_SERVICE_ROLE_ASSUME_ROLE_POLICY) which allows Batch to assume a role and manage resources.

    • Job Queue: aws.batch.JobQueue helps manage the dispatch and execution jobs in the compute environment. The queue is associated with the compute environment we created with specified priority.

    • Job Definition: aws.batch.JobDefinition describes how batch jobs should run in the environment. Here, we define the Docker container image to use and resources like the number of vCPUs and memory required.

    • Scheduling Policy: aws.batch.SchedulingPolicy is optional and gives you advanced options for job scheduling. The example policy uses a fair share policy to ensure that resources are shared fairly among jobs.

    • Exports: At the end of the Pulumi program, we use pulumi.export to output the ARNs of the AWS Batch resources, which could be used in other parts of your AWS infrastructure or applications.

    Make sure you replace placeholders like YOUR-DOCKER-IMAGE and YOUR_AMAZON_EC2_CONTAINER_SERVICE_ROLE_ASSUME_ROLE_POLICY with your actual Docker image path and policy document JSON.

    This is a simplified example. For more complex deep learning tasks, you may need to customize this setup further by adding specific configurations, like specialized EC2 instances or GPU resources, based on the requirements of your deep learning workload.