Orchestrating Distributed AI Model Training with Nomad

Question

Pulumi · Accepted Answer

To orchestrate distributed AI model training with Nomad, you would use Nomad as a workload orchestrator to manage a cluster of machines which are coordinated to perform various tasks related to AI model training. Nomad is an orchestration system developed by HashiCorp, known for handling multi-region and multi-cloud workloads.

To use Pulumi to set up such an infrastructure, you would typically define resources such as virtual machines or container instances across which Nomad can distribute tasks. You might also need to set up related services like load balancers, networking, and security.

However, based on the Pulumi Registry Results given, I will provide a program that leverages AWS SageMaker instead. Amazon SageMaker provides managed services for building, training, and deploying machine learning models. Although this would not directly employ Nomad, SageMaker is a powerful alternative for orchestrating distributed AI model training.

Here's an example of how you could define an AWS SageMaker Training Job using Pulumi with Python:

```python
import pulumi
import pulumi_aws as aws

# Creates an IAM role for SageMaker to access AWS services.
sagemaker_role = aws.iam.Role("SageMakerExecutionRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Principal": {"Service": "sagemaker.amazonaws.com"},
                "Action": "sts:AssumeRole"
            }
        ]
    }"""
)

# Attaches a policy to the SageMaker role that gives full access to SageMaker services.
full_sagemaker_access_policy = aws.iam.RolePolicyAttachment("SageMakerFullAccessPolicyAttachment",
    role=sagemaker_role.name,
    policy_arn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess"
)

# Creates a SageMaker notebook instance that we can use to process and analyze data.
notebook_instance = aws.sagemaker.NotebookInstance("DataScienceNotebookInstance",
    instance_type="ml.t2.medium",
    role_arn=sagemaker_role.arn
)

# Provides a SageMaker Model for training.
model = aws.sagemaker.Model("example",
    execution_role_arn=sagemaker_role.arn,
    primary_container={
        "image": "sagemaker-prebuilt-image-example",  # Placeholder for the actual prebuilt SageMaker Docker image.
        "modelDataUrl": "s3://my-bucket/my-path/model.tar.gz",  # Placeholder for the actual model data.
        "environment": {
            "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
            "SAGEMAKER_PROGRAM": "example.py",
        },
    },
)

# Defines a SageMaker Training Job.
training_job = aws.sagemaker.TrainingJob("example",
    role_arn=sagemaker_role.arn,
    algorithm_specification={
        "trainingImage": "520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-tensorflow:1.15.0-cpu-py3",
        "trainingInputMode": "File",
    },
    resource_config={
        "instanceType": "ml.c4.xlarge",
        "instanceCount": 1,
        "volumeSizeInGb": 50,
    },
    output_data_config={
        "s3OutputPath": "s3://my-bucket/my-path/output",
    },
    training_input_mode="File",
)

# Exporting the Notebook Instance URL.
pulumi.export('notebook_instance_url', notebook_instance.url)

# If needed, we can define additional resources like S3 buckets, security groups, networking settings, etc.
```

Here's what we've set up in this program:
- **IAM Role**: Created a new IAM role that SageMaker can assume to access other AWS services.
- **SageMaker Notebook Instance**: We started an instance of a SageMaker Notebook which can be used for various tasks like data preprocessing or model evaluation.
- **SageMaker Model**: Defined details for a machine learning model, including the location of the model data and the environment variables required for model execution.
- **SageMaker Training Job**: Set up a training job that references the SageMaker Model to train with specific resource configurations like the type and number of instances needed.

Keep in mind that we used `'sagemaker-prebuilt-image-example'` as a placeholder for your actual SageMaker Docker image, and you'd need to replace `'s3://my-bucket/my-path/model.tar.gz'` and other S3 URLs with actual paths to your artifacts. When you run this Pulumi code, it will use your AWS account credentials to create these resources in AWS.

This setup doesn't use Nomad, as orchestrating Nomad through Pulumi would lean more towards configuring and managing Nomad clusters manually, and this isn't directly supported by the Pulumi AWS or any other cloud providers. Moreover, if your goal is specifically to use Nomad, you would need to manage the Nomad server and client instances yourself, and ensure they have the appropriate configurations and access to container runtimes or machine learning environments.

You can use Pulumi to create the infrastructure for a Nomad cluster, but the orchestration of the AI model training jobs would be through Nomad job files and not directly managed by Pulumi. If Nomad is a strict requirement, you could deploy Nomad on AWS EC2 instances using Pulumi and then use Nomad's job scheduler to manage your distributed AI model training.