Multi-Account Strategy for Training Large Language Models

Question

Pulumi · Accepted Answer

To implement a multi-account strategy for training large language models, you would typically use cloud resources to manage multiple accounts, permissions, and possibly even the lifecycle of resources across these accounts. This approach can foster good security practices, such as isolation of resources, granting least privilege, and budget management.

In Pulumi, you can manage resources across multiple accounts within a cloud provider by configuring the provider with credentials for each account. You would also make use of services specifically designed for machine learning. For AWS, services like SageMaker, organizations, and IAM roles would be relevant. For Google Cloud, you might consider using Vertex AI or AI Platform. Azure offers Azure Machine Learning services.

For the scope of this explanation, let's focus on an example using AWS, as it provides a comprehensive set of services for machine learning and account management.

You would start by setting up an organization with multiple accounts, ensuring you have a centralized account that manages others—often referred to as the "master" account. Each sub-account could be dedicated to specific tasks, such as training, testing, and deploying language models.

Using Pulumi to roll out this strategy might look like the following:

1. Use `aws.organizations.Organization` to create an organization.
2. Use `aws.organizations.Account` to create sub-accounts for training models.
3. Set up IAM roles with `aws.iam.Role` to manage permissions between services and across accounts.
4. Set up resource groups or tagging strategies to organize resources.
5. Deploy an Amazon SageMaker instance or training job.

Below is an example program in Python using Pulumi's AWS package, demonstrating the creation of an organization and a sub-account for the purpose of training a large language model:

```python
import pulumi
import pulumi_aws as aws

# Create a new AWS organization
org = aws.organizations.Organization("my_org",
    feature_set="ALL",
    aws_service_access_principals=["cloudwatch.amazonaws.com", "sagemaker.amazonaws.com"],
    enabled_policy_types=["SERVICE_CONTROL_POLICY"]
)

# Create a sub-account within the organization for training models
train_model_account = aws.organizations.Account("train_model_account",
    email="train-model-account@example.com",
    name="TrainModel",
    role_name="OrganizationAccountAccessRole", # IAM role that AWS Organizations creates in the new member account.
    tags={
        "Environment": "Training"
    },
    opts=pulumi.ResourceOptions(parent=org)
)

# Assuming we have a pre-defined IAM policy for SageMaker operations, attach this policy to a role
sagemaker_role = aws.iam.Role("sagemaker_role",
    assume_role_policy=f"""{{
        "Version": "2012-10-17",
        "Statement": [{{
            "Effect": "Allow",
            "Principal": {{
                "Service": "sagemaker.amazonaws.com"
            }},
            "Action": "sts:AssumeRole"
        }}]
    }}""",
    tags={
        "Environment": "SageMaker"
    }
)

# Output the ARN of the sub-account (you can use this ARN to switch roles and manage resources in the sub-account)
pulumi.export("training_account_arn", train_model_account.arn)
pulumi.export("sagemaker_role_arn", sagemaker_role.arn)

# Additional resources like SageMaker notebooks, training jobs, etc., can be defined below using their respective Pulumi resource classes.
```

In this program:
- We first create an AWS organization that allows for service control policies and access to essential services needed for SageMaker.
- Next, we create a sub-account dedicated to training models, ensuring it's tagged appropriately for easy identification.
- An IAM role for SageMaker is created, with an assume role policy attached to it, enabling services like SageMaker to assume the role.
- Finally, we export the ARNs of the sub-account and the IAM role for SageMaker for later use, which might include switching roles or referencing in other stacks.

Please ensure you have appropriate AWS credentials configured on your environment before attempting to deploy this program. If you need to manage resources across different AWS sub-accounts, you'll need to configure the Pulumi AWS provider with credentials for those specific accounts.

For managing multi-account strategies across other cloud providers, you would follow similar patterns. The resources and services would change according to what's offered by those providers, but the overarching strategy remains quite similar: organization-level control, sub-account/resource isolation, and cross-account permissioning.