Scalable Checkpoint Storage for Large Language Models with EFS

Question

Pulumi · Accepted Answer

To set up a scalable checkpoint storage for large language models, you can use Amazon Elastic File System (EFS), which offers a simple, serverless, set-and-forget, elastic file system. It can be easily mounted onto several EC2 instances, allowing multiple processes to access the file storage at the same time. This is particularly useful when training large language models that require frequent checkpointing of data during the training process.

Here's what you need to create in AWS with Pulumi to set up scalable checkpoint storage using EFS:

1. **Amazon EFS File System**: This is the core of your checkpoint storage. It is a regional service that enables you to set up a file storage that is available across multiple availability zones. It automatically grows and shrinks as you add and remove files, and you pay only for what you use.

2. **EFS Mount Targets**: These are required for EC2 instances to access the EFS File System. You need to create a mount target in each availability zone from where your EC2 instances will connect to EFS.

3. **EFS Access Points**: This is an EFS feature that provides a way to manage application access to shared datasets on a file system. Access points can enforce a user identity for all file system requests that are made through the access point, and they can enforce a root directory within the file system that the access point will apply to all file system requests.

4. **Security Groups**: These are used to control the inbound and outbound traffic to your mount targets and EC2 instances that will access the EFS.

Let's write a Pulumi program in Python to create these resources.

```python
import pulumi
import pulumi_aws as aws

# Create an EFS File System
efs_file_system = aws.efs.FileSystem("myEfsFileSystem",
    tags={
        "Name": "MyProductCheckpointStorage"
    })

# Security group for EFS mount targets to allow access over NFS
efs_sg = aws.ec2.SecurityGroup("efs_security_group",
    description="Allow NFS traffic",
    ingress=[
        aws.ec2.SecurityGroupIngressArgs(
            from_port=2049,  # NFS uses port 2049
            to_port=2049,
            protocol="tcp",
            cidr_blocks=["0.0.0.0/0"]  # Allow traffic from any source. In production, scope this down.
        ),
    ])

# Subnets and Mount Targets for EFS
# Example: Create a mount target in each availability zone

# This is a list of subnet IDs where you want the mount targets to reside.
# In practice, you would fetch these programmatically based on your AWS VPC setup.
subnet_ids = ["subnet-00e11100ababababa", "subnet-06e22200fcfcfcfcf"]

# This snippet creates a list of mount targets
mount_targets = []
for i, subnet_id in enumerate(subnet_ids):
    mount_target = aws.efs.MountTarget(f"mount-target-{i}",
        file_system_id=efs_file_system.id,
        subnet_id=subnet_id,
        security_groups=[efs_sg.id]
    )
    mount_targets.append(mount_target)

# Access Point
efs_access_point = aws.efs.AccessPoint("myEfsAccessPoint",
    file_system_id=efs_file_system.id,
    posix_user=aws.efs.AccessPointPosixUserArgs(
        uid=1001,
        gid=1001,
    ),
    root_directory=aws.efs.AccessPointRootDirectoryArgs(
        path="/checkpoint-directory",
        creation_info=aws.efs.AccessPointRootDirectoryCreationInfoArgs(
            owner_gid=1001,
            owner_uid=1001,
            permissions="755"
        )
    ))

# Export the File System ID and Access Point ID
pulumi.export('efs_file_system_id', efs_file_system.id)
pulumi.export('efs_access_point_id', efs_access_point.id)
```

The above program performs the following actions:

- Creates an EFS file system.
- Creates a security group that allows NFS traffic on port 2049 (NFS is the network file system protocol used by EFS).
- Creates mount targets in each subnet specified by the `subnet_ids` list.
- Creates an EFS access point that enforces a root directory and user identity for applications using the file system for checkpoints.

You can run this program with the Pulumi CLI by navigating to the directory containing this file and running `pulumi up`. This will provision the resources defined above in your AWS account.

Remember that you will need AWS credentials configured where Pulumi can access them, which typically means setting the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables, or using an AWS credentials file.