GPU-Optimized EC2 Instances for Deep Learning

Question

Pulumi · Accepted Answer

To create GPU-optimized EC2 instances for deep learning on AWS with Pulumi, you'd typically use the `aws.ec2.Instance` resource. This resource allows you to launch an EC2 instance of a specified type. For deep learning workloads, AWS provides several instance types that are optimized for GPU-based computations, such as those belonging to the `p3` or `g4` families.

When choosing an instance type for deep learning, consider the following:
- **Compute capacity**: Ensure the GPU, CPU, and memory are suitable for your deep learning tasks.
- **Networking**: Check if high bandwidth networking is required for your workloads, as some GPU instances offer better networking capabilities.
- **Pricing**: GPU instances can be expensive, so you may want to use spot instances or reserved instances to save costs.

Below is a Pulumi program written in Python that provisions a GPU-optimized EC2 instance suitable for deep learning. The program will use a `p3.2xlarge` instance type, which is part of the `p3` family known for high-performance computing and deep learning tasks. We'll also include a security group to allow SSH access.

```python
import pulumi
import pulumi_aws as aws

# Create a new security group for the EC2 instance to allow SSH access
sec_group = aws.ec2.SecurityGroup('deep-learning-secgroup',
    description='Allow SSH inbound traffic',
    ingress=[
        # SSH access from anywhere
        {
            'protocol': 'tcp',
            'from_port': 22,
            'to_port': 22,
            'cidr_blocks': ['0.0.0.0/0'],
        },
    ]
)

# Provision a GPU-optimized EC2 instance for deep learning
gpu_instance = aws.ec2.Instance('deep-learning-instance',
    # 'p3.2xlarge' is a GPU instance type suitable for deep learning workloads
    instance_type='p3.2xlarge',
    # Choose an AMI that is optimized for GPU usage (e.g., Deep Learning Base AMI)
    ami='ami-0b294f219d14e6a82', # Example AMI ID, replace with a current GPU optimized AMI
    key_name='your-key-pair',  # Replace with your key pair name
    vpc_security_group_ids=[sec_group.id],
    # It is essential to use the correct EBS optimized setting and associate a public IP
    ebs_optimized=True,
    associate_public_ip_address=True,
    # You can specify other parameters like block device mappings, IAM roles, etc.
    tags={
        'Name': 'DeepLearningInstance',
    }
)

# Export the public IP of the GPU instance to access it later
 pulumi.export('gpu_instance_public_ip', gpu_instance.public_ip)
```

Here's a breakdown of what the script includes:

- **`SecurityGroup`**: A resource that defines a security group to allow inbound SSH traffic.

- **EC2 Instance Type**: We use `p3.2xlarge`, which is ideal for deep learning because of the powerful GPU it includes.

- **AMI**: You would replace `'ami-0b294f219d14e6a82'` with the actual AMI ID of an image that is GPU-optimized. AWS often provides specific AMIs tailored for deep learning tasks that come with pre-installed packages and drivers like CUDA.

- **Instance Settings**: Setting `ebs_optimized` to `True` for better disk performance and `associate_public_ip_address` to `True` to assign a public IP address to the instance for remote access.

- **Exports**: The public IP address of the GPU instance is exported as an output of the program, which you can use to access the instance remotely, for example, using SSH.

Please make sure you replace `'your-key-pair'` with the name of your SSH key pair already uploaded to AWS, and ensure that the AMI ID is up-to-date for the region you are deploying to. This script assumes you have AWS credentials and Pulumi set up to run in your environment.

To launch the instance, you will save this script to a file `__main__.py`, navigate to the directory with this file, and run `pulumi up` from the CLI. The instance will be provisioned according to the specifications defined in the script.