1. GPU-based AI Workload Processing on Scaleway Instances.


    To accomplish GPU-based AI workload processing on Scaleway instances using Pulumi, we'll create an instance with a GPU-based type that is suitable for such tasks. Please note that while Pulumi can be used to provision infrastructure across various cloud providers, the specific provider packages needed to deploy infrastructure on Scaleway are not available in the Pulumi's ecosystem at the time of writing this.

    Nonetheless, I'll guide you through setting up a cloud instance suitable for GPU-based AI workloads on AWS, which provides P2 or P3 instances types with NVIDIA GPUs that are often used for such tasks.

    Let's start by setting up a Pulumi program in Python that provisions an EC2 instance with an attached GPU for AI workloads:

    1. Setting up the environment: Ensure you have Pulumi installed and configured with AWS credentials.

    2. Setting up the Pulumi program: Create a new directory for your Pulumi program, initialize a new Pulumi stack, and install the pulumi_aws package.

    3. Creating the EC2 instance: The EC2 instance will be created with a GPU instance type, such as p2.xlarge or p3.2xlarge, and configured with the necessary bootstrapping scripts to set up your AI environment.

    Here's a complete Pulumi program to create a GPU-based EC2 instance:

    import pulumi import pulumi_aws as aws # Select the appropriate GPU-based instance type # In this example, we use the `p2.xlarge` which comes with NVIDIA K80 GPUs. # However, AWS offers a variety of other GPU-based instances that you may prefer, # such as the 'p3.2xlarge' for more demanding tasks. gpu_instance_type = 'p2.xlarge' # Define the ami-ID for the Deep Learning Base AMI (Amazon Linux) # Make sure to use the correct AMI ID for your AWS region deep_learning_ami_id = 'ami-0b294f219d14e6a82' # example AMI ID for US-West-1 # Create a new security group sec_group = aws.ec2.SecurityGroup('secgroup', description='Enable HTTP access', ingress=[ # Allow inbound SSH connections aws.ec2.SecurityGroupIngressArgs( protocol='tcp', from_port=22, to_port=22, cidr_blocks=[''], ), # Allow inbound HTTP connections (you might not need this depending on your workload) aws.ec2.SecurityGroupIngressArgs( protocol='tcp', from_port=80, to_port=80, cidr_blocks=[''], ), ], egress=[ # Allow all outbound traffic aws.ec2.SecurityGroupEgressArgs( protocol='-1', from_port=0, to_port=0, cidr_blocks=[''], ), ] ) # Provision the EC2 instance with GPU gpu_instance = aws.ec2.Instance('gpuInstance', instance_type=gpu_instance_type, ami=deep_learning_ami_id, key_name='your-key-name', # Replace with your key name for SSH access security_groups=[sec_group.name], # Attach the security group associate_public_ip_address=True, # Set to False if you do not need a public IP # User data to bootstrap the instance - This can be a script to install CUDA or any other setup needed user_data=""" #!/bin/bash echo "Bootstrapping instance for AI workloads..." sudo yum update -y # Add additional setup commands as needed """, # We enable monitoring to keep an eye on the instance performance monitoring=True, ) # Export the public IP to access the instance pulumi.export('gpuInstancePublicIp', gpu_instance.public_ip)

    Here's what the program does:

    • It sets up a security group to allow inbound SSH and HTTP traffic, and allows all outbound traffic.
    • It provisions an EC2 instance of type p2.xlarge with a deep learning AMI. Replace the AMI ID with the correct one for your region.
    • It attaches a security group to our instance for network access configurations.
    • It includes a user data script for setting up the instance environment, which can be customized as needed to bootstrap the AI environment.

    Remember that before running pulumi up to deploy the resources, you should replace placeholders such as the AMI ID and key name with appropriate values for your setup. Ensure that you've set up the AWS access credentials using Pulumi secrets or environment variables.