1. On-Demand GPU Allocation for Deep Learning with Spotinst.


    In order to allocate on-demand GPU resources for deep learning purposes using Spotinst (which is now part of Spot by NetApp), you will typically want to leverage the Spotinst service for managing cloud compute resources more efficiently, often at a lower cost.

    Unfortunately, as of my current knowledge, there isn't a direct Pulumi provider for Spotinst. However, you can still achieve on-demand GPU allocation for your deep learning workload using Pulumi with the cloud provider's native services. For instance, if you are using AWS, you can use the EC2 service to launch instances with GPU capabilities (such as the p2, p3, and g4 instance types) and apply Spot pricing strategies for cost optimization.

    Below is how you can create an AWS EC2 spot instance with an attached GPU for deep learning purposes using Pulumi in Python. Note that Spotinst automations might not be available directly through Pulumi, but you can still benefit from Spot Instance pricing and manage requests using AWS-native resources.

    To start, you will need to create an EC2 SpotInstanceRequest and specify the GPU instance type you need. You will also specify the AMI (Amazon Machine Image) that supports your deep learning frameworks, like TensorFlow or PyTorch.

    Here's an example program that sets up a single GPU-backed EC2 Spot instance suitable for deep learning:

    import pulumi import pulumi_aws as aws # Define the AMI (Amazon Machine Image) # Here we are using Deep Learning Base AMI (Ubuntu 18.04) version 30.0 # Make sure to select the appropriate AMI for your region and deep learning needs ami_id = "ami-12345abcdef" # replace with the actual AMI ID # Define the instance type for the GPU instance # For example, 'p3.2xlarge' is a commonly used instance type for deep learning tasks instance_type = "p3.2xlarge" # Setup a spot instance request for the GPU instance gpu_spot_instance = aws.ec2.SpotInstanceRequest("gpuSpotInstance", spot_type="one-time", # For one-time requests that don't need to be maintained if interrupted instance_interruption_behavior="stop", # Option includes 'stop', 'terminate', or 'hibernate' ami=ami_id, instance_type=instance_type, key_name="my-key-pair", # Replace with your key pair name tags={ "Name": "deep-learning-instance", }) # Output the spot instance request ID and the instance ID (after it is fulfilled) pulumi.export('spot_instance_request_id', gpu_spot_instance.id) pulumi.export('instance_id', gpu_spot_instance.spot_instance_id)

    This code sets up a spot instance request that, once fulfilled, will give you an instance geared for deep learning with an attached GPU. Please replace the ami_id and key_name with the actual AMI ID and key pair you have. The AMI should be deep learning-friendly, preinstalled with software like CUDA, cuDNN, TensorFlow, and other relevant libraries. You can find suitable AMIs in the AWS Marketplace, or the public community AMIs that match your requirements.

    Remember to configure your credentials for AWS before running Pulumi, using either environment variables or the AWS CLI. For more information on how to set up AWS credentials for Pulumi, refer to AWS Setup.

    Once your instance is running, connect to it via SSH and begin setting up your deep learning environment, data, and models.

    Please note that pricing, availability, and instance interruption behavior are subject to AWS Spot Market conditions. Always review the current documentation and pricing information to avoid unexpected costs.