1. Vault SSH Role Management for Machine Learning Clusters


    Vault, by HashiCorp, is a tool for managing secrets and access to systems, including generating dynamic SSH credentials for secure access to various resources. In a machine learning (ML) environment, teams often need to programmatically access computational resources like clusters to train models, retrieve datasets, or manage deployments. Using Vault's SSH secrets engine, you can set up role-based access management to these resources, ensuring that only authorized personnel or services can access your ML clusters.

    To manage SSH role access for machine learning clusters, a Pulumi program would typically involve these high-level steps:

    1. Vault SSH Backend Configuration: Setting up and configuring the SSH secrets engine in Vault, if it isn't already set up.
    2. SSH Role Creation: Defining roles in Vault that specify the access level and parameters for SSH credentials.
    3. Integrating with Machine Learning Clusters: Associating the created roles with your ML clusters which may be running on Kubernetes, AWS, GCP, Azure or other platforms.

    Here is an example Pulumi program demonstrating how to use Vault with Pulumi to manage SSH roles for machine learning clusters:

    import pulumi import pulumi_vault as vault # Assume that Vault and your cloud provider are already setup and configured. # Step 1: Configure the SSH secrets backend in Vault if it isn't already set up. # The SSH secrets engine allows Vault to issue SSH credentials for accessing # machines and clusters. ssh_secret_backend = vault.SshSecretBackend("ssh-secret-backend", description="SSH backend for ML clusters", path="ssh_ml_cluster" # The path where the secrets engine will be accessible. ) # Step 2: Create an SSH role in Vault. This role defines the access level and parameters # for SSH credentials. The below role is an example that would allow access to client # machines within a specific CIDR. ssh_role = vault.SshSecretBackendRole("ml-cluster-ssh-role", backend=ssh_secret_backend.path, # Associate with the backend defined earlier. name="ml-cluster-ssh-role", ttl="1h", # The time-to-live for the SSH credentials. max_ttl="24h", # The maximum time-to-live for the credentials. key_type="otp", # One-time password or dynamic key. cidr_list="", # The CIDR block of your ML cluster. default_user="ml-user", # The default SSH username. allow_bare_domains=True, allowed_domains=["example.com"], # The domains where the role applies. ) # Step 3: The actual association of the Vault SSH role with your machine learning clusters # would depend on the specifics of your infrastructure, such as how SSH access is provisioned. # For example, you might use the generated credentials from the role to access VM instances # in a cloud provider or nodes within a Kubernetes cluster. # The program would look different if you are integrating with a Kubernetes cluster, # AWS EC2 instances, GCP Compute Engine instances, Azure VMs, or another platform. # You would need to write additional Pulumi code to either create or integrate with # existing compute resources where your ML workloads are running and use Vault's SSH # credentials for access management. # For a complete solution, you would take the credentials provided by Vault and configure # them in your cluster management tools or directly on the instances, typically using # additional Pulumi resources for the cloud provider you are using for the ML clusters. # Output the configured secret backend and SSH role names pulumi.export("ssh_secret_backend_path", ssh_secret_backend.path) pulumi.export("ssh_role_name", ssh_role.name)

    This program sets up a Vault SSH secret backend and creates a role for SSH access to your machine learning clusters. It creates a one-time password (OTP) based role with a 1-hour TTL, which can be used for accessing a hypothetical cluster in the CIDR block. The actual integration with your machine learning clusters would require additional Pulumi code corresponding to your infrastructure – for instance, code for your Kubernetes cluster setup, AWS EC2 instances, Google Compute Engine VMs, etc.

    Keep in mind that to apply this Pulumi program, your Pulumi and cloud environment must already be configured, and you will need appropriate permissions to manage Vault and access machine learning clusters.

    Please replace the hypothetical values in this program (like CIDR blocks, default users, domains, etc.) with ones that match your own infrastructure and requirements. This program is a starting point and requires further customization to fit your specific setup.