AI Model Experiment Tracking Dashboard with AWS Grafana

Question

Pulumi · Accepted Answer

To set up an AI Model Experiment Tracking Dashboard using AWS and Grafana, you would typically need to collect metrics and logs from your AI experiments, store this data, and then visualize it on a dashboard. AWS Grafana is an ideal tool for creating dashboards for visualizing this data.

AWS Managed Grafana is a service that allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. By directly connecting Grafana to multiple data sources like Amazon CloudWatch, AWS X-Ray, and others, you can use it to monitor your operational metrics and logs.

Below is a Pulumi program that sets up an AWS Managed Grafana workspace, which is the foundation for creating Grafana dashboards. For the purpose of this example, we are considering that you are already streaming your AI model experiment data to a compatible AWS data source such as Amazon CloudWatch.

The program performs the following steps:

1. Creates an AWS Managed Grafana workspace. This is a managed service, meaning AWS takes care of the installation, set-up, and maintenance of Grafana.
2. Defines an IAM role with permissions that Grafana will assume to interact with AWS services on your behalf. Depending on your use case, you might need additional permissions to access datasets from other AWS services.

Make sure to replace `"MyGrafanaAdminRole"` with the appropriate IAM Role or define fine-grained permissions depending on your security requirements.

```python
import pulumi
import pulumi_aws as aws
import pulumi_aws.grafana as grafana

# Defines an IAM role for Grafana to access AWS resources
grafana_role = aws.iam.Role("grafana-role",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": {
            "Effect": "Allow",
            "Principal": {"Service": "grafana.amazonaws.com"},
            "Action": "sts:AssumeRole"
        }
    }"""
)

# Attaches a policy to the IAM role for Grafana. Replace this with your desired policy.
grafana_role_policy = aws.iam.RolePolicyAttachment("grafana-role-policy",
    role=grafana_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AmazonManagedGrafanaViewerPolicy"
)

# Create a new Grafana workspace
grafana_workspace = grafana.Workspace("grafana-workspace",
    account_access_type="CURRENT_ACCOUNT", # You can specify "ORGANIZATION" for organization-wide access.
    authentication_providers=["AWS_SSO"],  # Authentication method, can be "AWS_SSO" or "SAML".
    name="ai-model-experiments",           # Name of the Grafana workspace.
    description="AI Model Experiment Tracking Dashboard",
    role_arn=grafana_role.arn,            # Associate the IAM role with the workspace.
    permission_type="CUSTOMER_MANAGED",   # "SERVICE_MANAGED" or "CUSTOMER_MANAGED" permissions.
    # vpc_configuration example for VPC access. Uncomment and configure as needed.
    # vpc_configuration={
    #     "securityGroupIds": ["sg-12345678"],
    #     "subnetIds": ["subnet-12345678", "subnet-87654321"]
    # },
    # Define user and organizational roles, SAML config, and more as needed.
)

pulumi.export("grafana_workspace_endpoint", grafana_workspace.endpoint) # Export the endpoint to access the Grafana workspace.
```

After deploying the above Pulumi program, you can access your AWS Managed Grafana workspace using the exported endpoint URL. From there, you can start creating dashboards that track the metrics from your AI experiments.

Please note that you will need to configure data sources within Grafana to connect to your metrics storage, such as Amazon CloudWatch, Amazon S3, or any compatible data source. This can be done from the Grafana workspace console UI by adding data source configurations and creating queries to visualize your experiment metrics.

If you use additional AWS services or have specific integration requirements, you will need to add the corresponding permissions to the IAM role and configure the necessary resources in your Pulumi program.