1. Observability for AI Infrastructure using AWS Managed Grafana

    Python

    To build observability into your AI infrastructure using AWS Managed Grafana, you can leverage Pulumi to provision an Amazon Managed Grafana workspace. Grafana is an open-source platform for monitoring and observability that allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. AWS Managed Grafana is a fully managed service that is developed together with Grafana Labs and based on open-source Grafana. Automated provisioning, setup, scaling, and maintenance of your Grafana servers ensures that you can focus on your core business.

    Here's what we will do in this Pulumi program:

    1. Create an AWS Managed Grafana workspace, which serves as the foundation for our observability setup.
    2. Configure the Grafana workspace with necessary permissions and integrations.
    3. Set up authentication and network access.

    Please make sure you have the AWS provider configured in your Pulumi setup. This typically means having your AWS credentials set up in a way that Pulumi can access them, either by setting up the AWS CLI or by using environment variables.

    Now, let's look at the Pulumi program written in Python that sets up an AWS Managed Grafana workspace.

    import pulumi import pulumi_aws as aws # Create a Grafana workspace grafana_workspace = aws.grafana.Workspace("aiObservabilityGrafana", # Specify a name for the Grafana workspace name="ai-observability", # Specify the initial version of Grafana to deploy grafana_version="latest", # Set the access permissions for the workspace permission_type="CUSTOMER_MANAGED", # Optionally, specify additional data sources you want to integrate with the Grafana workspace data_sources=["PROMETHEUS", "AWS_XRAY"], # Optional VPC configuration # If your AI services are running within a VPC, specify subnet and security group IDs to integrate Grafana into the same VPC vpc_configuration=aws.grafana.WorkspaceVpcConfigurationArgs( subnet_ids=["YOUR_SUBNET_ID"], security_group_ids=["YOUR_SECURITY_GROUP_ID"], ), # Set up authentication providers (AWS SSO, Google, GitHub, etc.) authentication_providers=["AWS_SSO"], # If you have specific organizational requirements, you can specify the organizational units here organizational_units=["YOUR_ORGANIZATIONAL_UNIT"], ) # Export the Grafana workspace URL pulumi.export('grafana_url', grafana_workspace.endpoint)

    Here's an explanation of the code:

    • grafana_workspace: This is a Pulumi resource representing the AWS Managed Grafana workspace. We give it a logical name aiObservabilityGrafana, which is used in Pulumi's state management, not AWS.

    • name: A user-friendly name for the Grafana workspace.

    • grafana_version: Specifies the version of Grafana to deploy. In this case, we're using the latest available version.

    • permission_type: Defines whether AWS manages permissions or you do. For more elaborate setups, you would select "CUSTOMER_MANAGED".

    • data_sources: Grafana can connect to various data sources. If your AI infrastructure emits metrics to Prometheus or sends trace data to AWS X-Ray, you can list them here to integrate with Grafana.

    • vpc_configuration: If your infrastructure is contained within a VPC, this setting allows Grafana to access resources within that VPC by specifying subnet and security group IDs.

    • authentication_providers: Specifies the authentication mechanisms you want to use for your Grafana workspace. AWS Single Sign-On (AWS SSO) is used in this case, but other providers like Google and GitHub can also be included.

    • organizational_units: If you are using AWS Organizations, specify the OU IDs here to adhere to your company's organizational structure.

    Lastly, pulumi.export is used to output the Grafana workspace URL after it has been deployed. This URL is used to access the Grafana dashboard in your web browser.

    In a real-world scenario, you may need additional configurations, such as setting up IAM roles and permissions, integrating with additional data sources, or customizing the user authentication further.

    Once you run this program using Pulumi CLI, a Grafana workspace will be created, and you can start adding dashboards and data sources to visualize the metrics from your AI infrastructure.


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