1. AI Workload Performance Dashboards with Grafana


    To create AI Workload Performance Dashboards with Grafana using Pulumi, you would typically do the following:

    1. Provision the Grafana instance: Set up Grafana in a way that it is accessible to present the dashboards. You could either use a cloud-hosted Grafana service (like Aiven for Grafana) or deploy Grafana on a cloud instance (e.g., using AWS EC2 or another provider's equivalent).

    2. Create Grafana resources: Once Grafana is running, you'll need to define resources such as dashboards, data sources, and users. Pulumi allows you to provision these resources as code.

    3. Monitor and visualize data: You can configure Grafana to connect to your data sources (like Prometheus for metric collection or ElasticSearch for log data) and use this data to create informative and interactive dashboards.

    4. Automate and manage: Using Pulumi, you can automate the provisioning of the Grafana environment and manage changes over time through Infrastructure as Code practices.

    Here's a basic Pulumi program written in Python to illustrate how you could set up a Grafana instance with Pulumi. This uses the aiven and grafana Pulumi providers to create a cloud-hosted Grafana instance and a simple dashboard. Make sure you have an Aiven account, or you could adapt this to use a different Grafana hosting solution.

    import pulumi import pulumi_aiven as aiven import pulumi_grafana as grafana # Replace these variables with your own configuration information. # In a production environment, sensitive values should be pulled from a config or secrets management system. aiven_project_name = "my-aiven-project" grafana_cloud_name = "aws-eu-west-3" grafana_service_name = "my-grafana-instance" grafana_plan = "startup-4" # Select the plan that fits your needs on Aiven. grafana_admin_password = pulumi.Config('grafana').require_secret('admin_password') # Deploy Grafana instance on Aiven. grafana_service = aiven.Grafana( "my-grafana-service", project=aiven_project_name, cloud_name=grafana_cloud_name, plan=grafana_plan, service_name=grafana_service_name, grafana_user_config=aiven.GrafanaUserConfigArgs( service_to_fork_from="", public_access=aiven.ServicePublicAccessArgs( grafana=True ) ) ) # After setting up the Grafana service, we can create a basic dashboard. dashboard_config = { "uid": "dMyiq60Zz", "title": "AI Workload Performance", "tags": ["templated"], "timezone": "browser", "schemaVersion": 16, "version": 0, "refresh": "25s", "panels": [ { "type": "graph", "title": "CPU Usage", "id": 1, "targets": [ { "datasource": "${DS_PROMETHEUS}", "expr": "100 - (avg by(instance)(irate(node_cpu_seconds_total{mode='idle'}[5m])) * 100)" } ] } ] } # Create the Grafana Dashboard. grafana_dashboard = grafana.Dashboard( "ai-workload-dashboard", config_json=pulumi.Output.secret(dashboard_config) ) # Export the URL of the Grafana instance. pulumi.export('grafana_url', grafana_service.service_uri)

    In this program:

    • We instantiate a Grafana service with aiven.Grafana to provide hosted Grafana instance. (Aiven Grafana Docs)
    • We use grafana.Dashboard to create a new Grafana dashboard with a simple panel that can visualize CPU Usage. (Grafana Dashboard Docs)
    • The grafana_url output can be used to access the Grafana UI once the Pulumi program is applied and the instance is ready.

    Remember, you will need to handle the proper configuration of data sources and possibly configure fine-grained access control depending on your requirements. The above program is a simple start to illustrate the pattern, and in practice, you would build out more complex dashboards and configuration.