1. Real-time Deep Learning Inference Metrics via Grafana CloudWatch Datasource


    To set up real-time deep learning inference metrics using a Grafana CloudWatch datasource, we'll create a Pulumi program that carries out a series of steps:

    1. AWS CloudWatch Dashboard: The first step is to create a CloudWatch Dashboard in AWS. This dashboard allows us to visualize metrics and create customized views of the data that AWS services are sending to CloudWatch.

    2. AWS SageMaker Data Quality Job Definition: Since the focus is on deep learning inference, we'll assume there's an existing AWS SageMaker endpoint serving the model. To monitor the quality of data being processed by this endpoint, we will define a Data Quality Job in SageMaker, which can emit metrics to CloudWatch.

    3. Grafana CloudWatch Datasource: To visualize these metrics, we will set up a Grafana Data Source with CloudWatch as the backend.

    The following Python program using Pulumi will guide you through setting up the necessary infrastructure:

    import pulumi import pulumi_aws as aws import pulumi_grafana as grafana # Step 1: Create CloudWatch Dashboard # Replace "dashboard_body" with the actual JSON configuration for your CloudWatch dashboard. dashboard = aws.cloudwatch.Dashboard("deepLearningDashboard", dashboard_name="DeepLearningInferenceMetrics", dashboard_body="""{ "widgets": [ { "type": "metric", "x": 0, "y": 0, "width": 12, "height": 6, "properties": { "metrics": [ // Define specific metrics that you want to monitor ], "period": 300, "stat": "Average", "region": "us-west-2", "title": "Inference Metrics" } } ] }""" ) # Step 2: Define AWS SageMaker Data Quality Job Definition # Define the data quality job configuration. Make sure to replace placeholders with appropriate values. data_quality_job_definition = aws.sagemaker.DataQualityJobDefinition("dataQualityJob", role_arn="arn:aws:iam::ACCOUNT_ID:role/SageMakerRole", # Replace with the proper IAM role ARN job_resources={ "cluster_config": { "instance_count": 1, "instance_type": "ml.m5.large", "volume_size_in_gb": 30, }, }, # Other configurations go here, such as network_config, data_quality_app_specification, etc. ) # Step 3: Setup Grafana CloudWatch Data Source # This will use the Grafana provider to configure a CloudWatch data source. # Ensure you have the correct Grafana API credentials configured for Pulumi either via Provider # or using Pulumi config secrets for GRAFANA_AUTH_TOKEN and the url for your Grafana instance. grafana_cloudwatch_datasource = grafana.DataSource("cloudWatchDataSource", name="AWS CloudWatch", type="cloudwatch", url="https://monitoring.us-west-2.amazonaws.com", # Change your region accordingly jsonData={ "authType": "keys", "defaultRegion": "us-west-2" }, secureJsonData={ "accessKey": "YOUR_ACCESS_KEY", # Use Pulumi Config to handle secrets "secretKey": "YOUR_SECRET_KEY", } ) pulumi.export('dashboard_url', dashboard.dashboard_arn) pulumi.export('grafana_datasource_name', grafana_cloudwatch_datasource.name)


    • We first create a CloudWatch Dashboard with a customizable body that defines the various widgets and metrics to display. The JSON structure within dashboard_body can be tailor-made to highlight the metrics you are interested in.

    • The SageMaker Data Quality Job Definition is set up to periodically assess the quality of data used for inferences. It uses a predefined IAM role with the necessary permissions. You will need to modify the placeholders like ACCOUNT_ID, SageMakerRole, and other parameters to match your specific setup.

    • In the third step, we set up a Grafana DataSource which connects to CloudWatch. It's important to securely handle your AWS credentials (YOUR_ACCESS_KEY and YOUR_SECRET_KEY). For this example, we've hard-coded them, but in practice, you should use Pulumi Config to manage secrets.

    Finally, we export the ARN (Amazon Resource Name) for the dashboard and the name of the Grafana data source, which allows you to access these resources after they're created.

    Make sure to replace placeholders such as YOUR_ACCESS_KEY and YOUR_SECRET_KEY, and configure your Grafana API credentials before running the program. Pulumi will create the AWS resources and Grafana datasource, allowing you to monitor and visualize deep learning inference metrics in real-time.