1. Monitoring Large Language Model Inference Performance

    Python

    To monitor the performance of a Large Language Model (LLM) inference, you typically want to collect metrics on prediction latency, success rates, error rates, and other performance indicators. In a cloud environment, these metrics are often gathered using services tailored to machine learning models, such as AWS SageMaker, Azure Machine Learning, or Google AI Platform.

    Below is a Pulumi program that sets up AWS SageMaker Monitoring Schedules to demonstrate how you can monitor LLM inference performance. AWS SageMaker allows you to create monitoring schedules for models deployed on the platform. The monitoring schedule can be configured to specify how often to execute the monitoring job that tests the deployed model predicting with sample input data to compute statistics about the performance.

    In the example, we'll define a SageMaker Endpoint, a Model, and a Monitoring Schedule that measures the performance of the model running at this Endpoint.

    Here's a detailed breakdown of each part of the setup:

    1. SageMaker Model: Represents the machine learning model itself. In AWS SageMaker, this is where you define the image containing the model and other configurations such as environment variables.

    2. SageMaker Endpoint: The model is hosted behind an endpoint. This can be thought of as the URL or URI you hit with inference requests. It's like a web server, but specifically for running inference on your model.

    3. SageMaker Endpoint Configuration: Specifies the configuration of the deployed SageMaker Endpoint, such as instance type and initial variant weights.

    4. SageMaker Monitoring Schedule: Sets up a recurring schedule that automatically monitors the quality of the machine learning model deployed to the endpoint. It can trigger a processing job to execute every time the schedule is due, which can then evaluate the performance and accuracy of the model.

    Please note that this example assumes you have a pre-existing trained model and you want to deploy it for inference.

    import pulumi import pulumi_aws as aws # Define an IAM role for SageMaker to assume sagemaker_role = aws.iam.Role("sagemaker-role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"} }] }""") # Attach policies to the IAM role for SageMaker sagemaker_role_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker-policy-attachment", role=sagemaker_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGE_MAKER_FULL_ACCESS) # Define a SageMaker Model model = aws.sagemaker.Model("llm-model", execution_role_arn=sagemaker_role.arn, primary_container={ "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/your-model-image:latest", "modelDataUrl": "s3://your-model-bucket/model.tar.gz", }, depends_on=[sagemaker_role_policy_attachment]) # Define the configuration for the SageMaker Endpoint endpoint_config = aws.sagemaker.EndpointConfiguration("llm-endpoint-config", production_variants=[{ "instanceType": "ml.m5.large", "modelName": model.name, "initialVariantWeight": 1, "variantName": "AllTraffic", }]) # Create a SageMaker Endpoint using the endpoint configuration endpoint = aws.sagemaker.Endpoint("llm-endpoint", endpoint_config_name=endpoint_config.name, depends_on=[endpoint_config]) # Define a Monitoring Schedule for the LLM Model monitoring_schedule = aws.sagemaker.MonitoringSchedule("llm-monitoring-schedule", monitoring_schedule_config={ "monitoringJobDefinition": { "roleArn": sagemaker_role.arn, "baselineConfig": { "statisticsResource": { "s3Uri": "s3://your-monitoring-bucket/statistics.json", # The baseline statistics file }, "constraintsResource": { "s3Uri": "s3://your-monitoring-bucket/constraints.json", # The baseline constraints file }, }, "monitoringOutputs": [{ "s3Output": { "s3Uri": "s3://your-monitoring-bucket/monitoring/results", "localPath": "/opt/ml/processing/output", "s3UploadMode": "Continuous", }, }], "monitoringResources": { # Here you can define the type and number of instances to run the monitoring jobs "clusterConfig": { "instanceType": "ml.m5.large", "instanceCount": 1, "volumeSizeInGb": 50, }, }, "appSpecification": { "imageUri": "123456789012.dkr.ecr.us-west-2.amazonaws.com/your-monitoring-image:latest", }, }, "monitoringScheduleName": "llm-model-performance", "scheduleConfig": { "scheduleExpression": "cron(0 * ? * * *)", # A cron expression to set the schedule frequency }, }, depends_on=[sagemaker_role_policy_attachment]) # Export the endpoint URL for easy access pulumi.export("endpoint_url", endpoint.endpoint_url)

    This Pulumi program does the following:

    • Creates a new IAM role that the SageMaker service can assume and attaches the necessary policy allowing it to fully manage SageMaker resources.

    • Defines a SageMaker model with the location of the model image (this would be your own model) and the S3 URL where the model data is stored.

    • Sets up an endpoint configuration and an endpoint. The endpoint is where the model is served for inference.

    • Defines a monitoring schedule that periodically executes a job to analyze the performance of the model. The job outputs are saved to a specific S3 location.

    • Exports the URL of the SageMaker endpoint to which inference requests can be sent.

    Each part is critical to set up a monitored and scalable inference environment for your Large Language Model. Using this setup, you can begin to gather metrics surrounding the performance of your model.

    Remember to replace placeholder values (your-model-image, your-model-bucket, your-monitoring-image, your-monitoring-bucket) with your actual information, including the correct ARNs and S3 paths for your use case. Also, the cron expression in the schedule config determines how often the monitoring job will run—customize it according to your monitoring needs.


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