promptfoo/examples/amazon-sagemaker

amazon-sagemaker (Amazon SageMaker Provider)

This example demonstrates how to evaluate models deployed on Amazon SageMaker endpoints using promptfoo.

You can run this example with:

npx promptfoo@latest init --example amazon-sagemaker

Purpose

This example shows how to:

• Connect to and evaluate models deployed on Amazon SageMaker endpoints
• Configure various model types (OpenAI, Anthropic, Llama, Mistral) running on SageMaker
• Compare performance between different SageMaker-hosted models
• Use transform functions to format prompts for specific model requirements
• Work with embeddings models on SageMaker

Prerequisites

1. AWS account with SageMaker access
2. Deployed SageMaker endpoints with your models
3. AWS credentials configured locally
4. Required npm packages:

   npm install -g @aws-sdk/client-sagemaker-runtime
   

Environment Variables

This example requires the following environment variables:

• AWS_ACCESS_KEY_ID - Your AWS access key
• AWS_SECRET_ACCESS_KEY - Your AWS secret key
• AWS_REGION - Optional, can also be specified in the configuration

You can set these in a .env file or directly in your environment.

Example Configurations

This example includes multiple configuration files demonstrating different SageMaker integration patterns:

• promptfooconfig.openai.yaml: OpenAI-compatible models on SageMaker
• promptfooconfig.anthropic.yaml: Anthropic Claude models on SageMaker
• promptfooconfig.jumpstart.yaml: AWS JumpStart foundation models
• promptfooconfig.llama.yaml: Llama 3.2 models on SageMaker JumpStart
• promptfooconfig.mistral.yaml: Mistral 7B v3 models on SageMaker (Hugging Face)
• promptfooconfig.llama-vs-mistral.yaml: Comparison between Llama and Mistral models
• promptfooconfig.embedding.yaml: Embedding models on SageMaker
• promptfooconfig.multimodel.yaml: Multiple model types on SageMaker
• promptfooconfig.transform.yaml: Transform functions for SageMaker endpoints

Running the Examples

1. Replace the endpoint names in the configuration files with your actual SageMaker endpoints
2. Run the evaluation using promptfoo:

# Run a specific configuration
promptfoo eval -c promptfooconfig.jumpstart.yaml

Testing Your Setup

This directory includes a test script to validate your SageMaker endpoint configuration before running a full evaluation:

# Basic test for an OpenAI-compatible endpoint
node test-sagemaker-provider.js --endpoint=my-endpoint --model-type=openai

# Test with an embedding endpoint
node test-sagemaker-provider.js --endpoint=my-embedding-endpoint --embedding=true

# Test with transforms
node test-sagemaker-provider.js --endpoint=my-endpoint --model-type=llama --transform=true

# Test with a custom transform file
node test-sagemaker-provider.js --endpoint=my-endpoint --transform=true --transform-file=transform.js

Transform Functions

The SageMaker provider supports transforming prompts before they're sent to the endpoint, which is particularly useful for formatting prompts according to specific model requirements.

Inline Transform

providers:
  - id: sagemaker:llama:your-endpoint
    config:
      region: us-west-2
      modelType: llama
      # Apply an inline transform
      transform: |
        return `<s>[INST] ${prompt} [/INST]`;

File-Based Transform

This example includes a sample transform file (transform.js) that shows how to create reusable transformations:

providers:
  - id: sagemaker:jumpstart:your-endpoint
    config:
      region: us-west-2
      modelType: jumpstart
      # Reference an external transform file
      transform: file://transform.js

The transform function receives the prompt and a context object containing the provider configuration:

module.exports = function (prompt, context) {
  // Access config values
  const maxTokens = context.config?.maxTokens || 256;

  // Return transformed input
  return {
    inputs: prompt,
    parameters: {
      max_new_tokens: maxTokens,
      temperature: 0.7,
    },
  };
};

JumpStart Models

JumpStart models require a specific input/output format. The provider handles this automatically when modelType: jumpstart is specified:

providers:
  - id: sagemaker:jumpstart:your-jumpstart-endpoint
    config:
      region: us-west-2
      modelType: jumpstart
      maxTokens: 256
      responseFormat:
        path: 'json.generated_text'

Rate Limiting with Delays

For better rate limiting with SageMaker endpoints, you can add delays between API calls:

providers:
  - id: sagemaker:your-endpoint
    config:
      region: us-west-2
      delay: 500 # Add a 500ms delay between API calls

Expected Results

After running the evaluation, you should expect to see:

1. A comparison of responses from your SageMaker endpoints across different prompts
2. Performance metrics for each endpoint and prompt combination
3. Any errors or issues with specific endpoints or configurations

Troubleshooting

"Batch inference failed" Errors

If you encounter "Batch inference failed" errors:

1. Add a delay parameter (at least 500ms recommended)
2. Verify you're using the correct modelType for your endpoint:
   • For Llama models: Use modelType: jumpstart
   • For Mistral models: Use modelType: huggingface
3. Ensure you've specified the correct contentType and acceptType as "application/json"
4. Check that your endpoint is active and functioning in the SageMaker console

Response Format Issues

If you're getting unusual responses or missing output:

1. Make sure you're using the correct JavaScript expression for your model type:
   • For Llama models (JumpStart): Use responseFormat.path: "json.generated_text"
   • For Mistral models (Hugging Face): Use responseFormat.path: "json[0].generated_text"

Transform Issues

If transforms aren't working correctly:

1. Check that your transform function returns a valid string or object
2. For file-based transforms, verify the file path is correct and the file is accessible
3. Use the test script with --transform=true to debug transform behavior

Rate Limiting

If you're still experiencing errors even with the correct configuration:

1. Increase the delay between requests (try 1000ms or higher)
2. Run fewer tests in parallel
3. Monitor your endpoint metrics in the SageMaker console