Bedrock Benchmark

This project contains source code and supporting files for a serverless application that you can deploy with the SAM CLI. It includes the following files and folders:

functions - Code for the application's Lambda functions to Call Bedrock APIs and populate the sample prompt catalog in DynamoDB.
statemachines - Definition for the state machine that orchestrates the bedrock benchmark workflow.
template.yaml - A template that defines the application's AWS resources.
scripts - The folder contains script to update/modify the prompt catalog in DynamoDB
srteamlit_app - The folder contains code to setup and run an streamlit app in SageMaker Studio to collect human feedback on LLM response

This application creates a bedrock benchmark workflow which runs on a pre-defined schedule. The application uses several AWS resources, including Step Functions state machines, DynamoDB, Lambda functions and an EventBridge rule trigger. These resources are defined in the template.yaml file in this project. You can update the template to add AWS resources through the same deployment process that updates your application code.

Deploy the sample application

To use the SAM CLI, you need the following tools:

SAM CLI - Install the SAM CLI
Python 3 installed

To build and deploy your application for the first time, run the following in your shell in system terminal instance on SageMaker Studio:

sam build
sam validate
sam deploy --guided

Bedrock Model

The following Bedrock base models for Text Generation are included for benchmarking.

Cohere : Command Text, Command-R, Command-R+
Jurassic : AI21 Ultra, AI21 Mid
Amazon : Titan Express, Titan Large
Meta : Llama2 13B Chat, Llama2 70B Chat, Llama3 8B Instruct, Llama3 70B Instruct
Anthropic : Claude Instant V1, Claude V2, Claude V2.1, Claude Sonnet, Claude Haiku, Claude Opus
Mistral: Mistral-7b-instruct, Mixtral-8x7b-instruct

Collected Metrics

The following metrics are collected for each invocation:

invocation latency
input token size
output token size
model inference parameters

Prompt Repository

Sample prompts are added to a Dynamodb table as the prompt catalog. You can run the 'create_prompt_catalog.py' inside /scripts folder to add/modify the prompt list at any time.

UI

We used Streamlit for a simple UI and deployed it to SageMaker Studio. Follow these steps to run the streamlit app.

sh setup.sh #install dependencies
sh run.sh #run the streamlit app
sh cleanup.sh #clean up

Cleanup

To delete the application that you created, use the AWS CLI. Assuming you used your project name for the stack name, you can run the following:

sam delete --stack-name "bedrockbenchmark"