Qwen in a Lambda

Updated at 11/09/2024

(Marking the date because of how fast LLM APIs in Python move and may introduce breaking changes by the time anyone else reads this!)

Intro:

• This is a minor research on how we can put Qwen GGUF model files into AWS Lambda using Docker and SAM CLI

• Adapted from https://makit.net/blog/llm-in-a-lambda-function/

  • As of September '24, some required OS packages are not included in the above guide and subsequently in the Dockerfile as potentially the llama-cpp-python @ 0.2.90 does not include the required OS packages (?)
  • Who knows if there's anything new and breaking that will appear in the future :shrugs:

Motivation:

• I wanted to find out if I can reduce my AWS spending by only leveraging on the capabilities of Lambda and not Lambda + Bedrock as both services would incur more costs in the long run.

• The idea was to fit a small language model which wouldn't be as resource intensive relatively speaking and to, hopefully, receive subsecond to second latency on a 128 - 256 mb memory configuration

• I wanted to use also GGUF models to use different levels of quantization to find out which is the best performance / file size to be loaded into memory

  • My experimentation lead to me using Qwen2 1.5b Q5_K_M as it had the best "performance" and "latency" locally to receive prompt and spit out JSON structure using llama-cpp

Prerequisites:

• Docker
• AWS SAM CLI
• AWS CLI
• Python 3.11
• ECR permissions
• Lambda permissions
• Download qwen2-1_5b-instruct-q5_k_m.gguf into qwen_fuction/function/
  • Or download any other .gguf models that you'd like and change your model path in app.y / LOCAL_PATH

Setup Guide:

• Install pip packages under qwen_function/function/requirements.txt (preferably in a venv/conda env)
• Run sam build / sam validate
• Run sam local start-api to test locally
• Run curl --header "Content-Type: application/json" \ --request POST \ --data '{"prompt":"hello"}' \ http://localhost:3000/generate to prompt the LLM
  • Or use your preferred API clients
• Run sam deploy --guided to deploy to AWS
• This will deploy a cloudformation stack consisting of an API gateway and a Lambda function

Metrics

• Localhost - Macbook M3 Pro 32 GB

• AWS

  • Initial config - 128mb, 30s timeout

    • Lambda timed out! Cold start was timing out the lambda

  • Adjusted config #1 - 512mb, 30s timeout

    • Lambda timed out! Cold start was timing out the lambda

  • Adjusted config #2 - 512mb, 30s timeout

    • Lambda timed out! Cold start was timing out the lambda

• Adjusted config #3 - 3008mb, 30s timeout - cold start

• Adjusted config #3 - 3008mb, 30s timeout - warm start

Observation

• Referring back to the pricing structure of Lambda,

  • Pricing
  • 1536 MB / 1.465 s / $0.024638 over 1000 Lambda invocations
    • Qwen2 1.5b had me cranking up the memory to 3008mb just to not time out and receive 4 - 11 seconds latency response!
  • Claude 3 Haiku / $0.00025 / $0.00125 over 1000 input tokens & 1000 output tokens / Asia - Tokyo

• It may be cheaper to just use a hosted LLM using AWS Bedrock, etc.. on the cloud as the pricing structure for Lambda w/ Qwen does not look more competitive compared to Claude 3 Haiku

• Furthermore, the API gateway timeout is not easily configurable beyond the 30s timeout, depending on your usecase, this may not be very ideal

• Results via local is dependant on your machine specs!! and may heavily skew your perception, expectation vs reality

• Depending on your use case also, the latency per lambda invocation and responses might incur poor user experiences

Conclusion

All in all, I think this was a fun little experiment even though it didn't quite pan out to the budget & latency requirement via Qwen 1.5b for my side project. Thanks to @makit again for the guide!