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Beam

This page covers how to use Beam within LangChain. It is broken into two parts: installation and setup, and then references to specific Beam wrappers.

Installation and Setup​

  • Create an account
  • Install the Beam CLI with curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh
  • Register API keys with beam configure
  • Set environment variables (BEAM_CLIENT_ID) and (BEAM_CLIENT_SECRET)
  • Install the Beam SDK pip install beam-sdk

Wrappers​

LLM​

There exists a Beam LLM wrapper, which you can access with

from langchain.llms.beam import Beam

Define your Beam app.​

This is the environment you’ll be developing against once you start the app. It's also used to define the maximum response length from the model.

llm = Beam(model_name="gpt2",
           name="langchain-gpt2-test",
           cpu=8,
           memory="32Gi",
           gpu="A10G",
           python_version="python3.8",
           python_packages=[
               "diffusers[torch]>=0.10",
               "transformers",
               "torch",
               "pillow",
               "accelerate",
               "safetensors",
               "xformers",],
           max_length="50",
           verbose=False)

Deploy your Beam app​

Once defined, you can deploy your Beam app by calling your model's _deploy() method.

llm._deploy()

Call your Beam app​

Once a beam model is deployed, it can be called by callying your model's _call() method. This returns the GPT2 text response to your prompt.

response = llm._call("Running machine learning on a remote GPU")

An example script which deploys the model and calls it would be:

from langchain.llms.beam import Beam
import time

llm = Beam(model_name="gpt2",
           name="langchain-gpt2-test",
           cpu=8,
           memory="32Gi",
           gpu="A10G",
           python_version="python3.8",
           python_packages=[
               "diffusers[torch]>=0.10",
               "transformers",
               "torch",
               "pillow",
               "accelerate",
               "safetensors",
               "xformers",],
           max_length="50",
           verbose=False)

llm._deploy()

response = llm._call("Running machine learning on a remote GPU")

print(response)