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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)

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