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Beam
 Contents 
Installation and Setup
LLM
Example of the Beam app
Deploy the Beam app
Call the Beam app
Beam#
Beam makes it easy to run code on GPUs, deploy scalable web APIs,
schedule cron jobs, and run massively parallel workloads — without managing any infrastructure.
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
LLM#
from langchain.llms.beam import Beam
Example of the 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 the Beam app#
Once defined, you can deploy your Beam app by calling your model’s _deploy() method.
llm._deploy()
Call the Beam app#
Once a beam model is deployed, it can be called by calling your model’s _call() method.
This returns the GPT2 text response to your prompt.
rtdocs_stable/api.python.langchain.com/en/stable/integrations/beam.html