ProvidersMoreBeamOn this pageBeamThis 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 accountInstall the Beam CLI with curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | shRegister API keys with beam configureSet environment variables (BEAM_CLIENT_ID) and (BEAM_CLIENT_SECRET)Install the Beam SDK pip install beam-sdkWrappers​LLM​There exists a Beam LLM wrapper, which you can access withfrom langchain.llms.beam import BeamDefine 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 Beamimport timellm = 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)PreviousBasetenNextBeautiful SoupInstallation and SetupWrappersLLMDefine your Beam app.Deploy your Beam appCall your Beam app