text: "<Your beam client id>"beam_client_secret = "<Your beam client secret>"# Set the environment variablesos.environ["BEAM_CLIENT_ID"] = beam_client_idos.environ["BEAM_CLIENT_SECRET"] = beam_client_secret# Run the beam configure commandbeam configure --clientId={beam_client_id} --clientSecret={beam_client_secret}Install the Beam SDK:pip install beam-sdkDeploy and call Beam directly from langchain!Note that a cold start might take a couple of minutes to return the response, but subsequent calls will be faster!from langchain.llms.beam import Beamllm = 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)PreviousBasetenNextBedrockCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.