Beam

docs/integrations/beam.md

@@ -0,0 +1,93 @@
+# 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](https://www.beam.cloud/)
+- 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
+
+```python
+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.
+```python
+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.
+This returns an app ID for the deployed app, which can then be used to call the model.
+
+```python
+app_id = 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.
+
+```python
+response = llm._call(prompt="Running machine learning on a remote GPU", appID=appID)
+```
+
+An example script which deploys the model and calls it would be:
+
+```python
+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)
+
+appID = llm._deploy()
+
+response = llm._call(prompt="Running machine learning on a remote GPU", appID=appID)
+
+print(response)
+```

langchain/llms/__init__.py

@@ -7,6 +7,7 @@
 from langchain.llms.anyscale import Anyscale
 from langchain.llms.bananadev import Banana
 from langchain.llms.base import BaseLLM
+from langchain.llms.beam import Beam
 from langchain.llms.cerebriumai import CerebriumAI
 from langchain.llms.cohere import Cohere
 from langchain.llms.deepinfra import DeepInfra
@@ -41,6 +42,7 @@
     "AlephAlpha",
     "Anyscale",
     "Banana",
+    "Beam",
     "CerebriumAI",
     "Cohere",
     "DeepInfra",
@@ -81,6 +83,7 @@
     "anthropic": Anthropic,
     "anyscale": Anyscale,
     "bananadev": Banana,
+    "beam": Beam,
     "cerebriumai": CerebriumAI,
     "cohere": Cohere,
     "deepinfra": DeepInfra,