mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
faa26650c9
# Beam Calls the Beam API wrapper to deploy and make subsequent calls to an instance of the gpt2 LLM in a cloud deployment. Requires installation of the Beam library and registration of Beam Client ID and Client Secret. Additional calls can then be made through the instance of the large language model in your code or by calling the Beam API. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
92 lines
2.3 KiB
Markdown
92 lines
2.3 KiB
Markdown
# 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.
|
||
|
||
```python
|
||
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("Running machine learning on a remote GPU")
|
||
```
|
||
|
||
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)
|
||
|
||
llm._deploy()
|
||
|
||
response = llm._call("Running machine learning on a remote GPU")
|
||
|
||
print(response)
|
||
``` |