https://github.com/bentoml/bentotgi

Science Score: 13.0%

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Low similarity (16.0%) to scientific vocabulary

Last synced: 10 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: bentoml
Language: Python
Default Branch: main
Size: 11.7 KB

Statistics

Stars: 3
Watchers: 3
Forks: 1
Open Issues: 1
Releases: 0

Created about 2 years ago · Last pushed over 1 year ago

Metadata Files

Readme

Self-host LLMs with TGI and BentoML

This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models using Hugging Face TGI, a toolkit that enables high-performance text generation for LLMs.

See here for a full list of BentoML example projects.

💡 This example is served as a basis for advanced code customization, such as custom model, inference logic or LMDeploy options. For simple LLM hosting with OpenAI compatible endpoint without writing any code, see OpenLLM.

Prerequisites

You have installed Python 3.8+ and pip. See the Python downloads page to learn more.
You have a basic understanding of key concepts in BentoML, such as Services. We recommend you read Quickstart first.
If you want to test the Service locally, you need a Nvidia GPU with at least 20G VRAM.
You have installed Docker as this example depends on a base Docker image ghcr.io/huggingface/text-generation-inference:2.0.4 to set up TGI.
This example uses Llama 3. Make sure you have gained access to the model.
(Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.

Set up the environment

Clone the repo.

bash git clone https://github.com/bentoml/BentoTGI.git cd BentoTGI

Make sure you are in the BentoTGI directory and mount it from your host machine (${PWD}) into a Docker container at /BentoTGI. This means that the files and folders in the current directory are available inside the container at the /BentoTGI.

bash docker run --runtime=nvidia --gpus all -v ${PWD}:/BentoTGI -v ~/bentoml:/root/bentoml -p 3000:3000 --entrypoint /bin/bash -it --workdir /BentoTGI ghcr.io/huggingface/text-generation-inference:2.0.4

Install dependencies.

bash cd llama-3-8b-instruct pip install -r requirements.txt

Download the model

Run the script to download Llama 3 to the BentoML Model Store.

bash python import_model.py

Run the BentoML Service

We have defined a BentoML Service in service.py. Run bentoml serve in your project directory to start the Service.

bash $ bentoml serve . 2024-06-06T10:31:45+0000 [INFO] [cli] Starting production HTTP BentoServer from "service:TGI" listening on http://localhost:3000 (Press CTRL+C to quit)

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

```bash curl -X 'POST' \ 'http://localhost:3000/generate' \ -H 'accept: text/event-stream' \ -H 'Content-Type: application/json' \ -d '{ "prompt": "Explain superconductors like I'\''m five years old", "max_tokens": 1024 }' ```

Python client

```python import bentoml with bentoml.SyncHTTPClient("http://localhost:3000") as client: response_generator = client.generate( prompt="Explain superconductors like I'm five years old", max_tokens=1024 ) for response in response_generator: print(response, end='') ```

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, then run the following command to deploy it.

bash bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.

Owner

Name: BentoML
Login: bentoml
Kind: organization
Location: San Francisco

Website: https://bentoml.com
Twitter: bentomlai
Repositories: 76
Profile: https://github.com/bentoml

The most flexible way to serve AI models in production

GitHub Events

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Pull request event: 3
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Last Year

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Last synced: 10 months ago

All Time

Total issues: 0
Total pull requests: 3
Average time to close issues: N/A
Average time to close pull requests: about 3 hours
Total issue authors: 0
Total pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 2
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 0
Pull requests: 2
Average time to close issues: N/A
Average time to close pull requests: about 1 hour
Issue authors: 0
Pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 1

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