llama-stack/docs/source/getting_started/distributions/self_hosted_distro/tgi.md

TGI Distribution

The llamastack/distribution-tgi distribution consists of the following provider configurations.

API	Inference	Agents	Memory	Safety	Telemetry
Provider(s)	remote::tgi	meta-reference	meta-reference, remote::pgvector, remote::chroma	meta-reference	meta-reference

Docker: Start the Distribution (Single Node GPU)

Note

This assumes you have access to GPU to start a TGI server with access to your GPU.

$ cd distributions/tgi/gpu && docker compose up

The script will first start up TGI server, then start up Llama Stack distribution server hooking up to the remote TGI provider for inference. You should be able to see the following outputs --

[text-generation-inference] | 2024-10-15T18:56:33.810397Z  INFO text_generation_router::server: router/src/server.rs:1813: Using config Some(Llama)
[text-generation-inference] | 2024-10-15T18:56:33.810448Z  WARN text_generation_router::server: router/src/server.rs:1960: Invalid hostname, defaulting to 0.0.0.0
[text-generation-inference] | 2024-10-15T18:56:33.864143Z  INFO text_generation_router::server: router/src/server.rs:2353: Connected
INFO:     Started server process [1]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)

To kill the server

docker compose down

Docker: Start the Distribution (Single Node CPU)

Note

This assumes you have an hosted endpoint compatible with TGI server.

$ cd distributions/tgi/cpu && docker compose up

Replace <ENTER_YOUR_TGI_HOSTED_ENDPOINT> in run.yaml file with your TGI endpoint.

inference:
  - provider_id: tgi0
    provider_type: remote::tgi
    config:
      url: <ENTER_YOUR_TGI_HOSTED_ENDPOINT>

Conda: TGI server + llama stack run

If you wish to separately spin up a TGI server, and connect with Llama Stack, you may use the following commands.

Start TGI server locally

• Please check the TGI Getting Started Guide to get a TGI endpoint.

docker run --rm -it -v $HOME/.cache/huggingface:/data -p 5009:5009 --gpus all ghcr.io/huggingface/text-generation-inference:latest --dtype bfloat16 --usage-stats on --sharded false --model-id meta-llama/Llama-3.1-8B-Instruct --port 5009

Start Llama Stack server pointing to TGI server

Via Conda

llama stack build --template tgi --image-type conda
# -- start a TGI server endpoint
llama stack run ./gpu/run.yaml

Via Docker

docker run --network host -it -p 5000:5000 -v ./run.yaml:/root/my-run.yaml --gpus=all llamastack/distribution-tgi --yaml_config /root/my-run.yaml

Make sure in you run.yaml file, you inference provider is pointing to the correct TGI server endpoint. E.g.

inference:
  - provider_id: tgi0
    provider_type: remote::tgi
    config:
      url: http://127.0.0.1:5009

(Optional) Update Model Serving Configuration

To serve a new model with tgi, change the docker command flag --model-id <model-to-serve>.

This can be done by edit the command args in compose.yaml. E.g. Replace "Llama-3.2-1B-Instruct" with the model you want to serve.

command: ["--dtype", "bfloat16", "--usage-stats", "on", "--sharded", "false", "--model-id", "meta-llama/Llama-3.2-1B-Instruct", "--port", "5009", "--cuda-memory-fraction", "0.3"]

or by changing the docker run command's --model-id flag

docker run --rm -it -v $HOME/.cache/huggingface:/data -p 5009:5009 --gpus all ghcr.io/huggingface/text-generation-inference:latest --dtype bfloat16 --usage-stats on --sharded false --model-id meta-llama/Llama-3.2-1B-Instruct --port 5009

In run.yaml, make sure you point the correct server endpoint to the TGI server endpoint serving your model.

inference:
  - provider_id: tgi0
    provider_type: remote::tgi
    config:
      url: http://127.0.0.1:5009