forked from phoenix-oss/llama-stack-mirror

feat: Add a new template for dell (#978 )

- Added new template `dell` and its documentation 
- Update docs 
- [minor] uv fix i came across 
- codegen for all templates 

Tested with 

```bash
export INFERENCE_PORT=8181
export DEH_URL=http://0.0.0.0:$INFERENCE_PORT
export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
export CHROMADB_HOST=localhost
export CHROMADB_PORT=6601
export CHROMA_URL=[http://$CHROMADB_HOST:$CHROMADB_PORT](about:blank)
export CUDA_VISIBLE_DEVICES=0
export LLAMA_STACK_PORT=8321

# build the stack template 
llama stack build --template=dell 

# start the TGI inference server 
podman run --rm -it --network host -v $HOME/.cache/huggingface:/data -e HF_TOKEN=$HF_TOKEN -p $INFERENCE_PORT:$INFERENCE_PORT --gpus $CUDA_VISIBLE_DEVICES [ghcr.io/huggingface/text-generation-inference](http://ghcr.io/huggingface/text-generation-inference) --dtype bfloat16 --usage-stats off --sharded false --cuda-memory-fraction 0.7 --model-id $INFERENCE_MODEL --port $INFERENCE_PORT --hostname 0.0.0.0

# start chroma-db for vector-io ( aka RAG )
podman run --rm -it --network host --name chromadb -v .:/chroma/chroma -e IS_PERSISTENT=TRUE chromadb/chroma:latest --port $CHROMADB_PORT --host $(hostname)

# build docker 
llama stack build --template=dell --image-type=container

# run llama stack server ( via docker )
podman run -it \
--network host \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
# NOTE: mount the llama-stack / llama-model directories if testing local changes 
-v /home/hjshah/git/llama-stack:/app/llama-stack-source -v /home/hjshah/git/llama-models:/app/llama-models-source \ localhost/distribution-dell:dev \
--port $LLAMA_STACK_PORT  \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env DEH_URL=$DEH_URL \
--env CHROMA_URL=$CHROMA_URL

# test the server 
cd <PATH_TO_LLAMA_STACK_REPO>
LLAMA_STACK_BASE_URL=http://0.0.0.0:$LLAMA_STACK_PORT pytest -s -v tests/client-sdk/agents/test_agents.py

```

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>

2025-02-06 14:14:39 -08:00

4.7 KiB

Raw Blame History

orphan: true

TGI Distribution

:maxdepth: 2
:hidden:

self

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

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`remote::tgi`
safety	`inline::llama-guard`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
telemetry	`inline::meta-reference`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`
vector_io	`inline::faiss`, `remote::chromadb`, `remote::pgvector`

You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 5001)
INFERENCE_MODEL: Inference model loaded into the TGI server (default: meta-llama/Llama-3.2-3B-Instruct)
TGI_URL: URL of the TGI server with the main inference model (default: http://127.0.0.1:8080}/v1)
TGI_SAFETY_URL: URL of the TGI server with the safety model (default: http://127.0.0.1:8081/v1)
SAFETY_MODEL: Name of the safety (Llama-Guard) model to use (default: meta-llama/Llama-Guard-3-1B)

Setting up TGI server

Please check the TGI Getting Started Guide to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:

export INFERENCE_PORT=8080
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0

docker run --rm -it \
  -v $HOME/.cache/huggingface:/data \
  -p $INFERENCE_PORT:$INFERENCE_PORT \
  --gpus $CUDA_VISIBLE_DEVICES \
  ghcr.io/huggingface/text-generation-inference:2.3.1 \
  --dtype bfloat16 \
  --usage-stats off \
  --sharded false \
  --cuda-memory-fraction 0.7 \
  --model-id $INFERENCE_MODEL \
  --port $INFERENCE_PORT

If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like meta-llama/Llama-Guard-3-1B using a script like:

export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1

docker run --rm -it \
  -v $HOME/.cache/huggingface:/data \
  -p $SAFETY_PORT:$SAFETY_PORT \
  --gpus $CUDA_VISIBLE_DEVICES \
  ghcr.io/huggingface/text-generation-inference:2.3.1 \
  --dtype bfloat16 \
  --usage-stats off \
  --sharded false \
  --model-id $SAFETY_MODEL \
  --port $SAFETY_PORT

Running Llama Stack

Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.

Via Docker

This method allows you to get started quickly without having to build the distribution code.

LLAMA_STACK_PORT=5001
docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  llamastack/distribution-tgi \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env TGI_URL=http://host.docker.internal:$INFERENCE_PORT

If you are using Llama Stack Safety / Shield APIs, use:

# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack

docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
  llamastack/distribution-tgi \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
  --env SAFETY_MODEL=$SAFETY_MODEL \
  --env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT

Via Conda

Make sure you have done uv pip install llama-stack and have the Llama Stack CLI available.

llama stack build --template tgi --image-type conda
llama stack run ./run.yaml
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env TGI_URL=http://127.0.0.1:$INFERENCE_PORT