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orphan: true
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# Dell Distribution of Llama Stack

```{toctree}
:maxdepth: 2
:hidden:

self
```

The `llamastack/distribution-dell` 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`, `inline::sentence-transformers` |
| 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::rag-runtime` |
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |


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

### Environment Variables

The following environment variables can be configured:

- `DEH_URL`: URL for the Dell inference server (default: `http://0.0.0.0:8181`)
- `DEH_SAFETY_URL`: URL for the Dell safety inference server (default: `http://0.0.0.0:8282`)
- `CHROMA_URL`: URL for the Chroma server (default: `http://localhost:6601`)
- `INFERENCE_MODEL`: Inference model loaded into the TGI server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)


## Setting up Inference server using Dell Enterprise Hub's custom TGI container.

NOTE: This is a placeholder to run inference with TGI. This will be updated to use [Dell Enterprise Hub's containers](https://dell.huggingface.co/authenticated/models) once verified.

```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
export CUDA_VISIBLE_DEVICES=0
export LLAMA_STACK_PORT=8321

docker run --rm -it \
  --pull always \
  --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 \
  --dtype bfloat16 \
  --usage-stats off \
  --sharded false \
  --cuda-memory-fraction 0.7 \
  --model-id $INFERENCE_MODEL \
  --port $INFERENCE_PORT --hostname 0.0.0.0
```

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:

```bash
export SAFETY_INFERENCE_PORT=8282
export DEH_SAFETY_URL=http://0.0.0.0:$SAFETY_INFERENCE_PORT
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1

docker run --rm -it \
  --pull always \
  --network host \
  -v $HOME/.cache/huggingface:/data \
  -e HF_TOKEN=$HF_TOKEN \
  -p $SAFETY_INFERENCE_PORT:$SAFETY_INFERENCE_PORT \
  --gpus $CUDA_VISIBLE_DEVICES \
  ghcr.io/huggingface/text-generation-inference \
  --dtype bfloat16 \
  --usage-stats off \
  --sharded false \
  --cuda-memory-fraction 0.7 \
  --model-id $SAFETY_MODEL \
  --hostname 0.0.0.0 \
  --port $SAFETY_INFERENCE_PORT
```

## Dell distribution relies on ChromaDB for vector database usage

You can start a chroma-db easily using docker.
```bash
# This is where the indices are persisted
mkdir -p $HOME/chromadb

podman run --rm -it \
  --network host \
  --name chromadb \
  -v $HOME/chromadb:/chroma/chroma \
  -e IS_PERSISTENT=TRUE \
  chromadb/chroma:latest \
  --port $CHROMADB_PORT \
  --host $CHROMADB_HOST
```

## 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.

```bash
docker run -it \
  --pull always \
  --network host \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v $HOME/.llama:/root/.llama \
  # NOTE: mount the llama-stack / llama-model directories if testing local changes else not needed
  -v /home/hjshah/git/llama-stack:/app/llama-stack-source -v /home/hjshah/git/llama-models:/app/llama-models-source \
  # localhost/distribution-dell:dev if building / testing locally
  llamastack/distribution-dell\
  --port $LLAMA_STACK_PORT  \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env DEH_URL=$DEH_URL \
  --env CHROMA_URL=$CHROMA_URL

```

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

```bash
# 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

export SAFETY_INFERENCE_PORT=8282
export DEH_SAFETY_URL=http://0.0.0.0:$SAFETY_INFERENCE_PORT
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B

docker run \
  -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v $HOME/.llama:/root/.llama \
  -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
  llamastack/distribution-dell \
  --config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env DEH_URL=$DEH_URL \
  --env SAFETY_MODEL=$SAFETY_MODEL \
  --env DEH_SAFETY_URL=$DEH_SAFETY_URL \
  --env CHROMA_URL=$CHROMA_URL
```

### Via Conda

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

```bash
llama stack build --template dell --image-type conda
llama stack run dell
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env DEH_URL=$DEH_URL \
  --env CHROMA_URL=$CHROMA_URL
```

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

```bash
llama stack run ./run-with-safety.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env DEH_URL=$DEH_URL \
  --env SAFETY_MODEL=$SAFETY_MODEL \
  --env DEH_SAFETY_URL=$DEH_SAFETY_URL \
  --env CHROMA_URL=$CHROMA_URL
```
