forked from phoenix-oss/llama-stack-mirror
1ae61e8d5f
# What does this PR do? start_stack.sh was using --yaml-config which is deprecated. a bunch of distro docs also mentioned --yaml-config. Replaces all instances and logic for --yaml-config with --config resolves #2189 Signed-off-by: Charlie Doern <cdoern@redhat.com>
149 lines
4.7 KiB
Markdown
149 lines
4.7 KiB
Markdown
---
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orphan: true
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---
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<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# TGI Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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self
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```
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The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `remote::tgi`, `inline::sentence-transformers` |
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| safety | `inline::llama-guard` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
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- `INFERENCE_MODEL`: Inference model loaded into the TGI server (default: `meta-llama/Llama-3.2-3B-Instruct`)
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- `TGI_URL`: URL of the TGI server with the main inference model (default: `http://127.0.0.1:8080/v1`)
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- `TGI_SAFETY_URL`: URL of the TGI server with the safety model (default: `http://127.0.0.1:8081/v1`)
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- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
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## Setting up TGI server
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Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
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```bash
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export INFERENCE_PORT=8080
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export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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export CUDA_VISIBLE_DEVICES=0
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docker run --rm -it \
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--pull always \
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-v $HOME/.cache/huggingface:/data \
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-p $INFERENCE_PORT:$INFERENCE_PORT \
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--gpus $CUDA_VISIBLE_DEVICES \
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ghcr.io/huggingface/text-generation-inference:2.3.1 \
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--dtype bfloat16 \
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--usage-stats off \
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--sharded false \
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--cuda-memory-fraction 0.7 \
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--model-id $INFERENCE_MODEL \
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--port $INFERENCE_PORT
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```
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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:
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```bash
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export SAFETY_PORT=8081
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export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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export CUDA_VISIBLE_DEVICES=1
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docker run --rm -it \
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--pull always \
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-v $HOME/.cache/huggingface:/data \
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-p $SAFETY_PORT:$SAFETY_PORT \
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--gpus $CUDA_VISIBLE_DEVICES \
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ghcr.io/huggingface/text-generation-inference:2.3.1 \
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--dtype bfloat16 \
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--usage-stats off \
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--sharded false \
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--model-id $SAFETY_MODEL \
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--port $SAFETY_PORT
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```
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## Running Llama Stack
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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.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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LLAMA_STACK_PORT=8321
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docker run \
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-it \
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--pull always \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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llamastack/distribution-tgi \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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# You need a local checkout of llama-stack to run this, get it using
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# git clone https://github.com/meta-llama/llama-stack.git
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cd /path/to/llama-stack
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docker run \
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-it \
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--pull always \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ~/.llama:/root/.llama \
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-v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
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llamastack/distribution-tgi \
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--config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
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--env SAFETY_MODEL=$SAFETY_MODEL \
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--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
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```
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### Via Conda
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Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
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```bash
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llama stack build --template tgi --image-type conda
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llama stack run ./run.yaml
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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llama stack run ./run-with-safety.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT \
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--env SAFETY_MODEL=$SAFETY_MODEL \
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--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
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```
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