forked from phoenix-oss/llama-stack-mirror
272d3359ee
# What does this PR do? The builtin implementation of code interpreter is not robust and has a really weak sandboxing shell (the `bubblewrap` container). Given the availability of better MCP code interpreter servers coming up, we should use them instead of baking an implementation into the Stack and expanding the vulnerability surface to the rest of the Stack. This PR only does the removal. We will add examples with how to integrate with MCPs in subsequent ones. ## Test Plan Existing tests.
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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--yaml-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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