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https://github.com/meta-llama/llama-stack.git
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5b1e69e58e
## What does this PR do? See issue: #747 -- `uv` is just plain better. This PR does the bare minimum of replacing `pip install` by `uv pip install` and ensuring `uv` exists in the environment. ## Test Plan First: create new conda, `uv pip install -e .` on `llama-stack` -- all is good. Next: run `llama stack build --template together` followed by `llama stack run together` -- all good Next: run `llama stack build --template together --image-name yoyo` followed by `llama stack run together --image-name yoyo` -- all good Next: fresh conda and `uv pip install -e .` and `llama stack build --template together --image-type venv` -- all good. Docker: `llama stack build --template together --image-type container` works!
133 lines
3.8 KiB
Markdown
133 lines
3.8 KiB
Markdown
---
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orphan: true
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---
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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-{{ name }}` distribution consists of the following provider configurations.
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{{ providers_table }}
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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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{% if run_config_env_vars %}
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### Environment Variables
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The following environment variables can be configured:
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{% for var, (default_value, description) in run_config_env_vars.items() %}
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- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
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{% endfor %}
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{% endif %}
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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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-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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-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=5001
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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llamastack/distribution-{{ name }} \
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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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-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-{{ name }} \
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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 {{ name }} --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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