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## 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!
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Meta Reference Distribution
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self
The llamastack/distribution-{{ name }} distribution consists of the following provider configurations:
{{ providers_table }}
Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
{% if run_config_env_vars %}
Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
{{ var }}: {{ description }} (default:{{ default_value }}) {% endfor %} {% endif %}
Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models. Run llama model list to see the available models to download, and llama model download to download the checkpoints.
$ ls ~/.llama/checkpoints
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
Running the Distribution
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 \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
If you are using Llama Stack Safety / Shield APIs, use:
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
Via Conda
Make sure you have done uv pip install llama-stack and have the Llama Stack CLI available.
llama stack build --template {{ name }} --image-type conda
llama stack run distributions/{{ name }}/run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
If you are using Llama Stack Safety / Shield APIs, use:
llama stack run distributions/{{ name }}/run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B