mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 02:53:30 +00:00

# What does this PR do?

- fixes to nvidia inference provider to account for strategy update
- update nvidia templates

## Test Plan

```
llama stack run ./llama_stack/templates/nvidia/run.yaml --port 5000

LLAMA_STACK_BASE_URL="http://localhost:5000" pytest -v tests/client-sdk/inference/test_inference.py --html=report.html --self-contained-html
```
<img width="1288" alt="image"
src="https://github.com/user-attachments/assets/d20f9aea-525e-47de-a5be-586e022e0d55"
/>

**NOTE**
- vision inference broken
- tool calling broken
- /completion broken

cc @mattf @cdgamarose-nv  for improving NVIDIA inference adapter

## Sources

Please link relevant resources if necessary.


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

2025-01-15 18:49:36 -08:00

2.4 KiB

Raw Blame History

NVIDIA Distribution

The llamastack/distribution-nvidia 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::nvidia`
memory	`inline::faiss`
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::code-interpreter`, `inline::memory-runtime`

Environment Variables

The following environment variables can be configured:

LLAMASTACK_PORT: Port for the Llama Stack distribution server (default: 5001)
NVIDIA_API_KEY: NVIDIA API Key (default: ``)

Models

The following models are available by default:

meta-llama/Llama-3-8B-Instruct (meta/llama3-8b-instruct)
meta-llama/Llama-3-70B-Instruct (meta/llama3-70b-instruct)
meta-llama/Llama-3.1-8B-Instruct (meta/llama-3.1-8b-instruct)
meta-llama/Llama-3.1-70B-Instruct (meta/llama-3.1-70b-instruct)
meta-llama/Llama-3.1-405B-Instruct-FP8 (meta/llama-3.1-405b-instruct)
meta-llama/Llama-3.2-1B-Instruct (meta/llama-3.2-1b-instruct)
meta-llama/Llama-3.2-3B-Instruct (meta/llama-3.2-3b-instruct)
meta-llama/Llama-3.2-11B-Vision-Instruct (meta/llama-3.2-11b-vision-instruct)
meta-llama/Llama-3.2-90B-Vision-Instruct (meta/llama-3.2-90b-vision-instruct)

Prerequisite: API Keys

Make sure you have access to a NVIDIA API Key. You can get one by visiting https://build.nvidia.com/.

Running Llama Stack with NVIDIA

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 ./run.yaml:/root/my-run.yaml \
  llamastack/distribution-nvidia \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env NVIDIA_API_KEY=$NVIDIA_API_KEY

Via Conda

llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
  --port 5001 \
  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
  --env INFERENCE_MODEL=$INFERENCE_MODEL

2.4 KiB Raw Blame History