forked from phoenix-oss/llama-stack-mirror
b76bef169c
# 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.
73 lines
2.4 KiB
Markdown
73 lines
2.4 KiB
Markdown
# NVIDIA Distribution
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The `llamastack/distribution-nvidia` 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::nvidia` |
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| memory | `inline::faiss` |
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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::code-interpreter`, `inline::memory-runtime` |
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
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- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
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### Models
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The following models are available by default:
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- `meta-llama/Llama-3-8B-Instruct (meta/llama3-8b-instruct)`
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- `meta-llama/Llama-3-70B-Instruct (meta/llama3-70b-instruct)`
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- `meta-llama/Llama-3.1-8B-Instruct (meta/llama-3.1-8b-instruct)`
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- `meta-llama/Llama-3.1-70B-Instruct (meta/llama-3.1-70b-instruct)`
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- `meta-llama/Llama-3.1-405B-Instruct-FP8 (meta/llama-3.1-405b-instruct)`
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- `meta-llama/Llama-3.2-1B-Instruct (meta/llama-3.2-1b-instruct)`
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- `meta-llama/Llama-3.2-3B-Instruct (meta/llama-3.2-3b-instruct)`
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- `meta-llama/Llama-3.2-11B-Vision-Instruct (meta/llama-3.2-11b-vision-instruct)`
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- `meta-llama/Llama-3.2-90B-Vision-Instruct (meta/llama-3.2-90b-vision-instruct)`
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### Prerequisite: API Keys
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Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/).
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## Running Llama Stack with NVIDIA
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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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-v ./run.yaml:/root/my-run.yaml \
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llamastack/distribution-nvidia \
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--yaml-config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env NVIDIA_API_KEY=$NVIDIA_API_KEY
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```
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### Via Conda
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```bash
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llama stack build --template nvidia --image-type conda
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llama stack run ./run.yaml \
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--port 5001 \
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--env NVIDIA_API_KEY=$NVIDIA_API_KEY
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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