add nvidia distribution (#565)

# What does this PR do?

adds nvidia template for creating a distribution using inference adapter
for NVIDIA NIMs.

## Test Plan

Please describe:
Build llama stack distribution for nvidia using the template, docker and
conda.
```bash
(.venv) local-cdgamarose@a4u8g-0006:~/llama-stack$ llama-stack-client configure --endpoint http://localhost:5000
Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5000
(.venv) local-cdgamarose@a4u8g-0006:~/llama-stack$ llama-stack-client models list
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ identifier                       ┃ provider_id ┃ provider_resource_id       ┃ metadata ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ Llama3.1-8B-Instruct             │ nvidia      │ meta/llama-3.1-8b-instruct │ {}       │
│ meta-llama/Llama-3.2-3B-Instruct │ nvidia      │ meta/llama-3.2-3b-instruct │ {}       │
└──────────────────────────────────┴─────────────┴────────────────────────────┴──────────┘
(.venv) local-cdgamarose@a4u8g-0006:~/llama-stack$ llama-stack-client inference chat-completion --message "hello, write me a 2 sentence poem"
ChatCompletionResponse(
    completion_message=CompletionMessage(
        content='Here is a 2 sentence poem:\n\nThe sun sets slow and paints the sky, \nA gentle hue of pink that makes me sigh.',
        role='assistant',
        stop_reason='end_of_turn',
        tool_calls=[]
    ),
    logprobs=None
)
```

## Before submitting

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

---------

Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>

docs/source/distributions/index.md

@@ -20,6 +20,7 @@ If so, we suggest:
   - {dockerhub}`distribution-remote-vllm` ([Guide](self_hosted_distro/remote-vllm))
   - {dockerhub}`distribution-meta-reference-gpu` ([Guide](self_hosted_distro/meta-reference-gpu))
   - {dockerhub}`distribution-tgi` ([Guide](self_hosted_distro/tgi))
+  - {dockerhub} `distribution-nvidia` ([Guide](self_hosted_distro/nvidia))
 
 - **Are you running on a "regular" desktop machine?**
 If so, we suggest:

docs/source/distributions/remote_hosted_distro/nvidia.md

@@ -0,0 +1,65 @@
+# 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:
+
+- `${env.INFERENCE_MODEL} (None)`
+
+
+### Prerequisite: API Keys
+
+Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](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.
+
+```bash
+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
+
+```bash
+llama stack build --template nvidia --image-type conda
+llama stack run ./run.yaml \
+  --port 5001 \
+  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
+  --env INFERENCE=$INFERENCE_MODEL
+```

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -0,0 +1,60 @@
+# NVIDIA Distribution
+
+The `llamastack/distribution-nvidia` distribution consists of the following provider configurations.
+
+| API | Provider(s) |
+|-----|-------------|
+| agents | `inline::meta-reference` |
+| inference | `remote::nvidia` |
+| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
+| safety | `inline::llama-guard` |
+| telemetry | `inline::meta-reference` |
+
+
+### 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:
+
+- `${env.INFERENCE_MODEL} (None)`
+
+
+### Prerequisite: API Keys
+
+Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](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.
+
+```bash
+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
+
+```bash
+llama stack build --template nvidia --image-type conda
+llama stack run ./run.yaml \
+  --port 5001 \
+  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
+```