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Merge branch 'main' into add-watsonx-inference-adapter

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55
docs/source/distributions/building_distro.md
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@ -176,7 +176,11 @@ distribution_spec:
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
image_name: ollama
image_type: conda
# If some providers are external, you can specify the path to the implementation
external_providers_dir: /etc/llama-stack/providers.d
```
```
@ -184,6 +188,57 @@ llama stack build --config llama_stack/templates/ollama/build.yaml
```
:::
:::{tab-item} Building with External Providers
Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently or use community-provided providers.
To build a distribution with external providers, you need to:
1. Configure the `external_providers_dir` in your build configuration file:
```yaml
# Example my-external-stack.yaml with external providers
version: '2'
distribution_spec:
description: Custom distro for CI tests
providers:
inference:
- remote::custom_ollama
# Add more providers as needed
image_type: container
image_name: ci-test
# Path to external provider implementations
external_providers_dir: /etc/llama-stack/providers.d
```
Here's an example for a custom Ollama provider:
```yaml
adapter:
adapter_type: custom_ollama
pip_packages:
- ollama
- aiohttp
- llama-stack-provider-ollama # This is the provider package
config_class: llama_stack_ollama_provider.config.OllamaImplConfig
module: llama_stack_ollama_provider
api_dependencies: []
optional_api_dependencies: []
```
The `pip_packages` section lists the Python packages required by the provider, as well as the
provider package itself. The package must be available on PyPI or can be provided from a local
directory or a git repository (git must be installed on the build environment).
2. Build your distribution using the config file:
```
llama stack build --config my-external-stack.yaml
```
For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external.md).
:::
:::{tab-item} Building Container
```{admonition} Podman Alternative

14
docs/source/distributions/self_hosted_distro/nvidia.md
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@ -7,7 +7,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
|-----|-------------|
| agents | `inline::meta-reference` |
| datasetio | `inline::localfs` |
| eval | `inline::meta-reference` |
| eval | `remote::nvidia` |
| inference | `remote::nvidia` |
| post_training | `remote::nvidia` |
| safety | `remote::nvidia` |
@ -22,13 +22,13 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
The following environment variables can be configured:
- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`)
- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`)
- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`)
- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)
- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
- `NVIDIA_EVALUATOR_URL`: URL for the NeMo Evaluator Service (default: `http://0.0.0.0:7331`)
- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
@ -58,7 +58,7 @@ The following models are available by default:
Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
### Deploy NeMo Microservices Platform
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/documentation/latest/nemo-microservices/latest-early_access/set-up/deploy-as-platform/index.html) for platform prerequisites and instructions to install and deploy the platform.
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
## Supported Services
Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
@ -118,7 +118,7 @@ curl --location "$NEMO_URL/v1/deployment/model-deployments" \
}
}'
```
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/documentation/latest/nemo-microservices/latest-early_access/get-started/tutorials/deploy-nims.html#) for more information on how to deploy a NIM and verify it's available for inference.
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
You can also remove a deployed NIM to free up GPU resources, if needed.
```sh
@ -171,7 +171,3 @@ llama stack run ./run.yaml \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
--env INFERENCE_MODEL=$INFERENCE_MODEL
```
### Example Notebooks
You can reference the Jupyter notebooks in `docs/notebooks/nvidia/` for example usage of these APIs.
- [Llama_Stack_NVIDIA_E2E_Flow.ipynb](/docs/notebooks/nvidia/Llama_Stack_NVIDIA_E2E_Flow.ipynb) contains an end-to-end workflow for running inference, customizing, and evaluating models using your deployed NeMo Microservices platform.

2
docs/source/distributions/self_hosted_distro/remote-vllm.md
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@ -44,7 +44,7 @@ The following environment variables can be configured:
In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
[supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
that we only use GPUs here for demonstration purposes.
that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
### Setting up vLLM server on AMD GPU