From 0d06c654d0655fd511b62e2cc72111e61d52a90f Mon Sep 17 00:00:00 2001 From: Jash Gulabrai <37194352+JashG@users.noreply.github.com> Date: Fri, 18 Apr 2025 19:13:18 -0400 Subject: [PATCH] feat: Update NVIDIA to GA docs; remove notebook reference until ready (#1999) # What does this PR do? - Update NVIDIA documentation links to GA docs - Remove reference to notebooks until merged [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) Co-authored-by: Jash Gulabrai --- docs/source/distributions/self_hosted_distro/nvidia.md | 8 ++------ llama_stack/templates/nvidia/doc_template.md | 8 ++------ 2 files changed, 4 insertions(+), 12 deletions(-) diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md index 539d18d92..0922cb512 100644 --- a/docs/source/distributions/self_hosted_distro/nvidia.md +++ b/docs/source/distributions/self_hosted_distro/nvidia.md @@ -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. diff --git a/llama_stack/templates/nvidia/doc_template.md b/llama_stack/templates/nvidia/doc_template.md index 8818e55c1..068dd7ac3 100644 --- a/llama_stack/templates/nvidia/doc_template.md +++ b/llama_stack/templates/nvidia/doc_template.md @@ -31,7 +31,7 @@ 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. @@ -91,7 +91,7 @@ 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 @@ -144,7 +144,3 @@ 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.