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# What does this PR do? This PR adds support for NVIDIA's NeMo Evaluator API to the Llama Stack eval module. The integration enables users to evaluate models via the Llama Stack interface. ## 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.*] 1. Added unit tests and successfully ran from root of project: `./scripts/unit-tests.sh tests/unit/providers/nvidia/test_eval.py` ``` tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_cancel PASSED tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_result PASSED tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_status PASSED tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_register_benchmark PASSED tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_run_eval PASSED ``` 2. Verified I could build the Llama Stack image: `LLAMA_STACK_DIR=$(pwd) llama stack build --template nvidia --image-type venv` Documentation added to `llama_stack/providers/remote/eval/nvidia/README.md` --------- Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com> |
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
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Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Render locally
pip install -r requirements.txt
cd docs
python -m sphinx_autobuild source _build
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack