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# What does this PR do? When registering a dataset for NVIDIA, the DatasetsRoutingTable expects `nvidia` to be passed via the `provider_id` [here](https://github.com/llamastack/llama-stack/blob/main/llama_stack/core/routing_tables/datasets.py#L61). This PR fixes a notebook to correctly use `provider_id`. <!-- If resolving an issue, uncomment and update the line below --> Closes #3308 ## Test Plan Manually execute the notebook steps to verify the dataset is registered. Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com> |
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| openapi_generator | ||
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| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| original_rfc.md | ||
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| README.md | ||
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 Github page.
Render locally
From the llama-stack root directory, run the following command to render the docs locally:
uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
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