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e9bcb0e827
Add a new --export-dir flag to the `llama stack build` command that allows users to export container build artifacts to a specified directory instead of building the container directly. This feature is useful for: - Building containers in different environments - Sharing build configurations - Customizing the build process The exported tarball includes: - Containerfile (Dockerfile) - Run configuration file (if building from config) - External provider files (if specified) - Build script for assistance The tarball is named with a timestamp for uniqueness: <distro-name>_<timestamp>.tar.gz Documentation has been updated in building_distro.md to reflect this new functionality as well as integration tests. Signed-off-by: Sébastien Han <seb@redhat.com> |
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| .. | ||
| _static | ||
| notebooks | ||
| openapi_generator | ||
| resources | ||
| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| readme.md | ||
| requirements.txt | ||
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