PyPi users

The desired wheel can now be installed from PyPi with pip install nixl[cu12] or pip install nixl[cu13].

For backwards compatibility, pip install nixl installs automatically nixl[cu12], continuing to work seamlessly for CUDA 12 users without requiring changes to downstream project dependencies.

CUDA 13 users must use pip install nixl[cu13] to install NIXL.

If both nixl-cu12 and nixl-cu13 are installed at the same time in an environment, nixl-cu13 takes precedence.

Python installation from source

Pip installations from source code through pip install . now require additional steps to build and install the nixl meta-package:

On CUDA 12:

pip install .
pip install meson meson-python pybind11 tomlkit
meson setup build
ninja -C build
pip install build/src/bindings/python/nixl-meta/nixl-*-py3-none-any.whl

On CUDA 13:

pip install .
pip install meson meson-python pybind11 tomlkit
./contrib/tomlutil.py --wheel-name nixl-cu13 pyproject.toml
meson setup build
ninja -C build
pip install build/src/bindings/python/nixl-meta/nixl-*-py3-none-any.whl

See also this for a full example in docker.

API changes

• [Device API] Asynchronous post functions now return NIXL_IN_PROG to signal that an operation is in flight, providing a clearer status for non-blocking calls (#911).
• [Device API] Added support for worker_id selection in the backend, allowing for more granular performance tuning with multiple QPs driven by a single thread (#938).

Bugfixes

• [Libfabric] Corrected an issue with offset calculations that could cause data corruption in certain transfer scenarios (#883).
• [Libfabric] Fixed a bug in handling asymmetrical rail configurations on heterogeneous nodes (#908).
• [Libfabric] Addressed issues with metadata handling for partial and multi-load transfers (#976, #986).
• [Bindings] Fixed an error in the Python API when NIXL_LOG_LEVEL was set to TRACE (#890).
• [Plugins] Reduced logging noise by changing messages for plugins with missing external dependencies from ERROR to INFO (#967).

Known issues

• [GPUNETIO] The GPUNETIO plugin is not available in CUDA 13 environments. This will be addressed in a future release.

Benchmarks

• nixlbench now allows compilation even if the etcd development libraries are not found on the system (#959).
• The PostXferReq timer in nixlbench was changed to provide more accurate latency measurements (#944).
• For CUDA 13, the nixlbench PyTorch dependency is now installed from the stable channel (#943).

Full Changelog: https://github.com/ai-dynamo/nixl/compare/0.7.0...0.7.1