Installation ================= Prerequisites ------------- RaNNC works only with CUDA devices (CPU only or TPU environments are not supported). RaNNC requires the following libraries and tools at runtime. * *CUDA*: A CUDA runtime (Version 11) must be available in the runtime environment. * *NCCL*: NCCL (Version >= 2.11.4) must be available in the runtime environment. * *MPI*: A program using RaNNC must be launched with MPI. MPI libraries must also be available at runtime. RaNNC has been tested with OpenMPI v4.0.7. * *libstd++*: ``libstd++`` must support ``GLIBCXX_3.4.21`` to use the distributed ``pip`` packages (these packages are built with gcc 7.5.0). Installation ------------ The current version (``0.7.5``) of RaNNC requires PyTorch v1.11.0. ``pip`` packages for ``linux_x86_64`` are available for the following combinations of Python and CUDA versions. * Python version: 3,7, 3.8, 3.9, 3.10 * CUDA version: 11.3 The following commands install PyToch and RaNNC with pip. (The package version should be specified as ``0.7.5+cu[CUDA_VERSION_WITHOUT_DOT]``) .. code-block:: bash pip install torch==1.11.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html pip install pyrannc==0.7.5+cu113 -f https://nict-wisdom.github.io/rannc/installation.html Use the following links to manually download the packages. * :download:`For Python 3.7 / CUDA 11.3 ` * :download:`For Python 3.8 / CUDA 11.3 ` * :download:`For Python 3.9 / CUDA 11.3 ` * :download:`For Python 3.10 / CUDA 11.3 ` If the above packages do not match your Python/CUDA versions, create a suitable package using ``Makefile`` in ``docker/``. ``make.sh`` shows the commands to create wheel packages.