Facebook has released a stable version of the library for machine learning PyTorch 1.0. This iteration added support for large cloud platforms, a C ++ interface, a set of JIT compilers, and various improvements.
The stable version received a set of JIT compilers that eliminate the dependence of the code on the Python interpreter. The model code is transformed into Torch Script - a superstructure over Python. Keeping the opportunity to work with the model in the Python environment, the user can download it to other projects not related to this language. So, the PyTorch developers state that the code processed in this way can be used in the C ++ API.
torch.distributed package and the
torch.nn.parallel.DistributedDataParallel module are completely redesigned.
torch.distributed now has better performance and works asynchronously with the Gloo, NCCL and MPI libraries.
The developers added a C ++ wrapper to PyTorch 1.0. It contains analogs of Python interface components, such as
torch.data. According to the creators, the new interface should provide high performance for C ++ applications. True, the C ++ API is still experimental, but it can be used in projects now.
To improve the efficiency of working with PyTorch 1.0, a Torch Hub repository has been created, which stores pre-trained models of neural networks. You can publish your own development using the
hubconf.py file, after which the model will be available for download by any user via the
Support for C extensions and the module
torch.utils.trainer were removed from the library.
Facebook released the preliminary version of PyTorch 1.0 at the beginning of October 2018, and in two months the developers brought the framework to a stable state.