AI News, pytorch/pytorch


PyTorch is a Python package that provides two high-level features: You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

See also the HUD At a granular level, PyTorch is a library that consists of the following components: Usually one uses PyTorch either as: Elaborating further: If you use NumPy, then you have used Tensors (a.k.a ndarray).

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

can write your new neural network layers in Python itself, using your favorite libraries and

you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward. The

hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.

Hence, PyTorch is quite fast – whether you run small or large neural networks.

Commands to install from binaries via Conda or pip wheels are on our website: If you are installing from source, we highly recommend installing an Anaconda environment. You

If you want to compile with CUDA support, install If you want to disable CUDA support, export environment variable NO_CUDA=1. Other

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for

multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should

increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us. Sending

PyTorch is currently maintained by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan with major contributions coming from 10s of talented individuals in various forms and means. A

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