| commit | 7e13138eb6f6fcc6705ee66ca8cddc1432e1fb3e | [log] [tgz] |
|---|---|---|
| author | Soumith Chintala <soumith@gmail.com> | Mon Mar 19 17:47:54 2018 -0400 |
| committer | GitHub <noreply@github.com> | Mon Mar 19 17:47:54 2018 -0400 |
| tree | d3cafb43679a25e358c2974fcc1dda09dc3ff502 | |
| parent | 6f80023c29e0fb55f46a32c4931bc5d4ba749846 [diff] |
Revert "Enable resetting of batchnorm running stats and cumulative ("simple") moving average" (#5892)
* Revert "Port ATen and JIT C++ tests to Catch2 (#5788)"
This reverts commit 6f80023c29e0fb55f46a32c4931bc5d4ba749846.
* Revert "Fix error message for cat-ing zero-dim tensors (#5819)"
This reverts commit cf2e1760490d369e93017b9425279b235c10772d.
* Revert "Softmax symbolic should account for negative dim (#5846)"
This reverts commit ba64724aeea8ad5d4b50cd1154fca5a011618333.
* Revert "[fft][1 of 3] build system and helpers to support cuFFT and MKL (#5855)"
This reverts commit 22ef8e5654c45d1f5404e3add6ad19678c0b80a9.
* Revert "Don't modify requires_grad when running DataParallel in no_grad mode (#5880)"
This reverts commit d11b7fbd1c49ed7bd84c89d286e2763e6ba55f51.
* Revert "fix some methods not showing up in doc (#5882)"
This reverts commit 24fca0efb289a069929639783d1c050b79e591c0.
* Revert "ReduceOps cleanup and set_num_threads (#5723)"
This reverts commit 84400d5531500e1a3fbcfe8a3f2865f982405861.
* Revert "introduce shape_as_tensor and reshape_from_variable_shape (#5824)"
This reverts commit f446b82e70ca0aa42fffa58469c28b6bce51d021.
* Revert "Enable resetting of batchnorm running moments and cumulative ("simple") moving average (#5766)"
This reverts commit 99b1f6cfad85a4856550cc1e787afd7ff9e6c6aa.
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.
We are in an early-release beta. Expect some adventures and rough edges.
| System | 2.7 | 3.5 |
|---|---|---|
| Linux CPU | ||
| Linux GPU | ||
| Windows GPU | — |
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).
PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.
We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, math operations, linear algebra, reductions. And they are fast!
PyTorch has a unique way of building neural networks: using and replaying a tape recorder.
Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.
With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.
While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.
PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use NumPy / SciPy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.
PyTorch is designed to be intuitive, linear in thought and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.
PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends (TH, THC, THNN, THCUNN) are written as independent libraries with a C99 API. They are mature and have been tested for years.
Hence, PyTorch is quite fast – whether you run small or large neural networks.
The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.
Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward and with minimal abstractions.
You can write new neural network layers in Python using the torch API or your favorite NumPy-based libraries such as SciPy.
If you want to write your layers in C/C++, we provide an extension API based on cffi that is efficient and with minimal boilerplate. There is no wrapper code that needs to be written. You can see a tutorial here and an example here.
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 will get a high-quality BLAS library (MKL) and you get a controlled compiler version regardless of your Linux distro.
Once you have Anaconda installed, here are the instructions.
If you want to compile with CUDA support, install
If you want to disable CUDA support, export environment variable NO_CUDA=1.
On Linux
export CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" # [anaconda root directory] # Install basic dependencies conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing # Add LAPACK support for the GPU conda install -c pytorch magma-cuda80 # or magma-cuda90 if CUDA 9
On macOS
export CMAKE_PREFIX_PATH=[anaconda root directory] conda install numpy pyyaml setuptools cmake cffi typing
git clone --recursive https://github.com/pytorch/pytorch cd pytorch
On Linux
python setup.py install
On macOS
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
Dockerfile is supplied to build images with cuda support and cudnn v7. Build as usual
docker build -t pytorch .
Alternatively, if you want to use a runtime image, you can use the pre-built one from Docker Hub and run with nvidia-docker:
nvidia-docker run --rm -ti --ipc=host pytorch/pytorch:latest
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.
Installation instructions and binaries for previous PyTorch versions may be found on our website.
Three pointers to get you started:
PyTorch has a 90 day release cycle (major releases). It's current state is Beta, we expect no obvious bugs. Please let us know if you encounter a bug by filing an issue.
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
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 a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.
PyTorch is a community driven project with several skillful engineers and researchers contributing to it.
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 non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Kopf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.
Note: this project is unrelated to hughperkins/pytorch with the same name. Hugh is a valuable contributor in the Torch community and has helped with many things Torch and PyTorch.