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.. image:: docs/source/_static/img/pytorch-logo-dark.png
:width: 40%
:alt: Pytorch Logo
:align: center


PyTorch is a python package that provides two high-level features: -
Tensor computation (like numpy) with strong GPU acceleration - Deep
Neural Networks built on a tape-based autograd system

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.

- `More About PyTorch <#more-about-pytorch>`__
- `Installation <#installation>`__
- `Binaries <#binaries>`__
- `From source <#from-source>`__
- `Getting Started <#getting-started>`__
- `Communication <#communication>`__
- `Releases and Contributing <#releases-and-contributing>`__
- `The Team <#the-team>`__

| Python | **``Linux CPU``** | **``Linux GPU``** |
| 2.7.8 | |Build Status| | |
| 2.7 | |Build Status| | |Build Status GPU 2||
| 3.5 | |Build Status| | |Build Status GPU 3||
| Nightly | |Build Status| | |

More about PyTorch

At a granular level, PyTorch is a library that consists of the following

| \_ | \_ |
| torch | a Tensor library like NumPy, with strong GPU |
| | support |
| torch.autograd | a tape based automatic differentiation library |
| | that supports all differentiable Tensor |
| | operations in torch |
| torch.nn | a neural networks library deeply integrated with |
| | autograd designed for maximum flexibility |
| torch.optim | an optimization package to be used with torch.nn |
| | with standard optimization methods such as SGD, |
| | RMSProp, LBFGS, Adam etc. |
| torch.multiprocessing | python multiprocessing, but with magical memory |
| | sharing of torch Tensors across processes. |
| | Useful for data loading and hogwild training. |
| torch.utils | DataLoader, Trainer and other utility functions |
| | for convenience |
| torch.legacy(.nn/.optim) | legacy code that has been ported over from torch |
| | for backward compatibility reasons |

Usually one uses PyTorch either as:

- A replacement for numpy to use the power of GPUs.
- a deep learning research platform that provides maximum flexibility
and speed

Elaborating further:

A GPU-ready Tensor library

If you use numpy, then you have used Tensors (a.k.a ndarray).

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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!

Dynamic Neural Networks: Tape based Autograd

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 `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.

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Python first

PyTorch is not a Python binding into a monolothic 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.

Imperative experiences

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
straight-forward. 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.

Fast and Lean

| PyTorch has minimal framework overhead. We integrate acceleration
libraries such as Intel MKL and NVIDIA (CuDNN, NCCL) to maximize
speed. At the core, it's 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

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

Extensions without pain

Writing new neural network modules, or interfacing with PyTorch's Tensor
API was designed to be straight-forward 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 an
example here <>`__.



- Anaconda

.. code:: bash

conda install pytorch torchvision -c soumith

From source

Instructions for an Anaconda environment.

If you want to compile with CUDA support, install - `NVIDIA
CUDA <>`__ 7.5 or above -
`NVIDIA CuDNN <>`__ v5.x

Install optional dependencies

.. code:: bash

export CMAKE_PREFIX_PATH=[anaconda root directory]

# Install basic dependencies
conda install numpy mkl setuptools cmake gcc cffi

# On Linux, add LAPACK support for the GPU
conda install -c soumith magma-cuda75 # or magma-cuda80 if CUDA 8.0

Install PyTorch

.. code:: bash

pip install -r requirements.txt
python install

Getting Started

Three pointers to get you started: - `Tutorials: notebooks to get you
started with understanding and using
PyTorch <>`__ - `Examples: easy to
understand pytorch code across all
domains <>`__ - The API Reference:


- forums: discuss implementations, research, etc.
- github issues: bug reports, feature requests, install issues, RFCs,
thoughts, etc.
- slack: general chat, online discussions, collaboration etc. . If you need a slack invite, ping us at
- newsletter: no-noise, one-way email newsletter with important
announcements about pytorch. You can sign-up here:

Releases and Contributing

PyTorch has a 90 day release cycle (major releases). It's current state
is Beta (v0.1.6), 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.

**For the next release cycle, these are the 3 big features we are
planning to add:**

1. `Distributed
PyTorch <>`__ (a draft
implementation is present in this
`branch <>`__ )
2. Backward of Backward - Backpropagating through the optimization
process itself. Some past and recent papers such as `Double
Backprop <>`__
and `Unrolled GANs <>`__ need this.
3. Lazy Execution Engine for autograd - This will enable us to
optionally introduce caching and JIT compilers to optimize autograd

The Team

PyTorch is a community driven project with several skillful engineers
and researchers contributing to it.

PyTorch is currently maintained by `Adam
Paszke <>`__, `Sam
Gross <>`__ and `Soumith
Chintala <>`__ with major contributions coming from 10s
of talented individuals in various forms and means. A non-exhaustive but
growing list needs to mention: Sergey Zagoruyko, Adam Lerer, Francisco
Massa, Andreas Kopf, James Bradbury, Zeming Lin, Yuandong Tian,
Guillaume Lample, Marat Dukhan, Natalia Gimelshein.

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.

.. |Build Status| image::
.. |Build Status GPU 2| image::
.. |Build Status GPU 3| image::

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