1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
|
--------------------------------------------------------------------------------
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.
- [More about PyTorch](#more-about-pytorch)
- [Installation](#installation)
- [Binaries](#binaries)
- [From Source](#from-source)
- [Docker Image](#docker-image)
- [Building the Documentation](#building-the-documentation)
- [Previous Versions](#previous-versions)
- [Getting Started](#getting-started)
- [Communication](#communication)
- [Releases and Contributing](#releases-and-contributing)
- [The Team](#the-team)
| System | 2.7 | 3.5 | 3.6 |
| :---: | :---: | :---: | :--: |
| Linux CPU | [](https://ci.pytorch.org/jenkins/job/pytorch-master/) | [](https://ci.pytorch.org/jenkins/job/pytorch-master/) | <center>—</center> |
| Linux GPU | [](https://ci.pytorch.org/jenkins/job/pytorch-master/) | [](https://ci.pytorch.org/jenkins/job/pytorch-master/) | <center>—</center> |
| Windows CPU / GPU | <center>—</center> | [](https://ci.pytorch.org/jenkins/job/pytorch-builds/job/pytorch-win-ws2016-cuda9-cudnn7-py3-trigger/) | <center>—</center> |
| Linux (ppc64le) CPU | [](https://powerci.osuosl.org/job/pytorch-master-nightly-py2-linux-ppc64le/) | — | [](https://powerci.osuosl.org/job/pytorch-master-nightly-py3-linux-ppc64le/) |
| Linux (ppc64le) GPU | [](https://powerci.osuosl.org/job/pytorch-linux-cuda9-cudnn7-py2-mpi-build-test-gpu/) | — | [](https://powerci.osuosl.org/job/pytorch-linux-cuda92-cudnn7-py3-mpi-build-test-gpu/) |
See also the [ci.pytorch.org HUD](https://ezyang.github.io/pytorch-ci-hud/build/pytorch-master).
## More About PyTorch
At a granular level, PyTorch is a library that consists of the following components:
| Component | Description |
| ---- | --- |
| **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.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 |
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).
PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerates the
computation 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
[torch-autograd](https://github.com/twitter/torch-autograd),
[autograd](https://github.com/HIPS/autograd),
[Chainer](https://chainer.org), 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.
### Python First
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](http://www.numpy.org/) / [SciPy](https://www.scipy.org/) / [scikit-learn](http://scikit-learn.org) 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 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.
### Fast and Lean
PyTorch has minimal framework overhead. We integrate acceleration libraries
such as [Intel MKL](https://software.intel.com/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 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.
### Extensions Without Pain
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](https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html).
If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate.
There is no wrapper code that needs to be written. You can see [a tutorial here](https://pytorch.org/tutorials/advanced/cpp_extension.html) and [an example here](https://github.com/pytorch/extension-cpp).
## Installation
### Binaries
Commands to install from binaries via Conda or pip wheels are on our website:
[https://pytorch.org](https://pytorch.org)
### From Source
If you are installing from source, we highly recommend installing an [Anaconda](https://www.continuum.io/downloads) 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](https://www.continuum.io/downloads) installed, here are the instructions.
If you want to compile with CUDA support, install
- [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads) 7.5 or above
- [NVIDIA cuDNN](https://developer.nvidia.com/cudnn) v6.x or above
If you want to disable CUDA support, export environment variable `NO_CUDA=1`.
Other potentially useful environment variables may be found in `setup.py`.
#### Install Dependencies
Common
```
conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
```
On Linux
```bash
# Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda90 # or [magma-cuda80 | magma-cuda92 | magma-cuda100 ] depending on your cuda version
```
#### Get the PyTorch Source
```bash
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
```
#### Install PyTorch
On Linux
```bash
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
python setup.py install
```
On macOS
```bash
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
```
On Windows
At least Visual Studio 2017 Update 3 (version 15.3.3 with the toolset 14.11) and [NVTX](https://docs.nvidia.com/gameworks/content/gameworkslibrary/nvtx/nvidia_tools_extension_library_nvtx.htm) are needed.
If the version of Visual Studio 2017 is higher than 15.4.5, installing of "VC++ 2017 version 15.4 v14.11 toolset" is strongly recommended.
<br/> If the version of Visual Studio 2017 is lesser than 15.3.3, please update Visual Studio 2017 to the latest version along with installing "VC++ 2017 version 15.4 v14.11 toolset".
<br/> There is no guarantee of the correct building with VC++ 2017 toolsets, others than version 15.4 v14.11.
<br/> "VC++ 2017 version 15.4 v14.11 toolset" might be installed onto already installed Visual Studio 2017 by running its installation once again and checking the corresponding checkbox under "Individual components"/"Compilers, build tools, and runtimes".
For building against CUDA 8.0 Visual Studio 2015 Update 3 (version 14.0), and the [patch](https://download.microsoft.com/download/8/1/d/81dbe6bb-ed92-411a-bef5-3a75ff972c6a/vc14-kb4020481.exe) are needed to be installed too.
The details of the patch can be found [here](https://support.microsoft.com/en-gb/help/4020481/fix-link-exe-crashes-with-a-fatal-lnk1000-error-when-you-use-wholearch).
NVTX is a part of CUDA distributive, where it is called "Nsight Compute". For installing it onto already installed CUDA run CUDA installation once again and check the corresponding checkbox.
Be sure that CUDA with Nsight Compute is installed after Visual Studio 2017.
```cmd
cmd
REM [Optional] The following two lines are needed for Python 2.7, but the support for it is very experimental.
set MSSdk=1
set FORCE_PY27_BUILD=1
REM [Optional] As for CUDA 8, VS2015 Update 3 is required; use the following line.
set "CUDAHOSTCXX=%VS140COMNTOOLS%..\..\VC\bin\amd64\cl.exe"
set CMAKE_GENERATOR=Visual Studio 15 2017 Win64
set DISTUTILS_USE_SDK=1
REM Run "Visual Studio 2017 Developer Command Prompt"
for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,16^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=14.11
python setup.py install
```
### Docker Image
Dockerfile is supplied to build images with cuda support and cudnn v7. You can pass `-e PYTHON_VERSION=x.y` flag to specify which Python version is to be used by Miniconda, or leave it unset to use the default. Build from pytorch repo directory as docker needs to copy git repo into docker filesystem while building the image.
```
docker build -t pytorch -f docker/pytorch/Dockerfile .
```
You can also pull a pre-built docker image from Docker Hub and run with nvidia-docker,
but this is not currently maintained and will pull PyTorch 0.2.
```
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`.
### Building the Documentation
To build documentation in various formats, you will need [Sphinx](http://www.sphinx-doc.org) and the
readthedocs theme.
```
cd docs/
pip install -r requirements.txt
```
You can then build the documentation by running ``make <format>`` from the
``docs/`` folder. Run ``make`` to get a list of all available output formats.
### Previous Versions
Installation instructions and binaries for previous PyTorch versions may be found
on [our website](https://pytorch.org/previous-versions).
## Getting Started
Three pointers to get you started:
- [Tutorials: get you started with understanding and using PyTorch](https://pytorch.org/tutorials/)
- [Examples: easy to understand pytorch code across all domains](https://github.com/pytorch/examples)
- [The API Reference](https://pytorch.org/docs/)
## Communication
* forums: discuss implementations, research, etc. https://discuss.pytorch.org
* GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
* Slack: The [PyTorch Slack](https://pytorch.slack.com/) hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration etc. If you are a beginner looking for help, the primary medium is [PyTorch Forums](https://discuss.pytorch.org). If you need a slack invite, please fill this form: https://goo.gl/forms/PP1AGvNHpSaJP8to1
* newsletter: no-noise, one-way email newsletter with important announcements about pytorch. You can sign-up here: https://eepurl.com/cbG0rv
## Releases and Contributing
PyTorch has a 90 day release cycle (major releases). Please let us know if you encounter a bug by [filing an issue](https://github.com/pytorch/pytorch/issues).
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.
## The Team
PyTorch is a community driven project with several skillful engineers and researchers contributing to it.
PyTorch is currently maintained by [Adam Paszke](https://apaszke.github.io/), [Sam Gross](https://github.com/colesbury), [Soumith Chintala](http://soumith.ch) and [Gregory Chanan](https://github.com/gchanan) with major contributions coming from hundreds 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](https://github.com/hughperkins/pytorch) with the same name. Hugh is a valuable contributor in the Torch community and has helped with many things Torch and PyTorch.
## License
PyTorch is BSD-style licensed, as found in the LICENSE file.
|
