PyTorch

PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. It is one of the most popular deep learning frameworks, alongside others such as TensorFlow, offering free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.

A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, Uber's Pyro, Hugging Face's Transformers, and Catalyst.

PyTorch provides two high-level features:
* Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)
* Deep neural networks built on a tape-based automatic differentiation system

History

In 2001, Torch was written and released under a GPL license. It was a machine-learning library written in C++, supporting methods including neural networks, SVM, hidden Markov models, etc. It was improved to Torch7 in 2012. Development on Torch ceased in 2018 and was subsumed by the PyTorch project.

Meta (formerly known as Facebook) operates both PyTorch and Convolutional Architecture for Fast Feature Embedding ( Caffe2), but models defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange ( ONNX) project was created by Meta and Microsoft in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018. In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation.

PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms.

PyTorch tensors

PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable NVIDIA GPU. PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm and Apple's Metal Framework.

PyTorch supports various sub-types of Tensors.

Note that the term "tensor" here does not carry the same meaning as tensor in mathematics or physics. The meaning of the word in machine learning is only superficially related to its original meaning as a certain kind of object in linear algebra. Tensors in PyTorch are simply multi-dimensional arrays.

PyTorch neural networks

PyTorch defines a module called nn (torch.nn) to describe neural networks and to support training. This module offers a comprehensive collection of building blocks for neural networks, including various layers and activation functions, enabling the construction of complex models. Networks are built by inheriting from the torch.nn module and defining the sequence of operations in the forward() function.

Example

The following program shows the low-level functionality of the library with a simple example.

import torch
dtype = torch.float
device = torch.device("cpu") # Execute all calculations on the CPU
# device = torch.device("cuda:0") # Executes all calculations on the GPU

# Create a tensor and fill it with random numbers
a = torch.randn(2, 3, device=device, dtype=dtype)
print(a)
# Output: tensor( -1.1884, 0.8498, -1.7129
# 0.8816, 0.1944, 0.5847)

b = torch.randn(2, 3, device=device, dtype=dtype)
print(b)
# Output: tensor( 0.7178, -0.8453, -1.3403
# 1.3262, 1.1512, -1.7070)

print(a * b)
# Output: tensor( -0.8530, -0.7183, 2.58
# 1.1692, 0.2238, -0.9981)

print(a.sum())
# Output: tensor(-2.1540)

print(a , 2 # Output of the element in the third column of the second row (zero-based)
# Output: tensor(0.5847)

print(a.max())
# Output: tensor(0.8498)

The following code-block defines a neural network with linear layers using the nn module.

from torch import nn # Import the nn sub-module from PyTorch

class NeuralNetwork(nn.Module): # Neural networks are defined as classes
def __init__(self): # Layers and variables are defined in the __init__ method
super().__init__() # Must be in every network.
self.flatten = nn.Flatten() # Construct a flattening layer.
self.linear_relu_stack = nn.Sequential( # Construct a stack of layers.
nn.Linear(28 * 28, 512), # Linear Layers have an input and output shape
nn.ReLU(), # ReLU is one of many activation functions provided by nn
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10),
)

def forward(self, x): # This function defines the forward pass.
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits

See also

* Comparison of deep learning software
* Differentiable programming
* DeepSpeed

References

External links

*

{{Differentiable computing

Deep learning software
Facebook software
Free science software
Free software programmed in C
Free software programmed in Python
Open-source artificial intelligence
Python (programming language) scientific libraries
Software using the BSD license