|Developer(s)||Facebook's AI Research lab (FAIR)|
|Initial release||September 2016|
1.10.0 / 21 October 2021
|Type||Library for machine learning and deep learning|
PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab (FAIR). It is 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, PyTorch Lightning, 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 type-based automatic differentiation system
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 Facebook and Microsoft in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018.
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 supports various sub-types of Tensors.
Note that the term "tensor" here does not carry the same meaning as in mathematics or physics. The meaning of the word in those areas is only tangentially related to the one in Machine Learning. In mathematics, a tensor is a certain kind of object in linear algebra, while in physics the term "tensor" usually refers to what mathematicians call a tensor field.
PyTorch uses a method called automatic differentiation. A recorder records what operations have performed, and then it replays it backward to compute the gradients. This method is especially powerful when building neural networks to save time on one epoch by calculating differentiation of the parameters at the forward pass.
torch.optim is a module that implements various optimization algorithms used for building neural networks. Most of the commonly used methods are already supported, so there is no need to build them from scratch.
PyTorch autograd makes it easy to define computational graphs and take gradients, but raw autograd can be a bit too low-level for defining complex neural networks. This is where the
nn module can help.
The following program shows the functionality of the library with a simple example
import torch dtype = torch.float device = torch.device("cpu") # This executes all calculations on the CPU # device = torch.device("cuda:0") # This executes all calculations on the GPU # Creation of a tensor and filling of a tensor with random numbers a = torch.randn(2, 3, device=device, dtype=dtype) print(a) # Output of tensor A # Output: tensor([[-1.1884, 0.8498, -1.7129], # [-0.8816, 0.1944, 0.5847]]) # Creation of a tensor and filling of a tensor with random numbers b = torch.randn(2, 3, device=device, dtype=dtype) print(b) # Output of tensor B # Output: tensor([[ 0.7178, -0.8453, -1.3403], # [ 1.3262, 1.1512, -1.7070]]) print(a*b) # Output of a multiplication of the two tensors # Output: tensor([[-0.8530, -0.7183, 2.58], # [-1.1692, 0.2238, -0.9981]]) print(a.sum()) # Output of the sum of all elements in tensor A # Output: tensor(-2.1540) print(a[1,2]) # Output of the element in the third column of the second row # Output: tensor(0.5847) print(a.min()) # Output of the minimum value in tensor A # Output: tensor(-1.7129)
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FAIR is accustomed to working with PyTorch – a deep learning framework optimized for achieving state of the art results in research, regardless of resource constraints. Unfortunately in the real world, most of us are limited by the computational capabilities of our smartphones and computers.
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- GitHub - catalyst-team/catalyst: Accelerated DL & RL, Catalyst-Team, 2019-12-05, retrieved 2019-12-05
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- "PyTorch – About". pytorch.org. Archived from the original on 2018-06-15. Retrieved 2018-06-11.
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- "An Introduction to PyTorch – A Simple yet Powerful Deep Learning Library". analyticsvidhya.com. 2018-02-22. Retrieved 2018-06-11.