PyTorch is an open-source end-to-end machine learning framework, based on the Torch library. It was developed by Facebook's AI Research lab and it is free and distributed under BSD license. PyTorch is implemented in Python and C++.
The PyTorch framework includes all the standard features from Torch, such as nn modules, autograd, and Tensor computation graphs, while also providing Pythonic interfaces and features such as callbacks and Tensor decorators.
PyTorch's user-friendly front-end, distributed training, and ecosystem of tools and libraries allow quick, scalable experimentation and efficient production.
It provides a high-level neural scripting language that is used to define deep learning models and train them. The deep neural networks that we build with PyTorch are mostly forward-only and do not have cycles.
The famous Tesla Autopilot software was built using Pytorch. Uber's Pyro, HuggingFace's Transformers, PyTorch Lightning, and Catalyst are just a few examples of deep learning applications designed with PyTorch..
1. Tensor computing with strong acceleration via GPU
2. Production Ready
3. Fast and Lean
4. Distributed Training
5. Robust Ecosystem
6. Deep neural networks built on a type-based automatic differentiation system.
7. Cloud Support
Uses of PyTorch
- Computer Vision
- Natural Language processing
If you already have python installed, you can install PyTorch via package managers like pip or conda. To install via pip, please make sure you have NumPy already installed then run:
pip install torch
To install using conda, just run:
conda install -c pytorch pytorch
For more detailed installation guidelines, visit Getting started with PyTorch.