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PyTorch is an open-source deep learning framework developed by Facebook’s AI Research lab (FAIR). It provides a flexible ecosystem for deep learning and artificial intelligence research and production.

PyTorch is widely adopted in both academia and industry, competing closely with TensorFlow as a leading deep learning framework.

Key Features

Feature Description
Dynamic Computational Graphs PyTorch uses dynamic computation graphs, allowing for changes to network architecture during runtime.
Tensor Computation PyTorch offers a comprehensive library for tensor operations, similar to NumPy, but with GPU acceleration.
Deep Learning Support It includes modules for constructing deep learning models, such as CNNs, RNNs, and transformers.
Autograd PyTorch’s automatic differentiation library, Autograd, enables easy computation of gradients, essential for training neural networks.
Rich Ecosystem PyTorch has a thriving ecosystem with numerous libraries and tools, such as TorchVision for computer vision, TorchText for natural language processing, etc.
Open-source and community support PyTorch is open-source and actively maintained on GitHub. It has a large and active community providing support, contributing to discussions, and developing third-party tools and libraries.


PyTorch can be installed via pip, conda, or other package managers. Here’s a common installation method using pip:

pip install torch

Visit the official PyTorch installation guide which provides detailed instructions tailored to specific operating systems and hardware configurations.

PyTorch Concepts

    PyTorch contributors

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