PyTorch
A python deep learning library based on Torch for building and training neural networks, used for applications in computer vision, natural language processing, and other areas
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| + | Tensor Computation | Provides tensor computation similar to NumPy, with robust GPU acceleration support. |
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| + | Automatic Differentiation | Enables automatic differentiation for creating and training deep neural networks. |
| + | Performance Mode | Transition seamlessly with TorchScript between eager mode for ease of use and flexibility and graph mode for speed and optimization for production. |
| + | Distributed Training | Scalable distributed training and performance optimization with support of asynchronous execution of collective operations using the torch.distributed backend. |
| + | Pre-configured Models | Includes pre-trained models like ResNet, AlexNet, SqueezeNet, VGG, DenseNet, and Inception. |
| + | Robust Ecosystem | A rich community-built ecosystem of tools and libraries for various domains, from computer vision to reinforcement learning. |
| + | Scalable | Deploy PyTorch models at scale using TorchServe, which is also environment agnostic allowing deploying models across different cloud platforms. |
| + | Logging and Metrics | Monitor model performance with built-in logging and metrics features in TorchServe. |
| + | RESTful Endpoints | Create RESTful endpoints for seamless application integration using TorchServe. |
| + | Rich Data Loading | Allows to import data from various sources like CSV, image files, and databases, simplifying data pre-processing |
| + | Automatic Model Optimization | Tools like Quantization Library (TorchQuantization) optimize model size and speed for deployment on resource-constrained devices |
| + | Visualization Tools | Libraries like TensorBoard integration aid in visualizing model training and performance |
System Requirements
| # | Minimum |
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| 1 |
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| # | Minimum |
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| 1 | macOS 10.15 (Catalina) or above |
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| # | Minimum | Recommended |
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| 1 |
| Windows 10 or greater |
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Ratings
4.505
| InfoWorld | 4.55 based on professional's opinion |
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Written in
Python, C++, CUDA, C
Initial Release
24 August 2016
Repository
License
Categories
Alternatives
Machine Learning
Deep Learning
Neural Networks
Deep Learning
Neural Networks