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 |
Developer
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Other contributors
Written in
Python, C++, CUDA, C
Initial Release
24 August 2016
Repository
License
Categories
Alternatives
Machine Learning
Massive Online Analysis TensorFlow Apache Mahout Apache Spark Apache MXNet Apache SystemDS Eclipse Deeplearning4j MALLET mlpack OpenCV Orange scikit-learn The Microsoft Cognitive Toolkit Torch Weka Yooreeka
Deep Learning
TensorFlow Apache MXNet Apache SystemDS Caffe Eclipse Deeplearning4j OpenNN The Microsoft Cognitive Toolkit Torch Weka
Neural Networks
OpenNN
Massive Online Analysis TensorFlow Apache Mahout Apache Spark Apache MXNet Apache SystemDS Eclipse Deeplearning4j MALLET mlpack OpenCV Orange scikit-learn The Microsoft Cognitive Toolkit Torch Weka Yooreeka
Deep Learning
TensorFlow Apache MXNet Apache SystemDS Caffe Eclipse Deeplearning4j OpenNN The Microsoft Cognitive Toolkit Torch Weka
Neural Networks
OpenNN