Audience
Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment
About PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.
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PyTorch Verified User Reviews
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Probability You Would Recommend?1 2 3 4 5 6 7 8 9 10
"Great open source machine learning framework" Posted 2022-08-03
Pros: - creates dynamic neural networks in Python
- GPU acceleration compatible
- easy transition between eager and graph modes
- scalable across distributed computing networks
- excellent documentation and community
- very flexible and fast machine learning
- free and open sourceCons: - very high learning curve
- requires significant power to run any sort of computationOverall: PyTorch is a great machine learning framework that is both flexible and fast. It's highly customizable and free, but very complicated to learn.
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