Gabriel Coronado
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PyTorch is a flexible and powerful framework for building and deploying AI models. Learn how to install, use, and contribute to PyTorch, and explore its features, ecosystem, and cloud support.
- Start Locally
Select your preferences and run the install command. Stable...
- Tutorials
Using User-Defined Triton Kernels with torch.compile. Large...
- Mobile
PyTorch Mobile. There is a growing need to execute ML models...
- Blog
June 23, 2024. Training MoEs at Scale with PyTorch. Over the...
- Ecosystem
depyf is a tool to help users understand and adapt to...
- Torchvision
Torchvision - PyTorch
- Docs
PyTorch documentation ¶. PyTorch is an optimized tensor...
- Join
According to statistics from MIT Sloan, 75% of top...
- Start Locally
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- Prerequisites
- Installation
- Verification
- Building from Source
- GeneratedCaptionsTabForHeroSec
Supported Windows Distributions
PyTorch is supported on the following Windows distributions: 1. Windows 7 and greater; Windows 10or greater recommended. 2. Windows Server 2008r2 and greater
Python
Currently, PyTorch on Windows only supports Python 3.8-3.11; Python 2.x is not supported. As it is not installed by default on Windows, there are multiple ways to install Python: 1. Chocolatey 2. Python website 3. Anaconda For a Chocolatey-based install, run the following command in an administrative command prompt:
Package Manager
To install the PyTorch binaries, you will need to use at least one of two supported package managers: Anaconda and pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python and pip.
Anaconda
To install PyTorch with Anaconda, you will need to open an Anaconda prompt via Start | Anaconda3 | Anaconda Prompt.
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor. From the command line, type: then enter the following code: The output should be something similar to: Additionally, to check if your GPU driver and CUDA is enabled and accessible by Py...
For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from sou...
Learn how to install PyTorch on Windows using Anaconda, pip, or from source. Choose your preferred CUDA version, Python version, and package manager.
PyTorch documentation ¶. PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Features described in this documentation are classified by release status: Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation.
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PyTorch is a library that provides tensor computation, tape-based autograd, TorchScript, and neural networks with maximum flexibility and speed. It is designed to be deeply integrated into Python and reuse your favorite packages such as NumPy, SciPy, and Cython.
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