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torch · PyPI
https://pypi.org/project/torch/
WEBMar 27, 2024 · PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
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Start Locally | PyTorch
https://pytorch.org/get-started/locally/
WEBConda. Pip. LibTorch. Source. Python. C++ / Java. CUDA 11.8. CUDA 12.1. ROCm 5.7. CPU. pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118. Installing on Windows. PyTorch can be installed and used on various Windows distributions.
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torchvision · PyPI
https://pypi.org/project/torchvision/
WEBMar 27, 2024 · Project description. torchvision. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. Installation. Please refer to the official instructions to install the stable versions of torch and torchvision on your system. To build source, refer to our contributing page.
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torch-geometric · PyPI
https://pypi.org/project/torch-geometric/
WEBMar 20, 2024 · Project description. Documentation | Paper | Colab Notebooks and Video Tutorials | External Resources | OGB Examples. PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
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torch — PyTorch 2.2 documentation
https://pytorch.org/docs/stable/torch.html
WEBShortcuts. torch ¶. The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serialization of Tensors …
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Quickstart — PyTorch Tutorials 2.2.2+cu121 documentation
https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
WEB# Get cpu, gpu or mps device for training. device = ("cuda" if torch. cuda. is_available else "mps" if torch. backends. mps. is_available else "cpu") print (f "Using {device} device") # Define model class NeuralNetwork (nn. Module): def __init__ (self): super (). __init__ self. flatten = nn. Flatten self. linear_relu_stack = nn. Sequential (nn ...
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PyTorch
https://pytorch.org/
WEBAnaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++. NOTE: Latest PyTorch requires Python 3.8 or later. For more details, see Python section below. PyTorch Build. Your OS.
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PyTorch documentation — PyTorch 2.2 documentation
https://pytorch.org/docs/stable/index.html
WEBPyTorch 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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torch.Tensor — PyTorch 2.2 documentation
https://pytorch.org/docs/stable/tensors.html
WEBThere are a few main ways to create a tensor, depending on your use case. To create a tensor with pre-existing data, use torch.tensor(). To create a tensor with specific size, use torch.* tensor creation ops (see Creation Ops ).
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GitHub - pytorch/pytorch: Tensors and Dynamic neural networks …
https://github.com/pytorch/pytorch
WEBPyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
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