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<pclass="lead">Select preferences and run the command to install PyTorch locally, or get started quickly with one of the supported cloud platforms.</p>
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<pclass="lead">Select all the Preferences and run the command to Install PyTorch Locally, or get started quickly with one of the supported Cloud Platforms.</p>
<pclass="hub-release-message">*This is a beta release - we will be collecting feedback and improving the PyTorch Hub over the coming months.</p>
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<spanid="hub-header">PyTorch</span><br>
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<spanid="hub-sub-header"></span>Hub</span>
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<pclass="lead">Discovering and Publishing models to a Pre-Trained Model Repository Designed for Research Exploration. <br> Check out the models for <ahref="#model-row">Researchers</a>, or learn <ahref="https://pytorch.org/docs/stable/hub.html">How It Works</a>.</p>
PyTorch Hub supports publishing pre-trained models (model definitions and pre-trained weights) to a GitHub repository by adding a simple <codeclass="hub-code-text">hubconf.py</code> file.
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PyTorch Hub supports publishing pre-trained models (model definitions and pre-trained weights) to a GitHub repository by adding a simple <codeclass="hub-code-text">hubconf.py</code> file.
Users can load pre-trained models using <codeclass="hub-code-text">torch.hub.load()</code> API.
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<pclass="how-it-works-text">Here’s an example showing how to load the <codeclass="hub-code-text">resnet18</code> entrypoint from the <codeclass="hub-code-text">pytorch/vision</code> repo.</p>
<buttonclass="full-docs-button"><ahref="https://pytorch.org/docs/stable/hub.html">See Full Documentation</a></button>
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Users can load pre-trained models using <codeclass="hub-code-text">torch.hub.load()</code> API.
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<pclass="how-it-works-text">Here’s an example showing how to load the <codeclass="hub-code-text">resnet18</code> entrypoint from the <codeclass="hub-code-text">pytorch/vision</code> repo.</p>
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<ahref="#" class="btn btn-lg with-right-arrow">Call to Action !!</a>
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