PyTorch: Difference between revisions

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PyTorch packages can be installed and managed using Conda. Conda-forge's Miniforge{{ref|Miniforge}} is an open-source application that allows you to create and manage conda environments.
PyTorch packages can be installed and managed using Conda. Conda-forge's Miniforge{{ref|Miniforge}} is an open-source application that allows you to create and manage conda environments.


Trained PyTorch Models can be exported using the [[ONNX]] file format, which in turn can be imported into the {{software}} as a [[Neural Network]] for an [[Inference Overlay]].
Trained PyTorch Models can be exported using the [[ONNX]] file format, which in turn can be imported into the {{software}} as a [[Region-based Convolutional Neural Network (Inference Overlay)|Region-based Convolutional Neural Network (RCNN)]] for an [[Inference Overlay]].




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* [[ONNX]]
* [[ONNX]]
* [[Inference Overlay]]
* [[Inference Overlay]]
* [[Neural Network]]
* [[Region-based Convolutional Neural Network (Inference Overlay)|RCNN]]
* [[Demo Training Data Project]]
* [[Demo Training Data Project]]


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}}
}}
</references>
</references>
|api=*[[Api session event editor neuralnetwork add]]
}}
}}
{{GeoDataOverview}}
{{GeoDataOverview}}

Latest revision as of 12:01, 13 July 2026

PyTorch[1] is a Python-based, open source, and production-ready AI framework that supports distributed training, graph mode, and cloud platforms.

PyTorch packages can be installed and managed using Conda. Conda-forge's Miniforge[2] is an open-source application that allows you to create and manage conda environments.

Trained PyTorch Models can be exported using the ONNX file format, which in turn can be imported into the Tygron Platform as a Region-based Convolutional Neural Network (RCNN) for an Inference Overlay.