PyTorch: Difference between revisions
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A PyTorch model can be converted to an [[ONNX]] file, which in turn can be imported into the {{software}} as a [[Neural Network]] for an [[Inference Overlay]]. | A PyTorch model can be converted to an [[ONNX]] file, which in turn can be imported into the {{software}} as a [[Neural Network]] for an [[Inference Overlay]]. | ||
PyTorch packages can be installed and managed using Conda. Conda-forge's | PyTorch packages can be installed and managed using Conda. Conda-forge's Miniforge<ref name="Miniforge"/> is an open-source application that allows you to create and manage conda environments. | ||
{{article end | {{article end | ||
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<references> | <references> | ||
<ref name="PyTorch">PyTorch ∙ found at: https://pytorch.org/ (last visited: 2025-02-04)</ref> | <ref name="PyTorch">PyTorch ∙ found at: https://pytorch.org/ (last visited: 2025-02-04)</ref> | ||
<ref name="Miniforge">Miniforge ∙ found at: https://conda-forge.org/download/ (last visited: 2025-10-13)</ref> | |||
</references> | </references> | ||
{{GeoDataOverview}} | {{GeoDataOverview}} | ||
Revision as of 12:52, 14 October 2025
PyTorch[1] is a Python-based, open source, and production-ready AI framework that supports distributed training, graph mode, and cloud platforms.
A PyTorch model can be converted to an ONNX file, which in turn can be imported into the Tygron Platform as a Neural Network for an Inference Overlay.
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.
How-to's
- How to export AI Training Data
- How to train your own AI model for an Inference Overlay
- How to import an ONNX file using drag and drop
- How to import an ONNX file for an Inference Overlay
- How to adjust a Neural Networks metadata
See also
References
- ↑ PyTorch ∙ found at: https://pytorch.org/ (last visited: 2025-02-04)
- ↑ Miniforge ∙ found at: https://conda-forge.org/download/ (last visited: 2025-10-13)