42 lines
1.8 KiB
Markdown
42 lines
1.8 KiB
Markdown
---
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name: Feature request
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about: Suggest an idea for this project
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title: ''
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labels: enhancement
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assignees: ''
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---
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## 🚀 Feature
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<!-- A clear and concise description of the feature proposal -->
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### Motivation
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<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
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### Pitch
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<!-- A clear and concise description of what you want to happen. -->
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### Alternatives
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<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
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### Additional context
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<!-- Add any other context or screenshots about the feature request here. -->
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______________________________________________________________________
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#### If you enjoy Lightning, check out our other projects! ⚡
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- [**Metrics**](https://github.com/PyTorchLightning/metrics): Machine learning metrics for distributed, scalable PyTorch applications.
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- [**Lite**](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_lite.html): enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
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- [**Flash**](https://github.com/PyTorchLightning/lightning-flash): The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
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- [**Bolts**](https://github.com/PyTorchLightning/lightning-bolts): Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
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- [**Lightning Transformers**](https://github.com/PyTorchLightning/lightning-transformers): Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.
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