All notable changes to the "pytorch-snippets" extension will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Added
pytorch:scheduler
snippets for learning rate scheduling. - Added torchvision dataset and model snippets
pytorch:torchvision:...
. - Added model loading from checkpoint or github repo
pytorch:checkpoint
,pytorch:github
. - Added sampler snippets
pytorch:sampler
- Snippets for Ignite and Fastai have been moved to seperate projects. Soo you can find them at github.com/svenbecker/vscode_fastai and github.com/svenbecker/vscode_ignite. This war primarily done to reduce the snippet overload.
pytorch:sequential
has been moved topytorch:container
- Updated official
pytorch:examples
- Added two new snippets
pytorch:dataset
andignite_metrics
- Added DataBlock API support for tabular data
fastai:tabular:datablock
- Changed
train
snippets for pytorch and fastai
- Fixed bugs in optimizer selection
- Added PyTorch Functional Snippets
pytorch:F:
- Added code snippets for fast metrics or loss selection (PyTorch and fastai)
- Added easy selection of neural network layers in PyTorch based on their type
pytorch:layer:
(conv, recurrent, etc.) - Added some more PyTorch snippets like for example optimizer selection, weight initialization etc.
- Added DataBlock API snippet for fastai
- Changed some code examples for common problems to be inline with the official examples provided by PyTorch
- Added lots of Fastai Snippets (including NLP, Computer Vision, Tabular Data and Collaborative Filtering)
- Initial release