This minor release introduces Python 3.6 compatibility hence the library can now be freely used on Google's Colab.

torchfunc.modules module was also extended, additions being:

• device function (so you can check device of PyTorch module or torch.Tensor)
• switch_device context manager which cast obj (e.g. torch.nn.Module or torch.Tensor) to specified device when with-in the block and casts it back after the block is finished
• {weight, bias, named}_parameters - yields parameters only if they are {weight, bias, named} in order to remove unnecessary if statements and clarify the intent.

A lot of breaking changes introduced in this release. This one can be considered as first semi-stable with features working correctly (or seemingly correctly).

Major Features and Improvements:

• New package hooks, where recorders are now located
• New module within recorders, registrators, responsible for easier registration of hooks based on indices within network or types of it's submodules/children.
• plot module removed and will probably be featured in separate library
• torchfunc.performance.tips now parses torchfunc.performance.technology.TensorCores tips
• Each object within torchfunc.performance package have now tips() method returning str describing steps one can take in order to possibly improve specific torch.nn.Module performance.
• Highly improved test coverage

Bug fixes:

• Proper registration of hooks via indices and types
• Fix object's representation (previously overriden by dataclasses.dataclass)

Hello :smile:,

This is initial release of torchfunc library, which currently should be considered as alpha.

To see what it's all about, check README.md.

To get in-depth info, check documentation.

Just hoping this will help you with day-to-day neural net tasks as it helped me :100: