torchenhanced 0.3.0

Wrappers for pytorch stuff I use on the daily

TorchEnhanced

Wrappers for pytorch stuff I use on the daily. Basically a minimal 'pytorch lightning', I was just not aware it existed at the time of creation.

Basic Usage

Install with pip install torchenhanced.

Here we describe how to use at a basic level the different components included in torchenhanced. There are many unrelated things it helps to do, so we dedicate a section to each.

Improved nn.Module

torchenhanced defines two new classes which are meant as stand-in for nn.Module.

DevModule Import with from torchenhanced import DevModule. DevModule is short for 'DeviceModule'. It is a nn.Module, but has an additional attribute device, which helps keeps track of the current device it is on.

Use it just like nn.Module, except it needs to be initialized with a device :

    class MyModule(DevModule):
        def __init__(hidden, device='cpu'):
            super().__init__(device)
            layer = nn.Linear(hidden,hidden,device=self.device)

Works just [STILL WIP]