Related projects

Below you can find other projects started by the same author and based on PyTorch as well:

torchlayers:

torchlayers is a library based on PyTorch providing automatic shape and dimensionality inference of torch.nn layers + additional building blocks featured in current SOTA architectures (e.g. Efficient-Net).

• Shape inference for most of torch.nn module (convolutional, recurrent, transformer, attention and linear layers)

• Dimensionality inference (e.g. torchlayers.Conv working as torch.nn.Conv1d/2d/3d based on input shape)

• Shape inference of user created modules

• Additional Keras-like layers (e.g. torchlayers.Reshape or torchlayers.StandardNormalNoise)

• Additional SOTA layers mostly from ImageNet competitions (e.g. PolyNet, Squeeze-And-Excitation, StochasticDepth.

• Useful defaults (same padding and default kernel_size=3 for Conv, dropout rates etc.)

• Zero overhead and torchscript support

You can read documentation over at https://github.com/szymonmaszke/torchlayers.

torchdata:

torchdata extends torch.utils.data.Dataset and equips it with functionalities known from tensorflow.data like map or cache.

• Use map, apply, reduce or filter directly on Dataset objects

• cache data in RAM/disk or via your own method (partial caching supported)

• Full PyTorch’s [Dataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) and [IterableDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset>) support

• General torchdata.maps like Flatten or Select

• Extensible interface (your own cache methods, cache modifiers, maps etc.)

• Useful torchdata.datasets classes designed for general tasks (e.g. file reading)

• Support for torchvision datasets (e.g. ImageFolder, MNIST, CIFAR10) via td.datasets.WrapDataset

• Minimal overhead (single call to super().__init__())

You can read documentation over at https://szymonmaszke.github.io/torchdata.

torchlambda:

torchlambda is a tool to deploy PyTorch models on Amazon’s AWS Lambda using AWS SDK for C++ and custom C++ runtime.

• Using statically compiled dependencies whole package is shrunk to only 30MB.

• Due to small size of compiled source code users can pass their models as AWS Lambda layers. Services like Amazon S3 are no longer necessary to load your model.

• torchlambda has it’s PyTorch & AWS dependencies always up to date because of continuous deployment run at 03:00 a.m. every day.

You can read project’s wiki over at https://github.com/szymonmaszke/torchlambda/wiki