**************** 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