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Related projects
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Below you can find other projects started by the same author and based on `PyTorch `__ as well:
`torchlayers: `__
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**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: `__
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**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: `__
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**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