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"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries",
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"description": "![](\"https://github.com/szymonmaszke/torchfunc/blob/master/assets/logos/medium.png\")
\n\n* Improve and analyse performance of your neural network (e.g. Tensor Cores compatibility)\n* Record/analyse internal state of `torch.nn.Module` as data passes through it\n* Do the above based on external conditions (using single `Callable` to specify it)\n* Day-to-day neural network related duties (model size, seeding, time measurements etc.)\n* Get information about your host operating system, `torch.nn.Module` device, CUDA\ncapabilities etc.\n\n\n| Version | Docs | Tests | Coverage | Style | PyPI | Python | PyTorch | Docker | Roadmap |\n|---------|------|-------|----------|-------|------|--------|---------|--------|---------|\n| [](https://github.com/szymonmaszke/torchfunc/releases) | [](https://szymonmaszke.github.io/torchfunc/) |  |  | [](https://codebeat.co/projects/github-com-szymonmaszke-torchfunc-master) | [](https://pypi.org/project/torchfunc/) | [](https://www.python.org/) | [](https://pytorch.org/) | [](https://hub.docker.com/r/szymonmaszke/torchfunc) | [](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md) |\n\n# :bulb: Examples\n\n__Check documentation here:__ [https://szymonmaszke.github.io/torchfunc](https://szymonmaszke.github.io/torchfunc)\n\n## 1. Getting performance tips\n\n- __Get instant performance tips about your module. All problems described by comments\nwill be shown by `torchfunc.performance.tips`:__\n\n```python\nclass Model(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.convolution = torch.nn.Sequential(\n torch.nn.Conv2d(1, 32, 3),\n torch.nn.ReLU(inplace=True), # Inplace may harm kernel fusion\n torch.nn.Conv2d(32, 128, 3, groups=32), # Depthwise is slower in PyTorch\n torch.nn.ReLU(inplace=True), # Same as before\n torch.nn.Conv2d(128, 250, 3), # Wrong output size for TensorCores\n )\n\n self.classifier = torch.nn.Sequential(\n torch.nn.Linear(250, 64), # Wrong input size for TensorCores\n torch.nn.ReLU(), # Fine, no info about this layer\n torch.nn.Linear(64, 10), # Wrong output size for TensorCores\n )\n\n def forward(self, inputs):\n convolved = torch.nn.AdaptiveAvgPool2d(1)(self.convolution(inputs)).flatten()\n return self.classifier(convolved)\n\n# All you have to do\nprint(torchfunc.performance.tips(Model()))\n```\n\n## 2. Seeding, weight freezing and others\n\n- __Seed globaly (including `numpy` and `cuda`), freeze weights, check inference time and model size:__\n\n```python\n# Inb4 MNIST, you can use any module with those functions\nmodel = torch.nn.Linear(784, 10)\ntorchfunc.seed(0)\nfrozen = torchfunc.module.freeze(model, bias=False)\n\nwith torchfunc.Timer() as timer:\n frozen(torch.randn(32, 784)\n print(timer.checkpoint()) # Time since the beginning\n frozen(torch.randn(128, 784)\n print(timer.checkpoint()) # Since last checkpoint\n\nprint(f\"Overall time {timer}; Model size: {torchfunc.sizeof(frozen)}\")\n```\n\n## 3. Record `torch.nn.Module` internal state\n\n- __Record and sum per-layer activation statistics as data passes through network:__\n\n```python\n# Still MNIST but any module can be put in it's place\nmodel = torch.nn.Sequential(\n torch.nn.Linear(784, 100),\n torch.nn.ReLU(),\n torch.nn.Linear(100, 50),\n torch.nn.ReLU(),\n torch.nn.Linear(50, 10),\n)\n# Recorder which sums all inputs to layers\nrecorder = torchfunc.hooks.recorders.ForwardPre(reduction=lambda x, y: x+y)\n# Record only for torch.nn.Linear\nrecorder.children(model, types=(torch.nn.Linear,))\n# Train your network normally (or pass data through it)\n...\n# Activations of all neurons of first layer!\nprint(recorder[1]) # You can also post-process this data easily with apply\n```\n\nFor other examples (and how to use condition), see [documentation](https://szymonmaszke.github.io/torchfunc/)\n\n# :wrench: Installation\n\n## :snake: [pip]()\n\n### Latest release:\n\n```shell\npip install --user torchfunc\n```\n\n### Nightly:\n\n```shell\npip install --user torchfunc-nightly\n```\n\n## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchfunc)\n\n__CPU standalone__ and various versions of __GPU enabled__ images are available\nat [dockerhub](https://hub.docker.com/r/szymonmaszke/torchfunc/tags).\n\nFor CPU quickstart, issue:\n\n```shell\ndocker pull szymonmaszke/torchfunc:18.04\n```\n\nNightly builds are also available, just prefix tag with `nightly_`. If you are going for `GPU` image make sure you have\n[nvidia/docker](https://github.com/NVIDIA/nvidia-docker) installed and it's runtime set.\n\n# :question: Contributing\n\nIf you find any issue or you think some functionality may be useful to others and fits this library, please [open new Issue](https://help.github.com/en/articles/creating-an-issue) or [create Pull Request](https://help.github.com/en/articles/creating-a-pull-request-from-a-fork).\n\nTo get an overview of things one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md).\n\n\n",
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