Metadata-Version: 2.1
Name: torchfunc-nightly
Version: 1653529239
Summary: PyTorch functions to improve performance, analyse models and make your life easier.
Home-page: https://github.com/szymonmaszke/torchfunc
Author: Szymon Maszke
Author-email: szymon.maszke@protonmail.com
License: MIT
Project-URL: Website, https://szymonmaszke.github.io/torchfunc
Project-URL: Documentation, https://szymonmaszke.github.io/torchfunc/#torchfunc
Project-URL: Issues, https://github.com/szymonmaszke/torchfunc/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc
Description: <img align="left" width="256" height="256" src="https://github.com/szymonmaszke/torchfunc/blob/master/assets/logos/medium.png">
        
        * Improve and analyse performance of your neural network (e.g. Tensor Cores compatibility)
        * Record/analyse internal state of `torch.nn.Module` as data passes through it
        * Do the above based on external conditions (using single `Callable` to specify it)
        * Day-to-day neural network related duties (model size, seeding, time measurements etc.)
        * Get information about your host operating system, `torch.nn.Module` device, CUDA
        capabilities etc.
        
        
        | Version | Docs | Tests | Coverage | Style | PyPI | Python | PyTorch | Docker | Roadmap |
        |---------|------|-------|----------|-------|------|--------|---------|--------|---------|
        | [![Version](https://img.shields.io/static/v1?label=&message=0.2.0&color=377EF0&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/releases) | [![Documentation](https://img.shields.io/static/v1?label=&message=docs&color=EE4C2C&style=for-the-badge)](https://szymonmaszke.github.io/torchfunc/)  | ![Tests](https://github.com/szymonmaszke/torchfunc/workflows/test/badge.svg) | ![Coverage](https://img.shields.io/codecov/c/github/szymonmaszke/torchfunc?label=%20&logo=codecov&style=for-the-badge) | [![codebeat](https://img.shields.io/static/v1?label=&message=CB&color=27A8E0&style=for-the-badge)](https://codebeat.co/projects/github-com-szymonmaszke-torchfunc-master) | [![PyPI](https://img.shields.io/static/v1?label=&message=PyPI&color=377EF0&style=for-the-badge)](https://pypi.org/project/torchfunc/) | [![Python](https://img.shields.io/static/v1?label=&message=3.6&color=377EF0&style=for-the-badge&logo=python&logoColor=F8C63D)](https://www.python.org/) | [![PyTorch](https://img.shields.io/static/v1?label=&message=>=1.2.0&color=EE4C2C&style=for-the-badge)](https://pytorch.org/) | [![Docker](https://img.shields.io/static/v1?label=&message=docker&color=309cef&style=for-the-badge)](https://hub.docker.com/r/szymonmaszke/torchfunc) | [![Roadmap](https://img.shields.io/static/v1?label=&message=roadmap&color=009688&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md) |
        
        # :bulb: Examples
        
        __Check documentation here:__ [https://szymonmaszke.github.io/torchfunc](https://szymonmaszke.github.io/torchfunc)
        
        ## 1. Getting performance tips
        
        - __Get instant performance tips about your module. All problems described by comments
        will be shown by `torchfunc.performance.tips`:__
        
        ```python
        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.convolution = torch.nn.Sequential(
                    torch.nn.Conv2d(1, 32, 3),
                    torch.nn.ReLU(inplace=True),  # Inplace may harm kernel fusion
                    torch.nn.Conv2d(32, 128, 3, groups=32),  # Depthwise is slower in PyTorch
                    torch.nn.ReLU(inplace=True),  # Same as before
                    torch.nn.Conv2d(128, 250, 3),  # Wrong output size for TensorCores
                )
        
                self.classifier = torch.nn.Sequential(
                    torch.nn.Linear(250, 64),  # Wrong input size for TensorCores
                    torch.nn.ReLU(),  # Fine, no info about this layer
                    torch.nn.Linear(64, 10),  # Wrong output size for TensorCores
                )
        
            def forward(self, inputs):
                convolved = torch.nn.AdaptiveAvgPool2d(1)(self.convolution(inputs)).flatten()
                return self.classifier(convolved)
        
        # All you have to do
        print(torchfunc.performance.tips(Model()))
        ```
        
        ## 2. Seeding, weight freezing and others
        
        - __Seed globaly (including `numpy` and `cuda`), freeze weights, check inference time and model size:__
        
        ```python
        # Inb4 MNIST, you can use any module with those functions
        model = torch.nn.Linear(784, 10)
        torchfunc.seed(0)
        frozen = torchfunc.module.freeze(model, bias=False)
        
        with torchfunc.Timer() as timer:
          frozen(torch.randn(32, 784)
          print(timer.checkpoint()) # Time since the beginning
          frozen(torch.randn(128, 784)
          print(timer.checkpoint()) # Since last checkpoint
        
        print(f"Overall time {timer}; Model size: {torchfunc.sizeof(frozen)}")
        ```
        
        ## 3. Record `torch.nn.Module` internal state
        
        - __Record and sum per-layer activation statistics as data passes through network:__
        
        ```python
        # Still MNIST but any module can be put in it's place
        model = torch.nn.Sequential(
            torch.nn.Linear(784, 100),
            torch.nn.ReLU(),
            torch.nn.Linear(100, 50),
            torch.nn.ReLU(),
            torch.nn.Linear(50, 10),
        )
        # Recorder which sums all inputs to layers
        recorder = torchfunc.hooks.recorders.ForwardPre(reduction=lambda x, y: x+y)
        # Record only for torch.nn.Linear
        recorder.children(model, types=(torch.nn.Linear,))
        # Train your network normally (or pass data through it)
        ...
        # Activations of all neurons of first layer!
        print(recorder[1]) # You can also post-process this data easily with apply
        ```
        
        For other examples (and how to use condition), see [documentation](https://szymonmaszke.github.io/torchfunc/)
        
        # :wrench: Installation
        
        ## :snake: [pip](<https://pypi.org/project/torchfunc/>)
        
        ### Latest release:
        
        ```shell
        pip install --user torchfunc
        ```
        
        ### Nightly:
        
        ```shell
        pip install --user torchfunc-nightly
        ```
        
        ## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchfunc)
        
        __CPU standalone__ and various versions of __GPU enabled__ images are available
        at [dockerhub](https://hub.docker.com/r/szymonmaszke/torchfunc/tags).
        
        For CPU quickstart, issue:
        
        ```shell
        docker pull szymonmaszke/torchfunc:18.04
        ```
        
        Nightly builds are also available, just prefix tag with `nightly_`. If you are going for `GPU` image make sure you have
        [nvidia/docker](https://github.com/NVIDIA/nvidia-docker) installed and it's runtime set.
        
        # :question: Contributing
        
        If 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).
        
        To get an overview of things one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md).
        
Keywords: pytorch torch functions performance visualize utils utilities recording
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
