Metadata-Version: 2.1
Name: torchrecorder
Version: 1.0.2
Summary: Record execution graphs of PyTorch neural networks
Home-page: https://github.com/ahgamut/torchrecorder
Author: Gautham Venkatasubramanian
Author-email: ahgamut@gmail.com
License: MIT
Description: `torchrecord`
        =============
        
        ![](https://readthedocs.org/projects/torchrecord/badge/?version=latest&style=flat)
        
        A small package to record execution graphs of neural networks in PyTorch.
        The package uses hooks and the `grad_fn` attribute to record information.  
        This can be used to generate visualizations at different scope depths. 
        
        Licensed under MIT License.
        View documentation at https://torchrecord.readthedocs.io/
        
        ## Installation
        
        Requirements:
        
        * Python3.6+
        * [PyTorch](https://pytorch.org) v1.3 or greater (the `cpu` version)
        * The [Graphviz](https://graphviz.gitlab.io) library and `graphviz` [python package](https://graphviz.readthedocs.io/en/stable/manual.html).
        
        
        Install this package:
        
        ```
        $ pip install torchrecord
        ```
        
        ## Acknowledgements
        
        This is inspired from [`szagoruyko/pytorchviz`](https://github.com/szagoruyko/pytorchviz).  This package
        differs from `pytorchviz` as it provides rendering at multiple depths.
        
        Note that for rendering a network during training, you can use TensorBoard and
        [`torch.utils.tensorboard.SummaryWriter.add_graph`](https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_graph),
        which records and renders to a `protobuf` in a single step.  The intended usage of `torchrecord` is for
        presentation purposes.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
