https://github.com/inferno-pytorch/inferno

A utility library around PyTorch
https://github.com/inferno-pytorch/inferno

deep-learning neural-networks pytorch

Last synced: 3 months ago
JSON representation

A utility library around PyTorch

• Host: GitHub
• URL: https://github.com/inferno-pytorch/inferno
• Owner: inferno-pytorch
• License: other
• Archived: true
• Created: 2017-06-07T19:19:48.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T20:28:08.000Z (almost 2 years ago)
• Last Synced: 2024-05-23T06:47:51.233Z (6 months ago)
• Topics: deep-learning, neural-networks, pytorch
• Language: Python
• Homepage:
• Size: 38.7 MB
• Stars: 243
• Watchers: 12
• Forks: 41
• Open Issues: 19
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - inferno

README

        

=======

Inferno

=======

.. image:: https://anaconda.org/conda-forge/inferno/badges/version.svg   

        :target: https://anaconda.org/conda-forge/inferno

.. image:: https://travis-ci.org/inferno-pytorch/inferno.svg?branch=master

        :target: https://travis-ci.org/inferno-pytorch/inferno

..

  TODO new docs shield goes here, see https://github.com/inferno-pytorch/inferno/issues/139

  .. image:: https://readthedocs.org/projects/inferno-pytorch/badge/?version=latest

          :target: http://inferno-pytorch.readthedocs.io/en/latest/?badge=latest

          :alt: Documentation Status

.. image:: http://svgshare.com/i/2j7.svg

Inferno is a little library providing utilities and convenience functions/classes around 

`PyTorch `_. 

It's a work-in-progress, but the releases from v0.4 on should be fairly stable! 

* Free software: Apache Software License 2.0

* Documentation: http://inferno-pytorch.readthedocs.io (Work in Progress).

Features

--------

Current features include: 

  *   a basic 

      `Trainer class `_ 

      to encapsulate the training boilerplate (iteration/epoch loops, validation and checkpoint creation),

  *   a `graph API `_ for building models with complex architectures, powered by `networkx `_. 

  *   `easy data-parallelism `_ over multiple GPUs, 

  *   `a submodule `_ for `torch.nn.Module`-level parameter initialization,

  *   `a submodule `_ for data preprocessing / transforms,

  *   `support `_ for `Tensorboard `_ (best with atleast `tensorflow-cpu `_ installed)

  *   `a callback API `_ to enable flexible interaction with the trainer,

  *   `various utility layers `_ with more underway,

  *   `a submodule `_ for volumetric datasets, and more!

  

.. code:: python

  import torch.nn as nn

  from inferno.io.box.cifar import get_cifar10_loaders

  from inferno.trainers.basic import Trainer

  from inferno.trainers.callbacks.logging.tensorboard import TensorboardLogger

  from inferno.extensions.layers.convolutional import ConvELU2D

  from inferno.extensions.layers.reshape import Flatten

  # Fill these in:

  LOG_DIRECTORY = '...'

  SAVE_DIRECTORY = '...'

  DATASET_DIRECTORY = '...'

  DOWNLOAD_CIFAR = True

  USE_CUDA = True

  # Build torch model

  model = nn.Sequential(

      ConvELU2D(in_channels=3, out_channels=256, kernel_size=3),

      nn.MaxPool2d(kernel_size=2, stride=2),

      ConvELU2D(in_channels=256, out_channels=256, kernel_size=3),

      nn.MaxPool2d(kernel_size=2, stride=2),

      ConvELU2D(in_channels=256, out_channels=256, kernel_size=3),

      nn.MaxPool2d(kernel_size=2, stride=2),

      Flatten(),

      nn.Linear(in_features=(256 * 4 * 4), out_features=10),

      nn.LogSoftmax(dim=1)

  )

  # Load loaders

  train_loader, validate_loader = get_cifar10_loaders(DATASET_DIRECTORY,

                                                      download=DOWNLOAD_CIFAR)

  # Build trainer

  trainer = Trainer(model) \

    .build_criterion('NLLLoss') \

    .build_metric('CategoricalError') \

    .build_optimizer('Adam') \

    .validate_every((2, 'epochs')) \

    .save_every((5, 'epochs')) \

    .save_to_directory(SAVE_DIRECTORY) \

    .set_max_num_epochs(10) \

    .build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),

                                    log_images_every='never'),

                  log_directory=LOG_DIRECTORY)

  # Bind loaders

  trainer \

      .bind_loader('train', train_loader) \

      .bind_loader('validate', validate_loader)

  if USE_CUDA:

    trainer.cuda()

  # Go!

  trainer.fit()

To visualize the training progress, navigate to `LOG_DIRECTORY` and fire up tensorboard with 

.. code:: bash

  $ tensorboard --logdir=${PWD} --port=6007

and navigate to `localhost:6007` with your browser.

Installation

------------------------

Conda packages for python >= 3.6 for all distributions are availaible on conda-forge:

.. code:: bash

  $ conda install -c pytorch -c conda-forge inferno

Future Features: 

------------------------

Planned features include: 

  *   a class to encapsulate Hogwild! training over multiple GPUs, 

  *   minimal shape inference with a dry-run,

  *   proper packaging and documentation,

  *   cutting-edge fresh-off-the-press implementations of what the future has in store. :)

Credits

---------

All contributors are listed here_. 

.. _here: https://inferno-pytorch.github.io/inferno/html/authors.html

This package was partially generated with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template + lots of work by Thorsten. 

.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage