======= 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 <https://github.com/pytorch/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 <https://github.com/nasimrahaman/inferno/tree/master/docs#preparing-the-trainer>_ to encapsulate the training boilerplate (iteration/epoch loops, validation and checkpoint creation),
• a graph API <https://github.com/nasimrahaman/inferno/blob/master/inferno/extensions/containers/graph.py> for building models with complex architectures, powered by networkx <https://github.com/networkx/networkx>.
• easy data-parallelism <https://github.com/nasimrahaman/inferno/tree/master/docs#using-gpus>_ over multiple GPUs,
• a submodule <https://github.com/nasimrahaman/inferno/blob/master/inferno/extensions/initializers>_ for torch.nn.Module-level parameter initialization,
• a submodule <https://github.com/nasimrahaman/inferno/blob/master/inferno/io/transform>_ for data preprocessing / transforms,
• support <https://github.com/nasimrahaman/inferno/tree/master/docs#using-tensorboard>_ for Tensorboard <https://www.tensorflow.org/get_started/summaries_and_tensorboard> (best with atleast tensorflow-cpu <https://github.com/tensorflow/tensorflow> installed)
• a callback API <https://github.com/nasimrahaman/inferno/tree/master/docs#setting-up-callbacks>_ to enable flexible interaction with the trainer,
• various utility layers <https://github.com/nasimrahaman/inferno/tree/master/inferno/extensions/layers>_ with more underway,
• a submodule <https://github.com/nasimrahaman/inferno/blob/master/inferno/io/volumetric>_ 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