Ignite Your Networks!
=====================
:mod:`ignite` is a high-level library to help with training neural networks in PyTorch.
- ignite helps you write compact but full-featured training loops in a few lines of code
- you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop
to train and validate your model with occasional checkpointing:
.. image:: https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png
:target: https://raw.githubusercontent.com/pytorch/ignite/master/assets/ignite_vs_bare_pytorch.png
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or
things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of
your code when "rolling your own" training loop.
Installation
============
From `pip `_:
.. code:: bash
pip install pytorch-ignite
From `conda `_:
.. code:: bash
conda install ignite -c pytorch
From source:
.. code:: bash
pip install git+https://github.com/pytorch/ignite
Nightly releases
----------------
From pip:
.. code:: bash
pip install --pre pytorch-ignite
From conda (this suggests to install `pytorch nightly release `_ instead
of stable version as dependency):
.. code:: bash
conda install ignite -c pytorch-nightly
.. toctree::
:maxdepth: 2
:caption: Notes
concepts
quickstart
examples
faq
.. toctree::
:maxdepth: 2
:caption: Package Reference
engine
handlers
metrics
distributed
exceptions
utils
.. toctree::
:maxdepth: 2
:caption: Contrib Package Reference
contrib/engines
contrib/metrics
contrib/handlers
.. automodule:: ignite
:members:
.. toctree::
:maxdepth: 1
:caption: Team
about
governance