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