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README

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A Practical Debugging Tool for Training Deep Neural Networks

A better status screen for deep learning.

InstallationDocsExperimentsLicenseCitation

CI Lint Doc Coverage License: MIT Code style: black arXiv


pip install cockpit-for-pytorch

Cockpit is a visual and statistical debugger specifically designed for deep learning. Training a deep neural network is often a pain! Successfully training such a network usually requires either years of intuition or expensive parameter searches involving lots of trial and error. Traditional debuggers provide only limited help: They can find syntactical errors but not training bugs such as ill-chosen learning rates. Cockpit offers a closer, more meaningful look into the training process with multiple well-chosen instruments.


CockpitAnimation

Installation

To install Cockpit simply run

pip install cockpit-for-pytorch

Conda environment

For convenience, we also provide a conda environment, which can be installed via the conda yml file. It includes all the necessary requirements to build the docs, execute the tests and run the examples.

Documentation

The documentation provides a full tutorial on how to get started using Cockpit as well as a detailed documentation of its API.

Experiments

To showcase the capabilities of Cockpit we performed several experiments illustrating the usefulness of our debugging tool. The code for the experiments can be found in a separate repository. For a discussion of those experiments please refer to our paper.

License

Distributed under the MIT License. See LICENSE for more information.

Citation

If you use Cockpit, please consider citing:

[Frank Schneider, Felix Dangel, Philipp Hennig

Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

arXiv 2102.06604](http://arxiv.org/abs/2102.06604)

@misc{schneider2021cockpit,
   title={{Cockpit: A Practical Debugging Tool for Training Deep Neural Networks}},
   author={Frank Schneider and Felix Dangel and Philipp Hennig},
   year={2021},
   eprint={2102.06604},
   archivePrefix={arXiv},
   primaryClass={cs.LG}
}

Core symbols most depended-on inside this repo

should_compute
called by 39
cockpit/quantities/quantity.py
load_from_cache
called by 17
cockpit/quantities/quantity.py
save_to_cache
called by 16
cockpit/quantities/quantity.py
check_data
called by 16
cockpit/instruments/utils_instruments.py
get_individual_losses
called by 13
cockpit/context.py
plot
called by 13
cockpit/plotter.py
get_compare_fn
called by 13
tests/test_quantities/utils.py
get_batch_size
called by 12
cockpit/context.py

Shape

Method 306
Function 124
Class 66

Languages

Python100%

Modules by API surface

cockpit/quantities/quantity.py43 symbols
cockpit/quantities/alpha.py24 symbols
cockpit/plotter.py24 symbols
cockpit/quantities/bin_adaptation.py18 symbols
cockpit/quantities/hess_max_ev.py16 symbols
cockpit/cockpit.py16 symbols
cockpit/quantities/grad_hist.py15 symbols
cockpit/context.py15 symbols
tests/test_quantities/test_grad_hist.py14 symbols
tests/test_quantities/test_alpha.py14 symbols
tests/utils/problem.py13 symbols
examples/_utils_deepobs.py13 symbols

For agents

$ claude mcp add cockpit \
  -- python -m otcore.mcp_server <graph>

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