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Last Commit
Apr. 18, 2019
Dec. 13, 2018


Pure Python dashboard for monitoring deep learning experiments (like TensorBoard for PyTorch/MXNet/etc, without a browser)



  • Automatically discovers new experiments in a directory tree, and updates plots in real-time

  • Fully responsive native app, no fiddly Python-Javascript bridge or browsers involved

  • Visualize tensors (activations, filters) interactively with the mouse (zoom/pan)

  • Fully customizable plots using MatPlotLib. See what your network is really up to!

  • Fast logging and out-of-process drawing. Don't slow your training down to have fancy graphs

  • Easy remote monitoring of experiments (e.g. in a cluster over SSH)


The main OverBoard GUI uses Python 3; however, experiments can be logged from both Python 2 and 3 scripts.

The main dependencies are PyQt 5 and PyQtGraph. These can be installed as follows:

  • With Conda: conda install pyqt pyqtgraph -c anaconda

  • With pip: pip install pyqt5 pyqtgraph

Finally, OverBoard itself can be installed with: pip install overboard


  • Main interface: python3 -m overboard <logs-directory>

  • Logging experiments is simple:

from overboard import Logger

with Logger('./logs') as logger:
  for iteration in range(100):
    logger.append({'loss': 0, 'error': 0})

See the examples directory for more details.

  • examples/ Generate some test logs.
  • examples/ The mandatory MNIST example. Also includes custom MatPlotLib plots.

Remote experiments

The easiest way to monitor remote experiments is to mount their directory over SFTP, and point OverBoard to it.

Tested with: SSHFS (Linux, available in most distros), FUSE (Mac), SFTP NetDrive (Windows).

Since most of these don't allow OverBoard to monitor log files with the default light-weight method, the plots may not update automatically; in that case use the command-line argument --force-reopen-files.


João Henriques, Visual Geometry Group (VGG), University of Oxford

Latest Releases
 Apr. 1 2019
 Dec. 29 2018