Metadata-Version: 2.1
Name: overboard
Version: 0.5.1
Summary: Pure Python dashboard for monitoring deep learning experiments
Home-page: https://github.com/jotaf98/overboard
Author: Joao Henriques
License: UNKNOWN
Description: # OverBoard
        OverBoard is a lightweight yet powerful dashboard to monitor your experiments.
        
        <p align="center">
        <img align="center" alt="editor" src="https://raw.githubusercontent.com/jotaf98/overboard/master/demo.gif" />
        </p>
        
        
        ## Features
        
        - A table of hyper-parameters with Python-syntax filtering
        
        - Multiple views of the same data (i.e. custom X/Y axes)
        
        - Hyper-parameter visualisation (i.e. bubble plots)
        
        - Percentile intervals for multiple runs (i.e. shaded plots)
        
        - Custom visualisations (tensors and any custom plot with familiar MatPlotLib syntax)
        
        - Fast client-side rendering (the training code is kept lightweight)
        
        
        ## Installation
        
        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`
        
        
        ## Usage
        
        - Main interface: `python3 -m overboard <logs-directory>`
        
        - Logging experiments is simple:
        ```python
        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/synthetic.py`: Generate some test logs.
        - `examples/mnist.py`: 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](https://github.com/libfuse/sshfs) (Linux, available in most distros), [FUSE](https://osxfuse.github.io/) (Mac), [SFTP NetDrive](https://www.nsoftware.com/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`.
        
        
        ## Author
        
        [João Henriques](http://www.robots.ox.ac.uk/~joao/), [Visual Geometry Group (VGG)](http://www.robots.ox.ac.uk/~vgg/), University of Oxford
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
