Metadata-Version: 2.1
Name: overboard
Version: 0.6.0
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`.
        
        Depending on the remote server's configuration (e.g. firewall settings), you might need to use a VPN to tunnel to the server's network, to ensure that the right ports are not blocked to you (i.e. having SSH access does not guarantee SFTP access from an external network).
        
        
        ## 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
