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Start tensorboard in Jupyter! Jupyter notebook integration for tensorboard

Project description

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Tensorboard Integration for Jupyter Notebook.

A jupyter server extension for jupyter notebook and tensorboard (a visualization tool for tensorflow) which provides graphical user interface for tensorboard start, manage and stop in jupyter interface.

Installation

  1. Be sure that tensorflow(-gpu)>=1.3.0 has been installed. If not, you should install or upgrade your tensorflow>=1.3.0 first, and tensorbaord is a dependency of tensorflow so that it is automatically installed. This package does not have a tensorbaord or tensorflow dependency because there are several versions of tensorflow, for example, tensorflow and tensorflow-gpu, if jupyter_tensorboard requires any version of the tensorflow, it will install the version and abrupt the environment. Any way, you must be sure you have tensorflow(-gpu) installed before install this package.

  2. Install the pip package. The python version must be the same as Jupyter: if you start jupyter notebook in python3, pip3 may be used to install the package

    pip(3) install jupyter_tensorboard

    NOTE:

    The python version are important, you must be sure that your jupyter, jupyter_tensorboard, tensorflow have the same python version. If your tensorflow python and jupyter python versions are different, e.g., use tensorflow in py2 but jupyter starts in py3, both versions of tensorflow(py2 and py3) should be installed, and jupyter_tensorboard should install to py3, in accordance with jupyter.

  3. Enabling the notebook server to load the server extension. A jupyter subcommand is provided for this. You can enable the serverextension and the configurator nbextensions listed below for the current user with

    jupyter tensorboard enable --user

    The command accepts the same flags as the jupyter serverextension command provided by notebook versions >= 5.0, including --system to enable in system-wide config (the default), or --sys-prefix to enable in config files inside python’s sys.prefix, such as for a virtual environment. The provided jupyter tensorboard command can also be used to disable.

  4. Restart the jupyter notebook server.

Usage

Once jupyter_tensorboard is installed and enabled, and your notebook server has been restarted, you should be able to find the interfaces to manage tensorboard instances.

  • In notebook tree view, select a directory, a tensorboard button will be presented. Click the button, a new browser tab will be opened to show the tensorboard interface with the proposed directory as logdir.

https://github.com/lspvic/jupyter_tensorboard/raw/master/docs/_static/tensorboard_button.png
  • In notebook tree view, click the tensorbaord menu in new and a new tensorbaord instance is started with current directory as logdir.

https://github.com/lspvic/jupyter_tensorboard/raw/master/docs/_static/tensorboard_menu.png
  • In notebook running tab, a list of tensorboard instances are showed. Managing operations such as browsing, navigating, shutdown can be found here.

https://github.com/lspvic/jupyter_tensorboard/raw/master/docs/_static/tensorboard_list.png
  • The tensorbaord instance interface is in http://jupyter-host/tensorboard/<name>/ with the instance names increasing from 1.

https://github.com/lspvic/jupyter_tensorboard/raw/master/docs/_static/tensorboard_url.png

Troubleshooting

If you encounter problems with this server extension, you can:

  • check the issue page for this repository. If you can’t find one that fits your problem, please create a new one!

For debugging, useful information can (sometimes) be found by:

  • Checking for error messages in the browser’s Javascript console.

  • Checking for messages in the notebook server’s logs. This is particularly useful when the server is run with the –debug flag, to get as many logs as possible.

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