AI Notebooks - Tutorial - Use tensorboard inside notebooks

How to use tensorboard inside AI Notebooks

Last updated 1st September, 2022.


The purpose of this tutorial is to show how it is possible to launch a TensorBoard inside an AI Notebooks.

TensorBoard is a tool made by TensorFlow, for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.

TensorBoard provides a visual interface :


The tutorial presents a simple example of launching TensorBoard in a notebook.


  • access to the OVHcloud Control Panel
  • an AI Notebooks project created inside a Public Cloud project
  • a user for AI Notebooks


Launch and access Jupyter notebook

If you want to launch it from the OVHcloud Control Panel, just create a new notebook and select TensorFlow docker image.

If you want to launch it with the CLI, just choose the name of your notebook (<notebook-name>) and the number of GPUs (<nb-gpus>) your want and use the following command:

ovhai notebook run tensorflow jupyterlab \
    --name <notebook-name> \
    --gpu <nb-gpus>

You can then reach your notebook's URL once the notebook is running.

Experiment with example notebook

All source code for this tutorial can be found here.

The example notebook is based on the Fashion MNIST dataset.


Then access the example notebook via the following path:

ai-training-examples > notebooks > computer-vision > image-classification > tensorflow > tensorboard > notebook_tutorial_tensorboard.ipynb

The aim of this tutorial is to show how it is possible, thanks to TensorBoard, to see the dynamic display of different metrics.

A preview of this notebook can be found on GitHub.

Go further

  • Another way to track your training with an AI notebook is to use Weights & Biases. Follow this tutorial.
  • If you want to launch TensorBoard in a job, please refer to this documentation.


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