You can get help on Gitter, GitHub, or by email.

Pick one way to install atoti:

Python package

atoti is available as a Python package on the public PyPI repository and can thus be installed with Python package managers such as pip or Poetry.

Install atoti and its JupyterLab extension:

pip install atoti[jupyterlab]

Installing graphviz is also recommended as it can be used to display the schema of an atoti session.

Conda package

Install Miniconda 64-bit or Anaconda 64-bit.


Conda 64-bit is required since recent versions of some packages are not available with Conda 32-bit.

Add the conda-forge channel:

conda config --add channels conda-forge

Add the atoti channel:

conda config --add channels

Create a new Conda environment:

conda create --name atoti

Activate it:

conda activate atoti

Install atoti and its JupyterLab extension:

conda install atoti atoti-jupyterlab python

Docker image

The atoti image on DockerHub contains the atoti library and its JupyterLab extension ready to use.


This Docker image is a good playground to try atoti with JupyterLab locally. However, it should not be used as a base image for deploying atoti applications on the cloud since JupyterLab will not be needed in this situation. Starting from a bare Python image would be more suited for that.

Pull the image:

docker pull atoti/atoti

Create a volume where the data (notebooks, files, etc.) can be persisted when the Docker container is stopped:

docker volume create atoti-volume

Run the Docker image using the created volume:

docker run --publish 8888:8888 --volume atoti-volume:/home/jovyan/work atoti/atoti

Open the URL printed by Jupyter Server (e.g. http://localhost:8888?token=XXXXX with its security token).


Changing the port

Jupyter Server can listen to another port than 8888 by changing the publish mapping (--publish XXXX:XXXX) and the PORT environment variable:

docker run --env PORT=9999 --publish 9999:9999 --volume atoti-volume:/home/jovyan/work atoti/atoti

Building a custom image

Our Dockerfile can be used as a starting point for custom images (e.g. to pre-install other dependencies).