# Docker Images¶

Each image installs the full Dask conda package (including the distributed scheduler), Numpy, and Pandas on top of a Miniconda installation on top of a Debian image.

These images are large, around 1GB.

• daskdev/dask: This a normal debian + miniconda image with the full Dask conda package (including the distributed scheduler), Numpy, and Pandas. This image is about 1GB in size.
• daskdev/dask-notebook: This is based on the Jupyter base-notebook image and so it is suitable for use both normally as a Jupyter server, and also as part of a JupyterHub deployment. It also includes a matching Dask software environment described above. This image is about 2GB in size.

## Example¶

Here is a simple example on the local host network

docker run -it --network host daskdev/dask dask-scheduler  # start scheduler



## Extensibility¶

Users can mildly customize the software environment by populating the environment variables EXTRA_APT_PACKAGES, EXTRA_CONDA_PACKAGES, and EXTRA_PIP_PACKAGES. If these environment variables are set, they will trigger calls to the following respectively:

apt-get install $EXTRA_APT_PACKAGES conda install$EXTRA_CONDA_PACKAGES
pip install \$EXTRA_PIP_PACKAGES


Note that using these can significantly delay the container from starting, especially when using apt, or conda (pip is relatively fast).

Remember that it is important for software versions to match between Dask workers and Dask clients. As a result, it is often useful to include the same extra packages in both Jupyter and Worker images.

## Source¶

Docker files are maintained at https://github.com/dask/dask-docker. This repository also includes a docker-compose configuration.