The MintPy Docker container#

Docker allows you to run MintPy in a dedicated container, which is essentially an efficient virtual machine. Check here for the installation instruction.

1. Pulling the mintpy Docker image#

We publish the mintpy Docker images in the GitHub Container Registry at ghcr.io/insarlab/mintpy.

The latest stable released version can be pulled to your local machine via the latest tag as:

docker pull ghcr.io/insarlab/mintpy:latest

The latest development version (the current HEAD of the main branch) can be pulled via the develop tag as:

docker pull ghcr.io/insarlab/mintpy:develop

Note that both latest and develop are rolling tags, meaning they change as MintPy evolves. Thus, in a production system, one may want to use a specific version for reproducebility. This is available (since version 1.3.3) via the version tag as:

docker pull ghcr.io/insarlab/mintpy:v1.3.3

2. Running the mintpy Docker container#

Run the following to start an interactive shell session in the container with a host path to the data directory using volumes:

docker run -it -v </path/to/data/dir>:/home/mambauser/data ghcr.io/insarlab/mintpy:latest
# use "docker run --name" option to name the container, e.g. "--name mintpy"
# then enter the running container as "docker exec -it mintpy"

# now inside the container
cd data/FernandinaSenDT128/mintpy
smallbaselineApp.py FernandinaSenDT128.txt

Or run mintpy executables directly as:

docker run -it -v </path/to/data/dir>:/home/mambauser/data ghcr.io/insarlab/mintpy:latest smallbaselineApp.py --help
docker run -it -v </path/to/data/dir>:/home/mambauser/data ghcr.io/insarlab/mintpy:latest smallbaselineApp.py /home/mambauser/data/FernandinaSenDT128/mintpy/FernandinaSenDT128.txt

Or run the following to launch the Jupyter Lab server, then copy and paste the printed http://localhost:8888/lab?token= url in a browser.

# to launch a Jupyter Notebook frontend, replace "lab" with "notebook" in the command below
docker run -p 8888:8888 -it ghcr.io/insarlab/mintpy:latest jupyter lab

Or launch the Jupyter server with custom startup options as:

docker run -p 8888:8888 -it ghcr.io/insarlab/mintpy:latest jupyter {lab,notebook} [JUPYTER_OPTIONS]
# to see all the custom startup option:
docker run -p 8888:8888 -it ghcr.io/insarlab/mintpy:latest jupyter {lab,notebook} --help-all

Notes#

• The container image is built using the mambaorg/micromamba as a base. To manage conda environments inside the container use the micromamba command. For more information on micromamba, see: https://github.com/mamba-org/mamba#micromamba

• Docker tightly maps user/group ids (uid/gid) inside and outside the container. By default, a mambauser with uid=1000 and gid=1000 will run inside the container and write files as that user. If you mount in a volume, files written to that volume will be owned by the user on your local machine with uid=1000 and gid=1000. On linux and mac these are the default uid/gid values, but on a shared or managed system, these may not be your uid/gid values. You can override the users running inside the container with the --user argument to docker run, see: https://docs.docker.com/engine/reference/run/#user