Containerizing GAMS with Standard Python Libraries using Docker

This article discusses a complex task of containerizing GAMS (General Algebraic Modeling System) along with standard Python libraries in a single Docker container, due to the absence of readily available online resources and GAMS's own limited Docker documentation.

Here are the key steps we undertook to resolve the issue:

1.Preparation of Docker Base Image: We started with a base image of Ubuntu in Docker. Then, we installed necessary dependencies and Python libraries that our application required, like GCC, Git, Ninja-build, libglib2.0-0, etc.

2.Installation of Anaconda: We installed the Miniconda distribution to help manage our Python environment.

3.Installation of GAMS: Downloaded GAMS installer and ran it. Included GAMS in our environment path to ensure it was accessible for execution.

4.Integration of Application Code: Copied our application code files, including Python scripts and supporting modules, into the Docker container's workspace.

5.Provision of GAMS license: Added the GAMS license file into the appropriate directory within the Docker container.


6.Creation of Entrypoint Scripts: Developed shell scripts ( and to set up the conda environment, install Python packages, and initiate our Python applications respectively. Ensured that these scripts were executable.

7.Entrypoint Configuration: Configured Docker to run our script upon launching a container, which in turn would initiate our script.


This process enabled us to create a Docker image that could run GAMS and standard Python libraries in harmony. This image could be deployed seamlessly in various environments without worrying about dependencies, offering the benefits of containerization for our GAMS application.

Preparation of Entrypoint Scripts: As our application relies on several Python libraries and a specific Python environment managed by conda, we developed a shell script ( to set up this environment within the Docker container. This script activates the 'gams' conda environment, installs GAMS Python bindings, and other required Python packages. We also created another shell script ( that is responsible for initiating our Python applications.

A key reason we required was because, while building the Docker image, we weren't within the context of a shell session, and thus couldn't directly activate a conda environment. This issue could potentially cause failures when trying to install Python packages or run Python scripts that rely on the specific Python environment. By creating a separate script to manage conda setup, we were able to establish the necessary environment during runtime instead of build-time, ensuring the required Python packages and configurations were correctly set up.

Entrypoint Configuration: We configured Docker to run our script upon launching a container. This script ensures the necessary conda environment and Python packages are set up and then triggers to run our Python applications.

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