wc_env_manager: Tools for managing computing environments for whole-cell modeling

wc_env_manager helps modelers and software developers setup customizable computing environments for developing, testing, and running whole-cell (WC) models and WC modeling software. This makes it easy for modelers and software developers to install and configure the numerous dependencies required for WC modeling. This helps modelers and software developers focus on developing WC models and software tools, rather than on installing, configuring, and maintaining complicated dependencies.

In addition, wc_env_manager facilitates collaboration by helping WC modelers and software developers share a common base computing environment with third party dependencies. Furthermore, wc_env_manager helps software developers anticipate and debug issues in deployment by enabling developers to replicate similar environments to those used to test and deploy WC models and tools in systems such as Amazon EC2, CircleCI, and Heroku.

wc_env_manager uses Docker to setup a customizable computing environment that contains all of the software packages needed to run WC models and WC modeling software. This includes

• Required non-Python packages

• Required Python packages from PyPI and other sources

• WC models and WC modeling tools

• Optionally, local packages on the user’s machine such as clones of these WC models and WC modeling tools

wc_env_manager supports both the development and deployment of WC models and WC modeling tools:

• Development: wc_env_manager can run WC models and WC modeling software that is located on the user’s machine. This is useful for testing WC models and WC modeling software before committing it to GitHub.

• Deployment: wc_env_manager can run WC models and WC modeling software from external sources such as GitHub.

Contents

• 1. Installation
  • 1.1. Requirements
  • 1.2. Installing the latest revision from GitHub
• 2. Overview
  • 2.1. Features
  • 2.2. How wc_env_manager works
  • 2.3. Caveats and troubleshooting
• 3. Tutorial for users of WC models and WC modeling tools
  • 3.1. Pulling existing Docker images
  • 3.2. Building containers for WC modeling
  • 3.3. Using containers to run WC models and WC modeling tools
  • 3.4. Using WC modeling computing environments with an external IDE such as PyCharm
  • 3.5. Exiting and removing containers
• 4. Tutorial for developers of WC models and WC modeling tools
  • 4.1. Pulling existing Docker images
  • 4.2. Building containers for WC modeling
  • 4.3. Using containers to run WC models and WC modeling tools
  • 4.4. Using containers to develop WC models and WC modeling tools
  • 4.5. Using WC modeling computing environments with an external IDE such as PyCharm
  • 4.6. Exiting and removing containers
• 5. Tutorial for administrators of the wc_env and wc_env_dependencies images
  • 5.1. Create contexts for building the wc_env and wc_env_dependencies images
  • 5.2. Create Dockerfile templates for wc_env and wc_env_dependencies
  • 5.3. Set the configuration for wc_env_manager
  • 5.4. Build the wc_env and wc_env_dependencies Docker images
  • 5.5. Push the wc_env and wc_env_dependencies Docker images to DockerHub
• 6. API documentation
  • 6.1. wc_env_manager package
    • 6.1.1. Subpackages
    • 6.1.2. Submodules
    • 6.1.3. wc_env_manager.__main__ module
    • 6.1.4. wc_env_manager._version module
    • 6.1.5. wc_env_manager.core module
    • 6.1.6. Module contents
• 7. About
  • 7.1. License
  • 7.2. Development team
  • 7.3. Acknowledgements
  • 7.4. Questions and comments