Migrating from Docker to Machine

In some situations, the Docker executor is not the right fit for your builds. For example, if using a Docker resource class provides a lack of memory or there is a requirement for more dedicated CPU power. Moving to a dedicated virtual machine can help alleviate these issues. However, changing out an executor is not as simple as replacing a few lines of configuration. You will also need to consider the tools and libraries required to be installed for your application and tests.

The most up-to-date list of pre-installed software can be found on the image builder page. You can also visit the CircleCI community forum for more information.

Additional packages can be installed with sudo apt-get install <package>. If the package in question is not found, sudo apt-get update may be required before installing it.

Machine executors come installed with Docker, which can be used to run your application within a container rather than installing additional dependencies. We recommend using a custom Docker image rather than a CircleCI convenience image. CircleCI convenience images are built under the assumption they will be used with the Docker executor and may be tricky to work around. Since each machine executor environment is a dedicated virtual machine, commands to run background containers can be used as normal.

If you have Docker Layer Caching (DLC) enabled for your account, machine executors can utilize this to cache your image layers for subsequent runs.

Machine executors offer double the memory and a more isolated environment for running builds. However, there is additional overhead regarding spin-up time. Depending on the approach taken for running the application, there may be more time taken to install the required dependencies or pull your Docker image. The Docker executor will also cache as many layers as possible from your image during spin-up, as opposed to the machine executor, where DLC will need to be enabled.

All executors have their pros and cons, which have been laid out here to help you decide which is right for your pipelines.

We have more details on each specific executor in the Execution Environments Overview. This page includes links to specific memory and vCPU allocation details. It also shows how to implement each one in your own configuration.