This module is a collection of utility functions for the overall communication and the session management of the getML engine.

In order to log into the engine, you have to open your favorite internet browser and enter http://localhost:1709 in the navigation bar. This tells it to connect to a local TCP socket at port 1709 opened by the getML monitor. This will only be possible from within the same device!

The appearing page will prompt you for your email address and password or, if you haven’t registered yet, enable you to create a user account. The account management and all associated data is hosted by AWS in Frankfurt, Germany.

Note that while all your data and results stay locally at your device, the login does require a working internet connection in order to authenticate your account. For more information please see the official documentation at


First of all, you need to start the getML engine. Next, you need to create a new project or load an existing one.


After doing all calculations for today you can shut down the getML engine.


The Python process and the getML engine must be located on the same machine. If you intend to run the engine on a remote host, make sure to start your Python session on that device as well. Also, when using SSH sessions, make sure to start Python using python & followed by disown or using nohup python. This ensures the Python process and all the script it has to run won’t be killed the moment your remote connection becomes unstable and you are able to recover them later on (see Remote access).

All data frame objects and models in the getML engine are bundled in projects. When loading an existing project, the current memory of the engine will be flushed and all changes applied to DataFrame instances after calling their save() method will be lost. Afterwards, all Pipeline will be loaded into memory automatically. The data frame objects will not be loaded automatically since they consume significantly more memory than the pipelines. They can be loaded manually using load_data_frame() or load().

The getML engine reflects the separation of data into individual projects on the level of the filesystem too. All data belonging to a single project is stored in a dedicated folder in the ‘projects’ directory located in ‘.getML’ in your home folder. These projects can be copied and shared between different platforms and architectures without any loss of information. However, you must copy the entire project and not just individual data frames or pipelines.



Deletes a project.


Checks if the getML engine is running.

launch([allow_push_notifications, …])

Launches the getML engine.


List all projects on the getML engine.


List all projects on the getML engine that are currently running.


Creates a new project or loads an existing one.


Shuts down the getML engine.


Suspends a project that is currently running.