Experimaestro is a versatile tool for designing and managing complex workflows. It enables the definition of tasks and their dependencies, ensuring orderly execution within a workflow. Key features of Experimaestro include:

  • Task Automation: Automates repetitive tasks, facilitating large-scale experiments, especially useful when varying parameters or datasets.
  • Extensibility: Designed for flexibility, Experimaestro can easily be integrated with existing libraries, serving the diverse needs of data science and research.
  • Reproducibility: Maintains comprehensive records of experiments, including parameters and environments, supporting the essential research principle of reproducibility.
  • User Interface: Offers a user interface for workflow management and visualization, complementing its primary back-end functionality.

Difference with Other Projects

Experimaestro differentiates itself from traditional job scheduling software like OAR and Slurm, which focus more on resource allocation than on managing experimental workflows. It also stands apart from other experiment management tools like Comet, Sacred, FGLab, and Sumatra. For instance, Comet emphasizes collaboration and note-taking for machine learning experiments but is not open-source and focuses on single-shot experiments. Sumatra and FGLab, based on parameter files, offer less flexibility. Sacred, though open-source and allowing for pre-processing steps, doesn't support the construction of complex experimental plans like Experimaestro.

Experimaestro's distinct features include:

  1. Comprehensive Task Composition: It allows for the composition of types and tasks within an experimental plan.
  2. Parameter Monitoring: Offers a clear method to monitor experimental parameters using tags.
  3. Automated Output Organization: Efficiently manages task outputs in the file system, simplifying result storage.
  4. Imperative Experiment Definition: Unlike other tools that define experiments declaratively, Experimaestro adopts an imperative approach, enhancing flexibility in complex experimental planning.

Outline of the documentation


You can follow the tutorial

Experimental plan

An experimental plan is based on configuration and tasks, and define which tasks should be run with which parameters. Within an experiment, tags can be used to track experimental parameters.


A connector allow to specify how to access files on the computer where a task will be launched, and how to run processes on this computer. Two basic connectors exist, for localhost and SSH accesses (alpha).


A launcher specifies how a given task can be run. The most basic method is direct execution, but experimaestro can launch and monitor oar (planned) and slurm (planned) jobs.


Experimaestro and datamaestro are described in the following paper

Benjamin Piwowarski. 2020. Experimaestro and Datamaestro: Experiment and Dataset Managers (for IR). In Proceedings of the 43rd International ACM SIGIR