MSKS (short for Meeseek) is a lightweight Python tool for running interdependent tasks in a reproducible manner. Reproducible environments are created using Conda, the source code for the task is pinned to a specific version in a given repository. By design MSKS is somewhat aimed at machine learning experiments, although there is a design guideline to to keep the system quite generic supporting tasks that may not involve model training.
- Task - A combination of a fixed point in a repository, an entry point and arguments to the entrypoint. Each task is identified by a hash identifier based on these parameters. A task can depend on outputs of other tasks
- Environment - An Conda environment in which a given task is run in. The environment that is specified by the source code descriptor and is created on demand if needed.
- Repository / commit: Each task needs a Git repository to get source code from and a specific commit ID to specify which version of the code to use.
- Entrypoint - One or more ways to interact with the code. Entypoints are described using a description file in the source code which specifies what command is actually run, what arguments are accepted and what outputs are produced.
You can install MSKS using Pip: pip install msks. If successful, you can then use MSKS CLI by executing msks.
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The development of this package was supported by Sloveninan research agency (ARRS) project Z2-1866.
Copyright 2022 Luka Čehovin Zajc
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