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   "source": [
    "# mml on a cluster\n",
    "\n",
    "Especially for large scale or hardware demanding experiments it is nice to outsource experiments to a cluster. It is straightforward to set up `mml` for compatible use throughout multiple systems. `mml` ships with a `cluster` configuration provided within `configs/sys`. It loads the environment variables `MML_CLUSTER_DATA_PATH`, `MML_CLUSTER_RESULTS_PATH` and `MML_CLUSTER_WORKERS` exported in the `mml.env` file instead of the local ones (adding even more system configurations can be achieved similarly via additional `sys` config files). Now the only difference in calling `mml` becomes:"
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   "source": [
    "!mml ... sys=cluster "
   ]
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    "Of course the respective paths must be available on the other system and kept in sync (e.g. via `rsync`). It is possible to manage cluster jobs and data syncing via a `JobRunner` - see the `mml-lsf` plugin for an example on how to interact easily with an LSF cluster. "
   ]
  }
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