Reproducibility and Sharing Results¶
Reproducible research depends on being able to recreate an experiment's environment, code, and data at any later point. This page gives a short overview of the practices involved.
Before you begin¶
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Submit and manage jobs on the cluster with Slurm.
Environment¶
Pin the exact versions of Python and its dependencies so an experiment can be
recreated on any node. See the portability guide for managing
dependencies and lockfiles with uv.
Version control¶
Track code changes with Git and push to a hosted remote such as GitHub or GitLab. Tag or reference the exact commit used to produce a given set of results, so the code behind any experiment or publication stays retrievable.
Sharing datasets¶
To share a dataset with the Mila community, request its addition to
/network/datasets.
See the Datasets page for
ways to publicly share a Mila-hosted dataset — Academic Torrent, Google
Drive, and registering a DOI for citation.
For datasets that do not need to live on Mila's storage, the Hugging Face Hub hosts and versions datasets alongside model cards and loading scripts, and GitHub (or GitLab) works well for small datasets kept alongside the code that produces or consumes them.
Research paper distribution¶
When publishing results, bundle the code, environment, and dataset references readers need to reproduce them — for example through a public repository release alongside the paper.
Acknowledging Mila
Researchers are requested to acknowledge Mila when publications or reports are published on research that used compute resources provided by the organization. Our ability to showcase the successful use of our resources builds an important case for long-term and sustainable funding of Mila and our operations, i.e. have more compute resources available to researchers!
The exact wording of the acknowledgment may vary, but please ensure that Mila is mentioned. The following can be used as a guideline:
"This research was enabled in part by compute resources provided by Mila (mila.quebec)."
If you find useful some of the software developed at Mila or the technical help you can get from our experts you can also mention it in the acknowledgment:
"This research was enabled in part by compute resources, software and technical help provided by Mila (mila.quebec)."
Next step¶
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Monitor and Optimize Experiments
Understand why metrics matter, plan their collection before a job starts, and choose the right tool to optimize both models and resource usage.
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Use the dashboard to identify and reduce wasted GPU resources.