Reproducibility and Open Science Practices in Machine Learning // 20.May.2026

What is reproducibility and why do we need to care about it in machine learning research? What are the best practices for sharing machine learning models in your research? Join us for this one day event focused on machine learning and reproducibility. It is possible to obtain 1 ECTS / Certificate by actively participating to the event and doing some extra homework.

Registration is mandatory, so that we can ensure enough seats for everyone (and free snacks!).

Where

LUMI AI Factory Hub, TUAS, 4th Floor, Maarintie 8, Espoo.

Online via zoom (this is also possible but we cannot guarantee the same level of experience as those coming to the in-person event). 

More details will be sent a few days before the event.

Schedule

The event happens on the 20th May 2026. The schedule below is being updated and comes from a previous run of the event.

• 9:00 || Motivational intro: Fundamentals of reproducibility in Machine Learning (Enrico Glerean)
• 9:20 || Practicalities (EG):
  • https://noppe.2.rahtiapp.fi/welcome -> HAKA login (noppe workspace password given during the workshop)
• 9:45 || Jupyter reproducibility (Luca Ferranti)
• 10:00 || Environment reproducibility (Simo Tuomisto)
  • Motivation: environment reproducibility good [pip install in default bad]
    • Demo:
      • Show environment
      • Create container from environment: https://github.com/simo-tuomisto/micromamba-apptainer
      • Create apptainer in CSC machine https://coderefinery.github.io/hpc-containers/intro_and_motivation/#
• 10:30 || break
• 10:45 How do you track your work / training: MLflow works for trad ML and DL (Hossein Firooz and ST)
  • Why do we track the training process? What are the approaches? WandB, MLFlow (HF)
  • Examples demo with MLflow (ST)
• 11:30 || Overfitting & overreproducing (LF)
• 12:00 || Lunch - on your own
• 13:00 || How do you create reproducible DL training? How do you reproduce the training & creation? Lightning (ST) (30min)
  • Motivation:
    • Dataset
    • Model
    • Trainer
    • CLI: “main()”
    • configuration management
    • checkpointing
  • Example model in PyTorch Lightning
• 13:45 || break
• 14:00 || Model sharing How do you share models? Huggingface (ST)
  • Model card
    • Model parameters
    • Model structure as a code
  • Model weights & used tokenizers
    • Storage formats: safetensors
• 14:15 || Scaling: How do you read how big players are doing it and how do you get there? (HF)
  • How to understand the hardware?
  • DataLoader
    • Parallelism and workers.
    • Sampling and generator
    • Random number handling
  • DP -> DDP -> Model / Tensor Parallel -> Deepspeed
    • Demo on triton
• 14:55 || Outro “What next?”, good reproducibility practices of any research project (ie. the coderefinery workshop). https://coderefinery.github.io/reproducible-research/ (EG)
• 15:00 || The end

For info and questions please contact Enrico Glerean.