Training
Reproducibility and open science practices in machine learning
20 mei 2026 09:00 - 15:30
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 training event focused on machine learning and reproducibility.
Praktische info:
20 mei 2026 09:00 - 15:30
6,5 uur
LUMI AI Factory Hun Otaniemi, 4th floor - Maarintie 8, Espoo, Finland
Engels
Doelgroep: Researchers in machine learning
georganiseerd door:
Leertraject
Schedule
- 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/#
- Demo:
- Motivation: environment reproducibility good [pip install in default bad]
- 10:30 || break
- 10:45 How do you track your work / training: MLflow works for trad ML and DL (Hossein Firooz and 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
- Motivation:
- 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
- Model card
- 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
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