ML-INFN is the INFN initiative to coordinate efforts on the development and deployment of Machine Learning algorithms across its research lines. As part of our programme, we organize training events to discuss base and advanced Machine Learning topics with time to go through the code. We call them hackathons.
The first and second hackathons were targeting machine-learning beginners. The material used for those events is available in another GitHub repository.
The third event is targeting advanced users, and the related material is collected in this repository.
Contents is organized per topic in different folders. When documentation beyond the Jupyter notebook is needed, a README.md file is included in the sub-directory.
advanced_jupyter: tricks and suggestions to get the most out of your Jupyter-powered instance including remote access and pipelining multiple notebooks.introduction_to_gnns: a gentle introduction to Graph Neural Networks using synthetic dataex: material for the hackathon exercisesunet: Lung Segmentation on Chest X-Ray images with U-Netdomain_adaptation: Domain Adaptation for model-independent training in High Energy Physicsgnn_transformers: Introduction to Graph Neural Networks and Transformers with applications from High Energy Physics- `xai: Introduction to Explainable Artificial Intelligence algorithms with applications from Bioinformatics and Biogenetics
Tests on the notebooks are run frequently on the different setups being prepared for the hackathon event.
Run all tests with:
python3 -m pytest tests/test_notebooks.py -v --durations=01844.80s call tests/test_notebooks.py::test_xai │·····················
772.03s call tests/test_notebooks.py::test_DA_ML │·····················
714.65s call tests/test_notebooks.py::test_transformers │·····················
238.66s call tests/test_notebooks.py::test_ex_gnn │·····················
169.10s call tests/test_notebooks.py::test_unet_train_only │·····················
141.26s call tests/test_notebooks.py::test_intro_gnn │·····················
31.29s call tests/test_notebooks.py::test_intro_pytorch │·····················
24.72s call tests/test_notebooks.py::test_DA_ML_SimpleDNN │·····················
17.23s call tests/test_advanced_jupyter.py::test_snakemake │·····················
17.03s call tests/test_notebooks.py::test_unet_arch │·····················
12.32s call tests/test_notebooks.py::test_unet_predict_only │·····················
3.46s call tests/test_notebooks.py::test_unet_loss │·····················
3.43s call tests/test_notebooks.py::test_unet_generator │·····················
1.17s call tests/test_notebooks.py::test_unet_intro │·····················
1397.29s call tests/test_notebooks.py::test_xai
1102.10s call tests/test_notebooks.py::test_transformers
556.91s call tests/test_notebooks.py::test_DA_ML
186.14s call tests/test_notebooks.py::test_ex_gnn
180.75s call tests/test_notebooks.py::test_unet_train_only
145.65s call tests/test_notebooks.py::test_intro_gnn
28.84s call tests/test_notebooks.py::test_intro_pytorch
19.30s call tests/test_notebooks.py::test_DA_ML_SimpleDNN
17.04s call tests/test_notebooks.py::test_unet_arch
15.57s call tests/test_advanced_jupyter.py::test_snakemake
13.05s call tests/test_notebooks.py::test_unet_predict_only
3.10s call tests/test_notebooks.py::test_unet_generator
3.03s call tests/test_notebooks.py::test_unet_loss
1.00s call tests/test_notebooks.py::test_unet_intro
14519.75s call tests/test_notebooks.py::test_transformers
11855.75s call tests/test_notebooks.py::test_xai
3579.68s call tests/test_notebooks.py::test_DA_ML
771.67s call tests/test_notebooks.py::test_ex_gnn
696.70s call tests/test_notebooks.py::test_unet_train_only
143.61s call tests/test_notebooks.py::test_intro_gnn
44.79s call tests/test_notebooks.py::test_DA_ML_SimpleDNN
19.27s call tests/test_advanced_jupyter.py::test_snakemake
12.89s call tests/test_notebooks.py::test_unet_arch
9.78s call tests/test_notebooks.py::test_unet_predict_only
6.37s call tests/test_notebooks.py::test_intro_pytorch
4.44s call tests/test_notebooks.py::test_unet_generator
3.92s call tests/test_notebooks.py::test_unet_loss
1.07s call tests/test_notebooks.py::test_unet_intro
1632.26s call tests/test_notebooks.py::test_xai
643.56s call tests/test_notebooks.py::test_DA_ML
609.55s call tests/test_notebooks.py::test_transformers
318.69s call tests/test_notebooks.py::test_unet_train_only
224.93s call tests/test_notebooks.py::test_ex_gnn
182.76s call tests/test_notebooks.py::test_intro_gnn
48.19s call tests/test_notebooks.py::test_intro_pytorch
25.11s call tests/test_notebooks.py::test_DA_ML_SimpleDNN
21.48s call tests/test_advanced_jupyter.py::test_snakemake
17.07s call tests/test_notebooks.py::test_unet_predict_only
3.94s call tests/test_notebooks.py::test_unet_generator
3.66s call tests/test_notebooks.py::test_unet_loss
1.52s call tests/test_notebooks.py::test_unet_intro
0.91s call tests/test_notebooks.py::test_unet_arch
Code is released under OSI-approved MIT license.
The documentation provided in the form of Jupyter notebooks is released under CC-BY-NC-SA license.