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dsm:assignments:02 [2024/12/22 14:55] andrei.niculae1004 [Competition] |
dsm:assignments:02 [2025/10/06 20:14] (current) andrei.niculae1004 |
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| ====== Competition ====== | ====== Competition ====== | ||
| - | The competition is hosted on Kaggle at [[https://www.kaggle.com/t/2d3d10cda52f4fadb43658af5fed9e3c|this link]]. | + | |
| + | <hidden> | ||
| + | The competition is hosted on Kaggle at [[https://www.kaggle.com/competitions/dsm-2025|this link]]. | ||
| Each competitor will participate individually. Please login using your **student mail** (@stud.acs.upb.ro) and check this box. | Each competitor will participate individually. Please login using your **student mail** (@stud.acs.upb.ro) and check this box. | ||
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| {{:dsm:assignments:kaggle_mail_share.png?500|}} | {{:dsm:assignments:kaggle_mail_share.png?500|}} | ||
| - | We provide a [[https://www.kaggle.com/code/andreiniculae/dsm-2024-starting-code|starter code]] which demonstrates how to read the data, train a network and make a submission. You are encouraged to start your work from this notebook. | + | We provide a [[https://www.kaggle.com/code/andreiniculae/dsm-2025-starting-code|starter code]] which demonstrates how to read the data, train a network and make a submission. You are encouraged to start your work from this notebook. |
| Beating the baseline on the private leaderboard will reward **1p**, top 3 on the private leaderboard will have their final exam grade equal to 10 (4p). | Beating the baseline on the private leaderboard will reward **1p**, top 3 on the private leaderboard will have their final exam grade equal to 10 (4p). | ||
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| * Using additional training data apart from the data provided is not allowed. | * Using additional training data apart from the data provided is not allowed. | ||
| * Searching on the internet for the clean dataset or labels for the test set is not allowed. | * Searching on the internet for the clean dataset or labels for the test set is not allowed. | ||
| - | * You will need to provide the jupyter notebook/python script that was used to train the model that generated your submitted solution. Thus, the solution has to be reproducible. You can use the following snippet to ensure that the starting seed is the same: | + | * You will need to provide the jupyter notebook/python script that was used to train the model that generated your submitted solution. Upload it on [[https://curs.upb.ro/2025/mod/assign/view.php?id=24468|moodle]] |
| - | + | </hidden> | |
| - | <code> | + | |
| - | def seed_everything(seed=42): | + | |
| - | random.seed(seed) | + | |
| - | os.environ['PYTHONHASHSEED'] = str(seed) | + | |
| - | np.random.seed(seed) | + | |
| - | torch.manual_seed(seed) | + | |
| - | torch.cuda.manual_seed(seed) | + | |
| - | torch.cuda.manual_seed_all(seed) | + | |
| - | torch.backends.cudnn.deterministic = True | + | |
| - | torch.backends.cudnn.benchmark = False | + | |
| - | </code> | + | |
