Result table

This table was generated on 2023-10-12 at 05:33. See more results here. See last results here.

results
project_namegroup_namehostnamestatusTimeRMSEAccuracyerror_msg
goat
Master-IASD
boldeagle
Success
110.19
0.863
0.1
None
claraliorarafael
Master-IASD
boldeagle
Success
182.3
0.873
25.16
None
the-boring-group
Master-IASD
boldeagle
Success
198.35
0.877
27.52
None
prestige-worldwide
Master-IASD
boldeagle
Success
23.4
0.886
25.63
None
equipe-404
Master-IASD
boldeagle
Success
130.75
0.891
0.0
None
matrix-brigade
Master-IASD
boldeagle
Success
104.62
0.891
24.99
None
a-m-y-group
Master-IASD
boldeagle
Success
58.54
0.903
10.28
None
bot
Master-IASD
boldeagle
Success
21.4
0.915
25.28
None
deeprec
Master-IASD
boldeagle
Success
311.99
0.915
24.92
None
descente-optimale
Master-IASD
boldeagle
Success
25.55
0.924
21.81
None
la-grosse-descente
Master-IASD
boldeagle
Success
125.41
0.926
0.0
None
forecastors
Master-IASD
boldeagle
Success
21.19
0.971
29.86
None
deepdeepmf
Master-IASD
boldeagle
Success
0.58
1.037
0.0
None
average
profs
boldeagle
Success
0.58
1.037
0.0
None
random
profs
boldeagle
Success
0.55
1.838
13.79
None
lecun-team
Master-IASD
boldeagle
Error
0
100
0
bytes: b'Traceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/lecun-team/generate.py", line 39, in \n model.make_output(batch_size = 65536, tqdm_on = False)\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/lecun-team/algorithms/folded_deep_matrix_factorization_imprv.py", line 308, in make_output\n test_users_explicit_ds = self.R[test_users_idx].float() / 5\n ~~~~~~^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.43 GiB (GPU 0; 10.91 GiB total capacity; 662.84 MiB already allocated; 1.32 GiB free; 3.04 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n'

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