| project_name | group_name | hostname | status | Time | RMSE | Accuracy | error_msg |
|---|
code-brain-fuel | Master-IASD | coktailjet | Success | 77.3 | 0.854 | 21.37 | None | BestOf2023-2 | profs | coktailjet | Success | 186.24 | 0.861 | 26.07 | None | psg-iasd | Master-IASD | coktailjet | Success | 222.54 | 0.865 | 0.0 | None | gitlegs | Master-IASD | coktailjet | Success | 5.99 | 0.866 | 0.19 | None | tetech | Master-IASD | coktailjet | Success | 29.07 | 0.874 | 25.3 | None | freshtomatoes | Master-IASD | coktailjet | Success | 122.65 | 0.878 | 0.32 | None | matrixe | Master-IASD | coktailjet | Success | 128.74 | 0.919 | 0.0 | None | esi | Master-IASD | coktailjet | Success | 6.65 | 0.935 | 31.51 | None | closeai | Master-IASD | coktailjet | Success | 5.5 | 0.943 | 24.02 | None | elcoma | Master-IASD | coktailjet | Success | 47.89 | 1.014 | 29.76 | None | theshawshankredemption | Master-IASD | coktailjet | Success | 0.8 | 1.037 | 0.0 | None | average | profs | coktailjet | Success | 0.46 | 1.037 | 0.0 | None | just-do-it | Master-IASD | coktailjet | Success | 7.74 | 1.139 | 11.81 | None | random | profs | coktailjet | Success | 0.45 | 1.83 | 14.19 | None | alexandre-verinotableandefficientmodel | Master-IASD | coktailjet | Error | 0 | 100 | 0 | bytes: b'/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/NCF.py:120: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don\'t have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n model.load_state_dict(torch.load(\'best_model.pth\'))\nTraceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/generate.py", line 129, in \n table = NCF.complete_matrix(model, R, F)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/NCF.py", line 175, in complete_matrix\n title_date_ids = torch.LongTensor(encoded_titles_dates[list(item_ids)]).to(device)\n ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/venv/lib/python3.11/site-packages/torch/_tensor.py", line 1083, in __array__\n return self.numpy()\n ^^^^^^^^^^^^\nTypeError: can\'t convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.\n' | anxisa | Master-IASD | coktailjet | Error | 0 | 100 | 0 | bytes: b'Traceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/anxisa/generate.py", line 22, in \n R_train = np.load("ratings_train.npy")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/anxisa/venv/lib/python3.11/site-packages/numpy/lib/npyio.py", line 427, in load\n fid = stack.enter_context(open(os_fspath(file), "rb"))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: \'ratings_train.npy\'\n' | elon | Master-IASD | coktailjet | Error | 0 | 100 | 0 | bytes: b'Traceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/elon/generate.py", line 146, in \n table, test_RMSE, test_accuracy = model.fit(R_train, R_test)\n ^^^^^^^\nNameError: name \'R_train\' is not defined\n' | rimaya | Master-IASD | coktailjet | Error | 0 | 100 | 0 | bytes: b'2024-10-04 07:06:38.751439: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n2024-10-04 07:06:39.162041: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-10-04 07:06:39.162131: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-10-04 07:06:39.231817: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-10-04 07:06:39.388495: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-10-04 07:06:42.091195: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\nTraceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/rimaya/generate.py", line 55, in \n users = torch.tensor(user_indices, dtype=torch.long)\n ^^^^^\nNameError: name \'torch\' is not defined\n' | BestOf2023-1 | profs | coktailjet | Error | 0 | 100 | 0 | bytes: b'Traceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/profs/BestOf2023-1/generate.py", line 39, in \n model.make_output(batch_size = 65536, tqdm_on = False)\n File "/home/lamsade/testplatform/test-platform-a1/repos/profs/BestOf2023-1/algorithms/folded_deep_matrix_factorization_imprv.py", line 343, 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; 44.35 GiB total capacity; 605.75 MiB already allocated; 1.38 GiB free; 638.00 MiB 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' | palm | Master-IASD | coktailjet | Success | 96.31 | NaN | 0.0 | None |
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