renaissance-movie-lens_0
[2024-12-05T02:13:41.784Z] Running test renaissance-movie-lens_0 ...
[2024-12-05T02:13:41.784Z] ===============================================
[2024-12-05T02:13:41.784Z] renaissance-movie-lens_0 Start Time: Thu Dec 5 02:13:40 2024 Epoch Time (ms): 1733364820996
[2024-12-05T02:13:41.784Z] variation: NoOptions
[2024-12-05T02:13:41.784Z] JVM_OPTIONS:
[2024-12-05T02:13:41.784Z] { \
[2024-12-05T02:13:41.784Z] echo ""; echo "TEST SETUP:"; \
[2024-12-05T02:13:41.784Z] echo "Nothing to be done for setup."; \
[2024-12-05T02:13:41.784Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17333639874731/renaissance-movie-lens_0"; \
[2024-12-05T02:13:41.784Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17333639874731/renaissance-movie-lens_0"; \
[2024-12-05T02:13:41.784Z] echo ""; echo "TESTING:"; \
[2024-12-05T02:13:41.784Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17333639874731/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-05T02:13:41.784Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17333639874731/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-05T02:13:41.784Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-05T02:13:41.784Z] echo "Nothing to be done for teardown."; \
[2024-12-05T02:13:41.784Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17333639874731/TestTargetResult";
[2024-12-05T02:13:41.784Z]
[2024-12-05T02:13:41.784Z] TEST SETUP:
[2024-12-05T02:13:41.784Z] Nothing to be done for setup.
[2024-12-05T02:13:41.784Z]
[2024-12-05T02:13:41.784Z] TESTING:
[2024-12-05T02:13:45.572Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-05T02:13:48.065Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-12-05T02:13:50.556Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-05T02:13:51.330Z] Training: 60056, validation: 20285, test: 19854
[2024-12-05T02:13:51.330Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-05T02:13:51.330Z] GC before operation: completed in 59.925 ms, heap usage 188.729 MB -> 37.412 MB.
[2024-12-05T02:13:55.802Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:13:59.240Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:14:01.713Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:14:05.153Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:14:05.925Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:14:07.518Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:14:10.028Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:14:19.401Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:14:19.402Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:14:19.402Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:14:19.402Z] Movies recommended for you:
[2024-12-05T02:14:19.402Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:14:19.402Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:14:19.402Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20312.541 ms) ======
[2024-12-05T02:14:19.402Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-05T02:14:19.402Z] GC before operation: completed in 81.135 ms, heap usage 326.820 MB -> 50.474 MB.
[2024-12-05T02:14:19.402Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:14:19.402Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:14:19.402Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:14:21.873Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:14:23.470Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:14:25.069Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:14:26.662Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:14:28.257Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:14:28.257Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:14:28.257Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:14:29.030Z] Movies recommended for you:
[2024-12-05T02:14:29.030Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:14:29.030Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:14:29.030Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17142.605 ms) ======
[2024-12-05T02:14:29.030Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-05T02:14:29.030Z] GC before operation: completed in 71.490 ms, heap usage 1.864 GB -> 56.130 MB.
[2024-12-05T02:14:31.521Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:14:33.994Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:14:36.477Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:14:38.974Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:14:40.587Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:14:42.179Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:14:43.785Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:14:45.386Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:14:45.386Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:14:45.386Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:14:45.386Z] Movies recommended for you:
[2024-12-05T02:14:45.386Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:14:45.386Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:14:45.386Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16801.803 ms) ======
[2024-12-05T02:14:45.386Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-05T02:14:45.386Z] GC before operation: completed in 71.937 ms, heap usage 1.152 GB -> 55.766 MB.
[2024-12-05T02:14:47.867Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:14:50.516Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:14:53.009Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:14:55.494Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:14:57.087Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:14:58.683Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:15:00.282Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:15:01.879Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:15:01.879Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:15:01.879Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:15:01.879Z] Movies recommended for you:
[2024-12-05T02:15:01.879Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:15:01.879Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:15:01.879Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16510.634 ms) ======
[2024-12-05T02:15:01.879Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-05T02:15:02.826Z] GC before operation: completed in 79.633 ms, heap usage 1.379 GB -> 56.369 MB.
[2024-12-05T02:15:04.457Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:15:06.933Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:15:10.364Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:15:11.956Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:15:13.559Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:15:15.154Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:15:16.769Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:15:18.361Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:15:18.361Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:15:18.361Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:15:18.361Z] Movies recommended for you:
[2024-12-05T02:15:18.361Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:15:18.361Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:15:18.361Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16384.077 ms) ======
[2024-12-05T02:15:18.361Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-05T02:15:18.361Z] GC before operation: completed in 80.061 ms, heap usage 317.217 MB -> 52.012 MB.
[2024-12-05T02:15:20.857Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:15:23.489Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:15:26.923Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:15:28.526Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:15:30.129Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:15:31.722Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:15:33.319Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:15:34.921Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:15:34.921Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:15:34.921Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:15:34.921Z] Movies recommended for you:
[2024-12-05T02:15:34.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:15:34.921Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:15:34.921Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16451.142 ms) ======
[2024-12-05T02:15:34.921Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-05T02:15:34.921Z] GC before operation: completed in 85.253 ms, heap usage 1.517 GB -> 56.730 MB.
[2024-12-05T02:15:37.406Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:15:39.891Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:15:43.316Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:15:44.912Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:15:46.505Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:15:48.100Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:15:49.691Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:15:51.305Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:15:51.305Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:15:51.305Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:15:51.305Z] Movies recommended for you:
[2024-12-05T02:15:51.305Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:15:51.305Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:15:51.305Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16390.515 ms) ======
[2024-12-05T02:15:51.305Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-05T02:15:52.081Z] GC before operation: completed in 82.175 ms, heap usage 1.723 GB -> 57.037 MB.
[2024-12-05T02:15:54.555Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:15:57.041Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:15:59.511Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:16:01.994Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:16:03.589Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:16:05.177Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:16:06.775Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:16:08.374Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:16:08.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:16:08.374Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:16:08.374Z] Movies recommended for you:
[2024-12-05T02:16:08.374Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:16:08.374Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:16:08.374Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16624.944 ms) ======
[2024-12-05T02:16:08.374Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-05T02:16:08.374Z] GC before operation: completed in 79.866 ms, heap usage 2.335 GB -> 57.577 MB.
[2024-12-05T02:16:10.856Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:16:13.354Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:16:16.021Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:16:18.523Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:16:20.121Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:16:21.714Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:16:23.305Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:16:24.902Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:16:24.902Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:16:24.902Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:16:24.902Z] Movies recommended for you:
[2024-12-05T02:16:24.902Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:16:24.902Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:16:24.902Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16565.439 ms) ======
[2024-12-05T02:16:24.902Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-05T02:16:24.902Z] GC before operation: completed in 88.200 ms, heap usage 2.520 GB -> 57.246 MB.
[2024-12-05T02:16:27.385Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:16:29.861Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:16:33.302Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:16:34.901Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:16:36.531Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:16:38.126Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:16:39.722Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:16:41.313Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:16:41.313Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:16:41.313Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:16:42.083Z] Movies recommended for you:
[2024-12-05T02:16:42.083Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:16:42.083Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:16:42.083Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16656.671 ms) ======
[2024-12-05T02:16:42.083Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-05T02:16:42.083Z] GC before operation: completed in 81.068 ms, heap usage 1.724 GB -> 57.253 MB.
[2024-12-05T02:16:44.693Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:16:47.175Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:16:49.667Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:16:52.157Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:16:53.799Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:16:54.579Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:16:56.176Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:16:57.779Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:16:57.779Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:16:58.548Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:16:58.549Z] Movies recommended for you:
[2024-12-05T02:16:58.549Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:16:58.549Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:16:58.549Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16420.768 ms) ======
[2024-12-05T02:16:58.549Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-05T02:16:58.549Z] GC before operation: completed in 86.060 ms, heap usage 1.349 GB -> 56.690 MB.
[2024-12-05T02:17:01.036Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:17:03.593Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:17:06.070Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:17:08.550Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:17:10.151Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:17:11.754Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:17:13.366Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:17:14.982Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:17:14.982Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:17:14.982Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:17:14.982Z] Movies recommended for you:
[2024-12-05T02:17:14.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:17:14.982Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:17:14.982Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16684.269 ms) ======
[2024-12-05T02:17:14.982Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-05T02:17:14.982Z] GC before operation: completed in 86.640 ms, heap usage 2.819 GB -> 57.300 MB.
[2024-12-05T02:17:17.465Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:17:19.948Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:17:22.426Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:17:24.904Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:17:26.507Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:17:28.293Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:17:29.893Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:17:31.488Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:17:31.488Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:17:31.488Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:17:31.488Z] Movies recommended for you:
[2024-12-05T02:17:31.488Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:17:31.488Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:17:31.488Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16325.753 ms) ======
[2024-12-05T02:17:31.488Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-05T02:17:31.488Z] GC before operation: completed in 80.436 ms, heap usage 1.350 GB -> 57.117 MB.
[2024-12-05T02:17:33.972Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:17:36.459Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:17:38.950Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:17:41.444Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:17:43.047Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:17:44.643Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:17:46.268Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:17:47.864Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:17:47.864Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:17:47.864Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:17:47.864Z] Movies recommended for you:
[2024-12-05T02:17:47.864Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:17:47.864Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:17:47.864Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16477.421 ms) ======
[2024-12-05T02:17:47.864Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-05T02:17:47.864Z] GC before operation: completed in 80.534 ms, heap usage 1.283 GB -> 56.733 MB.
[2024-12-05T02:17:50.344Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:17:52.819Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:17:56.251Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:17:57.850Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:17:59.445Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:18:01.039Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:18:02.632Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:18:04.225Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:18:04.225Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:18:04.225Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:18:04.225Z] Movies recommended for you:
[2024-12-05T02:18:04.225Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:18:04.225Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:18:04.225Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16417.143 ms) ======
[2024-12-05T02:18:04.225Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-05T02:18:04.225Z] GC before operation: completed in 76.843 ms, heap usage 1.255 GB -> 56.864 MB.
[2024-12-05T02:18:06.725Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:18:09.217Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:18:12.652Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:18:14.248Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:18:15.863Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:18:17.460Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:18:19.054Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:18:20.652Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:18:21.423Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:18:21.423Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:18:21.423Z] Movies recommended for you:
[2024-12-05T02:18:21.423Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:18:21.423Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:18:21.423Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16583.630 ms) ======
[2024-12-05T02:18:21.423Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-05T02:18:21.423Z] GC before operation: completed in 86.991 ms, heap usage 483.984 MB -> 52.689 MB.
[2024-12-05T02:18:23.906Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:18:26.390Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:18:28.871Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:18:31.344Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:18:32.941Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:18:34.546Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:18:36.155Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:18:36.934Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:18:37.711Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:18:37.711Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:18:37.711Z] Movies recommended for you:
[2024-12-05T02:18:37.711Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:18:37.711Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:18:37.711Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16441.921 ms) ======
[2024-12-05T02:18:37.711Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-05T02:18:37.711Z] GC before operation: completed in 88.193 ms, heap usage 104.766 MB -> 54.979 MB.
[2024-12-05T02:18:40.390Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:18:42.869Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:18:45.356Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:18:47.842Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:18:49.436Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:18:51.052Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:18:52.698Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:18:54.292Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:18:54.292Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:18:54.292Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:18:54.292Z] Movies recommended for you:
[2024-12-05T02:18:54.292Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:18:54.292Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:18:54.292Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16527.186 ms) ======
[2024-12-05T02:18:54.292Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-05T02:18:54.292Z] GC before operation: completed in 80.045 ms, heap usage 2.578 GB -> 57.361 MB.
[2024-12-05T02:18:56.780Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:18:59.263Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:19:01.734Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:19:04.218Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:19:05.815Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:19:07.404Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:19:09.001Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:19:10.595Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:19:10.595Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:19:10.595Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:19:10.595Z] Movies recommended for you:
[2024-12-05T02:19:10.595Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:19:10.595Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:19:10.595Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16538.755 ms) ======
[2024-12-05T02:19:10.595Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-05T02:19:11.366Z] GC before operation: completed in 94.009 ms, heap usage 471.991 MB -> 52.768 MB.
[2024-12-05T02:19:13.855Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T02:19:16.349Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T02:19:18.838Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T02:19:21.342Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T02:19:22.944Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T02:19:23.716Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T02:19:25.325Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T02:19:26.920Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T02:19:27.763Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-12-05T02:19:27.763Z] The best model improves the baseline by 14.43%.
[2024-12-05T02:19:27.763Z] Movies recommended for you:
[2024-12-05T02:19:27.763Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T02:19:27.763Z] There is no way to check that no silent failure occurred.
[2024-12-05T02:19:27.763Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16447.338 ms) ======
[2024-12-05T02:19:28.534Z] -----------------------------------
[2024-12-05T02:19:28.534Z] renaissance-movie-lens_0_PASSED
[2024-12-05T02:19:28.534Z] -----------------------------------
[2024-12-05T02:19:28.534Z]
[2024-12-05T02:19:28.534Z] TEST TEARDOWN:
[2024-12-05T02:19:28.534Z] Nothing to be done for teardown.
[2024-12-05T02:19:28.534Z] renaissance-movie-lens_0 Finish Time: Thu Dec 5 02:19:27 2024 Epoch Time (ms): 1733365167798