[2023-04-19T10:37:21.065Z] Running test renaissance-movie-lens_0 ... [2023-04-19T10:37:21.065Z] =============================================== [2023-04-19T10:37:21.065Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 10:37:20 2023 Epoch Time (ms): 1681900640101 [2023-04-19T10:37:21.065Z] variation: NoOptions [2023-04-19T10:37:21.065Z] JVM_OPTIONS: [2023-04-19T10:37:21.065Z] { \ [2023-04-19T10:37:21.065Z] echo ""; echo "TEST SETUP:"; \ [2023-04-19T10:37:21.065Z] echo "Nothing to be done for setup."; \ [2023-04-19T10:37:21.065Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818995653055/renaissance-movie-lens_0"; \ [2023-04-19T10:37:21.065Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818995653055/renaissance-movie-lens_0"; \ [2023-04-19T10:37:21.065Z] echo ""; echo "TESTING:"; \ [2023-04-19T10:37:21.065Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/openjdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818995653055/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2023-04-19T10:37:21.065Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818995653055/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2023-04-19T10:37:21.065Z] echo ""; echo "TEST TEARDOWN:"; \ [2023-04-19T10:37:21.065Z] echo "Nothing to be done for teardown."; \ [2023-04-19T10:37:21.065Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_16818995653055/TestTargetResult"; [2023-04-19T10:37:21.065Z] [2023-04-19T10:37:21.065Z] TEST SETUP: [2023-04-19T10:37:21.065Z] Nothing to be done for setup. [2023-04-19T10:37:21.065Z] [2023-04-19T10:37:21.065Z] TESTING: [2023-04-19T10:37:25.132Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2023-04-19T10:37:27.795Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2023-04-19T10:37:33.442Z] Got 100004 ratings from 671 users on 9066 movies. [2023-04-19T10:37:33.442Z] Training: 60056, validation: 20285, test: 19854 [2023-04-19T10:37:33.442Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2023-04-19T10:37:33.442Z] GC before operation: completed in 231.265 ms, heap usage 138.272 MB -> 26.048 MB. [2023-04-19T10:37:40.764Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:37:43.719Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:37:47.787Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:37:50.751Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:37:52.045Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:37:54.695Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:37:55.626Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:37:58.642Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:37:58.642Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:37:59.576Z] The best model improves the baseline by 14.52%. [2023-04-19T10:37:59.576Z] Movies recommended for you: [2023-04-19T10:37:59.576Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:37:59.576Z] There is no way to check that no silent failure occurred. [2023-04-19T10:37:59.576Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24822.901 ms) ====== [2023-04-19T10:37:59.576Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2023-04-19T10:37:59.576Z] GC before operation: completed in 336.509 ms, heap usage 209.874 MB -> 42.215 MB. [2023-04-19T10:38:23.148Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:38:23.148Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:38:23.148Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:38:23.148Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:38:23.148Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:38:23.148Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:38:23.148Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:38:23.148Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:38:23.148Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:38:23.148Z] The best model improves the baseline by 14.52%. [2023-04-19T10:38:23.148Z] Movies recommended for you: [2023-04-19T10:38:23.148Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:38:23.148Z] There is no way to check that no silent failure occurred. [2023-04-19T10:38:23.148Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (18687.138 ms) ====== [2023-04-19T10:38:23.148Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2023-04-19T10:38:23.148Z] GC before operation: completed in 304.003 ms, heap usage 61.758 MB -> 40.558 MB. [2023-04-19T10:38:23.148Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:38:23.148Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:38:26.833Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:38:27.766Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:38:30.276Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:38:31.216Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:38:33.135Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:38:34.071Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:38:35.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:38:35.374Z] The best model improves the baseline by 14.52%. [2023-04-19T10:38:35.374Z] Movies recommended for you: [2023-04-19T10:38:35.374Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:38:35.374Z] There is no way to check that no silent failure occurred. [2023-04-19T10:38:35.374Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17404.475 ms) ====== [2023-04-19T10:38:35.374Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2023-04-19T10:38:35.374Z] GC before operation: completed in 245.010 ms, heap usage 188.415 MB -> 41.404 MB. [2023-04-19T10:38:37.291Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:38:39.841Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:38:43.189Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:38:46.063Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:38:46.997Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:38:48.467Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:38:49.936Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:38:50.868Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:38:50.868Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:38:51.800Z] The best model improves the baseline by 14.52%. [2023-04-19T10:38:51.800Z] Movies recommended for you: [2023-04-19T10:38:51.800Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:38:51.800Z] There is no way to check that no silent failure occurred. [2023-04-19T10:38:51.800Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16298.596 ms) ====== [2023-04-19T10:38:51.800Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2023-04-19T10:38:51.800Z] GC before operation: completed in 201.103 ms, heap usage 127.482 MB -> 41.466 MB. [2023-04-19T10:38:54.077Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:38:56.354Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:38:59.683Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:39:01.604Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:39:03.905Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:39:16.328Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:39:25.936Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:39:25.936Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:39:25.936Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:39:25.936Z] The best model improves the baseline by 14.52%. [2023-04-19T10:39:25.936Z] Movies recommended for you: [2023-04-19T10:39:25.936Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:39:25.936Z] There is no way to check that no silent failure occurred. [2023-04-19T10:39:25.936Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16401.240 ms) ====== [2023-04-19T10:39:25.936Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2023-04-19T10:39:25.936Z] GC before operation: completed in 211.559 ms, heap usage 183.756 MB -> 42.074 MB. [2023-04-19T10:39:25.936Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:39:25.936Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:39:25.936Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:39:25.936Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:39:25.936Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:39:25.936Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:39:25.936Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:39:25.936Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:39:25.936Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:39:25.936Z] The best model improves the baseline by 14.52%. [2023-04-19T10:39:25.936Z] Movies recommended for you: [2023-04-19T10:39:25.936Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:39:25.936Z] There is no way to check that no silent failure occurred. [2023-04-19T10:39:25.936Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16551.280 ms) ====== [2023-04-19T10:39:25.936Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2023-04-19T10:39:25.936Z] GC before operation: completed in 192.577 ms, heap usage 184.723 MB -> 41.957 MB. [2023-04-19T10:39:27.848Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:39:29.765Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:39:32.043Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:39:36.103Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:39:36.103Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:39:38.384Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:39:39.322Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:39:40.257Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:39:42.312Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:39:42.312Z] The best model improves the baseline by 14.52%. [2023-04-19T10:39:42.312Z] Movies recommended for you: [2023-04-19T10:39:42.312Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:39:42.312Z] There is no way to check that no silent failure occurred. [2023-04-19T10:39:42.312Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16006.355 ms) ====== [2023-04-19T10:39:42.312Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2023-04-19T10:39:42.312Z] GC before operation: completed in 195.268 ms, heap usage 129.913 MB -> 41.863 MB. [2023-04-19T10:39:43.246Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:39:47.154Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:39:48.085Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:39:50.398Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:39:53.044Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:39:53.978Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:39:54.909Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:39:56.841Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:39:56.841Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:39:56.841Z] The best model improves the baseline by 14.52%. [2023-04-19T10:39:56.841Z] Movies recommended for you: [2023-04-19T10:39:56.841Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:39:56.841Z] There is no way to check that no silent failure occurred. [2023-04-19T10:39:56.841Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15876.379 ms) ====== [2023-04-19T10:39:56.841Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2023-04-19T10:39:58.365Z] GC before operation: completed in 183.572 ms, heap usage 94.849 MB -> 42.043 MB. [2023-04-19T10:39:59.296Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:40:02.792Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:40:04.098Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:40:15.774Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:40:28.608Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:40:28.608Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:40:28.608Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:40:28.608Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:40:28.608Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:40:28.608Z] The best model improves the baseline by 14.52%. [2023-04-19T10:40:28.608Z] Movies recommended for you: [2023-04-19T10:40:28.608Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:40:28.608Z] There is no way to check that no silent failure occurred. [2023-04-19T10:40:28.608Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15825.228 ms) ====== [2023-04-19T10:40:28.608Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2023-04-19T10:40:28.608Z] GC before operation: completed in 139.954 ms, heap usage 186.494 MB -> 42.236 MB. [2023-04-19T10:40:28.608Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:40:28.608Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:40:28.608Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:40:28.608Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:40:28.608Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:40:28.608Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:40:28.608Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:40:28.608Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:40:30.864Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:40:30.864Z] The best model improves the baseline by 14.52%. [2023-04-19T10:40:30.864Z] Movies recommended for you: [2023-04-19T10:40:30.864Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:40:30.864Z] There is no way to check that no silent failure occurred. [2023-04-19T10:40:30.864Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16089.030 ms) ====== [2023-04-19T10:40:30.864Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2023-04-19T10:40:30.864Z] GC before operation: completed in 235.947 ms, heap usage 146.613 MB -> 42.136 MB. [2023-04-19T10:40:31.800Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:40:35.408Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:40:38.809Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:40:41.812Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:40:44.804Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:40:46.763Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:40:48.698Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:40:51.204Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:40:51.204Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:40:51.204Z] The best model improves the baseline by 14.52%. [2023-04-19T10:40:51.204Z] Movies recommended for you: [2023-04-19T10:40:51.204Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:40:51.204Z] There is no way to check that no silent failure occurred. [2023-04-19T10:40:51.204Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (21201.733 ms) ====== [2023-04-19T10:40:51.204Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2023-04-19T10:40:51.204Z] GC before operation: completed in 189.329 ms, heap usage 78.073 MB -> 41.587 MB. [2023-04-19T10:40:54.204Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:40:57.196Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:41:01.706Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:41:03.625Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:41:05.567Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:41:08.626Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:41:11.570Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:41:13.065Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:41:13.065Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:41:13.065Z] The best model improves the baseline by 14.52%. [2023-04-19T10:41:13.065Z] Movies recommended for you: [2023-04-19T10:41:13.065Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:41:13.065Z] There is no way to check that no silent failure occurred. [2023-04-19T10:41:13.065Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (21631.319 ms) ====== [2023-04-19T10:41:13.065Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2023-04-19T10:41:13.065Z] GC before operation: completed in 251.141 ms, heap usage 122.477 MB -> 44.069 MB. [2023-04-19T10:41:14.983Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:41:17.979Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:41:20.958Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:41:23.984Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:41:25.934Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:41:27.217Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:41:29.167Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:41:30.695Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:41:30.695Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:41:30.695Z] The best model improves the baseline by 14.52%. [2023-04-19T10:41:32.005Z] Movies recommended for you: [2023-04-19T10:41:32.005Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:41:32.005Z] There is no way to check that no silent failure occurred. [2023-04-19T10:41:32.005Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18258.798 ms) ====== [2023-04-19T10:41:32.005Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2023-04-19T10:41:32.005Z] GC before operation: completed in 156.571 ms, heap usage 138.063 MB -> 42.230 MB. [2023-04-19T10:41:34.662Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:41:36.592Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:42:14.629Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:42:14.629Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:42:14.630Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:42:14.630Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:42:14.630Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:42:14.630Z] The best model improves the baseline by 14.52%. [2023-04-19T10:42:14.630Z] Movies recommended for you: [2023-04-19T10:42:14.630Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:42:14.630Z] There is no way to check that no silent failure occurred. [2023-04-19T10:42:14.630Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16215.366 ms) ====== [2023-04-19T10:42:14.630Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2023-04-19T10:42:14.630Z] GC before operation: completed in 154.619 ms, heap usage 336.223 MB -> 42.409 MB. [2023-04-19T10:42:14.630Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:42:14.630Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:42:14.630Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:42:14.630Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:42:14.630Z] The best model improves the baseline by 14.52%. [2023-04-19T10:42:14.630Z] Movies recommended for you: [2023-04-19T10:42:14.630Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:42:14.630Z] There is no way to check that no silent failure occurred. [2023-04-19T10:42:14.630Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15760.203 ms) ====== [2023-04-19T10:42:14.630Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2023-04-19T10:42:14.630Z] GC before operation: completed in 145.085 ms, heap usage 96.234 MB -> 41.946 MB. [2023-04-19T10:42:14.630Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:42:14.630Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:42:15.923Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:42:16.855Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:42:18.523Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:42:19.457Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:42:19.457Z] The best model improves the baseline by 14.52%. [2023-04-19T10:42:19.457Z] Movies recommended for you: [2023-04-19T10:42:19.457Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:42:19.457Z] There is no way to check that no silent failure occurred. [2023-04-19T10:42:19.457Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15344.535 ms) ====== [2023-04-19T10:42:19.457Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2023-04-19T10:42:19.458Z] GC before operation: completed in 158.502 ms, heap usage 127.844 MB -> 42.107 MB. [2023-04-19T10:42:21.374Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:42:26.033Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:42:26.966Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:42:28.880Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:42:30.183Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:42:31.484Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:42:32.419Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:42:34.334Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:42:34.335Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:42:34.335Z] The best model improves the baseline by 14.52%. [2023-04-19T10:42:34.335Z] Movies recommended for you: [2023-04-19T10:42:34.335Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:42:34.335Z] There is no way to check that no silent failure occurred. [2023-04-19T10:42:34.335Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15677.642 ms) ====== [2023-04-19T10:42:34.335Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2023-04-19T10:42:35.267Z] GC before operation: completed in 169.183 ms, heap usage 212.023 MB -> 42.360 MB. [2023-04-19T10:42:37.544Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:42:39.462Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:42:42.785Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:42:43.737Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:42:46.015Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:42:46.956Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:42:48.245Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:42:50.160Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:42:50.160Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:42:50.160Z] The best model improves the baseline by 14.52%. [2023-04-19T10:42:50.160Z] Movies recommended for you: [2023-04-19T10:42:50.160Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:42:50.160Z] There is no way to check that no silent failure occurred. [2023-04-19T10:42:50.160Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15578.969 ms) ====== [2023-04-19T10:42:50.160Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2023-04-19T10:42:51.091Z] GC before operation: completed in 175.728 ms, heap usage 231.813 MB -> 42.428 MB. [2023-04-19T10:42:53.366Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:42:55.278Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:42:57.194Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:43:00.147Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:43:01.443Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:43:02.743Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:43:03.674Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:43:06.693Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:43:06.693Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:43:06.693Z] The best model improves the baseline by 14.52%. [2023-04-19T10:43:06.693Z] Movies recommended for you: [2023-04-19T10:43:06.693Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:43:06.693Z] There is no way to check that no silent failure occurred. [2023-04-19T10:43:06.693Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15086.595 ms) ====== [2023-04-19T10:43:06.693Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2023-04-19T10:43:06.693Z] GC before operation: completed in 133.888 ms, heap usage 128.223 MB -> 42.229 MB. [2023-04-19T10:43:08.217Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T10:43:45.343Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T10:43:45.343Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T10:43:45.343Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T10:43:45.343Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T10:43:45.343Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T10:43:45.343Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T10:43:45.343Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T10:43:45.343Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T10:43:45.343Z] The best model improves the baseline by 14.52%. [2023-04-19T10:43:45.343Z] Movies recommended for you: [2023-04-19T10:43:45.343Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T10:43:45.343Z] There is no way to check that no silent failure occurred. [2023-04-19T10:43:45.343Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (15452.453 ms) ====== [2023-04-19T10:43:45.343Z] ----------------------------------- [2023-04-19T10:43:45.343Z] renaissance-movie-lens_0_PASSED [2023-04-19T10:43:45.343Z] ----------------------------------- [2023-04-19T10:43:45.343Z] [2023-04-19T10:43:45.343Z] TEST TEARDOWN: [2023-04-19T10:43:45.343Z] Nothing to be done for teardown. [2023-04-19T10:43:45.343Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 10:43:21 2023 Epoch Time (ms): 1681901001421