Test Results Summary Service

renaissance-movie-lens_0

[2023-04-19T18:28:45.721Z] Running test renaissance-movie-lens_0 ... [2023-04-19T18:28:45.721Z] =============================================== [2023-04-19T18:28:45.721Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 05:04:07 2023 Epoch Time (ms): 1681898647616 [2023-04-19T18:28:45.721Z] variation: NoOptions [2023-04-19T18:28:45.721Z] JVM_OPTIONS: [2023-04-19T18:28:45.721Z] { \ [2023-04-19T18:28:45.721Z] echo ""; echo "TEST SETUP:"; \ [2023-04-19T18:28:45.721Z] echo "Nothing to be done for setup."; \ [2023-04-19T18:28:45.721Z] mkdir -p "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_16818895873227/renaissance-movie-lens_0"; \ [2023-04-19T18:28:45.721Z] cd "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_16818895873227/renaissance-movie-lens_0"; \ [2023-04-19T18:28:45.721Z] echo ""; echo "TESTING:"; \ [2023-04-19T18:28:45.721Z] "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/openjdkbinary/j2sdk-image/bin/java" -jar "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_16818895873227/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2023-04-19T18:28:45.721Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/..; rm -f -r "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_16818895873227/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2023-04-19T18:28:45.721Z] echo ""; echo "TEST TEARDOWN:"; \ [2023-04-19T18:28:45.721Z] echo "Nothing to be done for teardown."; \ [2023-04-19T18:28:45.721Z] } 2>&1 | tee -a "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_16818895873227/TestTargetResult"; [2023-04-19T18:28:45.721Z] [2023-04-19T18:28:45.721Z] TEST SETUP: [2023-04-19T18:28:45.721Z] Nothing to be done for setup. [2023-04-19T18:28:45.721Z] [2023-04-19T18:28:45.721Z] TESTING: [2023-04-19T18:29:18.729Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2023-04-19T18:29:30.935Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2023-04-19T18:30:03.870Z] Got 100004 ratings from 671 users on 9066 movies. [2023-04-19T18:30:04.526Z] Training: 60056, validation: 20285, test: 19854 [2023-04-19T18:30:04.526Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2023-04-19T18:30:05.839Z] GC before operation: completed in 1578.605 ms, heap usage 149.008 MB -> 27.672 MB. [2023-04-19T18:31:00.949Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:31:29.123Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:32:01.975Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:32:25.620Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:32:40.845Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:32:55.335Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:33:15.962Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:33:30.442Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:33:31.072Z] 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. [2023-04-19T18:33:31.703Z] The best model improves the baseline by 14.52%. [2023-04-19T18:33:33.750Z] Movies recommended for you: [2023-04-19T18:33:33.750Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:33:33.750Z] There is no way to check that no silent failure occurred. [2023-04-19T18:33:33.750Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (207622.615 ms) ====== [2023-04-19T18:33:33.750Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2023-04-19T18:33:36.615Z] GC before operation: completed in 3128.431 ms, heap usage 639.751 MB -> 48.661 MB. [2023-04-19T18:34:04.615Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:34:28.275Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:34:51.916Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:35:21.162Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:35:31.442Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:35:45.925Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:36:02.950Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:36:17.391Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:36:19.441Z] 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. [2023-04-19T18:36:20.070Z] The best model improves the baseline by 14.52%. [2023-04-19T18:36:20.697Z] Movies recommended for you: [2023-04-19T18:36:20.697Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:36:20.697Z] There is no way to check that no silent failure occurred. [2023-04-19T18:36:20.697Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (164269.570 ms) ====== [2023-04-19T18:36:20.697Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2023-04-19T18:36:22.753Z] GC before operation: completed in 1963.825 ms, heap usage 679.892 MB -> 47.447 MB. [2023-04-19T18:36:47.958Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:37:11.635Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:37:35.368Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:37:55.440Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:38:10.258Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:38:24.015Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:38:38.485Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:38:53.023Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:38:54.331Z] 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. [2023-04-19T18:38:54.960Z] The best model improves the baseline by 14.52%. [2023-04-19T18:38:55.592Z] Movies recommended for you: [2023-04-19T18:38:55.592Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:38:55.592Z] There is no way to check that no silent failure occurred. [2023-04-19T18:38:55.592Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (152772.629 ms) ====== [2023-04-19T18:38:55.592Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2023-04-19T18:38:57.642Z] GC before operation: completed in 1663.148 ms, heap usage 485.906 MB -> 46.855 MB. [2023-04-19T18:39:21.305Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:39:41.398Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:40:09.300Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:40:29.428Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:40:41.763Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:40:56.207Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:41:10.682Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:41:23.648Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:41:25.701Z] 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. [2023-04-19T18:41:25.701Z] The best model improves the baseline by 14.52%. [2023-04-19T18:41:27.014Z] Movies recommended for you: [2023-04-19T18:41:27.014Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:41:27.014Z] There is no way to check that no silent failure occurred. [2023-04-19T18:41:27.014Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (149351.662 ms) ====== [2023-04-19T18:41:27.014Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2023-04-19T18:41:28.342Z] GC before operation: completed in 1513.442 ms, heap usage 428.558 MB -> 47.187 MB. [2023-04-19T18:41:52.040Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:42:19.939Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:42:43.599Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:43:04.833Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:43:17.045Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:43:31.509Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:43:46.014Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:44:00.556Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:44:01.865Z] 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. [2023-04-19T18:44:01.865Z] The best model improves the baseline by 14.52%. [2023-04-19T18:44:03.188Z] Movies recommended for you: [2023-04-19T18:44:03.188Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:44:03.188Z] There is no way to check that no silent failure occurred. [2023-04-19T18:44:03.188Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (154695.601 ms) ====== [2023-04-19T18:44:03.188Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2023-04-19T18:44:04.501Z] GC before operation: completed in 1359.241 ms, heap usage 447.693 MB -> 47.473 MB. [2023-04-19T18:44:25.941Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:44:49.637Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:45:09.699Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:45:29.782Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:45:41.987Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:45:55.285Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:46:09.700Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:46:21.976Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:46:23.284Z] 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. [2023-04-19T18:46:23.922Z] The best model improves the baseline by 14.52%. [2023-04-19T18:46:24.566Z] Movies recommended for you: [2023-04-19T18:46:24.566Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:46:24.566Z] There is no way to check that no silent failure occurred. [2023-04-19T18:46:24.566Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (140445.720 ms) ====== [2023-04-19T18:46:24.566Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2023-04-19T18:46:25.879Z] GC before operation: completed in 1364.120 ms, heap usage 430.844 MB -> 47.314 MB. [2023-04-19T18:46:46.052Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:47:09.718Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:47:31.577Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:47:51.730Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:48:02.020Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:48:14.346Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:48:28.785Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:48:41.030Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:48:43.689Z] 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. [2023-04-19T18:48:43.689Z] The best model improves the baseline by 14.52%. [2023-04-19T18:48:44.322Z] Movies recommended for you: [2023-04-19T18:48:44.322Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:48:44.322Z] There is no way to check that no silent failure occurred. [2023-04-19T18:48:44.322Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (138320.362 ms) ====== [2023-04-19T18:48:44.322Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2023-04-19T18:48:45.631Z] GC before operation: completed in 1436.726 ms, heap usage 414.866 MB -> 47.496 MB. [2023-04-19T18:49:05.850Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:49:29.553Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:49:49.801Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:50:09.893Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:50:22.105Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:50:34.332Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:50:48.751Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:51:00.964Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:51:03.017Z] 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. [2023-04-19T18:51:03.017Z] The best model improves the baseline by 14.52%. [2023-04-19T18:51:03.672Z] Movies recommended for you: [2023-04-19T18:51:03.672Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:51:03.672Z] There is no way to check that no silent failure occurred. [2023-04-19T18:51:03.672Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (138006.190 ms) ====== [2023-04-19T18:51:03.672Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2023-04-19T18:51:05.720Z] GC before operation: completed in 1521.795 ms, heap usage 426.623 MB -> 47.810 MB. [2023-04-19T18:51:25.792Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:51:47.009Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:52:10.667Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:52:27.690Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:52:42.138Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:52:54.330Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:53:06.521Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:53:20.061Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:53:21.370Z] 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. [2023-04-19T18:53:22.000Z] The best model improves the baseline by 14.52%. [2023-04-19T18:53:22.629Z] Movies recommended for you: [2023-04-19T18:53:22.629Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:53:22.629Z] There is no way to check that no silent failure occurred. [2023-04-19T18:53:22.629Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (137366.380 ms) ====== [2023-04-19T18:53:22.630Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2023-04-19T18:53:23.963Z] GC before operation: completed in 1446.176 ms, heap usage 422.603 MB -> 47.579 MB. [2023-04-19T18:53:44.042Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:54:07.925Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:54:28.099Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:54:49.067Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:55:01.261Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:55:13.455Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:55:25.674Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:55:37.877Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:55:39.931Z] 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. [2023-04-19T18:55:40.559Z] The best model improves the baseline by 14.52%. [2023-04-19T18:55:41.188Z] Movies recommended for you: [2023-04-19T18:55:41.188Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:55:41.188Z] There is no way to check that no silent failure occurred. [2023-04-19T18:55:41.188Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (137119.279 ms) ====== [2023-04-19T18:55:41.188Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2023-04-19T18:55:43.357Z] GC before operation: completed in 1942.006 ms, heap usage 434.983 MB -> 47.727 MB. [2023-04-19T18:56:04.177Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:56:24.264Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:56:47.998Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:57:08.073Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:57:18.361Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:57:30.556Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T18:57:45.599Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T18:57:57.852Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T18:57:59.165Z] 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. [2023-04-19T18:57:59.165Z] The best model improves the baseline by 14.52%. [2023-04-19T18:57:59.794Z] Movies recommended for you: [2023-04-19T18:57:59.794Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T18:57:59.794Z] There is no way to check that no silent failure occurred. [2023-04-19T18:57:59.794Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (136708.877 ms) ====== [2023-04-19T18:57:59.794Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2023-04-19T18:58:01.102Z] GC before operation: completed in 1130.441 ms, heap usage 437.921 MB -> 47.396 MB. [2023-04-19T18:58:21.179Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T18:58:41.238Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T18:59:05.282Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T18:59:25.385Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T18:59:35.654Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T18:59:50.089Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:00:02.308Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:00:14.504Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:00:16.925Z] 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. [2023-04-19T19:00:16.925Z] The best model improves the baseline by 14.52%. [2023-04-19T19:00:18.231Z] Movies recommended for you: [2023-04-19T19:00:18.231Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:00:18.231Z] There is no way to check that no silent failure occurred. [2023-04-19T19:00:18.231Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (136814.443 ms) ====== [2023-04-19T19:00:18.231Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2023-04-19T19:00:18.859Z] GC before operation: completed in 1145.841 ms, heap usage 346.119 MB -> 44.088 MB. [2023-04-19T19:00:38.974Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:01:02.627Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:01:22.827Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:01:42.922Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:01:55.373Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:02:07.594Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:02:22.069Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:02:36.506Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:02:37.134Z] 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. [2023-04-19T19:02:37.134Z] The best model improves the baseline by 14.52%. [2023-04-19T19:02:38.455Z] Movies recommended for you: [2023-04-19T19:02:38.455Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:02:38.455Z] There is no way to check that no silent failure occurred. [2023-04-19T19:02:38.455Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (139109.037 ms) ====== [2023-04-19T19:02:38.455Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2023-04-19T19:02:39.766Z] GC before operation: completed in 1819.364 ms, heap usage 445.563 MB -> 47.049 MB. [2023-04-19T19:02:59.832Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:03:20.410Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:03:44.077Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:04:04.305Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:04:16.512Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:04:28.743Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:04:43.330Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:04:55.564Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:04:56.194Z] 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. [2023-04-19T19:04:56.823Z] The best model improves the baseline by 14.52%. [2023-04-19T19:04:57.454Z] Movies recommended for you: [2023-04-19T19:04:57.454Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:04:57.454Z] There is no way to check that no silent failure occurred. [2023-04-19T19:04:57.454Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (137558.362 ms) ====== [2023-04-19T19:04:57.454Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2023-04-19T19:04:59.505Z] GC before operation: completed in 1921.139 ms, heap usage 433.107 MB -> 43.648 MB. [2023-04-19T19:05:19.619Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:05:39.702Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:06:03.839Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:06:23.902Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:06:34.183Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:06:48.619Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:07:00.816Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:07:15.270Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:07:15.902Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2023-04-19T19:07:16.532Z] The best model improves the baseline by 14.52%. [2023-04-19T19:07:17.160Z] Movies recommended for you: [2023-04-19T19:07:17.160Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:07:17.160Z] There is no way to check that no silent failure occurred. [2023-04-19T19:07:17.160Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (137654.051 ms) ====== [2023-04-19T19:07:17.160Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2023-04-19T19:07:19.220Z] GC before operation: completed in 1922.952 ms, heap usage 427.547 MB -> 43.687 MB. [2023-04-19T19:07:39.513Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:07:59.591Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:08:23.251Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:08:43.325Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:08:56.044Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:09:08.297Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:09:20.496Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:09:32.721Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:09:35.588Z] 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. [2023-04-19T19:09:35.588Z] The best model improves the baseline by 14.52%. [2023-04-19T19:09:36.220Z] Movies recommended for you: [2023-04-19T19:09:36.221Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:09:36.221Z] There is no way to check that no silent failure occurred. [2023-04-19T19:09:36.221Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (137238.327 ms) ====== [2023-04-19T19:09:36.221Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2023-04-19T19:09:38.290Z] GC before operation: completed in 1781.825 ms, heap usage 428.305 MB -> 43.129 MB. [2023-04-19T19:09:58.375Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:10:18.600Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:10:42.246Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:11:02.382Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:11:14.593Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:11:26.781Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:11:41.443Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:11:53.642Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:11:55.692Z] 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. [2023-04-19T19:11:55.692Z] The best model improves the baseline by 14.52%. [2023-04-19T19:11:57.047Z] Movies recommended for you: [2023-04-19T19:11:57.047Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:11:57.047Z] There is no way to check that no silent failure occurred. [2023-04-19T19:11:57.047Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (138568.457 ms) ====== [2023-04-19T19:11:57.047Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2023-04-19T19:11:58.361Z] GC before operation: completed in 1694.162 ms, heap usage 487.491 MB -> 45.582 MB. [2023-04-19T19:12:18.442Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:12:42.192Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:13:05.867Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:13:23.082Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:13:37.520Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:13:49.729Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:14:04.328Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:14:16.524Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:14:17.839Z] 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. [2023-04-19T19:14:17.839Z] The best model improves the baseline by 14.52%. [2023-04-19T19:14:19.230Z] Movies recommended for you: [2023-04-19T19:14:19.230Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:14:19.230Z] There is no way to check that no silent failure occurred. [2023-04-19T19:14:19.230Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (140506.265 ms) ====== [2023-04-19T19:14:19.230Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2023-04-19T19:14:20.538Z] GC before operation: completed in 1299.507 ms, heap usage 526.312 MB -> 43.863 MB. [2023-04-19T19:14:40.599Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:15:00.667Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:15:24.333Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:15:44.475Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:15:56.283Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:16:08.481Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:16:22.918Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:16:35.149Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:16:36.461Z] 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. [2023-04-19T19:16:36.461Z] The best model improves the baseline by 14.52%. [2023-04-19T19:16:37.091Z] Movies recommended for you: [2023-04-19T19:16:37.091Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:16:37.091Z] There is no way to check that no silent failure occurred. [2023-04-19T19:16:37.091Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (137039.640 ms) ====== [2023-04-19T19:16:37.091Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2023-04-19T19:16:39.135Z] GC before operation: completed in 1691.526 ms, heap usage 526.087 MB -> 44.079 MB. [2023-04-19T19:16:59.228Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-19T19:17:20.177Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-19T19:17:43.900Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-19T19:18:00.945Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-19T19:18:15.383Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-19T19:18:27.580Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-19T19:18:41.251Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-19T19:18:53.687Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-19T19:18:55.002Z] 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. [2023-04-19T19:18:55.002Z] The best model improves the baseline by 14.52%. [2023-04-19T19:18:55.632Z] Movies recommended for you: [2023-04-19T19:18:55.633Z] WARNING: This benchmark provides no result that can be validated. [2023-04-19T19:18:55.633Z] There is no way to check that no silent failure occurred. [2023-04-19T19:18:55.633Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (136868.327 ms) ====== [2023-04-19T19:18:58.495Z] ----------------------------------- [2023-04-19T19:18:58.495Z] renaissance-movie-lens_0_PASSED [2023-04-19T19:18:58.495Z] ----------------------------------- [2023-04-19T19:18:58.495Z] [2023-04-19T19:18:58.495Z] TEST TEARDOWN: [2023-04-19T19:18:58.495Z] Nothing to be done for teardown. [2023-04-19T19:18:59.123Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 05:54:23 2023 Epoch Time (ms): 1681901663982