renaissance-movie-lens_0
[2024-11-29T00:20:33.399Z] Running test renaissance-movie-lens_0 ...
[2024-11-29T00:20:33.399Z] ===============================================
[2024-11-29T00:20:33.399Z] renaissance-movie-lens_0 Start Time: Thu Nov 28 19:00:25 2024 Epoch Time (ms): 1732842025766
[2024-11-29T00:20:33.399Z] variation: NoOptions
[2024-11-29T00:20:33.399Z] JVM_OPTIONS:
[2024-11-29T00:20:33.399Z] { \
[2024-11-29T00:20:33.399Z] echo ""; echo "TEST SETUP:"; \
[2024-11-29T00:20:33.399Z] echo "Nothing to be done for setup."; \
[2024-11-29T00:20:33.399Z] mkdir -p "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_17328396808047/renaissance-movie-lens_0"; \
[2024-11-29T00:20:33.399Z] cd "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_17328396808047/renaissance-movie-lens_0"; \
[2024-11-29T00:20:33.399Z] echo ""; echo "TESTING:"; \
[2024-11-29T00:20:33.399Z] "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/jdkbinary/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_17328396808047/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-29T00:20:33.399Z] 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_17328396808047/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-29T00:20:33.399Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-29T00:20:33.399Z] echo "Nothing to be done for teardown."; \
[2024-11-29T00:20:33.399Z] } 2>&1 | tee -a "/export/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_sparcv9_solaris/aqa-tests/TKG/../TKG/output_17328396808047/TestTargetResult";
[2024-11-29T00:20:33.399Z]
[2024-11-29T00:20:33.399Z] TEST SETUP:
[2024-11-29T00:20:33.399Z] Nothing to be done for setup.
[2024-11-29T00:20:33.399Z]
[2024-11-29T00:20:33.399Z] TESTING:
[2024-11-29T00:20:40.541Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-29T00:20:45.291Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-29T00:20:55.654Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-29T00:20:56.281Z] Training: 60056, validation: 20285, test: 19854
[2024-11-29T00:20:56.281Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-29T00:20:56.923Z] GC before operation: completed in 462.466 ms, heap usage 159.001 MB -> 27.746 MB.
[2024-11-29T00:21:14.028Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:21:21.500Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:21:30.114Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:21:38.844Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:21:44.753Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:21:51.549Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:21:53.590Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:21:58.336Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:21:58.964Z] 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.
[2024-11-29T00:21:58.964Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:21:59.593Z] Movies recommended for you:
[2024-11-29T00:21:59.593Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:21:59.593Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:21:59.593Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (62741.695 ms) ======
[2024-11-29T00:21:59.593Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-29T00:22:00.222Z] GC before operation: completed in 643.421 ms, heap usage 214.093 MB -> 41.604 MB.
[2024-11-29T00:22:06.087Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:22:13.226Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:22:20.369Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:22:25.118Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:22:29.872Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:22:33.622Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:22:37.371Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:22:41.114Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:22:41.743Z] 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.
[2024-11-29T00:22:42.374Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:22:42.374Z] Movies recommended for you:
[2024-11-29T00:22:42.374Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:22:42.374Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:22:42.374Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (42402.004 ms) ======
[2024-11-29T00:22:42.374Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-29T00:22:43.000Z] GC before operation: completed in 563.370 ms, heap usage 145.832 MB -> 42.207 MB.
[2024-11-29T00:22:48.862Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:22:56.532Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:23:01.284Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:23:07.154Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:23:10.910Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:23:14.667Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:23:18.414Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:23:22.163Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:23:22.790Z] 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.
[2024-11-29T00:23:22.790Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:23:22.790Z] Movies recommended for you:
[2024-11-29T00:23:22.790Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:23:22.790Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:23:22.790Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (40009.301 ms) ======
[2024-11-29T00:23:22.790Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-29T00:23:23.417Z] GC before operation: completed in 556.056 ms, heap usage 621.719 MB -> 47.427 MB.
[2024-11-29T00:23:29.318Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:23:35.197Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:23:41.063Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:23:48.206Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:23:52.979Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:23:55.837Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:24:00.822Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:24:02.862Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:24:03.491Z] 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.
[2024-11-29T00:24:03.491Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:24:03.491Z] Movies recommended for you:
[2024-11-29T00:24:03.491Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:24:03.491Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:24:03.491Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (40186.893 ms) ======
[2024-11-29T00:24:03.491Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-29T00:24:04.118Z] GC before operation: completed in 454.706 ms, heap usage 579.289 MB -> 47.469 MB.
[2024-11-29T00:24:09.987Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:24:15.861Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:24:21.727Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:24:27.617Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:24:31.401Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:24:35.144Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:24:38.896Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:24:42.653Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:24:43.281Z] 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.
[2024-11-29T00:24:43.281Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:24:43.281Z] Movies recommended for you:
[2024-11-29T00:24:43.281Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:24:43.281Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:24:43.281Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (39297.612 ms) ======
[2024-11-29T00:24:43.281Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-29T00:24:43.908Z] GC before operation: completed in 440.186 ms, heap usage 571.556 MB -> 47.742 MB.
[2024-11-29T00:24:49.784Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:24:54.537Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:25:00.440Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:25:05.547Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:25:09.292Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:25:13.038Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:25:16.798Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:25:19.651Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:25:20.278Z] 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.
[2024-11-29T00:25:20.278Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:25:20.278Z] Movies recommended for you:
[2024-11-29T00:25:20.278Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:25:20.278Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:25:20.278Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (36411.702 ms) ======
[2024-11-29T00:25:20.278Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-29T00:25:20.905Z] GC before operation: completed in 439.633 ms, heap usage 577.836 MB -> 47.581 MB.
[2024-11-29T00:25:25.655Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:25:31.524Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:25:37.391Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:25:43.260Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:25:46.117Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:25:48.967Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:25:52.718Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:25:55.566Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:25:56.192Z] 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.
[2024-11-29T00:25:56.192Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:25:56.821Z] Movies recommended for you:
[2024-11-29T00:25:56.821Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:25:56.821Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:25:56.821Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (35880.753 ms) ======
[2024-11-29T00:25:56.821Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-29T00:25:56.821Z] GC before operation: completed in 430.828 ms, heap usage 570.667 MB -> 47.790 MB.
[2024-11-29T00:26:02.692Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:26:08.256Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:26:14.127Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:26:18.873Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:26:22.619Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:26:25.470Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:26:29.215Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:26:32.195Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:26:32.822Z] 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.
[2024-11-29T00:26:32.822Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:26:32.822Z] Movies recommended for you:
[2024-11-29T00:26:32.822Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:26:32.822Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:26:32.822Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (35790.234 ms) ======
[2024-11-29T00:26:32.822Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-29T00:26:33.451Z] GC before operation: completed in 378.973 ms, heap usage 581.438 MB -> 48.093 MB.
[2024-11-29T00:26:38.195Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:26:44.068Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:26:49.940Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:26:54.718Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:26:58.596Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:27:01.447Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:27:05.808Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:27:07.922Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:27:08.547Z] 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.
[2024-11-29T00:27:08.547Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:27:09.182Z] Movies recommended for you:
[2024-11-29T00:27:09.182Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:27:09.182Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:27:09.182Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (35716.219 ms) ======
[2024-11-29T00:27:09.182Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-29T00:27:09.182Z] GC before operation: completed in 400.966 ms, heap usage 599.686 MB -> 50.358 MB.
[2024-11-29T00:27:15.042Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:27:19.786Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:27:25.646Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:27:31.518Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:27:34.371Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:27:37.252Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:27:41.001Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:27:43.854Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:27:44.481Z] 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.
[2024-11-29T00:27:45.110Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:27:45.110Z] Movies recommended for you:
[2024-11-29T00:27:45.110Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:27:45.110Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:27:45.110Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (35617.845 ms) ======
[2024-11-29T00:27:45.110Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-29T00:27:45.739Z] GC before operation: completed in 475.463 ms, heap usage 619.020 MB -> 48.395 MB.
[2024-11-29T00:27:51.610Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:27:56.360Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:28:02.232Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:28:08.586Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:28:11.439Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:28:14.300Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:28:18.051Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:28:20.909Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:28:21.537Z] 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.
[2024-11-29T00:28:22.165Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:28:22.165Z] Movies recommended for you:
[2024-11-29T00:28:22.165Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:28:22.165Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:28:22.165Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (36586.031 ms) ======
[2024-11-29T00:28:22.165Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-29T00:28:22.796Z] GC before operation: completed in 402.723 ms, heap usage 587.725 MB -> 47.671 MB.
[2024-11-29T00:28:27.550Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:28:33.422Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:28:39.296Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:28:44.037Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:28:47.803Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:28:50.659Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:28:54.432Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:28:57.285Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:28:57.912Z] 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.
[2024-11-29T00:28:57.912Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:28:57.912Z] Movies recommended for you:
[2024-11-29T00:28:57.912Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:28:57.912Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:28:57.912Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (35577.222 ms) ======
[2024-11-29T00:28:57.912Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-29T00:28:58.540Z] GC before operation: completed in 381.754 ms, heap usage 583.724 MB -> 47.883 MB.
[2024-11-29T00:29:03.289Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:29:09.745Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:29:15.610Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:29:20.362Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:29:24.116Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:29:27.868Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:29:30.719Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:29:34.473Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:29:34.473Z] 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.
[2024-11-29T00:29:34.473Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:29:35.099Z] Movies recommended for you:
[2024-11-29T00:29:35.099Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:29:35.099Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:29:35.099Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (36540.859 ms) ======
[2024-11-29T00:29:35.099Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-29T00:29:35.727Z] GC before operation: completed in 400.806 ms, heap usage 578.646 MB -> 48.128 MB.
[2024-11-29T00:29:40.488Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:29:46.384Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:29:52.254Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:29:57.015Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:30:00.772Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:30:04.236Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:30:07.121Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:30:10.869Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:30:10.869Z] 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.
[2024-11-29T00:30:10.869Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:30:11.495Z] Movies recommended for you:
[2024-11-29T00:30:11.495Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:30:11.495Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:30:11.495Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (35804.901 ms) ======
[2024-11-29T00:30:11.495Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-29T00:30:11.495Z] GC before operation: completed in 372.828 ms, heap usage 584.172 MB -> 47.791 MB.
[2024-11-29T00:30:17.360Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:30:22.109Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:30:27.981Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:30:32.727Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:30:36.476Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:30:39.330Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:30:43.082Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:30:45.934Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:30:46.561Z] 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.
[2024-11-29T00:30:46.561Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:30:46.561Z] Movies recommended for you:
[2024-11-29T00:30:46.562Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:30:46.562Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:30:46.562Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (35064.601 ms) ======
[2024-11-29T00:30:46.562Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-29T00:30:47.189Z] GC before operation: completed in 371.414 ms, heap usage 598.639 MB -> 48.269 MB.
[2024-11-29T00:30:51.932Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:30:57.821Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:31:04.303Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:31:08.123Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:31:11.873Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:31:14.724Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:31:18.471Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:31:22.219Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:31:22.219Z] 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.
[2024-11-29T00:31:22.219Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:31:22.846Z] Movies recommended for you:
[2024-11-29T00:31:22.846Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:31:22.846Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:31:22.846Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (35545.915 ms) ======
[2024-11-29T00:31:22.846Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-29T00:31:22.846Z] GC before operation: completed in 366.079 ms, heap usage 531.488 MB -> 48.020 MB.
[2024-11-29T00:31:28.712Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:31:33.583Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:31:39.452Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:31:44.200Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:31:47.946Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:31:50.799Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:31:54.545Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:31:57.395Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:31:58.021Z] 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.
[2024-11-29T00:31:58.021Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:31:58.021Z] Movies recommended for you:
[2024-11-29T00:31:58.021Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:31:58.021Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:31:58.021Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (35084.943 ms) ======
[2024-11-29T00:31:58.021Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-29T00:31:58.649Z] GC before operation: completed in 522.666 ms, heap usage 540.464 MB -> 47.897 MB.
[2024-11-29T00:32:03.729Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:32:09.608Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:32:15.476Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:32:20.230Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:32:23.088Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:32:26.833Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:32:29.688Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:32:33.438Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:32:33.438Z] 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.
[2024-11-29T00:32:33.438Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:32:34.065Z] Movies recommended for you:
[2024-11-29T00:32:34.065Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:32:34.065Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:32:34.065Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (35382.374 ms) ======
[2024-11-29T00:32:34.065Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-29T00:32:34.694Z] GC before operation: completed in 500.943 ms, heap usage 579.600 MB -> 47.987 MB.
[2024-11-29T00:32:39.441Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:32:45.309Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:32:51.175Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:32:55.938Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:32:58.798Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:33:02.150Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:33:05.904Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:33:08.796Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:33:09.424Z] 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.
[2024-11-29T00:33:09.424Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:33:10.052Z] Movies recommended for you:
[2024-11-29T00:33:10.052Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:33:10.052Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:33:10.052Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (35413.616 ms) ======
[2024-11-29T00:33:10.052Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-29T00:33:10.052Z] GC before operation: completed in 458.146 ms, heap usage 629.147 MB -> 50.993 MB.
[2024-11-29T00:33:15.930Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-29T00:33:20.685Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-29T00:33:26.557Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-29T00:33:32.514Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-29T00:33:35.365Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-29T00:33:38.216Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-29T00:33:41.962Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-29T00:33:44.818Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-29T00:33:45.446Z] 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.
[2024-11-29T00:33:46.072Z] The best model improves the baseline by 14.52%.
[2024-11-29T00:33:46.072Z] Movies recommended for you:
[2024-11-29T00:33:46.072Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-29T00:33:46.072Z] There is no way to check that no silent failure occurred.
[2024-11-29T00:33:46.072Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (35697.777 ms) ======
[2024-11-29T00:33:46.697Z] -----------------------------------
[2024-11-29T00:33:46.697Z] renaissance-movie-lens_0_PASSED
[2024-11-29T00:33:46.697Z] -----------------------------------
[2024-11-29T00:33:46.697Z]
[2024-11-29T00:33:46.697Z] TEST TEARDOWN:
[2024-11-29T00:33:46.697Z] Nothing to be done for teardown.
[2024-11-29T00:33:47.324Z] renaissance-movie-lens_0 Finish Time: Thu Nov 28 19:13:40 2024 Epoch Time (ms): 1732842820355