[2024-10-30T00:12:32.796Z] Running test renaissance-movie-lens_0 ... [2024-10-30T00:12:32.796Z] =============================================== [2024-10-30T00:12:33.127Z] renaissance-movie-lens_0 Start Time: Wed Oct 30 00:12:32 2024 Epoch Time (ms): 1730247152834 [2024-10-30T00:12:33.127Z] variation: NoOptions [2024-10-30T00:12:33.127Z] JVM_OPTIONS: [2024-10-30T00:12:33.127Z] { \ [2024-10-30T00:12:33.127Z] echo ""; echo "TEST SETUP:"; \ [2024-10-30T00:12:33.127Z] echo "Nothing to be done for setup."; \ [2024-10-30T00:12:33.127Z] mkdir -p "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17302459809478\\renaissance-movie-lens_0"; \ [2024-10-30T00:12:33.127Z] cd "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17302459809478\\renaissance-movie-lens_0"; \ [2024-10-30T00:12:33.127Z] echo ""; echo "TESTING:"; \ [2024-10-30T00:12:33.127Z] "c:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/jdkbinary/j2sdk-image\\bin\\java" -jar "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17302459809478\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2024-10-30T00:12:33.127Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17302459809478\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-10-30T00:12:33.128Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-10-30T00:12:33.128Z] echo "Nothing to be done for teardown."; \ [2024-10-30T00:12:33.128Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17302459809478\\TestTargetResult"; [2024-10-30T00:12:33.128Z] [2024-10-30T00:12:33.128Z] TEST SETUP: [2024-10-30T00:12:33.128Z] Nothing to be done for setup. [2024-10-30T00:12:33.128Z] [2024-10-30T00:12:33.128Z] TESTING: [2024-10-30T00:12:43.665Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-10-30T00:12:44.352Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-10-30T00:12:48.133Z] Got 100004 ratings from 671 users on 9066 movies. [2024-10-30T00:12:48.133Z] Training: 60056, validation: 20285, test: 19854 [2024-10-30T00:12:48.133Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-10-30T00:12:48.133Z] GC before operation: completed in 226.980 ms, heap usage 131.973 MB -> 26.207 MB. [2024-10-30T00:12:58.751Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:13:04.552Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:13:13.245Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:13:20.306Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:13:24.895Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:13:27.760Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:13:32.394Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:13:36.057Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:13:36.057Z] 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. [2024-10-30T00:13:36.057Z] The best model improves the baseline by 14.52%. [2024-10-30T00:13:36.362Z] Movies recommended for you: [2024-10-30T00:13:36.362Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:13:36.362Z] There is no way to check that no silent failure occurred. [2024-10-30T00:13:36.362Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (48164.585 ms) ====== [2024-10-30T00:13:36.362Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-10-30T00:13:36.679Z] GC before operation: completed in 366.915 ms, heap usage 219.166 MB -> 40.733 MB. [2024-10-30T00:13:43.767Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:13:49.475Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:13:55.203Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:14:02.269Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:14:05.107Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:14:08.766Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:14:12.395Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:14:16.086Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:14:16.437Z] 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. [2024-10-30T00:14:16.437Z] The best model improves the baseline by 14.52%. [2024-10-30T00:14:16.755Z] Movies recommended for you: [2024-10-30T00:14:16.755Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:14:16.755Z] There is no way to check that no silent failure occurred. [2024-10-30T00:14:16.755Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (40013.094 ms) ====== [2024-10-30T00:14:16.755Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-10-30T00:14:16.755Z] GC before operation: completed in 191.589 ms, heap usage 172.092 MB -> 41.246 MB. [2024-10-30T00:14:23.821Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:14:29.528Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:14:35.241Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:14:40.954Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:14:44.585Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:14:48.227Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:14:51.897Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:14:54.726Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:14:55.042Z] 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. [2024-10-30T00:14:55.042Z] The best model improves the baseline by 14.52%. [2024-10-30T00:14:55.385Z] Movies recommended for you: [2024-10-30T00:14:55.385Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:14:55.385Z] There is no way to check that no silent failure occurred. [2024-10-30T00:14:55.385Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (38553.276 ms) ====== [2024-10-30T00:14:55.385Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-10-30T00:14:55.385Z] GC before operation: completed in 179.469 ms, heap usage 556.552 MB -> 45.686 MB. [2024-10-30T00:15:02.439Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:15:08.186Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:15:13.946Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:15:20.985Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:15:24.602Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:15:27.430Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:15:31.112Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:15:34.746Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:15:34.746Z] 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. [2024-10-30T00:15:34.746Z] The best model improves the baseline by 14.52%. [2024-10-30T00:15:35.060Z] Movies recommended for you: [2024-10-30T00:15:35.060Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:15:35.060Z] There is no way to check that no silent failure occurred. [2024-10-30T00:15:35.060Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (39513.874 ms) ====== [2024-10-30T00:15:35.060Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-10-30T00:15:35.060Z] GC before operation: completed in 151.029 ms, heap usage 550.767 MB -> 46.064 MB. [2024-10-30T00:15:42.130Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:15:47.868Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:15:53.589Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:15:59.287Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:16:02.910Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:16:06.546Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:16:10.199Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:16:13.868Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:16:13.868Z] 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. [2024-10-30T00:16:13.868Z] The best model improves the baseline by 14.52%. [2024-10-30T00:16:13.868Z] Movies recommended for you: [2024-10-30T00:16:13.868Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:16:13.868Z] There is no way to check that no silent failure occurred. [2024-10-30T00:16:13.868Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (38811.327 ms) ====== [2024-10-30T00:16:13.868Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-10-30T00:16:14.188Z] GC before operation: completed in 155.221 ms, heap usage 531.314 MB -> 46.287 MB. [2024-10-30T00:16:21.260Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:16:26.969Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:16:32.707Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:16:38.405Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:16:42.040Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:16:45.673Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:16:49.340Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:16:52.192Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:16:52.513Z] 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. [2024-10-30T00:16:52.513Z] The best model improves the baseline by 14.52%. [2024-10-30T00:16:52.513Z] Movies recommended for you: [2024-10-30T00:16:52.513Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:16:52.513Z] There is no way to check that no silent failure occurred. [2024-10-30T00:16:52.513Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (38431.527 ms) ====== [2024-10-30T00:16:52.513Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-10-30T00:16:52.830Z] GC before operation: completed in 162.117 ms, heap usage 563.053 MB -> 46.216 MB. [2024-10-30T00:16:58.535Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:17:04.261Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:17:11.300Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:17:17.008Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:17:20.649Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:17:23.506Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:17:27.129Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:17:30.855Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:17:30.855Z] 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. [2024-10-30T00:17:30.855Z] The best model improves the baseline by 14.52%. [2024-10-30T00:17:30.855Z] Movies recommended for you: [2024-10-30T00:17:30.855Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:17:30.855Z] There is no way to check that no silent failure occurred. [2024-10-30T00:17:30.855Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (38077.954 ms) ====== [2024-10-30T00:17:30.855Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-10-30T00:17:30.855Z] GC before operation: completed in 138.798 ms, heap usage 533.743 MB -> 46.372 MB. [2024-10-30T00:17:37.892Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:17:42.479Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:17:49.545Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:17:55.260Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:17:58.104Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:18:01.724Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:18:05.378Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:18:08.240Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:18:08.943Z] 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. [2024-10-30T00:18:08.943Z] The best model improves the baseline by 14.52%. [2024-10-30T00:18:08.943Z] Movies recommended for you: [2024-10-30T00:18:08.943Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:18:08.943Z] There is no way to check that no silent failure occurred. [2024-10-30T00:18:08.943Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (37940.185 ms) ====== [2024-10-30T00:18:08.943Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-10-30T00:18:08.943Z] GC before operation: completed in 122.113 ms, heap usage 548.001 MB -> 46.664 MB. [2024-10-30T00:18:16.012Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:18:21.758Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:18:27.506Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:18:33.215Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:18:36.859Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:18:39.699Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:18:43.337Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:18:46.975Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:18:47.292Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2024-10-30T00:18:47.292Z] The best model improves the baseline by 14.52%. [2024-10-30T00:18:47.671Z] Movies recommended for you: [2024-10-30T00:18:47.671Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:18:47.671Z] There is no way to check that no silent failure occurred. [2024-10-30T00:18:47.671Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (38552.078 ms) ====== [2024-10-30T00:18:47.671Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-10-30T00:18:47.671Z] GC before operation: completed in 161.856 ms, heap usage 488.497 MB -> 46.315 MB. [2024-10-30T00:18:53.415Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:19:00.473Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:19:06.208Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:19:11.916Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:19:14.745Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:19:18.372Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:19:22.002Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:19:25.636Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:19:25.636Z] 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. [2024-10-30T00:19:25.636Z] The best model improves the baseline by 14.52%. [2024-10-30T00:19:25.636Z] Movies recommended for you: [2024-10-30T00:19:25.636Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:19:25.636Z] There is no way to check that no silent failure occurred. [2024-10-30T00:19:25.636Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (37903.174 ms) ====== [2024-10-30T00:19:25.636Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-10-30T00:19:25.951Z] GC before operation: completed in 180.224 ms, heap usage 551.763 MB -> 46.640 MB. [2024-10-30T00:19:31.704Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:19:37.399Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:19:44.455Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:19:50.168Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:19:52.976Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:19:56.587Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:20:00.270Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:20:03.966Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:20:03.966Z] 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. [2024-10-30T00:20:03.966Z] The best model improves the baseline by 14.52%. [2024-10-30T00:20:03.966Z] Movies recommended for you: [2024-10-30T00:20:03.966Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:20:03.966Z] There is no way to check that no silent failure occurred. [2024-10-30T00:20:03.966Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (38094.751 ms) ====== [2024-10-30T00:20:03.966Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-10-30T00:20:03.966Z] GC before operation: completed in 117.021 ms, heap usage 584.874 MB -> 48.782 MB. [2024-10-30T00:20:11.040Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:20:16.755Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:20:22.475Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:20:28.193Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:20:31.806Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:20:35.437Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:20:39.070Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:20:41.895Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:20:42.263Z] 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. [2024-10-30T00:20:42.263Z] The best model improves the baseline by 14.52%. [2024-10-30T00:20:42.263Z] Movies recommended for you: [2024-10-30T00:20:42.263Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:20:42.263Z] There is no way to check that no silent failure occurred. [2024-10-30T00:20:42.263Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (38366.064 ms) ====== [2024-10-30T00:20:42.263Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-10-30T00:20:42.576Z] GC before operation: completed in 128.735 ms, heap usage 601.445 MB -> 46.903 MB. [2024-10-30T00:20:48.277Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:20:53.974Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:21:01.034Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:21:06.756Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:21:09.584Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:21:13.204Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:21:16.820Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:21:20.444Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:21:20.444Z] 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. [2024-10-30T00:21:20.444Z] The best model improves the baseline by 14.52%. [2024-10-30T00:21:20.761Z] Movies recommended for you: [2024-10-30T00:21:20.761Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:21:20.761Z] There is no way to check that no silent failure occurred. [2024-10-30T00:21:20.761Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (38174.910 ms) ====== [2024-10-30T00:21:20.761Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-10-30T00:21:20.761Z] GC before operation: completed in 124.402 ms, heap usage 531.782 MB -> 46.675 MB. [2024-10-30T00:21:26.501Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:21:33.545Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:21:39.255Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:21:44.951Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:21:47.776Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:21:51.421Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:21:55.053Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:21:58.690Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:21:58.690Z] 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. [2024-10-30T00:21:58.690Z] The best model improves the baseline by 14.52%. [2024-10-30T00:21:58.690Z] Movies recommended for you: [2024-10-30T00:21:58.690Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:21:58.690Z] There is no way to check that no silent failure occurred. [2024-10-30T00:21:58.690Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (37863.508 ms) ====== [2024-10-30T00:21:58.690Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-10-30T00:21:58.690Z] GC before operation: completed in 126.742 ms, heap usage 540.667 MB -> 46.351 MB. [2024-10-30T00:22:05.780Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:22:11.485Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:22:17.186Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:22:22.899Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:22:26.525Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:22:30.155Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:22:33.788Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:22:36.626Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:22:37.298Z] 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. [2024-10-30T00:22:37.298Z] The best model improves the baseline by 14.52%. [2024-10-30T00:22:37.298Z] Movies recommended for you: [2024-10-30T00:22:37.298Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:22:37.298Z] There is no way to check that no silent failure occurred. [2024-10-30T00:22:37.298Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (38476.363 ms) ====== [2024-10-30T00:22:37.298Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-10-30T00:22:37.298Z] GC before operation: completed in 115.571 ms, heap usage 536.960 MB -> 46.561 MB. [2024-10-30T00:22:43.012Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:22:50.043Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:22:55.726Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:23:01.428Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:23:04.263Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:23:07.882Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:23:11.513Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:23:15.212Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:23:15.212Z] 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. [2024-10-30T00:23:15.212Z] The best model improves the baseline by 14.52%. [2024-10-30T00:23:15.212Z] Movies recommended for you: [2024-10-30T00:23:15.212Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:23:15.212Z] There is no way to check that no silent failure occurred. [2024-10-30T00:23:15.212Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (37787.246 ms) ====== [2024-10-30T00:23:15.212Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-10-30T00:23:15.524Z] GC before operation: completed in 117.906 ms, heap usage 550.244 MB -> 46.700 MB. [2024-10-30T00:23:21.265Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:23:28.321Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:23:34.066Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:23:39.870Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:23:42.699Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:23:46.326Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:23:49.959Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:23:52.791Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:23:53.106Z] 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. [2024-10-30T00:23:53.106Z] The best model improves the baseline by 14.52%. [2024-10-30T00:23:53.434Z] Movies recommended for you: [2024-10-30T00:23:53.434Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:23:53.434Z] There is no way to check that no silent failure occurred. [2024-10-30T00:23:53.434Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (37997.483 ms) ====== [2024-10-30T00:23:53.434Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-10-30T00:23:53.434Z] GC before operation: completed in 183.282 ms, heap usage 530.806 MB -> 46.534 MB. [2024-10-30T00:23:59.171Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:24:06.212Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:24:11.918Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:24:17.616Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:24:21.249Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:24:24.124Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:24:27.739Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:24:31.376Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:24:31.376Z] 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. [2024-10-30T00:24:31.376Z] The best model improves the baseline by 14.52%. [2024-10-30T00:24:31.376Z] Movies recommended for you: [2024-10-30T00:24:31.376Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:24:31.376Z] There is no way to check that no silent failure occurred. [2024-10-30T00:24:31.376Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (38009.637 ms) ====== [2024-10-30T00:24:31.376Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-10-30T00:24:31.694Z] GC before operation: completed in 196.339 ms, heap usage 554.240 MB -> 46.558 MB. [2024-10-30T00:24:37.400Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:24:44.438Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:24:50.141Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:24:55.836Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:24:58.660Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:25:02.274Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:25:05.907Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:25:09.527Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:25:09.527Z] 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. [2024-10-30T00:25:09.527Z] The best model improves the baseline by 14.52%. [2024-10-30T00:25:09.527Z] Movies recommended for you: [2024-10-30T00:25:09.527Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:25:09.527Z] There is no way to check that no silent failure occurred. [2024-10-30T00:25:09.527Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (37751.193 ms) ====== [2024-10-30T00:25:09.527Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-10-30T00:25:09.527Z] GC before operation: completed in 114.158 ms, heap usage 536.970 MB -> 46.763 MB. [2024-10-30T00:25:16.611Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-30T00:25:21.189Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-30T00:25:28.237Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-30T00:25:33.952Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-30T00:25:36.782Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-30T00:25:40.406Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-30T00:25:44.049Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-30T00:25:47.663Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-30T00:25:47.663Z] 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. [2024-10-30T00:25:47.663Z] The best model improves the baseline by 14.52%. [2024-10-30T00:25:47.663Z] Movies recommended for you: [2024-10-30T00:25:47.663Z] WARNING: This benchmark provides no result that can be validated. [2024-10-30T00:25:47.663Z] There is no way to check that no silent failure occurred. [2024-10-30T00:25:47.663Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (38103.521 ms) ====== [2024-10-30T00:25:48.322Z] ----------------------------------- [2024-10-30T00:25:48.322Z] renaissance-movie-lens_0_PASSED [2024-10-30T00:25:48.322Z] ----------------------------------- [2024-10-30T00:25:48.634Z] [2024-10-30T00:25:48.634Z] TEST TEARDOWN: [2024-10-30T00:25:48.634Z] Nothing to be done for teardown. [2024-10-30T00:25:48.634Z] renaissance-movie-lens_0 Finish Time: Wed Oct 30 00:25:48 2024 Epoch Time (ms): 1730247948593