[2024-09-06T02:08:15.046Z] Running test renaissance-movie-lens_0 ... [2024-09-06T02:08:15.349Z] =============================================== [2024-09-06T02:08:15.661Z] renaissance-movie-lens_0 Start Time: Fri Sep 6 02:08:15 2024 Epoch Time (ms): 1725588495352 [2024-09-06T02:08:15.661Z] variation: NoOptions [2024-09-06T02:08:15.974Z] JVM_OPTIONS: [2024-09-06T02:08:15.974Z] { \ [2024-09-06T02:08:15.974Z] echo ""; echo "TEST SETUP:"; \ [2024-09-06T02:08:15.974Z] echo "Nothing to be done for setup."; \ [2024-09-06T02:08:15.974Z] mkdir -p "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_1725587276451\\renaissance-movie-lens_0"; \ [2024-09-06T02:08:15.974Z] cd "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_1725587276451\\renaissance-movie-lens_0"; \ [2024-09-06T02:08:15.974Z] echo ""; echo "TESTING:"; \ [2024-09-06T02:08:15.974Z] "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_1725587276451\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2024-09-06T02:08:15.974Z] 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_1725587276451\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-09-06T02:08:15.974Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-09-06T02:08:15.974Z] echo "Nothing to be done for teardown."; \ [2024-09-06T02:08:15.974Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_1725587276451\\TestTargetResult"; [2024-09-06T02:08:15.974Z] [2024-09-06T02:08:15.974Z] TEST SETUP: [2024-09-06T02:08:15.974Z] Nothing to be done for setup. [2024-09-06T02:08:15.974Z] [2024-09-06T02:08:15.974Z] TESTING: [2024-09-06T02:08:26.501Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-09-06T02:08:28.076Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-09-06T02:08:31.754Z] Got 100004 ratings from 671 users on 9066 movies. [2024-09-06T02:08:31.754Z] Training: 60056, validation: 20285, test: 19854 [2024-09-06T02:08:31.754Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-09-06T02:08:31.754Z] GC before operation: completed in 223.035 ms, heap usage 181.884 MB -> 26.536 MB. [2024-09-06T02:08:42.365Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:08:51.034Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:08:59.700Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:09:06.753Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:09:11.339Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:09:14.967Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:09:19.545Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:09:23.204Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:09:23.204Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2024-09-06T02:09:23.204Z] The best model improves the baseline by 14.52%. [2024-09-06T02:09:23.526Z] Movies recommended for you: [2024-09-06T02:09:23.526Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:09:23.526Z] There is no way to check that no silent failure occurred. [2024-09-06T02:09:23.526Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (51576.585 ms) ====== [2024-09-06T02:09:23.526Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-09-06T02:09:23.876Z] GC before operation: completed in 319.310 ms, heap usage 456.680 MB -> 53.242 MB. [2024-09-06T02:09:30.914Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:09:37.976Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:09:43.687Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:09:50.742Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:09:53.580Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:09:57.205Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:10:00.834Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:10:04.449Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:10:04.768Z] 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. [2024-09-06T02:10:04.768Z] The best model improves the baseline by 14.52%. [2024-09-06T02:10:05.083Z] Movies recommended for you: [2024-09-06T02:10:05.083Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:10:05.083Z] There is no way to check that no silent failure occurred. [2024-09-06T02:10:05.083Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (41237.540 ms) ====== [2024-09-06T02:10:05.083Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-09-06T02:10:05.398Z] GC before operation: completed in 216.832 ms, heap usage 412.645 MB -> 45.135 MB. [2024-09-06T02:10:12.446Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:10:18.163Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:10:23.854Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:10:29.551Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:10:34.123Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:10:36.941Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:10:41.514Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:10:44.344Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:10:44.677Z] 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. [2024-09-06T02:10:44.677Z] The best model improves the baseline by 14.52%. [2024-09-06T02:10:44.677Z] Movies recommended for you: [2024-09-06T02:10:44.677Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:10:44.677Z] There is no way to check that no silent failure occurred. [2024-09-06T02:10:44.677Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (39548.690 ms) ====== [2024-09-06T02:10:44.677Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-09-06T02:10:44.991Z] GC before operation: completed in 163.959 ms, heap usage 510.379 MB -> 45.678 MB. [2024-09-06T02:10:52.029Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:10:57.766Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:11:03.475Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:11:10.513Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:11:14.145Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:11:16.968Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:11:21.554Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:11:24.382Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:11:24.704Z] 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. [2024-09-06T02:11:24.704Z] The best model improves the baseline by 14.52%. [2024-09-06T02:11:24.704Z] Movies recommended for you: [2024-09-06T02:11:24.704Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:11:24.704Z] There is no way to check that no silent failure occurred. [2024-09-06T02:11:24.704Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (39834.810 ms) ====== [2024-09-06T02:11:24.704Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-09-06T02:11:25.020Z] GC before operation: completed in 148.675 ms, heap usage 468.885 MB -> 46.013 MB. [2024-09-06T02:11:32.056Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:11:37.767Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:11:44.828Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:11:50.544Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:11:54.166Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:11:57.804Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:12:01.434Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:12:05.118Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:12:05.118Z] 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. [2024-09-06T02:12:05.118Z] The best model improves the baseline by 14.52%. [2024-09-06T02:12:05.441Z] Movies recommended for you: [2024-09-06T02:12:05.441Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:12:05.441Z] There is no way to check that no silent failure occurred. [2024-09-06T02:12:05.441Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (40298.346 ms) ====== [2024-09-06T02:12:05.441Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-09-06T02:12:05.441Z] GC before operation: completed in 144.602 ms, heap usage 473.830 MB -> 46.234 MB. [2024-09-06T02:12:11.152Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:12:18.226Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:12:23.955Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:12:29.665Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:12:33.290Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:12:36.959Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:12:40.583Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:12:44.217Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:12:44.217Z] 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. [2024-09-06T02:12:44.218Z] The best model improves the baseline by 14.52%. [2024-09-06T02:12:44.537Z] Movies recommended for you: [2024-09-06T02:12:44.537Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:12:44.537Z] There is no way to check that no silent failure occurred. [2024-09-06T02:12:44.537Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (39080.020 ms) ====== [2024-09-06T02:12:44.537Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-09-06T02:12:44.537Z] GC before operation: completed in 143.795 ms, heap usage 436.066 MB -> 46.040 MB. [2024-09-06T02:12:51.581Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:12:57.318Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:13:03.023Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:13:10.061Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:13:12.904Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:13:16.532Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:13:20.161Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:13:23.785Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:13:23.785Z] 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. [2024-09-06T02:13:23.785Z] The best model improves the baseline by 14.52%. [2024-09-06T02:13:23.785Z] Movies recommended for you: [2024-09-06T02:13:23.785Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:13:23.785Z] There is no way to check that no silent failure occurred. [2024-09-06T02:13:23.786Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (39087.221 ms) ====== [2024-09-06T02:13:23.786Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-09-06T02:13:23.786Z] GC before operation: completed in 147.349 ms, heap usage 419.738 MB -> 46.225 MB. [2024-09-06T02:13:30.863Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:13:36.611Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:13:42.327Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:13:48.015Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:13:51.660Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:13:55.270Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:13:58.900Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:14:02.518Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:14:02.518Z] 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. [2024-09-06T02:14:02.518Z] The best model improves the baseline by 14.52%. [2024-09-06T02:14:02.518Z] Movies recommended for you: [2024-09-06T02:14:02.518Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:14:02.518Z] There is no way to check that no silent failure occurred. [2024-09-06T02:14:02.518Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (38634.804 ms) ====== [2024-09-06T02:14:02.518Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-09-06T02:14:02.518Z] GC before operation: completed in 115.858 ms, heap usage 436.115 MB -> 46.526 MB. [2024-09-06T02:14:09.543Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:14:15.252Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:14:20.947Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:14:26.647Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:14:31.223Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:14:34.041Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:14:37.650Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:14:41.287Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:14:41.287Z] 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. [2024-09-06T02:14:41.287Z] The best model improves the baseline by 14.52%. [2024-09-06T02:14:41.287Z] Movies recommended for you: [2024-09-06T02:14:41.287Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:14:41.287Z] There is no way to check that no silent failure occurred. [2024-09-06T02:14:41.287Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (38760.185 ms) ====== [2024-09-06T02:14:41.287Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-09-06T02:14:41.600Z] GC before operation: completed in 120.833 ms, heap usage 418.463 MB -> 46.295 MB. [2024-09-06T02:14:48.635Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:14:54.325Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:15:00.052Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:15:05.739Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:15:09.351Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:15:12.973Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:15:16.631Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:15:19.461Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:15:20.133Z] 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. [2024-09-06T02:15:20.133Z] The best model improves the baseline by 14.52%. [2024-09-06T02:15:20.133Z] Movies recommended for you: [2024-09-06T02:15:20.133Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:15:20.133Z] There is no way to check that no silent failure occurred. [2024-09-06T02:15:20.133Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (38644.672 ms) ====== [2024-09-06T02:15:20.133Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-09-06T02:15:20.450Z] GC before operation: completed in 175.158 ms, heap usage 412.931 MB -> 46.438 MB. [2024-09-06T02:15:27.489Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:15:33.207Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:15:38.926Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:15:45.961Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:15:48.799Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:15:52.428Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:15:56.074Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:15:59.696Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:15:59.696Z] 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. [2024-09-06T02:15:59.696Z] The best model improves the baseline by 14.52%. [2024-09-06T02:16:00.013Z] Movies recommended for you: [2024-09-06T02:16:00.013Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:16:00.013Z] There is no way to check that no silent failure occurred. [2024-09-06T02:16:00.013Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (39591.263 ms) ====== [2024-09-06T02:16:00.013Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-09-06T02:16:00.013Z] GC before operation: completed in 121.705 ms, heap usage 423.948 MB -> 46.100 MB. [2024-09-06T02:16:07.050Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:16:12.814Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:16:18.516Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:16:24.219Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:16:27.853Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:16:31.476Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:16:35.091Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:16:38.726Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:16:38.726Z] 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. [2024-09-06T02:16:38.726Z] The best model improves the baseline by 14.52%. [2024-09-06T02:16:38.726Z] Movies recommended for you: [2024-09-06T02:16:38.726Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:16:38.726Z] There is no way to check that no silent failure occurred. [2024-09-06T02:16:38.726Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (38655.770 ms) ====== [2024-09-06T02:16:38.726Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-09-06T02:16:38.726Z] GC before operation: completed in 116.184 ms, heap usage 410.287 MB -> 46.317 MB. [2024-09-06T02:16:45.754Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:16:51.465Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:16:58.505Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:17:04.206Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:17:07.049Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:17:10.670Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:17:14.300Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:17:17.930Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:17:17.930Z] 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. [2024-09-06T02:17:17.930Z] The best model improves the baseline by 14.52%. [2024-09-06T02:17:17.930Z] Movies recommended for you: [2024-09-06T02:17:17.930Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:17:17.930Z] There is no way to check that no silent failure occurred. [2024-09-06T02:17:17.930Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (39087.096 ms) ====== [2024-09-06T02:17:17.930Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-09-06T02:17:17.930Z] GC before operation: completed in 121.962 ms, heap usage 412.249 MB -> 46.524 MB. [2024-09-06T02:17:24.966Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:17:30.716Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:17:36.432Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:17:42.146Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:17:45.779Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:17:49.423Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:17:53.053Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:17:56.700Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:17:56.700Z] 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. [2024-09-06T02:17:56.700Z] The best model improves the baseline by 14.52%. [2024-09-06T02:17:56.700Z] Movies recommended for you: [2024-09-06T02:17:56.700Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:17:56.700Z] There is no way to check that no silent failure occurred. [2024-09-06T02:17:56.700Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (38644.257 ms) ====== [2024-09-06T02:17:56.700Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-09-06T02:17:56.700Z] GC before operation: completed in 115.561 ms, heap usage 414.146 MB -> 46.207 MB. [2024-09-06T02:18:03.745Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:18:09.454Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:18:15.170Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:18:20.848Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:18:24.461Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:18:28.078Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:18:31.698Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:18:35.311Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:18:35.311Z] 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. [2024-09-06T02:18:35.311Z] The best model improves the baseline by 14.52%. [2024-09-06T02:18:35.311Z] Movies recommended for you: [2024-09-06T02:18:35.311Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:18:35.311Z] There is no way to check that no silent failure occurred. [2024-09-06T02:18:35.311Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (38510.551 ms) ====== [2024-09-06T02:18:35.311Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-09-06T02:18:35.311Z] GC before operation: completed in 107.991 ms, heap usage 407.785 MB -> 46.448 MB. [2024-09-06T02:18:42.352Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:18:48.054Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:18:53.809Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:18:59.496Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:19:03.105Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:19:06.737Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:19:10.357Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:19:13.996Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:19:13.996Z] 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. [2024-09-06T02:19:13.996Z] The best model improves the baseline by 14.52%. [2024-09-06T02:19:13.996Z] Movies recommended for you: [2024-09-06T02:19:13.996Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:19:13.996Z] There is no way to check that no silent failure occurred. [2024-09-06T02:19:13.996Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (38592.330 ms) ====== [2024-09-06T02:19:13.996Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-09-06T02:19:13.996Z] GC before operation: completed in 128.986 ms, heap usage 406.264 MB -> 46.543 MB. [2024-09-06T02:19:21.017Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:19:26.701Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:19:32.443Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:19:38.136Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:19:41.752Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:19:45.409Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:19:49.015Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:19:52.642Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:19:52.642Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2024-09-06T02:19:52.642Z] The best model improves the baseline by 14.52%. [2024-09-06T02:19:52.642Z] Movies recommended for you: [2024-09-06T02:19:52.642Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:19:52.642Z] There is no way to check that no silent failure occurred. [2024-09-06T02:19:52.642Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (38470.847 ms) ====== [2024-09-06T02:19:52.642Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-09-06T02:19:52.642Z] GC before operation: completed in 106.712 ms, heap usage 407.142 MB -> 48.651 MB. [2024-09-06T02:19:59.689Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:20:05.404Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:20:11.116Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:20:16.817Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:20:20.422Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:20:24.023Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:20:27.637Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:20:31.262Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:20:31.262Z] 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. [2024-09-06T02:20:31.262Z] The best model improves the baseline by 14.52%. [2024-09-06T02:20:31.262Z] Movies recommended for you: [2024-09-06T02:20:31.262Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:20:31.262Z] There is no way to check that no silent failure occurred. [2024-09-06T02:20:31.262Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (38532.497 ms) ====== [2024-09-06T02:20:31.262Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-09-06T02:20:31.262Z] GC before operation: completed in 129.207 ms, heap usage 462.475 MB -> 46.504 MB. [2024-09-06T02:20:38.307Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:20:44.035Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:20:49.754Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:20:55.447Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:20:59.065Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:21:02.689Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:21:06.302Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:21:09.929Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:21:09.929Z] 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. [2024-09-06T02:21:09.929Z] The best model improves the baseline by 14.52%. [2024-09-06T02:21:09.929Z] Movies recommended for you: [2024-09-06T02:21:09.929Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:21:09.929Z] There is no way to check that no silent failure occurred. [2024-09-06T02:21:09.929Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (38697.383 ms) ====== [2024-09-06T02:21:09.929Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-09-06T02:21:10.243Z] GC before operation: completed in 119.851 ms, heap usage 419.207 MB -> 46.638 MB. [2024-09-06T02:21:15.930Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-06T02:21:22.991Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-06T02:21:28.686Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-06T02:21:34.394Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-06T02:21:38.013Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-06T02:21:41.657Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-06T02:21:45.263Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-06T02:21:48.898Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-06T02:21:48.898Z] 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. [2024-09-06T02:21:48.898Z] The best model improves the baseline by 14.52%. [2024-09-06T02:21:48.898Z] Movies recommended for you: [2024-09-06T02:21:48.898Z] WARNING: This benchmark provides no result that can be validated. [2024-09-06T02:21:48.898Z] There is no way to check that no silent failure occurred. [2024-09-06T02:21:48.898Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (38818.681 ms) ====== [2024-09-06T02:21:49.569Z] ----------------------------------- [2024-09-06T02:21:49.569Z] renaissance-movie-lens_0_PASSED [2024-09-06T02:21:49.569Z] ----------------------------------- [2024-09-06T02:21:50.229Z] [2024-09-06T02:21:50.229Z] TEST TEARDOWN: [2024-09-06T02:21:50.229Z] Nothing to be done for teardown. [2024-09-06T02:21:50.229Z] renaissance-movie-lens_0 Finish Time: Fri Sep 6 02:21:50 2024 Epoch Time (ms): 1725589310034