[2024-11-23T05:33:50.715Z] Running test renaissance-movie-lens_0 ... [2024-11-23T05:33:50.715Z] =============================================== [2024-11-23T05:33:50.715Z] renaissance-movie-lens_0 Start Time: Sat Nov 23 05:33:48 2024 Epoch Time (ms): 1732340028257 [2024-11-23T05:33:50.715Z] variation: NoOptions [2024-11-23T05:33:50.715Z] JVM_OPTIONS: [2024-11-23T05:33:50.715Z] { \ [2024-11-23T05:33:50.715Z] echo ""; echo "TEST SETUP:"; \ [2024-11-23T05:33:50.715Z] echo "Nothing to be done for setup."; \ [2024-11-23T05:33:50.715Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323282433475/renaissance-movie-lens_0"; \ [2024-11-23T05:33:50.715Z] cd "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323282433475/renaissance-movie-lens_0"; \ [2024-11-23T05:33:50.715Z] echo ""; echo "TESTING:"; \ [2024-11-23T05:33:50.715Z] "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/jdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323282433475/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-11-23T05:33:50.715Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323282433475/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-11-23T05:33:50.715Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-11-23T05:33:50.715Z] echo "Nothing to be done for teardown."; \ [2024-11-23T05:33:50.715Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323282433475/TestTargetResult"; [2024-11-23T05:33:50.715Z] [2024-11-23T05:33:50.715Z] TEST SETUP: [2024-11-23T05:33:50.715Z] Nothing to be done for setup. [2024-11-23T05:33:50.715Z] [2024-11-23T05:33:50.715Z] TESTING: [2024-11-23T05:33:52.481Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-11-23T05:33:54.249Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2024-11-23T05:33:57.369Z] Got 100004 ratings from 671 users on 9066 movies. [2024-11-23T05:33:57.369Z] Training: 60056, validation: 20285, test: 19854 [2024-11-23T05:33:57.369Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-11-23T05:33:57.728Z] GC before operation: completed in 66.411 ms, heap usage 94.889 MB -> 38.244 MB. [2024-11-23T05:34:31.664Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:34:59.906Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:35:28.140Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:35:47.782Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:36:01.288Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:36:10.513Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:36:26.809Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:36:37.993Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:36:37.993Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:36:37.993Z] The best model improves the baseline by 14.43%. [2024-11-23T05:36:37.993Z] Movies recommended for you: [2024-11-23T05:36:37.993Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:36:37.993Z] There is no way to check that no silent failure occurred. [2024-11-23T05:36:37.993Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (160148.268 ms) ====== [2024-11-23T05:36:37.993Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-11-23T05:36:37.993Z] GC before operation: completed in 86.136 ms, heap usage 859.865 MB -> 74.524 MB. [2024-11-23T05:37:06.255Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:37:29.803Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:37:58.108Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:38:17.711Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:38:28.884Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:38:40.067Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:38:56.353Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:39:07.520Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:39:07.520Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:39:07.520Z] The best model improves the baseline by 14.43%. [2024-11-23T05:39:07.520Z] Movies recommended for you: [2024-11-23T05:39:07.520Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:39:07.520Z] There is no way to check that no silent failure occurred. [2024-11-23T05:39:07.520Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (149575.264 ms) ====== [2024-11-23T05:39:07.520Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-11-23T05:39:07.520Z] GC before operation: completed in 91.445 ms, heap usage 341.109 MB -> 80.420 MB. [2024-11-23T05:39:35.784Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:40:04.470Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:40:32.720Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:40:52.314Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:41:03.496Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:41:14.674Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:41:30.979Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:41:42.182Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:41:42.182Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:41:42.182Z] The best model improves the baseline by 14.43%. [2024-11-23T05:41:42.182Z] Movies recommended for you: [2024-11-23T05:41:42.182Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:41:42.182Z] There is no way to check that no silent failure occurred. [2024-11-23T05:41:42.182Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (153944.395 ms) ====== [2024-11-23T05:41:42.182Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-11-23T05:41:42.182Z] GC before operation: completed in 86.893 ms, heap usage 228.621 MB -> 80.804 MB. [2024-11-23T05:42:10.433Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:42:38.711Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:43:07.000Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:43:30.560Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:43:41.743Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:43:52.930Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:44:09.207Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:44:20.390Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:44:20.390Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:44:20.390Z] The best model improves the baseline by 14.43%. [2024-11-23T05:44:20.390Z] Movies recommended for you: [2024-11-23T05:44:20.390Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:44:20.390Z] There is no way to check that no silent failure occurred. [2024-11-23T05:44:20.390Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (158119.676 ms) ====== [2024-11-23T05:44:20.390Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-11-23T05:44:20.390Z] GC before operation: completed in 90.897 ms, heap usage 271.557 MB -> 81.253 MB. [2024-11-23T05:44:48.638Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:45:12.191Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:45:40.435Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:46:00.027Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:46:13.528Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:46:24.701Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:46:41.017Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:46:50.254Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:46:51.022Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:46:51.022Z] The best model improves the baseline by 14.43%. [2024-11-23T05:46:51.022Z] Movies recommended for you: [2024-11-23T05:46:51.022Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:46:51.022Z] There is no way to check that no silent failure occurred. [2024-11-23T05:46:51.022Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (151353.497 ms) ====== [2024-11-23T05:46:51.022Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-11-23T05:46:51.022Z] GC before operation: completed in 90.681 ms, heap usage 732.927 MB -> 81.642 MB. [2024-11-23T05:47:19.270Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:47:42.821Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:48:11.103Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:48:37.768Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:48:53.886Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:49:07.373Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:49:24.749Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:49:35.912Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:49:36.271Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:49:36.271Z] The best model improves the baseline by 14.43%. [2024-11-23T05:49:36.630Z] Movies recommended for you: [2024-11-23T05:49:36.630Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:49:36.631Z] There is no way to check that no silent failure occurred. [2024-11-23T05:49:36.631Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (165306.544 ms) ====== [2024-11-23T05:49:36.631Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-11-23T05:49:36.631Z] GC before operation: completed in 84.960 ms, heap usage 228.135 MB -> 81.265 MB. [2024-11-23T05:50:04.878Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:50:28.402Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:50:56.643Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:51:16.230Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:51:29.743Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:51:40.917Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:51:57.174Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:52:08.422Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:52:08.422Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:52:08.422Z] The best model improves the baseline by 14.43%. [2024-11-23T05:52:08.422Z] Movies recommended for you: [2024-11-23T05:52:08.423Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:52:08.423Z] There is no way to check that no silent failure occurred. [2024-11-23T05:52:08.423Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (150802.544 ms) ====== [2024-11-23T05:52:08.423Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-11-23T05:52:08.423Z] GC before operation: completed in 80.876 ms, heap usage 210.304 MB -> 80.323 MB. [2024-11-23T05:52:31.954Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:53:00.207Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:53:28.495Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:53:52.047Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:54:03.248Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:54:14.428Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:54:30.727Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:54:39.961Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:54:39.961Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:54:39.961Z] The best model improves the baseline by 14.43%. [2024-11-23T05:54:39.961Z] Movies recommended for you: [2024-11-23T05:54:39.961Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:54:39.961Z] There is no way to check that no silent failure occurred. [2024-11-23T05:54:39.962Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (152686.055 ms) ====== [2024-11-23T05:54:39.962Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-11-23T05:54:40.321Z] GC before operation: completed in 83.528 ms, heap usage 235.472 MB -> 81.806 MB. [2024-11-23T05:55:08.592Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:55:32.126Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:56:00.404Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:56:20.013Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:56:31.185Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:56:42.356Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:56:58.640Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:57:09.815Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:57:09.815Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:57:09.815Z] The best model improves the baseline by 14.43%. [2024-11-23T05:57:09.815Z] Movies recommended for you: [2024-11-23T05:57:09.815Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:57:09.815Z] There is no way to check that no silent failure occurred. [2024-11-23T05:57:09.815Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (148844.625 ms) ====== [2024-11-23T05:57:09.815Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-11-23T05:57:09.815Z] GC before operation: completed in 83.273 ms, heap usage 274.771 MB -> 81.693 MB. [2024-11-23T05:57:38.067Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T05:58:01.808Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T05:58:30.104Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T05:58:53.660Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T05:59:04.887Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T05:59:16.089Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T05:59:32.389Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T05:59:41.619Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T05:59:41.985Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T05:59:41.985Z] The best model improves the baseline by 14.43%. [2024-11-23T05:59:41.985Z] Movies recommended for you: [2024-11-23T05:59:41.985Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T05:59:41.985Z] There is no way to check that no silent failure occurred. [2024-11-23T05:59:41.985Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (152976.200 ms) ====== [2024-11-23T05:59:41.985Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-11-23T05:59:42.347Z] GC before operation: completed in 83.596 ms, heap usage 395.583 MB -> 81.947 MB. [2024-11-23T06:00:10.615Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:00:30.213Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:01:04.129Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:01:25.383Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:01:41.681Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:01:52.891Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:02:09.203Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:02:22.722Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:02:23.084Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:02:23.084Z] The best model improves the baseline by 14.43%. [2024-11-23T06:02:23.084Z] Movies recommended for you: [2024-11-23T06:02:23.084Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:02:23.084Z] There is no way to check that no silent failure occurred. [2024-11-23T06:02:23.084Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (160970.717 ms) ====== [2024-11-23T06:02:23.084Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-11-23T06:02:23.084Z] GC before operation: completed in 109.100 ms, heap usage 524.275 MB -> 81.774 MB. [2024-11-23T06:02:54.655Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:03:18.234Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:03:46.491Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:04:10.040Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:04:21.229Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:04:32.428Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:04:45.948Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:04:57.142Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:04:57.142Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:04:57.142Z] The best model improves the baseline by 14.43%. [2024-11-23T06:04:57.142Z] Movies recommended for you: [2024-11-23T06:04:57.142Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:04:57.142Z] There is no way to check that no silent failure occurred. [2024-11-23T06:04:57.142Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (154048.215 ms) ====== [2024-11-23T06:04:57.142Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-11-23T06:04:57.501Z] GC before operation: completed in 81.498 ms, heap usage 558.047 MB -> 70.954 MB. [2024-11-23T06:05:21.049Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:05:49.315Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:06:17.590Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:06:41.161Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:06:54.689Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:07:05.873Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:07:25.471Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:07:36.658Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:07:36.658Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:07:36.658Z] The best model improves the baseline by 14.43%. [2024-11-23T06:07:36.658Z] Movies recommended for you: [2024-11-23T06:07:36.658Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:07:36.658Z] There is no way to check that no silent failure occurred. [2024-11-23T06:07:36.658Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (158971.012 ms) ====== [2024-11-23T06:07:36.658Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-11-23T06:07:36.658Z] GC before operation: completed in 103.458 ms, heap usage 600.915 MB -> 81.991 MB. [2024-11-23T06:08:04.947Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:08:28.525Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:08:56.782Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:09:20.303Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:09:31.489Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:09:42.670Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:09:58.946Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:10:08.157Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:10:08.157Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:10:08.157Z] The best model improves the baseline by 14.43%. [2024-11-23T06:10:08.157Z] Movies recommended for you: [2024-11-23T06:10:08.157Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:10:08.157Z] There is no way to check that no silent failure occurred. [2024-11-23T06:10:08.157Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (151428.734 ms) ====== [2024-11-23T06:10:08.157Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-11-23T06:10:08.157Z] GC before operation: completed in 94.549 ms, heap usage 690.148 MB -> 81.709 MB. [2024-11-23T06:10:36.391Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:10:59.951Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:11:28.216Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:11:51.795Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:12:05.336Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:12:16.655Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:12:36.473Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:12:47.644Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:12:47.644Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:12:47.644Z] The best model improves the baseline by 14.43%. [2024-11-23T06:12:47.644Z] Movies recommended for you: [2024-11-23T06:12:47.644Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:12:47.644Z] There is no way to check that no silent failure occurred. [2024-11-23T06:12:47.644Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (158962.554 ms) ====== [2024-11-23T06:12:47.644Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-11-23T06:12:47.644Z] GC before operation: completed in 111.628 ms, heap usage 682.153 MB -> 81.911 MB. [2024-11-23T06:13:15.924Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:13:39.483Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:14:07.748Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:14:31.293Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:14:42.473Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:14:51.700Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:15:07.983Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:15:19.153Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:15:19.153Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:15:19.153Z] The best model improves the baseline by 14.43%. [2024-11-23T06:15:19.153Z] Movies recommended for you: [2024-11-23T06:15:19.153Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:15:19.153Z] There is no way to check that no silent failure occurred. [2024-11-23T06:15:19.153Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (151514.763 ms) ====== [2024-11-23T06:15:19.153Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-11-23T06:15:19.153Z] GC before operation: completed in 84.652 ms, heap usage 237.418 MB -> 81.937 MB. [2024-11-23T06:15:47.445Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:16:10.985Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:16:39.256Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:17:05.479Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:17:21.768Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:17:32.949Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:17:49.225Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:17:58.451Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:17:58.451Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:17:58.451Z] The best model improves the baseline by 14.43%. [2024-11-23T06:17:58.809Z] Movies recommended for you: [2024-11-23T06:17:58.809Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:17:58.809Z] There is no way to check that no silent failure occurred. [2024-11-23T06:17:58.809Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (160118.139 ms) ====== [2024-11-23T06:17:58.809Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-11-23T06:17:58.809Z] GC before operation: completed in 96.376 ms, heap usage 1.670 GB -> 82.113 MB. [2024-11-23T06:18:27.098Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:18:50.763Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:19:18.991Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:19:42.504Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:19:51.707Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:20:02.904Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:20:19.180Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:20:30.356Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:20:30.356Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:20:30.356Z] The best model improves the baseline by 14.43%. [2024-11-23T06:20:30.356Z] Movies recommended for you: [2024-11-23T06:20:30.356Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:20:30.356Z] There is no way to check that no silent failure occurred. [2024-11-23T06:20:30.356Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (150675.893 ms) ====== [2024-11-23T06:20:30.356Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-11-23T06:20:30.356Z] GC before operation: completed in 97.521 ms, heap usage 581.893 MB -> 81.914 MB. [2024-11-23T06:20:53.904Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:21:27.792Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:21:56.051Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:22:15.621Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:22:26.794Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:22:37.983Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:22:54.263Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:23:03.489Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:23:04.260Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:23:04.260Z] The best model improves the baseline by 14.43%. [2024-11-23T06:23:04.260Z] Movies recommended for you: [2024-11-23T06:23:04.260Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:23:04.260Z] There is no way to check that no silent failure occurred. [2024-11-23T06:23:04.260Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (154596.289 ms) ====== [2024-11-23T06:23:04.260Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-11-23T06:23:04.260Z] GC before operation: completed in 93.565 ms, heap usage 711.057 MB -> 82.153 MB. [2024-11-23T06:23:32.587Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T06:23:56.126Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T06:24:24.404Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T06:24:44.000Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T06:24:55.179Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T06:25:06.363Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T06:25:22.659Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T06:25:33.853Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T06:25:33.853Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T06:25:33.853Z] The best model improves the baseline by 14.43%. [2024-11-23T06:25:33.853Z] Movies recommended for you: [2024-11-23T06:25:33.853Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T06:25:33.853Z] There is no way to check that no silent failure occurred. [2024-11-23T06:25:33.853Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (148568.005 ms) ====== [2024-11-23T06:25:34.212Z] ----------------------------------- [2024-11-23T06:25:34.212Z] renaissance-movie-lens_0_PASSED [2024-11-23T06:25:34.212Z] ----------------------------------- [2024-11-23T06:25:34.212Z] [2024-11-23T06:25:34.212Z] TEST TEARDOWN: [2024-11-23T06:25:34.212Z] Nothing to be done for teardown. [2024-11-23T06:25:34.212Z] renaissance-movie-lens_0 Finish Time: Sat Nov 23 06:25:34 2024 Epoch Time (ms): 1732343134195