[2023-04-18T23:53:14.598Z] Running test renaissance-movie-lens_0 ... [2023-04-18T23:53:14.598Z] =============================================== [2023-04-18T23:53:14.598Z] renaissance-movie-lens_0 Start Time: Tue Apr 18 19:53:13 2023 Epoch Time (ms): 1681861993588 [2023-04-18T23:53:14.598Z] variation: NoOptions [2023-04-18T23:53:14.598Z] JVM_OPTIONS: [2023-04-18T23:53:14.598Z] { \ [2023-04-18T23:53:14.598Z] echo ""; echo "TEST SETUP:"; \ [2023-04-18T23:53:14.598Z] echo "Nothing to be done for setup."; \ [2023-04-18T23:53:14.598Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_16818612857371/renaissance-movie-lens_0"; \ [2023-04-18T23:53:14.598Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_16818612857371/renaissance-movie-lens_0"; \ [2023-04-18T23:53:14.598Z] echo ""; echo "TESTING:"; \ [2023-04-18T23:53:14.598Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/openjdkbinary/j2sdk-image/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 "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_16818612857371/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2023-04-18T23:53:14.598Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_16818612857371/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2023-04-18T23:53:14.598Z] echo ""; echo "TEST TEARDOWN:"; \ [2023-04-18T23:53:14.598Z] echo "Nothing to be done for teardown."; \ [2023-04-18T23:53:14.598Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_16818612857371/TestTargetResult"; [2023-04-18T23:53:14.598Z] [2023-04-18T23:53:14.598Z] TEST SETUP: [2023-04-18T23:53:14.598Z] Nothing to be done for setup. [2023-04-18T23:53:14.598Z] [2023-04-18T23:53:14.598Z] TESTING: [2023-04-18T23:53:15.215Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2023-04-18T23:53:16.500Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads. [2023-04-18T23:53:18.567Z] Got 100004 ratings from 671 users on 9066 movies. [2023-04-18T23:53:18.567Z] Training: 60056, validation: 20285, test: 19854 [2023-04-18T23:53:18.567Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2023-04-18T23:53:18.567Z] GC before operation: completed in 54.694 ms, heap usage 62.909 MB -> 37.176 MB. [2023-04-18T23:53:23.369Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:53:25.470Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:53:28.271Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:53:29.596Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:53:31.010Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:53:32.339Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:53:33.614Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:53:34.245Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:53:34.924Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:53:34.924Z] The best model improves the baseline by 14.34%. [2023-04-18T23:53:34.924Z] Movies recommended for you: [2023-04-18T23:53:34.924Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:53:34.924Z] There is no way to check that no silent failure occurred. [2023-04-18T23:53:34.924Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (16132.758 ms) ====== [2023-04-18T23:53:34.924Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2023-04-18T23:53:34.924Z] GC before operation: completed in 131.006 ms, heap usage 75.916 MB -> 52.790 MB. [2023-04-18T23:53:36.674Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:53:38.747Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:53:40.095Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:53:42.076Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:53:42.747Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:53:44.037Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:53:44.765Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:53:46.034Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:53:46.035Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:53:46.035Z] The best model improves the baseline by 14.34%. [2023-04-18T23:53:46.035Z] Movies recommended for you: [2023-04-18T23:53:46.035Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:53:46.035Z] There is no way to check that no silent failure occurred. [2023-04-18T23:53:46.035Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (10998.766 ms) ====== [2023-04-18T23:53:46.035Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2023-04-18T23:53:46.035Z] GC before operation: completed in 58.324 ms, heap usage 75.831 MB -> 52.552 MB. [2023-04-18T23:53:48.114Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:53:49.416Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:53:50.874Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:53:52.169Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:53:53.611Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:53:54.219Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:53:55.590Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:53:56.208Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:53:56.208Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:53:56.208Z] The best model improves the baseline by 14.34%. [2023-04-18T23:53:56.813Z] Movies recommended for you: [2023-04-18T23:53:56.813Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:53:56.813Z] There is no way to check that no silent failure occurred. [2023-04-18T23:53:56.813Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (10493.817 ms) ====== [2023-04-18T23:53:56.813Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2023-04-18T23:53:56.813Z] GC before operation: completed in 66.524 ms, heap usage 92.510 MB -> 51.031 MB. [2023-04-18T23:53:58.148Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:53:59.460Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:54:01.460Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:54:02.719Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:54:03.447Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:54:04.701Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:54:05.960Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:54:06.555Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:54:06.556Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:54:06.556Z] The best model improves the baseline by 14.34%. [2023-04-18T23:54:06.556Z] Movies recommended for you: [2023-04-18T23:54:06.556Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:54:06.556Z] There is no way to check that no silent failure occurred. [2023-04-18T23:54:06.556Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (10186.716 ms) ====== [2023-04-18T23:54:06.556Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2023-04-18T23:54:06.556Z] GC before operation: completed in 75.467 ms, heap usage 64.937 MB -> 53.128 MB. [2023-04-18T23:54:08.530Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:54:09.845Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:54:11.928Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:54:14.144Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:54:14.746Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:54:15.677Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:54:16.923Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:54:17.592Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:54:17.592Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:54:17.592Z] The best model improves the baseline by 14.34%. [2023-04-18T23:54:18.249Z] Movies recommended for you: [2023-04-18T23:54:18.249Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:54:18.249Z] There is no way to check that no silent failure occurred. [2023-04-18T23:54:18.249Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (11089.477 ms) ====== [2023-04-18T23:54:18.249Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2023-04-18T23:54:18.249Z] GC before operation: completed in 62.384 ms, heap usage 340.506 MB -> 53.414 MB. [2023-04-18T23:54:19.574Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:54:20.901Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:54:23.039Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:54:24.338Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:54:24.931Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:54:26.270Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:54:26.874Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:54:27.495Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:54:28.104Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:54:28.104Z] The best model improves the baseline by 14.34%. [2023-04-18T23:54:28.104Z] Movies recommended for you: [2023-04-18T23:54:28.104Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:54:28.104Z] There is no way to check that no silent failure occurred. [2023-04-18T23:54:28.104Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (10049.034 ms) ====== [2023-04-18T23:54:28.104Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2023-04-18T23:54:28.104Z] GC before operation: completed in 62.238 ms, heap usage 163.984 MB -> 49.950 MB. [2023-04-18T23:54:29.368Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:54:31.379Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:54:32.874Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:54:34.312Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:54:34.963Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:54:36.223Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:54:36.904Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:54:38.165Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:54:38.165Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:54:38.165Z] The best model improves the baseline by 14.34%. [2023-04-18T23:54:38.165Z] Movies recommended for you: [2023-04-18T23:54:38.165Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:54:38.165Z] There is no way to check that no silent failure occurred. [2023-04-18T23:54:38.165Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (10041.985 ms) ====== [2023-04-18T23:54:38.165Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2023-04-18T23:54:38.165Z] GC before operation: completed in 68.998 ms, heap usage 137.910 MB -> 50.155 MB. [2023-04-18T23:54:39.420Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:54:40.875Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:54:42.200Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:54:44.190Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:54:44.845Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:54:46.125Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:54:46.739Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:54:48.014Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:54:48.014Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:54:48.014Z] The best model improves the baseline by 14.34%. [2023-04-18T23:54:48.014Z] Movies recommended for you: [2023-04-18T23:54:48.014Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:54:48.014Z] There is no way to check that no silent failure occurred. [2023-04-18T23:54:48.014Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (9775.627 ms) ====== [2023-04-18T23:54:48.014Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2023-04-18T23:54:48.014Z] GC before operation: completed in 75.760 ms, heap usage 256.281 MB -> 50.485 MB. [2023-04-18T23:54:49.754Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:54:51.040Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:54:52.341Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:54:54.395Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:54:55.000Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:54:55.604Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:54:56.862Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:54:57.470Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:54:57.470Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:54:57.470Z] The best model improves the baseline by 14.34%. [2023-04-18T23:54:58.073Z] Movies recommended for you: [2023-04-18T23:54:58.073Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:54:58.073Z] There is no way to check that no silent failure occurred. [2023-04-18T23:54:58.073Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (9733.601 ms) ====== [2023-04-18T23:54:58.073Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2023-04-18T23:54:58.073Z] GC before operation: completed in 61.783 ms, heap usage 93.780 MB -> 50.259 MB. [2023-04-18T23:54:59.334Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:55:00.618Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:55:01.890Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:55:03.166Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:55:04.408Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:55:05.012Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:55:06.259Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:55:06.894Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:55:06.894Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:55:06.894Z] The best model improves the baseline by 14.34%. [2023-04-18T23:55:07.504Z] Movies recommended for you: [2023-04-18T23:55:07.504Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:55:07.504Z] There is no way to check that no silent failure occurred. [2023-04-18T23:55:07.504Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (9377.620 ms) ====== [2023-04-18T23:55:07.504Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2023-04-18T23:55:07.504Z] GC before operation: completed in 79.993 ms, heap usage 256.846 MB -> 50.441 MB. [2023-04-18T23:55:08.772Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:55:10.049Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:55:12.085Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:55:13.359Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:55:14.634Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:55:15.247Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:55:16.532Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:55:17.162Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:55:17.162Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:55:17.162Z] The best model improves the baseline by 14.34%. [2023-04-18T23:55:17.162Z] Movies recommended for you: [2023-04-18T23:55:17.162Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:55:17.162Z] There is no way to check that no silent failure occurred. [2023-04-18T23:55:17.162Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (9986.896 ms) ====== [2023-04-18T23:55:17.162Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2023-04-18T23:55:17.163Z] GC before operation: completed in 68.415 ms, heap usage 60.517 MB -> 50.315 MB. [2023-04-18T23:55:19.134Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:55:20.405Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:55:21.676Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:55:24.004Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:55:24.608Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:55:25.254Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:55:25.855Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:55:27.125Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:55:27.125Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:55:27.125Z] The best model improves the baseline by 14.34%. [2023-04-18T23:55:27.125Z] Movies recommended for you: [2023-04-18T23:55:27.125Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:55:27.125Z] There is no way to check that no silent failure occurred. [2023-04-18T23:55:27.125Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (9741.461 ms) ====== [2023-04-18T23:55:27.125Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2023-04-18T23:55:27.125Z] GC before operation: completed in 80.366 ms, heap usage 245.270 MB -> 50.367 MB. [2023-04-18T23:55:28.394Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:55:30.404Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:55:31.675Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:55:32.934Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:55:34.204Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:55:34.816Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:55:36.111Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:55:36.721Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:55:36.721Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:55:36.721Z] The best model improves the baseline by 14.34%. [2023-04-18T23:55:36.721Z] Movies recommended for you: [2023-04-18T23:55:36.721Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:55:36.721Z] There is no way to check that no silent failure occurred. [2023-04-18T23:55:36.721Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (9753.205 ms) ====== [2023-04-18T23:55:36.721Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2023-04-18T23:55:37.320Z] GC before operation: completed in 60.358 ms, heap usage 83.499 MB -> 53.997 MB. [2023-04-18T23:55:38.584Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:55:39.844Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:55:41.816Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:55:42.418Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:55:43.712Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:55:44.318Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:55:44.929Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:55:46.194Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:55:46.194Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:55:46.194Z] The best model improves the baseline by 14.34%. [2023-04-18T23:55:46.194Z] Movies recommended for you: [2023-04-18T23:55:46.194Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:55:46.194Z] There is no way to check that no silent failure occurred. [2023-04-18T23:55:46.194Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (9132.161 ms) ====== [2023-04-18T23:55:46.194Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2023-04-18T23:55:46.194Z] GC before operation: completed in 60.979 ms, heap usage 318.843 MB -> 50.399 MB. [2023-04-18T23:55:47.475Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:55:48.764Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:55:50.033Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:55:51.303Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:55:52.593Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:55:53.204Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:55:54.460Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:55:55.107Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:55:55.728Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:55:55.728Z] The best model improves the baseline by 14.34%. [2023-04-18T23:55:55.728Z] Movies recommended for you: [2023-04-18T23:55:55.728Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:55:55.728Z] There is no way to check that no silent failure occurred. [2023-04-18T23:55:55.728Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (9378.428 ms) ====== [2023-04-18T23:55:55.728Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2023-04-18T23:55:55.728Z] GC before operation: completed in 67.719 ms, heap usage 180.933 MB -> 50.387 MB. [2023-04-18T23:55:57.003Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:56:00.298Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:56:01.552Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:56:02.835Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:56:03.434Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:56:04.028Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:56:05.294Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:56:06.565Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:56:06.565Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:56:06.565Z] The best model improves the baseline by 14.34%. [2023-04-18T23:56:06.565Z] Movies recommended for you: [2023-04-18T23:56:06.565Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:56:06.565Z] There is no way to check that no silent failure occurred. [2023-04-18T23:56:06.565Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (10876.087 ms) ====== [2023-04-18T23:56:06.565Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2023-04-18T23:56:06.565Z] GC before operation: completed in 61.184 ms, heap usage 118.656 MB -> 50.424 MB. [2023-04-18T23:56:07.824Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:56:09.085Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:56:11.064Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:56:11.693Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:56:12.956Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:56:13.565Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:56:14.180Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:56:15.455Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:56:15.455Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:56:15.455Z] The best model improves the baseline by 14.34%. [2023-04-18T23:56:15.455Z] Movies recommended for you: [2023-04-18T23:56:15.455Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:56:15.455Z] There is no way to check that no silent failure occurred. [2023-04-18T23:56:15.455Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (8861.927 ms) ====== [2023-04-18T23:56:15.455Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2023-04-18T23:56:15.455Z] GC before operation: completed in 65.846 ms, heap usage 75.992 MB -> 50.167 MB. [2023-04-18T23:56:16.794Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:56:18.073Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:56:20.096Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:56:21.362Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:56:22.624Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:56:23.257Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:56:23.862Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:56:25.135Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:56:25.135Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:56:25.135Z] The best model improves the baseline by 14.34%. [2023-04-18T23:56:25.135Z] Movies recommended for you: [2023-04-18T23:56:25.135Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:56:25.135Z] There is no way to check that no silent failure occurred. [2023-04-18T23:56:25.135Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (9563.787 ms) ====== [2023-04-18T23:56:25.135Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2023-04-18T23:56:25.135Z] GC before operation: completed in 59.609 ms, heap usage 63.428 MB -> 53.410 MB. [2023-04-18T23:56:26.400Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:56:27.756Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:56:29.032Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:56:30.295Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:56:31.554Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:56:32.184Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:56:33.266Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:56:33.889Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:56:33.890Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:56:33.890Z] The best model improves the baseline by 14.34%. [2023-04-18T23:56:33.890Z] Movies recommended for you: [2023-04-18T23:56:33.890Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:56:33.890Z] There is no way to check that no silent failure occurred. [2023-04-18T23:56:33.890Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (9012.988 ms) ====== [2023-04-18T23:56:33.890Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2023-04-18T23:56:34.487Z] GC before operation: completed in 70.438 ms, heap usage 204.141 MB -> 50.567 MB. [2023-04-18T23:56:35.747Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2023-04-18T23:56:37.004Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2023-04-18T23:56:38.274Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2023-04-18T23:56:39.534Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2023-04-18T23:56:40.806Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2023-04-18T23:56:41.411Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2023-04-18T23:56:42.019Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2023-04-18T23:56:43.301Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2023-04-18T23:56:43.301Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2023-04-18T23:56:43.301Z] The best model improves the baseline by 14.34%. [2023-04-18T23:56:43.301Z] Movies recommended for you: [2023-04-18T23:56:43.301Z] WARNING: This benchmark provides no result that can be validated. [2023-04-18T23:56:43.301Z] There is no way to check that no silent failure occurred. [2023-04-18T23:56:43.301Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (9023.767 ms) ====== [2023-04-18T23:56:43.301Z] ----------------------------------- [2023-04-18T23:56:43.301Z] renaissance-movie-lens_0_PASSED [2023-04-18T23:56:43.301Z] ----------------------------------- [2023-04-18T23:56:43.301Z] [2023-04-18T23:56:43.301Z] TEST TEARDOWN: [2023-04-18T23:56:43.301Z] Nothing to be done for teardown. [2023-04-18T23:56:43.301Z] renaissance-movie-lens_0 Finish Time: Tue Apr 18 19:56:43 2023 Epoch Time (ms): 1681862203199