[2024-06-27T10:19:59.491Z] Running test renaissance-movie-lens_0 ... [2024-06-27T10:19:59.491Z] =============================================== [2024-06-27T10:19:59.815Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 10:19:59 2024 Epoch Time (ms): 1719483599530 [2024-06-27T10:20:00.141Z] variation: NoOptions [2024-06-27T10:20:00.141Z] JVM_OPTIONS: [2024-06-27T10:20:00.141Z] { \ [2024-06-27T10:20:00.141Z] echo ""; echo "TEST SETUP:"; \ [2024-06-27T10:20:00.141Z] echo "Nothing to be done for setup."; \ [2024-06-27T10:20:00.141Z] mkdir -p "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194825947502\\renaissance-movie-lens_0"; \ [2024-06-27T10:20:00.141Z] cd "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194825947502\\renaissance-movie-lens_0"; \ [2024-06-27T10:20:00.141Z] echo ""; echo "TESTING:"; \ [2024-06-27T10:20:00.141Z] "c:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/jdkbinary/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 "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194825947502\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2024-06-27T10:20:00.141Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194825947502\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-06-27T10:20:00.141Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-06-27T10:20:00.141Z] echo "Nothing to be done for teardown."; \ [2024-06-27T10:20:00.141Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17194825947502\\TestTargetResult"; [2024-06-27T10:20:00.467Z] [2024-06-27T10:20:00.467Z] TEST SETUP: [2024-06-27T10:20:00.467Z] Nothing to be done for setup. [2024-06-27T10:20:00.467Z] [2024-06-27T10:20:00.467Z] TESTING: [2024-06-27T10:20:11.135Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-06-27T10:20:11.846Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-06-27T10:20:14.812Z] Got 100004 ratings from 671 users on 9066 movies. [2024-06-27T10:20:15.143Z] Training: 60056, validation: 20285, test: 19854 [2024-06-27T10:20:15.143Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-06-27T10:20:15.471Z] GC before operation: completed in 49.573 ms, heap usage 45.974 MB -> 37.420 MB. [2024-06-27T10:20:26.223Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:20:33.438Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:20:42.288Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:20:48.118Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:20:51.852Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:20:55.576Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:21:00.266Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:21:03.244Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:21:03.970Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:21:03.970Z] The best model improves the baseline by 14.52%. [2024-06-27T10:21:03.970Z] Movies recommended for you: [2024-06-27T10:21:03.970Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:21:03.970Z] There is no way to check that no silent failure occurred. [2024-06-27T10:21:03.970Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (48750.799 ms) ====== [2024-06-27T10:21:03.970Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-06-27T10:21:04.311Z] GC before operation: completed in 61.787 ms, heap usage 154.389 MB -> 55.764 MB. [2024-06-27T10:21:11.477Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:21:17.343Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:21:24.516Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:21:30.331Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:21:34.047Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:21:37.764Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:21:41.509Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:21:45.229Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:21:45.578Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:21:45.578Z] The best model improves the baseline by 14.52%. [2024-06-27T10:21:45.578Z] Movies recommended for you: [2024-06-27T10:21:45.578Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:21:45.578Z] There is no way to check that no silent failure occurred. [2024-06-27T10:21:45.578Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (41525.915 ms) ====== [2024-06-27T10:21:45.578Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-06-27T10:21:45.907Z] GC before operation: completed in 58.328 ms, heap usage 200.500 MB -> 49.964 MB. [2024-06-27T10:21:51.740Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:21:58.923Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:22:04.750Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:22:10.564Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:22:14.284Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:22:17.992Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:22:21.701Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:22:25.416Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:22:25.746Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:22:25.746Z] The best model improves the baseline by 14.52%. [2024-06-27T10:22:25.746Z] Movies recommended for you: [2024-06-27T10:22:25.746Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:22:25.746Z] There is no way to check that no silent failure occurred. [2024-06-27T10:22:25.746Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (40101.795 ms) ====== [2024-06-27T10:22:25.746Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-06-27T10:22:25.746Z] GC before operation: completed in 60.678 ms, heap usage 115.875 MB -> 50.200 MB. [2024-06-27T10:22:32.917Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:22:38.749Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:22:44.575Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:22:51.761Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:22:54.662Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:22:58.392Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:23:02.118Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:23:05.029Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:23:05.356Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:23:05.684Z] The best model improves the baseline by 14.52%. [2024-06-27T10:23:05.684Z] Movies recommended for you: [2024-06-27T10:23:05.684Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:23:05.684Z] There is no way to check that no silent failure occurred. [2024-06-27T10:23:05.684Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (39755.919 ms) ====== [2024-06-27T10:23:05.684Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-06-27T10:23:05.684Z] GC before operation: completed in 58.644 ms, heap usage 332.154 MB -> 50.773 MB. [2024-06-27T10:23:12.850Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:23:18.745Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:23:25.913Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:23:31.751Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:23:34.668Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:23:38.378Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:23:42.106Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:23:45.831Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:23:45.831Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:23:45.831Z] The best model improves the baseline by 14.52%. [2024-06-27T10:23:46.167Z] Movies recommended for you: [2024-06-27T10:23:46.167Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:23:46.167Z] There is no way to check that no silent failure occurred. [2024-06-27T10:23:46.168Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (40379.064 ms) ====== [2024-06-27T10:23:46.168Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-06-27T10:23:46.168Z] GC before operation: completed in 61.223 ms, heap usage 210.276 MB -> 50.776 MB. [2024-06-27T10:23:51.986Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:23:59.163Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:24:05.010Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:24:10.834Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:24:14.557Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:24:17.473Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:24:21.190Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:24:24.896Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:24:25.226Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:24:25.226Z] The best model improves the baseline by 14.52%. [2024-06-27T10:24:25.226Z] Movies recommended for you: [2024-06-27T10:24:25.226Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:24:25.226Z] There is no way to check that no silent failure occurred. [2024-06-27T10:24:25.226Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (39112.701 ms) ====== [2024-06-27T10:24:25.226Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-06-27T10:24:25.226Z] GC before operation: completed in 60.857 ms, heap usage 127.326 MB -> 50.667 MB. [2024-06-27T10:24:31.047Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:24:38.224Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:24:44.046Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:24:49.861Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:24:53.575Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:24:57.286Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:25:01.001Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:25:03.904Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:25:04.236Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:25:04.597Z] The best model improves the baseline by 14.52%. [2024-06-27T10:25:04.597Z] Movies recommended for you: [2024-06-27T10:25:04.597Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:25:04.597Z] There is no way to check that no silent failure occurred. [2024-06-27T10:25:04.597Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (39190.550 ms) ====== [2024-06-27T10:25:04.597Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-06-27T10:25:04.597Z] GC before operation: completed in 60.683 ms, heap usage 332.904 MB -> 51.052 MB. [2024-06-27T10:25:10.424Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:25:17.606Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:25:23.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:25:29.275Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:25:33.019Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:25:35.912Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:25:39.630Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:25:43.352Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:25:43.352Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:25:43.352Z] The best model improves the baseline by 14.52%. [2024-06-27T10:25:43.682Z] Movies recommended for you: [2024-06-27T10:25:43.682Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:25:43.682Z] There is no way to check that no silent failure occurred. [2024-06-27T10:25:43.682Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (39048.228 ms) ====== [2024-06-27T10:25:43.682Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-06-27T10:25:43.682Z] GC before operation: completed in 60.055 ms, heap usage 226.859 MB -> 51.236 MB. [2024-06-27T10:25:49.508Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:25:56.678Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:26:02.527Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:26:08.346Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:26:12.193Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:26:15.106Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:26:18.821Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:26:22.553Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:26:22.879Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:26:22.879Z] The best model improves the baseline by 14.52%. [2024-06-27T10:26:22.879Z] Movies recommended for you: [2024-06-27T10:26:22.879Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:26:22.879Z] There is no way to check that no silent failure occurred. [2024-06-27T10:26:22.879Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (39212.184 ms) ====== [2024-06-27T10:26:22.880Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-06-27T10:26:22.880Z] GC before operation: completed in 58.114 ms, heap usage 155.355 MB -> 51.014 MB. [2024-06-27T10:26:28.719Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:26:35.922Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:26:41.783Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:26:47.613Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:26:51.337Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:26:54.240Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:26:57.965Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:27:01.686Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:27:02.054Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:27:02.054Z] The best model improves the baseline by 14.52%. [2024-06-27T10:27:02.054Z] Movies recommended for you: [2024-06-27T10:27:02.054Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:27:02.054Z] There is no way to check that no silent failure occurred. [2024-06-27T10:27:02.054Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (39112.458 ms) ====== [2024-06-27T10:27:02.054Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-06-27T10:27:02.054Z] GC before operation: completed in 62.672 ms, heap usage 194.755 MB -> 51.117 MB. [2024-06-27T10:27:07.874Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:27:15.038Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:27:20.848Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:27:26.655Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:27:30.376Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:27:34.093Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:27:37.809Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:27:41.532Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:27:41.532Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:27:41.532Z] The best model improves the baseline by 14.52%. [2024-06-27T10:27:41.532Z] Movies recommended for you: [2024-06-27T10:27:41.532Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:27:41.532Z] There is no way to check that no silent failure occurred. [2024-06-27T10:27:41.532Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (39451.732 ms) ====== [2024-06-27T10:27:41.532Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-06-27T10:27:41.532Z] GC before operation: completed in 61.110 ms, heap usage 70.442 MB -> 50.888 MB. [2024-06-27T10:27:48.700Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:27:54.527Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:28:00.349Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:28:06.220Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:28:09.934Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:28:13.665Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:28:17.423Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:28:20.337Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:28:21.052Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:28:21.052Z] The best model improves the baseline by 14.52%. [2024-06-27T10:28:21.052Z] Movies recommended for you: [2024-06-27T10:28:21.052Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:28:21.052Z] There is no way to check that no silent failure occurred. [2024-06-27T10:28:21.052Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (39445.874 ms) ====== [2024-06-27T10:28:21.052Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-06-27T10:28:21.052Z] GC before operation: completed in 59.481 ms, heap usage 126.388 MB -> 50.980 MB. [2024-06-27T10:28:28.220Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:28:34.051Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:28:39.918Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:28:45.744Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:28:49.479Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:28:53.200Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:28:56.916Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:28:59.840Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:29:00.171Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:29:00.512Z] The best model improves the baseline by 14.52%. [2024-06-27T10:29:00.512Z] Movies recommended for you: [2024-06-27T10:29:00.512Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:29:00.512Z] There is no way to check that no silent failure occurred. [2024-06-27T10:29:00.512Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (39317.221 ms) ====== [2024-06-27T10:29:00.512Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-06-27T10:29:00.512Z] GC before operation: completed in 59.409 ms, heap usage 326.090 MB -> 51.399 MB. [2024-06-27T10:29:06.342Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:29:13.503Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:29:19.351Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:29:25.166Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:29:28.879Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:29:31.775Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:29:35.493Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:29:39.200Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:29:39.200Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:29:39.200Z] The best model improves the baseline by 14.52%. [2024-06-27T10:29:39.531Z] Movies recommended for you: [2024-06-27T10:29:39.531Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:29:39.531Z] There is no way to check that no silent failure occurred. [2024-06-27T10:29:39.531Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (38946.526 ms) ====== [2024-06-27T10:29:39.531Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-06-27T10:29:39.531Z] GC before operation: completed in 60.693 ms, heap usage 191.341 MB -> 51.043 MB. [2024-06-27T10:29:45.345Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:29:52.500Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:29:58.314Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:30:04.113Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:30:07.822Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:30:10.731Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:30:15.418Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:30:18.325Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:30:18.657Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:30:18.657Z] The best model improves the baseline by 14.52%. [2024-06-27T10:30:18.657Z] Movies recommended for you: [2024-06-27T10:30:18.657Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:30:18.657Z] There is no way to check that no silent failure occurred. [2024-06-27T10:30:18.657Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (39143.478 ms) ====== [2024-06-27T10:30:18.657Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-06-27T10:30:18.657Z] GC before operation: completed in 60.552 ms, heap usage 330.855 MB -> 51.269 MB. [2024-06-27T10:30:25.821Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:30:31.664Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:30:37.500Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:30:43.363Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:30:47.073Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:30:50.782Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:30:54.502Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:30:57.422Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:30:57.757Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:30:57.757Z] The best model improves the baseline by 14.52%. [2024-06-27T10:30:58.103Z] Movies recommended for you: [2024-06-27T10:30:58.103Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:30:58.103Z] There is no way to check that no silent failure occurred. [2024-06-27T10:30:58.103Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (39272.253 ms) ====== [2024-06-27T10:30:58.103Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-06-27T10:30:58.103Z] GC before operation: completed in 63.578 ms, heap usage 68.655 MB -> 51.154 MB. [2024-06-27T10:31:03.903Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:31:11.071Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:31:16.922Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:31:22.741Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:31:26.457Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:31:30.179Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:31:33.901Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:31:36.803Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:31:37.132Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:31:37.132Z] The best model improves the baseline by 14.52%. [2024-06-27T10:31:37.463Z] Movies recommended for you: [2024-06-27T10:31:37.463Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:31:37.463Z] There is no way to check that no silent failure occurred. [2024-06-27T10:31:37.463Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (39300.582 ms) ====== [2024-06-27T10:31:37.463Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-06-27T10:31:37.463Z] GC before operation: completed in 61.813 ms, heap usage 130.193 MB -> 51.014 MB. [2024-06-27T10:31:43.295Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:31:50.481Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:31:56.316Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:32:02.169Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:32:05.892Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:32:08.787Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:32:12.499Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:32:16.217Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:32:16.217Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:32:16.217Z] The best model improves the baseline by 14.52%. [2024-06-27T10:32:16.563Z] Movies recommended for you: [2024-06-27T10:32:16.563Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:32:16.563Z] There is no way to check that no silent failure occurred. [2024-06-27T10:32:16.563Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (38979.333 ms) ====== [2024-06-27T10:32:16.563Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-06-27T10:32:16.563Z] GC before operation: completed in 59.769 ms, heap usage 223.159 MB -> 51.127 MB. [2024-06-27T10:32:22.364Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:32:29.518Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:32:35.368Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:32:41.236Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:32:44.987Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:32:47.904Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:32:52.590Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:32:55.510Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:32:55.839Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:32:55.839Z] The best model improves the baseline by 14.52%. [2024-06-27T10:32:55.839Z] Movies recommended for you: [2024-06-27T10:32:55.839Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:32:55.839Z] There is no way to check that no silent failure occurred. [2024-06-27T10:32:55.839Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (39388.809 ms) ====== [2024-06-27T10:32:55.839Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-06-27T10:32:55.839Z] GC before operation: completed in 60.871 ms, heap usage 70.596 MB -> 51.214 MB. [2024-06-27T10:33:01.660Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-27T10:33:08.832Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-27T10:33:14.642Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-27T10:33:20.486Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-27T10:33:24.209Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-27T10:33:27.103Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-27T10:33:30.814Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-27T10:33:34.527Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-27T10:33:34.527Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-27T10:33:34.527Z] The best model improves the baseline by 14.52%. [2024-06-27T10:33:34.527Z] Movies recommended for you: [2024-06-27T10:33:34.527Z] WARNING: This benchmark provides no result that can be validated. [2024-06-27T10:33:34.527Z] There is no way to check that no silent failure occurred. [2024-06-27T10:33:34.527Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (38647.343 ms) ====== [2024-06-27T10:33:35.223Z] ----------------------------------- [2024-06-27T10:33:35.223Z] renaissance-movie-lens_0_PASSED [2024-06-27T10:33:35.223Z] ----------------------------------- [2024-06-27T10:33:35.539Z] [2024-06-27T10:33:35.539Z] TEST TEARDOWN: [2024-06-27T10:33:35.539Z] Nothing to be done for teardown. [2024-06-27T10:33:35.863Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 10:33:35 2024 Epoch Time (ms): 1719484415551