[2025-03-04T23:16:21.945Z] Running test renaissance-movie-lens_0 ... [2025-03-04T23:16:21.945Z] =============================================== [2025-03-04T23:16:21.945Z] renaissance-movie-lens_0 Start Time: Tue Mar 4 23:16:21 2025 Epoch Time (ms): 1741130181204 [2025-03-04T23:16:21.945Z] variation: NoOptions [2025-03-04T23:16:21.945Z] JVM_OPTIONS: [2025-03-04T23:16:21.945Z] { \ [2025-03-04T23:16:21.945Z] echo ""; echo "TEST SETUP:"; \ [2025-03-04T23:16:21.945Z] echo "Nothing to be done for setup."; \ [2025-03-04T23:16:21.945Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17411280099614/renaissance-movie-lens_0"; \ [2025-03-04T23:16:21.945Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17411280099614/renaissance-movie-lens_0"; \ [2025-03-04T23:16:21.945Z] echo ""; echo "TESTING:"; \ [2025-03-04T23:16:21.945Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/jdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17411280099614/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-03-04T23:16:21.945Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17411280099614/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-03-04T23:16:21.945Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-03-04T23:16:21.945Z] echo "Nothing to be done for teardown."; \ [2025-03-04T23:16:21.945Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17411280099614/TestTargetResult"; [2025-03-04T23:16:21.945Z] [2025-03-04T23:16:21.945Z] TEST SETUP: [2025-03-04T23:16:21.945Z] Nothing to be done for setup. [2025-03-04T23:16:21.945Z] [2025-03-04T23:16:21.945Z] TESTING: [2025-03-04T23:16:26.373Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-03-04T23:16:32.477Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-03-04T23:16:42.293Z] Got 100004 ratings from 671 users on 9066 movies. [2025-03-04T23:16:42.293Z] Training: 60056, validation: 20285, test: 19854 [2025-03-04T23:16:42.293Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-03-04T23:16:42.293Z] GC before operation: completed in 567.794 ms, heap usage 180.877 MB -> 27.272 MB. [2025-03-04T23:17:01.191Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:17:11.086Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:17:22.795Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:17:31.046Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:17:36.617Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:17:41.583Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:17:47.197Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:17:51.718Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:17:52.480Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:17:52.480Z] The best model improves the baseline by 14.52%. [2025-03-04T23:17:53.256Z] Movies recommended for you: [2025-03-04T23:17:53.256Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:17:53.256Z] There is no way to check that no silent failure occurred. [2025-03-04T23:17:53.256Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (70271.188 ms) ====== [2025-03-04T23:17:53.256Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-03-04T23:17:54.026Z] GC before operation: completed in 877.712 ms, heap usage 393.236 MB -> 45.501 MB. [2025-03-04T23:18:00.988Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:18:10.920Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:18:16.591Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:18:24.982Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:18:29.491Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:18:34.420Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:18:40.065Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:18:44.578Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:18:46.183Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:18:46.183Z] The best model improves the baseline by 14.52%. [2025-03-04T23:18:46.183Z] Movies recommended for you: [2025-03-04T23:18:46.183Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:18:46.183Z] There is no way to check that no silent failure occurred. [2025-03-04T23:18:46.183Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (52343.102 ms) ====== [2025-03-04T23:18:46.183Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-03-04T23:18:46.953Z] GC before operation: completed in 653.884 ms, heap usage 324.625 MB -> 42.414 MB. [2025-03-04T23:18:55.520Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:19:04.224Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:19:11.447Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:19:18.649Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:19:22.261Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:19:26.999Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:19:32.271Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:19:35.911Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:19:36.729Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:19:36.729Z] The best model improves the baseline by 14.52%. [2025-03-04T23:19:36.729Z] Movies recommended for you: [2025-03-04T23:19:36.729Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:19:36.729Z] There is no way to check that no silent failure occurred. [2025-03-04T23:19:36.729Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (49843.557 ms) ====== [2025-03-04T23:19:36.729Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-03-04T23:19:37.549Z] GC before operation: completed in 459.333 ms, heap usage 500.998 MB -> 46.471 MB. [2025-03-04T23:19:44.795Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:19:53.502Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:20:02.225Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:20:10.927Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:20:15.635Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:20:20.501Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:20:25.225Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:20:30.507Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:20:30.507Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:20:30.507Z] The best model improves the baseline by 14.52%. [2025-03-04T23:20:31.323Z] Movies recommended for you: [2025-03-04T23:20:31.324Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:20:31.324Z] There is no way to check that no silent failure occurred. [2025-03-04T23:20:31.324Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (53836.210 ms) ====== [2025-03-04T23:20:31.324Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-03-04T23:20:31.324Z] GC before operation: completed in 513.715 ms, heap usage 423.291 MB -> 46.785 MB. [2025-03-04T23:20:38.534Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:20:47.230Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:20:54.464Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:21:03.165Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:21:07.867Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:21:12.591Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:21:17.296Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:21:22.063Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:21:22.063Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:21:22.063Z] The best model improves the baseline by 14.52%. [2025-03-04T23:21:22.879Z] Movies recommended for you: [2025-03-04T23:21:22.879Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:21:22.879Z] There is no way to check that no silent failure occurred. [2025-03-04T23:21:22.879Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (51170.699 ms) ====== [2025-03-04T23:21:22.879Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-03-04T23:21:22.880Z] GC before operation: completed in 388.034 ms, heap usage 436.332 MB -> 47.069 MB. [2025-03-04T23:21:30.684Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:21:37.907Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:21:45.175Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:21:52.414Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:21:57.119Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:22:00.750Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:22:05.481Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:22:10.202Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:22:10.202Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:22:10.202Z] The best model improves the baseline by 14.52%. [2025-03-04T23:22:10.202Z] Movies recommended for you: [2025-03-04T23:22:10.202Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:22:10.202Z] There is no way to check that no silent failure occurred. [2025-03-04T23:22:10.202Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (47442.814 ms) ====== [2025-03-04T23:22:10.202Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-03-04T23:22:11.021Z] GC before operation: completed in 491.525 ms, heap usage 420.498 MB -> 46.921 MB. [2025-03-04T23:22:18.365Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:22:28.751Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:22:35.959Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:22:43.189Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:22:46.822Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:22:50.450Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:22:55.200Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:22:59.874Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:22:59.874Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:22:59.874Z] The best model improves the baseline by 14.52%. [2025-03-04T23:23:00.686Z] Movies recommended for you: [2025-03-04T23:23:00.686Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:23:00.686Z] There is no way to check that no silent failure occurred. [2025-03-04T23:23:00.686Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (49311.326 ms) ====== [2025-03-04T23:23:00.686Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-03-04T23:23:00.686Z] GC before operation: completed in 389.405 ms, heap usage 461.696 MB -> 47.168 MB. [2025-03-04T23:23:07.849Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:23:15.046Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:23:23.747Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:23:31.595Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:23:35.207Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:23:39.914Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:23:44.653Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:23:48.297Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:23:49.118Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:23:49.118Z] The best model improves the baseline by 14.52%. [2025-03-04T23:23:49.932Z] Movies recommended for you: [2025-03-04T23:23:49.932Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:23:49.932Z] There is no way to check that no silent failure occurred. [2025-03-04T23:23:49.932Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (48874.755 ms) ====== [2025-03-04T23:23:49.932Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-03-04T23:23:49.932Z] GC before operation: completed in 345.722 ms, heap usage 477.886 MB -> 47.502 MB. [2025-03-04T23:23:57.168Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:24:04.434Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:24:13.106Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:24:20.441Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:24:24.618Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:24:28.248Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:24:32.956Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:24:37.694Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:24:37.694Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:24:37.694Z] The best model improves the baseline by 14.52%. [2025-03-04T23:24:38.524Z] Movies recommended for you: [2025-03-04T23:24:38.524Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:24:38.524Z] There is no way to check that no silent failure occurred. [2025-03-04T23:24:38.524Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (48278.871 ms) ====== [2025-03-04T23:24:38.524Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-03-04T23:24:38.524Z] GC before operation: completed in 384.042 ms, heap usage 490.985 MB -> 47.257 MB. [2025-03-04T23:24:47.194Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:24:54.474Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:25:03.198Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:25:10.676Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:25:14.495Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:25:18.462Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:25:24.038Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:25:27.902Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:25:28.773Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:25:28.773Z] The best model improves the baseline by 14.52%. [2025-03-04T23:25:28.773Z] Movies recommended for you: [2025-03-04T23:25:28.773Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:25:28.773Z] There is no way to check that no silent failure occurred. [2025-03-04T23:25:28.773Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (50562.272 ms) ====== [2025-03-04T23:25:28.773Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-03-04T23:25:29.648Z] GC before operation: completed in 580.755 ms, heap usage 464.738 MB -> 47.400 MB. [2025-03-04T23:25:37.177Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:25:44.734Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:25:52.272Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:25:57.223Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:25:59.966Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:26:05.069Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:26:05.942Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:26:09.732Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:26:09.732Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:26:09.732Z] The best model improves the baseline by 14.52%. [2025-03-04T23:26:09.732Z] Movies recommended for you: [2025-03-04T23:26:09.732Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:26:09.732Z] There is no way to check that no silent failure occurred. [2025-03-04T23:26:09.732Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (40251.345 ms) ====== [2025-03-04T23:26:09.732Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-03-04T23:26:10.604Z] GC before operation: completed in 261.870 ms, heap usage 456.871 MB -> 47.065 MB. [2025-03-04T23:26:16.788Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:26:26.375Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:26:36.539Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:26:42.582Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:26:48.510Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:26:50.270Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:26:56.995Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:26:58.780Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:26:59.647Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:26:59.647Z] The best model improves the baseline by 14.52%. [2025-03-04T23:26:59.647Z] Movies recommended for you: [2025-03-04T23:26:59.647Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:26:59.647Z] There is no way to check that no silent failure occurred. [2025-03-04T23:26:59.647Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (49742.552 ms) ====== [2025-03-04T23:26:59.647Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-03-04T23:27:00.510Z] GC before operation: completed in 362.769 ms, heap usage 486.697 MB -> 47.381 MB. [2025-03-04T23:27:08.039Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:27:15.597Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:27:23.075Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:27:30.379Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:27:35.291Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:27:40.063Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:27:44.858Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:27:50.830Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:27:50.830Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:27:50.830Z] The best model improves the baseline by 14.52%. [2025-03-04T23:27:50.830Z] Movies recommended for you: [2025-03-04T23:27:50.830Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:27:50.831Z] There is no way to check that no silent failure occurred. [2025-03-04T23:27:50.831Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (50835.639 ms) ====== [2025-03-04T23:27:50.831Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-03-04T23:27:51.661Z] GC before operation: completed in 511.172 ms, heap usage 471.098 MB -> 47.518 MB. [2025-03-04T23:28:00.472Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:28:07.763Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:28:16.551Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:28:22.625Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:28:28.655Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:28:32.372Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:28:36.652Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:28:41.458Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:28:41.458Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:28:41.458Z] The best model improves the baseline by 14.52%. [2025-03-04T23:28:42.285Z] Movies recommended for you: [2025-03-04T23:28:42.285Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:28:42.285Z] There is no way to check that no silent failure occurred. [2025-03-04T23:28:42.285Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (50195.785 ms) ====== [2025-03-04T23:28:42.285Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-03-04T23:28:42.285Z] GC before operation: completed in 347.372 ms, heap usage 466.951 MB -> 47.199 MB. [2025-03-04T23:28:49.615Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:28:55.595Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:29:04.418Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:29:10.403Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:29:17.007Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:29:19.739Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:29:24.530Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:29:29.285Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:29:30.108Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:29:30.108Z] The best model improves the baseline by 14.52%. [2025-03-04T23:29:30.108Z] Movies recommended for you: [2025-03-04T23:29:30.108Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:29:30.108Z] There is no way to check that no silent failure occurred. [2025-03-04T23:29:30.108Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (47883.422 ms) ====== [2025-03-04T23:29:30.108Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-03-04T23:29:30.108Z] GC before operation: completed in 372.360 ms, heap usage 466.040 MB -> 47.425 MB. [2025-03-04T23:29:37.977Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:29:45.278Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:29:54.075Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:30:01.392Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:30:06.196Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:30:09.870Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:30:14.687Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:30:19.608Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:30:19.608Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:30:19.608Z] The best model improves the baseline by 14.52%. [2025-03-04T23:30:20.441Z] Movies recommended for you: [2025-03-04T23:30:20.441Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:30:20.441Z] There is no way to check that no silent failure occurred. [2025-03-04T23:30:20.441Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (49555.638 ms) ====== [2025-03-04T23:30:20.441Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-03-04T23:30:20.441Z] GC before operation: completed in 478.309 ms, heap usage 503.094 MB -> 47.516 MB. [2025-03-04T23:30:29.239Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:30:35.781Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:30:43.118Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:30:50.442Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:30:55.236Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:31:00.032Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:31:04.495Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:31:09.276Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:31:09.276Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:31:10.114Z] The best model improves the baseline by 14.52%. [2025-03-04T23:31:10.114Z] Movies recommended for you: [2025-03-04T23:31:10.114Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:31:10.114Z] There is no way to check that no silent failure occurred. [2025-03-04T23:31:10.114Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (49401.461 ms) ====== [2025-03-04T23:31:10.114Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-03-04T23:31:10.114Z] GC before operation: completed in 467.492 ms, heap usage 414.543 MB -> 47.265 MB. [2025-03-04T23:31:18.935Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:31:27.762Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:31:35.093Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:31:42.927Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:31:47.708Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:31:51.376Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:31:55.034Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:31:58.690Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:31:59.518Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:31:59.518Z] The best model improves the baseline by 14.52%. [2025-03-04T23:31:59.518Z] Movies recommended for you: [2025-03-04T23:31:59.518Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:31:59.518Z] There is no way to check that no silent failure occurred. [2025-03-04T23:31:59.518Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (49043.823 ms) ====== [2025-03-04T23:31:59.518Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-03-04T23:31:59.518Z] GC before operation: completed in 375.882 ms, heap usage 441.479 MB -> 47.302 MB. [2025-03-04T23:32:08.345Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:32:17.145Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:32:25.968Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:32:31.704Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:32:36.814Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:32:41.376Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:32:45.961Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:32:51.747Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:32:51.747Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:32:51.747Z] The best model improves the baseline by 14.52%. [2025-03-04T23:32:51.747Z] Movies recommended for you: [2025-03-04T23:32:51.747Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:32:51.747Z] There is no way to check that no silent failure occurred. [2025-03-04T23:32:51.747Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (52180.205 ms) ====== [2025-03-04T23:32:51.747Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-03-04T23:32:52.547Z] GC before operation: completed in 298.181 ms, heap usage 456.759 MB -> 47.536 MB. [2025-03-04T23:33:00.992Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T23:33:09.491Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T23:33:18.057Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T23:33:26.586Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T23:33:32.347Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T23:33:36.389Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T23:33:42.144Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T23:33:46.715Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T23:33:47.506Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T23:33:47.506Z] The best model improves the baseline by 14.52%. [2025-03-04T23:33:47.506Z] Movies recommended for you: [2025-03-04T23:33:47.506Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T23:33:47.506Z] There is no way to check that no silent failure occurred. [2025-03-04T23:33:47.506Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (55566.454 ms) ====== [2025-03-04T23:33:49.135Z] ----------------------------------- [2025-03-04T23:33:49.135Z] renaissance-movie-lens_0_PASSED [2025-03-04T23:33:49.135Z] ----------------------------------- [2025-03-04T23:33:49.135Z] [2025-03-04T23:33:49.135Z] TEST TEARDOWN: [2025-03-04T23:33:49.135Z] Nothing to be done for teardown. [2025-03-04T23:33:49.929Z] renaissance-movie-lens_0 Finish Time: Tue Mar 4 23:33:49 2025 Epoch Time (ms): 1741131229077