renaissance-movie-lens_0
[2025-02-06T11:07:50.130Z] Running test renaissance-movie-lens_0 ...
[2025-02-06T11:07:50.130Z] ===============================================
[2025-02-06T11:07:50.130Z] renaissance-movie-lens_0 Start Time: Thu Feb 6 11:07:49 2025 Epoch Time (ms): 1738840069665
[2025-02-06T11:07:50.130Z] variation: NoOptions
[2025-02-06T11:07:50.130Z] JVM_OPTIONS:
[2025-02-06T11:07:50.130Z] { \
[2025-02-06T11:07:50.130Z] echo ""; echo "TEST SETUP:"; \
[2025-02-06T11:07:50.130Z] echo "Nothing to be done for setup."; \
[2025-02-06T11:07:50.130Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17388377092746/renaissance-movie-lens_0"; \
[2025-02-06T11:07:50.130Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17388377092746/renaissance-movie-lens_0"; \
[2025-02-06T11:07:50.130Z] echo ""; echo "TESTING:"; \
[2025-02-06T11:07:50.130Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/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 "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17388377092746/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-06T11:07:50.130Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17388377092746/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-06T11:07:50.130Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-06T11:07:50.130Z] echo "Nothing to be done for teardown."; \
[2025-02-06T11:07:50.131Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17388377092746/TestTargetResult";
[2025-02-06T11:07:50.131Z]
[2025-02-06T11:07:50.131Z] TEST SETUP:
[2025-02-06T11:07:50.131Z] Nothing to be done for setup.
[2025-02-06T11:07:50.131Z]
[2025-02-06T11:07:50.131Z] TESTING:
[2025-02-06T11:07:55.892Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-06T11:08:01.819Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-06T11:08:13.857Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-06T11:08:15.506Z] Training: 60056, validation: 20285, test: 19854
[2025-02-06T11:08:15.506Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-06T11:08:15.506Z] GC before operation: completed in 281.797 ms, heap usage 80.002 MB -> 36.981 MB.
[2025-02-06T11:08:38.158Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:08:54.731Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:09:11.182Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:09:27.685Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:09:33.473Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:09:39.222Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:09:46.721Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:09:52.804Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:09:54.479Z] 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.
[2025-02-06T11:09:54.479Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:09:55.278Z] Movies recommended for you:
[2025-02-06T11:09:55.278Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:09:55.278Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:09:55.278Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (99200.699 ms) ======
[2025-02-06T11:09:55.278Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-06T11:09:55.278Z] GC before operation: completed in 312.315 ms, heap usage 332.674 MB -> 52.439 MB.
[2025-02-06T11:10:03.730Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:10:15.566Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:10:23.930Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:10:32.221Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:10:37.895Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:10:42.385Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:10:49.267Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:10:52.676Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:10:55.000Z] 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.
[2025-02-06T11:10:55.000Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:10:55.770Z] Movies recommended for you:
[2025-02-06T11:10:55.770Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:10:55.770Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:10:55.770Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (60369.696 ms) ======
[2025-02-06T11:10:55.770Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-06T11:10:55.770Z] GC before operation: completed in 267.052 ms, heap usage 132.108 MB -> 49.448 MB.
[2025-02-06T11:11:05.715Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:11:12.639Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:11:21.474Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:11:28.395Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:11:35.273Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:11:38.696Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:11:43.285Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:11:47.729Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:11:48.491Z] 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.
[2025-02-06T11:11:49.255Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:11:49.255Z] Movies recommended for you:
[2025-02-06T11:11:49.255Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:11:49.255Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:11:49.255Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (53310.402 ms) ======
[2025-02-06T11:11:49.255Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-06T11:11:49.255Z] GC before operation: completed in 223.537 ms, heap usage 249.222 MB -> 50.329 MB.
[2025-02-06T11:11:57.515Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:12:04.351Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:12:11.270Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:12:18.862Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:12:23.327Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:12:28.882Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:12:33.321Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:12:37.765Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:12:37.765Z] 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.
[2025-02-06T11:12:37.765Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:12:38.530Z] Movies recommended for you:
[2025-02-06T11:12:38.530Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:12:38.530Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:12:38.530Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (49077.230 ms) ======
[2025-02-06T11:12:38.530Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-06T11:12:38.530Z] GC before operation: completed in 216.215 ms, heap usage 131.800 MB -> 50.490 MB.
[2025-02-06T11:12:46.890Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:12:55.308Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:13:02.242Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:13:09.759Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:13:14.265Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:13:18.763Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:13:23.252Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:13:26.713Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:13:28.338Z] 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.
[2025-02-06T11:13:28.338Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:13:29.113Z] Movies recommended for you:
[2025-02-06T11:13:29.113Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:13:29.113Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:13:29.113Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (50194.528 ms) ======
[2025-02-06T11:13:29.113Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-06T11:13:29.113Z] GC before operation: completed in 349.734 ms, heap usage 289.118 MB -> 50.644 MB.
[2025-02-06T11:13:37.484Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:13:43.159Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:13:51.471Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:13:57.097Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:14:01.054Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:14:05.555Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:14:10.048Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:14:15.633Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:14:16.409Z] 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.
[2025-02-06T11:14:16.409Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:14:17.180Z] Movies recommended for you:
[2025-02-06T11:14:17.180Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:14:17.180Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:14:17.180Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (47574.343 ms) ======
[2025-02-06T11:14:17.180Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-06T11:14:17.180Z] GC before operation: completed in 271.877 ms, heap usage 388.278 MB -> 50.675 MB.
[2025-02-06T11:14:24.092Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:14:32.345Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:14:39.218Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:14:46.239Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:14:50.772Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:14:55.732Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:15:00.248Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:15:03.689Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:15:04.470Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-06T11:15:04.470Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:15:05.242Z] Movies recommended for you:
[2025-02-06T11:15:05.242Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:15:05.242Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:15:05.242Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (47820.574 ms) ======
[2025-02-06T11:15:05.242Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-06T11:15:05.242Z] GC before operation: completed in 234.915 ms, heap usage 393.613 MB -> 51.009 MB.
[2025-02-06T11:15:12.187Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:15:17.847Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:15:24.744Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:15:31.628Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:15:35.116Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:15:39.585Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:15:43.398Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:15:46.824Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:15:47.597Z] 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.
[2025-02-06T11:15:48.364Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:15:48.364Z] Movies recommended for you:
[2025-02-06T11:15:48.364Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:15:48.364Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:15:48.364Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (43151.725 ms) ======
[2025-02-06T11:15:48.364Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-06T11:15:48.364Z] GC before operation: completed in 245.786 ms, heap usage 188.419 MB -> 50.875 MB.
[2025-02-06T11:15:53.963Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:16:02.287Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:16:09.234Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:16:14.862Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:16:18.268Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:16:22.752Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:16:26.207Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:16:29.639Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:16:30.414Z] 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.
[2025-02-06T11:16:30.414Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:16:31.187Z] Movies recommended for you:
[2025-02-06T11:16:31.187Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:16:31.187Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:16:31.187Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (42496.517 ms) ======
[2025-02-06T11:16:31.187Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-06T11:16:31.187Z] GC before operation: completed in 210.760 ms, heap usage 145.258 MB -> 50.565 MB.
[2025-02-06T11:16:38.075Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:16:43.693Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:16:50.559Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:16:56.567Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:17:00.318Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:17:04.067Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:17:08.927Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:17:12.675Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:17:13.523Z] 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.
[2025-02-06T11:17:13.523Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:17:13.523Z] Movies recommended for you:
[2025-02-06T11:17:13.523Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:17:13.523Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:17:13.523Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (42175.865 ms) ======
[2025-02-06T11:17:13.523Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-06T11:17:13.523Z] GC before operation: completed in 256.748 ms, heap usage 118.369 MB -> 50.809 MB.
[2025-02-06T11:17:19.613Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:17:25.667Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:17:32.372Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:17:37.261Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:17:42.162Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:17:45.907Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:17:49.625Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:17:53.354Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:17:54.206Z] 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.
[2025-02-06T11:17:54.206Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:17:54.206Z] Movies recommended for you:
[2025-02-06T11:17:54.206Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:17:54.206Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:17:54.206Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (40852.960 ms) ======
[2025-02-06T11:17:54.206Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-06T11:17:55.054Z] GC before operation: completed in 208.770 ms, heap usage 118.961 MB -> 50.372 MB.
[2025-02-06T11:18:01.124Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:18:07.220Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:18:14.642Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:18:19.514Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:18:23.873Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:18:27.640Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:18:32.521Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:18:35.250Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:18:36.103Z] 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.
[2025-02-06T11:18:36.103Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:18:37.109Z] Movies recommended for you:
[2025-02-06T11:18:37.109Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:18:37.109Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:18:37.109Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (41886.556 ms) ======
[2025-02-06T11:18:37.109Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-06T11:18:37.109Z] GC before operation: completed in 206.940 ms, heap usage 371.382 MB -> 50.754 MB.
[2025-02-06T11:18:43.248Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:18:49.356Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:18:55.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:19:01.575Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:19:05.330Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:19:09.066Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:19:13.931Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:19:17.236Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:19:18.086Z] 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.
[2025-02-06T11:19:18.087Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:19:18.087Z] Movies recommended for you:
[2025-02-06T11:19:18.087Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:19:18.087Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:19:18.087Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (41565.225 ms) ======
[2025-02-06T11:19:18.087Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-06T11:19:18.949Z] GC before operation: completed in 217.097 ms, heap usage 295.486 MB -> 50.878 MB.
[2025-02-06T11:19:25.053Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:19:32.523Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:19:38.639Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:19:44.743Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:19:49.550Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:19:53.254Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:19:56.958Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:20:00.784Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:20:01.657Z] 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.
[2025-02-06T11:20:01.657Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:20:01.657Z] Movies recommended for you:
[2025-02-06T11:20:01.657Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:20:01.657Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:20:01.657Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (42882.146 ms) ======
[2025-02-06T11:20:01.657Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-06T11:20:01.657Z] GC before operation: completed in 155.021 ms, heap usage 120.142 MB -> 50.472 MB.
[2025-02-06T11:20:07.931Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:20:14.612Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:20:20.911Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:20:28.545Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:20:31.401Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:20:36.418Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:20:41.431Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:20:45.279Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:20:46.163Z] 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.
[2025-02-06T11:20:46.163Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:20:46.163Z] Movies recommended for you:
[2025-02-06T11:20:46.163Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:20:46.163Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:20:46.163Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (44394.296 ms) ======
[2025-02-06T11:20:46.163Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-06T11:20:46.163Z] GC before operation: completed in 212.941 ms, heap usage 118.332 MB -> 50.647 MB.
[2025-02-06T11:20:52.433Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:20:58.727Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:21:05.665Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:21:13.345Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:21:18.372Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:21:22.241Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:21:27.440Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:21:32.479Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:21:32.479Z] 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.
[2025-02-06T11:21:32.479Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:21:33.359Z] Movies recommended for you:
[2025-02-06T11:21:33.359Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:21:33.359Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:21:33.359Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (46661.227 ms) ======
[2025-02-06T11:21:33.359Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-06T11:21:33.359Z] GC before operation: completed in 230.491 ms, heap usage 82.932 MB -> 50.881 MB.
[2025-02-06T11:21:39.594Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:21:47.274Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:21:54.888Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:22:02.621Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:22:06.491Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:22:10.362Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:22:15.381Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:22:19.245Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:22:19.245Z] 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.
[2025-02-06T11:22:19.245Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:22:20.128Z] Movies recommended for you:
[2025-02-06T11:22:20.128Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:22:20.128Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:22:20.128Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (46546.943 ms) ======
[2025-02-06T11:22:20.128Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-06T11:22:20.128Z] GC before operation: completed in 233.364 ms, heap usage 118.998 MB -> 50.528 MB.
[2025-02-06T11:22:27.798Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:22:34.102Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:22:41.775Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:22:49.449Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:22:53.310Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:22:58.948Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:23:02.801Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:23:07.831Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:23:07.831Z] 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.
[2025-02-06T11:23:07.831Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:23:08.728Z] Movies recommended for you:
[2025-02-06T11:23:08.728Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:23:08.728Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:23:08.728Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (48560.937 ms) ======
[2025-02-06T11:23:08.728Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-06T11:23:08.728Z] GC before operation: completed in 222.364 ms, heap usage 185.403 MB -> 50.722 MB.
[2025-02-06T11:23:15.031Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:23:24.272Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:23:31.867Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:23:38.338Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:23:43.709Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:23:52.841Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:23:57.374Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:24:02.402Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:24:02.402Z] 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.
[2025-02-06T11:24:02.402Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:24:03.304Z] Movies recommended for you:
[2025-02-06T11:24:03.304Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:24:03.304Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:24:03.304Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (54140.090 ms) ======
[2025-02-06T11:24:03.304Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-06T11:24:03.304Z] GC before operation: completed in 257.200 ms, heap usage 236.970 MB -> 50.901 MB.
[2025-02-06T11:24:17.743Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T11:24:25.405Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T11:24:31.647Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T11:24:37.900Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T11:24:42.975Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T11:24:46.924Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T11:24:51.965Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T11:24:55.876Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T11:24:57.366Z] 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.
[2025-02-06T11:24:57.366Z] The best model improves the baseline by 14.52%.
[2025-02-06T11:24:57.366Z] Movies recommended for you:
[2025-02-06T11:24:57.366Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T11:24:57.366Z] There is no way to check that no silent failure occurred.
[2025-02-06T11:24:57.366Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (53825.436 ms) ======
[2025-02-06T11:24:58.258Z] -----------------------------------
[2025-02-06T11:24:58.258Z] renaissance-movie-lens_0_PASSED
[2025-02-06T11:24:58.258Z] -----------------------------------
[2025-02-06T11:24:58.258Z]
[2025-02-06T11:24:58.258Z] TEST TEARDOWN:
[2025-02-06T11:24:58.258Z] Nothing to be done for teardown.
[2025-02-06T11:24:58.258Z] renaissance-movie-lens_0 Finish Time: Thu Feb 6 11:24:57 2025 Epoch Time (ms): 1738841097942