renaissance-movie-lens_0
[2024-10-29T22:15:33.541Z] Running test renaissance-movie-lens_0 ...
[2024-10-29T22:15:33.541Z] ===============================================
[2024-10-29T22:15:33.541Z] renaissance-movie-lens_0 Start Time: Tue Oct 29 22:15:32 2024 Epoch Time (ms): 1730240132977
[2024-10-29T22:15:33.541Z] variation: NoOptions
[2024-10-29T22:15:33.541Z] JVM_OPTIONS:
[2024-10-29T22:15:33.541Z] { \
[2024-10-29T22:15:33.541Z] echo ""; echo "TEST SETUP:"; \
[2024-10-29T22:15:33.541Z] echo "Nothing to be done for setup."; \
[2024-10-29T22:15:33.541Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17302391083690/renaissance-movie-lens_0"; \
[2024-10-29T22:15:33.541Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17302391083690/renaissance-movie-lens_0"; \
[2024-10-29T22:15:33.541Z] echo ""; echo "TESTING:"; \
[2024-10-29T22:15:33.541Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17302391083690/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-29T22:15:33.541Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17302391083690/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-29T22:15:33.541Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-29T22:15:33.541Z] echo "Nothing to be done for teardown."; \
[2024-10-29T22:15:33.541Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17302391083690/TestTargetResult";
[2024-10-29T22:15:33.541Z]
[2024-10-29T22:15:33.541Z] TEST SETUP:
[2024-10-29T22:15:33.541Z] Nothing to be done for setup.
[2024-10-29T22:15:33.541Z]
[2024-10-29T22:15:33.541Z] TESTING:
[2024-10-29T22:15:37.601Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-29T22:15:39.506Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-29T22:15:44.756Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-29T22:15:44.756Z] Training: 60056, validation: 20285, test: 19854
[2024-10-29T22:15:44.756Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-29T22:15:44.756Z] GC before operation: completed in 243.707 ms, heap usage 104.413 MB -> 25.875 MB.
[2024-10-29T22:15:51.307Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:15:55.369Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:15:58.354Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:16:01.302Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:16:03.246Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:16:05.177Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:16:07.087Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:16:08.994Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:16:08.994Z] 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.
[2024-10-29T22:16:08.994Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:16:08.994Z] Movies recommended for you:
[2024-10-29T22:16:08.994Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:16:08.994Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:16:08.994Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (24304.209 ms) ======
[2024-10-29T22:16:08.994Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-29T22:16:09.923Z] GC before operation: completed in 304.343 ms, heap usage 115.057 MB -> 41.450 MB.
[2024-10-29T22:16:12.882Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:16:14.800Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:16:17.758Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:16:20.707Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:16:21.636Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:16:23.545Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:16:25.453Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:16:26.385Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:16:27.314Z] 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.
[2024-10-29T22:16:27.314Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:16:27.314Z] Movies recommended for you:
[2024-10-29T22:16:27.314Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:16:27.314Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:16:27.314Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17319.574 ms) ======
[2024-10-29T22:16:27.314Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-29T22:16:27.314Z] GC before operation: completed in 208.487 ms, heap usage 343.723 MB -> 41.317 MB.
[2024-10-29T22:16:30.261Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:16:33.184Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:16:34.277Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:16:37.221Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:16:39.129Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:16:40.066Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:16:41.972Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:16:42.901Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:16:43.830Z] 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.
[2024-10-29T22:16:43.830Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:16:43.830Z] Movies recommended for you:
[2024-10-29T22:16:43.830Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:16:43.830Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:16:43.830Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16368.037 ms) ======
[2024-10-29T22:16:43.830Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-29T22:16:43.830Z] GC before operation: completed in 240.429 ms, heap usage 114.310 MB -> 40.999 MB.
[2024-10-29T22:16:46.777Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:16:48.786Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:16:51.736Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:16:53.642Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:16:55.550Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:16:56.479Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:16:58.395Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:16:59.327Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:17:00.254Z] 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.
[2024-10-29T22:17:00.254Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:17:00.254Z] Movies recommended for you:
[2024-10-29T22:17:00.254Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:17:00.254Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:17:00.254Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16286.353 ms) ======
[2024-10-29T22:17:00.254Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-29T22:17:00.254Z] GC before operation: completed in 224.885 ms, heap usage 122.954 MB -> 41.430 MB.
[2024-10-29T22:17:02.255Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:17:05.203Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:17:08.152Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:17:10.084Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:17:11.992Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:17:12.922Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:17:14.834Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:17:15.766Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:17:16.694Z] 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.
[2024-10-29T22:17:16.694Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:17:16.694Z] Movies recommended for you:
[2024-10-29T22:17:16.694Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:17:16.694Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:17:16.694Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16102.905 ms) ======
[2024-10-29T22:17:16.694Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-29T22:17:16.694Z] GC before operation: completed in 183.508 ms, heap usage 168.143 MB -> 42.114 MB.
[2024-10-29T22:17:18.608Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:17:21.556Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:17:23.468Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:17:26.418Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:17:27.350Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:17:29.264Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:17:30.194Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:17:32.104Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:17:32.104Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T22:17:32.104Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:17:32.104Z] Movies recommended for you:
[2024-10-29T22:17:32.104Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:17:32.104Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:17:32.104Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15459.806 ms) ======
[2024-10-29T22:17:32.104Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-29T22:17:32.104Z] GC before operation: completed in 176.228 ms, heap usage 169.796 MB -> 55.103 MB.
[2024-10-29T22:17:35.050Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:17:37.353Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:17:39.262Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:17:41.168Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:17:43.075Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:17:44.005Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:17:45.913Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:17:46.842Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:17:47.770Z] 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.
[2024-10-29T22:17:47.770Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:17:47.770Z] Movies recommended for you:
[2024-10-29T22:17:47.770Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:17:47.770Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:17:47.770Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15206.780 ms) ======
[2024-10-29T22:17:47.770Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-29T22:17:47.770Z] GC before operation: completed in 183.538 ms, heap usage 227.150 MB -> 46.172 MB.
[2024-10-29T22:17:49.680Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:17:52.623Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:17:54.528Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:17:56.438Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:17:58.356Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:17:59.284Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:18:01.193Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:18:02.267Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:18:02.267Z] 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.
[2024-10-29T22:18:02.267Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:18:02.267Z] Movies recommended for you:
[2024-10-29T22:18:02.267Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:18:02.267Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:18:02.267Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14839.067 ms) ======
[2024-10-29T22:18:02.267Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-29T22:18:02.267Z] GC before operation: completed in 154.106 ms, heap usage 237.644 MB -> 42.533 MB.
[2024-10-29T22:18:05.217Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:18:07.130Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:18:09.035Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:18:11.980Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:18:12.911Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:18:13.839Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:18:15.747Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:18:16.674Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:18:17.603Z] 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.
[2024-10-29T22:18:17.603Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:18:17.603Z] Movies recommended for you:
[2024-10-29T22:18:17.603Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:18:17.603Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:18:17.603Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14777.325 ms) ======
[2024-10-29T22:18:17.603Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-29T22:18:17.603Z] GC before operation: completed in 157.453 ms, heap usage 203.268 MB -> 49.226 MB.
[2024-10-29T22:18:19.514Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:18:22.461Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:18:24.371Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:18:26.277Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:18:28.185Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:18:29.113Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:18:31.022Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:18:32.118Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:18:33.046Z] 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.
[2024-10-29T22:18:33.046Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:18:33.046Z] Movies recommended for you:
[2024-10-29T22:18:33.046Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:18:33.046Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:18:33.046Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15154.104 ms) ======
[2024-10-29T22:18:33.046Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-29T22:18:33.046Z] GC before operation: completed in 238.562 ms, heap usage 395.283 MB -> 46.041 MB.
[2024-10-29T22:18:34.958Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:18:36.868Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:18:39.493Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:18:41.408Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:18:43.316Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:18:44.243Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:18:46.151Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:18:47.080Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:18:47.080Z] 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.
[2024-10-29T22:18:47.080Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:18:47.080Z] Movies recommended for you:
[2024-10-29T22:18:47.080Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:18:47.080Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:18:47.080Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14430.976 ms) ======
[2024-10-29T22:18:47.080Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-29T22:18:48.008Z] GC before operation: completed in 216.578 ms, heap usage 257.104 MB -> 70.573 MB.
[2024-10-29T22:18:49.914Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:18:51.825Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:18:53.733Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:18:56.684Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:18:57.612Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:18:58.540Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:19:00.529Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:19:01.455Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:19:02.417Z] 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.
[2024-10-29T22:19:02.417Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:19:02.417Z] Movies recommended for you:
[2024-10-29T22:19:02.417Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:19:02.417Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:19:02.417Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14557.257 ms) ======
[2024-10-29T22:19:02.417Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-29T22:19:02.417Z] GC before operation: completed in 181.346 ms, heap usage 183.874 MB -> 49.345 MB.
[2024-10-29T22:19:04.333Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:19:07.345Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:19:09.247Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:19:11.152Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:19:12.080Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:19:13.986Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:19:14.918Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:19:16.827Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:19:16.827Z] 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.
[2024-10-29T22:19:16.827Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:19:16.827Z] Movies recommended for you:
[2024-10-29T22:19:16.827Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:19:16.827Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:19:16.828Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14439.840 ms) ======
[2024-10-29T22:19:16.828Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-29T22:19:16.828Z] GC before operation: completed in 205.638 ms, heap usage 187.581 MB -> 70.834 MB.
[2024-10-29T22:19:19.778Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:19:21.685Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:19:23.602Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:19:25.509Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:19:27.415Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:19:28.344Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:19:29.274Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:19:31.180Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:19:31.180Z] 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.
[2024-10-29T22:19:31.180Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:19:31.180Z] Movies recommended for you:
[2024-10-29T22:19:31.180Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:19:31.180Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:19:31.180Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14381.570 ms) ======
[2024-10-29T22:19:31.180Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-29T22:19:32.112Z] GC before operation: completed in 211.045 ms, heap usage 230.477 MB -> 70.593 MB.
[2024-10-29T22:19:34.023Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:19:35.933Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:19:37.841Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:19:41.972Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:19:43.060Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:19:43.060Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:19:43.994Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:19:45.905Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:19:45.905Z] 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.
[2024-10-29T22:19:45.905Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:19:45.905Z] Movies recommended for you:
[2024-10-29T22:19:45.905Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:19:45.905Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:19:45.905Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14346.466 ms) ======
[2024-10-29T22:19:45.905Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-29T22:19:45.905Z] GC before operation: completed in 211.987 ms, heap usage 209.155 MB -> 70.610 MB.
[2024-10-29T22:19:48.858Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:19:50.767Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:19:52.674Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:19:54.583Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:19:55.513Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:19:57.423Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:19:58.353Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:20:00.261Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:20:00.261Z] 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.
[2024-10-29T22:20:00.261Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:20:00.261Z] Movies recommended for you:
[2024-10-29T22:20:00.261Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:20:00.261Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:20:00.261Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14145.993 ms) ======
[2024-10-29T22:20:00.261Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-29T22:20:00.261Z] GC before operation: completed in 251.690 ms, heap usage 197.775 MB -> 70.507 MB.
[2024-10-29T22:20:03.302Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:20:05.209Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:20:07.119Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:20:09.024Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:20:10.934Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:20:11.865Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:20:13.773Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:20:14.704Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:20:14.704Z] 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.
[2024-10-29T22:20:14.704Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:20:14.704Z] Movies recommended for you:
[2024-10-29T22:20:14.704Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:20:14.704Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:20:14.704Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14509.748 ms) ======
[2024-10-29T22:20:14.704Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-29T22:20:15.633Z] GC before operation: completed in 225.308 ms, heap usage 244.577 MB -> 70.958 MB.
[2024-10-29T22:20:17.541Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:20:19.451Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:20:22.402Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:20:24.312Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:20:25.244Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:20:26.199Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:20:28.112Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:20:29.042Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:20:29.973Z] 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.
[2024-10-29T22:20:29.973Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:20:29.973Z] Movies recommended for you:
[2024-10-29T22:20:29.973Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:20:29.973Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:20:29.973Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14435.093 ms) ======
[2024-10-29T22:20:29.973Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-29T22:20:29.973Z] GC before operation: completed in 197.279 ms, heap usage 233.000 MB -> 70.796 MB.
[2024-10-29T22:20:31.933Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:20:33.846Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:20:36.793Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:20:38.703Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:20:39.634Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:20:41.544Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:20:42.477Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:20:43.407Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:20:45.107Z] 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.
[2024-10-29T22:20:45.107Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:20:45.107Z] Movies recommended for you:
[2024-10-29T22:20:45.108Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:20:45.108Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:20:45.108Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14312.317 ms) ======
[2024-10-29T22:20:45.108Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-29T22:20:45.108Z] GC before operation: completed in 232.249 ms, heap usage 219.925 MB -> 70.830 MB.
[2024-10-29T22:20:47.023Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T22:20:48.935Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T22:20:50.843Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T22:20:52.754Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T22:20:54.663Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T22:20:55.594Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T22:20:57.504Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T22:20:58.433Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T22:20:58.433Z] 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.
[2024-10-29T22:20:58.433Z] The best model improves the baseline by 14.52%.
[2024-10-29T22:20:58.433Z] Movies recommended for you:
[2024-10-29T22:20:58.433Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T22:20:58.433Z] There is no way to check that no silent failure occurred.
[2024-10-29T22:20:58.433Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14306.162 ms) ======
[2024-10-29T22:20:59.363Z] -----------------------------------
[2024-10-29T22:20:59.363Z] renaissance-movie-lens_0_PASSED
[2024-10-29T22:20:59.363Z] -----------------------------------
[2024-10-29T22:20:59.363Z]
[2024-10-29T22:20:59.363Z] TEST TEARDOWN:
[2024-10-29T22:20:59.363Z] Nothing to be done for teardown.
[2024-10-29T22:20:59.363Z] renaissance-movie-lens_0 Finish Time: Tue Oct 29 22:20:58 2024 Epoch Time (ms): 1730240458930