renaissance-movie-lens_0
[2024-09-26T05:52:05.807Z] Running test renaissance-movie-lens_0 ...
[2024-09-26T05:52:05.807Z] ===============================================
[2024-09-26T05:52:05.807Z] renaissance-movie-lens_0 Start Time: Thu Sep 26 05:52:05 2024 Epoch Time (ms): 1727329925594
[2024-09-26T05:52:05.807Z] variation: NoOptions
[2024-09-26T05:52:05.807Z] JVM_OPTIONS:
[2024-09-26T05:52:05.807Z] { \
[2024-09-26T05:52:05.807Z] echo ""; echo "TEST SETUP:"; \
[2024-09-26T05:52:05.807Z] echo "Nothing to be done for setup."; \
[2024-09-26T05:52:05.807Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17273288001084/renaissance-movie-lens_0"; \
[2024-09-26T05:52:05.807Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17273288001084/renaissance-movie-lens_0"; \
[2024-09-26T05:52:05.807Z] echo ""; echo "TESTING:"; \
[2024-09-26T05:52:05.807Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17273288001084/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-26T05:52:05.807Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17273288001084/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-26T05:52:05.807Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-26T05:52:05.807Z] echo "Nothing to be done for teardown."; \
[2024-09-26T05:52:05.807Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17273288001084/TestTargetResult";
[2024-09-26T05:52:05.807Z]
[2024-09-26T05:52:05.807Z] TEST SETUP:
[2024-09-26T05:52:05.807Z] Nothing to be done for setup.
[2024-09-26T05:52:05.807Z]
[2024-09-26T05:52:05.807Z] TESTING:
[2024-09-26T05:52:10.819Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-26T05:52:15.862Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-26T05:52:22.185Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-26T05:52:23.069Z] Training: 60056, validation: 20285, test: 19854
[2024-09-26T05:52:23.069Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-26T05:52:23.069Z] GC before operation: completed in 183.131 ms, heap usage 119.178 MB -> 36.423 MB.
[2024-09-26T05:52:38.783Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:52:49.810Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:52:57.525Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:53:03.846Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:53:08.896Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:53:12.809Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:53:16.687Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:53:20.577Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:53:20.577Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:53:20.577Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:53:21.461Z] Movies recommended for you:
[2024-09-26T05:53:21.461Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:53:21.461Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:53:21.461Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (57670.440 ms) ======
[2024-09-26T05:53:21.461Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-26T05:53:21.461Z] GC before operation: completed in 188.092 ms, heap usage 156.417 MB -> 48.114 MB.
[2024-09-26T05:53:26.683Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:53:30.569Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:53:35.600Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:53:40.644Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:53:43.465Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:53:45.289Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:53:48.097Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:53:49.921Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:53:49.921Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:53:49.921Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:53:49.921Z] Movies recommended for you:
[2024-09-26T05:53:49.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:53:49.921Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:53:49.921Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (29015.692 ms) ======
[2024-09-26T05:53:49.921Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-26T05:53:51.325Z] GC before operation: completed in 133.634 ms, heap usage 126.560 MB -> 48.993 MB.
[2024-09-26T05:53:54.245Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:53:57.054Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:54:00.921Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:54:03.740Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:54:05.552Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:54:07.362Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:54:09.190Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:54:12.005Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:54:12.005Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:54:12.005Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:54:12.005Z] Movies recommended for you:
[2024-09-26T05:54:12.005Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:54:12.005Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:54:12.005Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (21907.651 ms) ======
[2024-09-26T05:54:12.005Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-26T05:54:12.886Z] GC before operation: completed in 179.443 ms, heap usage 204.961 MB -> 49.357 MB.
[2024-09-26T05:54:15.694Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:54:19.556Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:54:23.459Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:54:27.333Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:54:30.155Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:54:32.976Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:54:36.857Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:54:39.684Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:54:39.684Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:54:39.684Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:54:40.575Z] Movies recommended for you:
[2024-09-26T05:54:40.575Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:54:40.575Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:54:40.575Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (27716.405 ms) ======
[2024-09-26T05:54:40.575Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-26T05:54:40.575Z] GC before operation: completed in 217.292 ms, heap usage 208.969 MB -> 49.672 MB.
[2024-09-26T05:54:45.630Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:54:50.770Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:54:57.069Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:55:02.110Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:55:04.936Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:55:07.753Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:55:12.152Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:55:14.974Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:55:14.974Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:55:14.974Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:55:14.974Z] Movies recommended for you:
[2024-09-26T05:55:14.974Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:55:14.974Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:55:14.974Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (34950.482 ms) ======
[2024-09-26T05:55:14.974Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-26T05:55:15.861Z] GC before operation: completed in 233.000 ms, heap usage 169.353 MB -> 49.882 MB.
[2024-09-26T05:55:20.893Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:55:25.930Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:55:32.220Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:55:36.366Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:55:39.224Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:55:43.104Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:55:45.923Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:55:49.940Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:55:49.940Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:55:49.940Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:55:50.825Z] Movies recommended for you:
[2024-09-26T05:55:50.825Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:55:50.825Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:55:50.825Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (34998.332 ms) ======
[2024-09-26T05:55:50.825Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-26T05:55:50.825Z] GC before operation: completed in 238.403 ms, heap usage 140.378 MB -> 49.875 MB.
[2024-09-26T05:55:55.849Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:56:00.885Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:56:04.766Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:56:09.787Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:56:12.615Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:56:15.444Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:56:19.328Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:56:22.143Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:56:22.143Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:56:22.143Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:56:22.143Z] Movies recommended for you:
[2024-09-26T05:56:22.143Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:56:22.143Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:56:22.143Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (31673.079 ms) ======
[2024-09-26T05:56:22.143Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-26T05:56:23.032Z] GC before operation: completed in 226.906 ms, heap usage 184.029 MB -> 49.994 MB.
[2024-09-26T05:56:28.075Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:56:33.098Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:56:38.138Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:56:43.169Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:56:45.984Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:56:49.185Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:56:53.056Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:56:55.694Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:56:55.694Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:56:55.694Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:56:55.694Z] Movies recommended for you:
[2024-09-26T05:56:55.694Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:56:55.694Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:56:55.694Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (33335.633 ms) ======
[2024-09-26T05:56:55.694Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-26T05:56:56.511Z] GC before operation: completed in 171.272 ms, heap usage 69.362 MB -> 50.113 MB.
[2024-09-26T05:57:01.231Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:57:04.866Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:57:09.570Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:57:14.263Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:57:16.881Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:57:19.510Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:57:23.136Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:57:25.775Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:57:25.775Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:57:25.775Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:57:25.775Z] Movies recommended for you:
[2024-09-26T05:57:25.775Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:57:25.775Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:57:25.775Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (29918.213 ms) ======
[2024-09-26T05:57:25.775Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-26T05:57:26.593Z] GC before operation: completed in 129.395 ms, heap usage 144.661 MB -> 50.046 MB.
[2024-09-26T05:57:31.317Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:57:35.488Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:57:39.119Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:57:43.847Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:57:47.461Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:57:50.074Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:57:52.680Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:57:56.315Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:57:56.315Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:57:56.315Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:57:57.141Z] Movies recommended for you:
[2024-09-26T05:57:57.141Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:57:57.141Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:57:57.141Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (30519.657 ms) ======
[2024-09-26T05:57:57.141Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-26T05:57:57.141Z] GC before operation: completed in 278.331 ms, heap usage 64.517 MB -> 50.051 MB.
[2024-09-26T05:58:01.842Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:58:06.556Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:58:12.470Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:58:17.168Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:58:19.786Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:58:22.396Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:58:26.161Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:58:28.305Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:58:29.130Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:58:29.130Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:58:29.130Z] Movies recommended for you:
[2024-09-26T05:58:29.130Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:58:29.130Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:58:29.130Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (32326.230 ms) ======
[2024-09-26T05:58:29.130Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-26T05:58:29.952Z] GC before operation: completed in 219.277 ms, heap usage 126.728 MB -> 49.832 MB.
[2024-09-26T05:58:34.688Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:58:39.428Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:58:44.136Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:58:48.887Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:58:51.633Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:58:55.249Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:58:57.872Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:59:01.485Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:59:01.485Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:59:01.485Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:59:01.485Z] Movies recommended for you:
[2024-09-26T05:59:01.485Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:59:01.485Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:59:01.485Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (32058.123 ms) ======
[2024-09-26T05:59:01.485Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-26T05:59:01.485Z] GC before operation: completed in 182.288 ms, heap usage 114.388 MB -> 50.193 MB.
[2024-09-26T05:59:07.380Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:59:12.092Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:59:17.357Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:59:20.972Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:59:23.585Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:59:26.202Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T05:59:28.817Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T05:59:32.438Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T05:59:32.438Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T05:59:32.438Z] The best model improves the baseline by 14.52%.
[2024-09-26T05:59:33.257Z] Movies recommended for you:
[2024-09-26T05:59:33.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T05:59:33.257Z] There is no way to check that no silent failure occurred.
[2024-09-26T05:59:33.257Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (31063.713 ms) ======
[2024-09-26T05:59:33.257Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-26T05:59:33.257Z] GC before operation: completed in 279.111 ms, heap usage 296.957 MB -> 50.342 MB.
[2024-09-26T05:59:37.974Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T05:59:42.688Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T05:59:48.587Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T05:59:52.242Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T05:59:55.868Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T05:59:58.487Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:00:02.088Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:00:04.718Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:00:05.534Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:00:05.534Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:00:05.534Z] Movies recommended for you:
[2024-09-26T06:00:05.534Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:00:05.534Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:00:05.534Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (32445.696 ms) ======
[2024-09-26T06:00:05.534Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-26T06:00:05.534Z] GC before operation: completed in 244.569 ms, heap usage 115.031 MB -> 49.956 MB.
[2024-09-26T06:00:10.814Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T06:00:15.510Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T06:00:21.373Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T06:00:26.077Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T06:00:28.697Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T06:00:32.355Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:00:34.958Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:00:38.577Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:00:38.577Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:00:38.577Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:00:38.577Z] Movies recommended for you:
[2024-09-26T06:00:38.577Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:00:38.577Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:00:38.578Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (33109.991 ms) ======
[2024-09-26T06:00:38.578Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-26T06:00:39.373Z] GC before operation: completed in 207.132 ms, heap usage 121.306 MB -> 50.095 MB.
[2024-09-26T06:00:43.940Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T06:00:49.680Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T06:00:55.403Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T06:00:59.999Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T06:01:02.166Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T06:01:05.677Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:01:09.164Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:01:11.684Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:01:11.684Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:01:12.481Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:01:12.481Z] Movies recommended for you:
[2024-09-26T06:01:12.481Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:01:12.481Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:01:12.481Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (33150.993 ms) ======
[2024-09-26T06:01:12.481Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-26T06:01:12.481Z] GC before operation: completed in 184.786 ms, heap usage 141.796 MB -> 50.201 MB.
[2024-09-26T06:01:18.205Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T06:01:22.761Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T06:01:27.320Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T06:01:31.866Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T06:01:35.366Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T06:01:37.904Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:01:41.385Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:01:43.907Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:01:44.692Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:01:44.692Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:01:44.692Z] Movies recommended for you:
[2024-09-26T06:01:44.692Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:01:44.692Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:01:44.692Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (32285.120 ms) ======
[2024-09-26T06:01:44.692Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-26T06:01:44.692Z] GC before operation: completed in 221.134 ms, heap usage 83.312 MB -> 49.969 MB.
[2024-09-26T06:01:49.258Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T06:01:52.863Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T06:01:56.337Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T06:01:58.850Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T06:02:01.377Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T06:02:03.003Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:02:05.534Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:02:09.039Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:02:09.039Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:02:09.039Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:02:09.832Z] Movies recommended for you:
[2024-09-26T06:02:09.832Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:02:09.832Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:02:09.832Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (24539.765 ms) ======
[2024-09-26T06:02:09.832Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-26T06:02:09.832Z] GC before operation: completed in 228.039 ms, heap usage 225.869 MB -> 50.166 MB.
[2024-09-26T06:02:15.549Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T06:02:20.088Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T06:02:25.804Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T06:02:30.360Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T06:02:33.871Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T06:02:36.411Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:02:39.924Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:02:42.455Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:02:43.248Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:02:43.248Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:02:43.248Z] Movies recommended for you:
[2024-09-26T06:02:43.248Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:02:43.248Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:02:43.248Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (33519.838 ms) ======
[2024-09-26T06:02:43.248Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-26T06:02:43.248Z] GC before operation: completed in 236.454 ms, heap usage 115.110 MB -> 50.275 MB.
[2024-09-26T06:02:48.326Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T06:02:54.032Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T06:02:59.722Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T06:03:04.264Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T06:03:06.811Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T06:03:09.339Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T06:03:12.844Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T06:03:15.360Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T06:03:16.146Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-26T06:03:16.146Z] The best model improves the baseline by 14.52%.
[2024-09-26T06:03:16.146Z] Movies recommended for you:
[2024-09-26T06:03:16.146Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T06:03:16.146Z] There is no way to check that no silent failure occurred.
[2024-09-26T06:03:16.146Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (32565.097 ms) ======
[2024-09-26T06:03:16.935Z] -----------------------------------
[2024-09-26T06:03:16.935Z] renaissance-movie-lens_0_PASSED
[2024-09-26T06:03:16.935Z] -----------------------------------
[2024-09-26T06:03:16.935Z]
[2024-09-26T06:03:16.935Z] TEST TEARDOWN:
[2024-09-26T06:03:16.935Z] Nothing to be done for teardown.
[2024-09-26T06:03:16.935Z] renaissance-movie-lens_0 Finish Time: Thu Sep 26 06:03:16 2024 Epoch Time (ms): 1727330596454