renaissance-movie-lens_0
[2024-09-05T07:39:50.859Z] Running test renaissance-movie-lens_0 ...
[2024-09-05T07:39:50.859Z] ===============================================
[2024-09-05T07:39:50.859Z] renaissance-movie-lens_0 Start Time: Thu Sep 5 07:39:50 2024 Epoch Time (ms): 1725521990728
[2024-09-05T07:39:50.859Z] variation: NoOptions
[2024-09-05T07:39:50.859Z] JVM_OPTIONS:
[2024-09-05T07:39:50.859Z] { \
[2024-09-05T07:39:50.859Z] echo ""; echo "TEST SETUP:"; \
[2024-09-05T07:39:50.859Z] echo "Nothing to be done for setup."; \
[2024-09-05T07:39:50.859Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17255212989108/renaissance-movie-lens_0"; \
[2024-09-05T07:39:50.859Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17255212989108/renaissance-movie-lens_0"; \
[2024-09-05T07:39:50.859Z] echo ""; echo "TESTING:"; \
[2024-09-05T07:39:50.860Z] "/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_17255212989108/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-05T07:39:50.860Z] 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_17255212989108/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-05T07:39:50.860Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-05T07:39:50.860Z] echo "Nothing to be done for teardown."; \
[2024-09-05T07:39:50.860Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17255212989108/TestTargetResult";
[2024-09-05T07:39:50.860Z]
[2024-09-05T07:39:50.860Z] TEST SETUP:
[2024-09-05T07:39:50.860Z] Nothing to be done for setup.
[2024-09-05T07:39:50.860Z]
[2024-09-05T07:39:50.860Z] TESTING:
[2024-09-05T07:39:54.537Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-05T07:39:58.204Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-09-05T07:40:04.795Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-05T07:40:04.796Z] Training: 60056, validation: 20285, test: 19854
[2024-09-05T07:40:04.796Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-05T07:40:04.796Z] GC before operation: completed in 93.049 ms, heap usage 106.491 MB -> 37.177 MB.
[2024-09-05T07:40:13.644Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:40:19.642Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:40:25.638Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:40:29.318Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:40:31.973Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:40:34.618Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:40:36.338Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:40:38.064Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:40:38.906Z] 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.
[2024-09-05T07:40:38.906Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:40:38.906Z] Movies recommended for you:
[2024-09-05T07:40:38.906Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:40:38.906Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:40:38.906Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34818.371 ms) ======
[2024-09-05T07:40:38.906Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-05T07:40:38.906Z] GC before operation: completed in 77.249 ms, heap usage 160.349 MB -> 54.127 MB.
[2024-09-05T07:40:42.593Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:40:46.266Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:40:49.946Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:40:51.657Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:40:53.373Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:40:54.205Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:40:55.918Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:40:57.634Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:40:57.635Z] 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.
[2024-09-05T07:40:57.635Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:40:57.635Z] Movies recommended for you:
[2024-09-05T07:40:57.635Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:40:57.635Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:40:57.635Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (18418.580 ms) ======
[2024-09-05T07:40:57.635Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-05T07:40:57.635Z] GC before operation: completed in 61.041 ms, heap usage 84.024 MB -> 53.824 MB.
[2024-09-05T07:40:59.345Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:41:02.005Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:41:04.659Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:41:06.371Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:41:08.090Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:41:08.919Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:41:10.628Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:41:11.462Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:41:11.462Z] 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.
[2024-09-05T07:41:11.462Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:41:12.290Z] Movies recommended for you:
[2024-09-05T07:41:12.290Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:41:12.290Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:41:12.290Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14406.048 ms) ======
[2024-09-05T07:41:12.290Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-05T07:41:12.290Z] GC before operation: completed in 58.447 ms, heap usage 324.233 MB -> 50.074 MB.
[2024-09-05T07:41:14.000Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:41:16.646Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:41:19.308Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:41:22.549Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:41:23.381Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:41:25.105Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:41:26.815Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:41:28.523Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:41:28.523Z] 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.
[2024-09-05T07:41:29.357Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:41:29.357Z] Movies recommended for you:
[2024-09-05T07:41:29.357Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:41:29.357Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:41:29.357Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (17050.300 ms) ======
[2024-09-05T07:41:29.357Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-05T07:41:29.357Z] GC before operation: completed in 90.115 ms, heap usage 321.513 MB -> 50.466 MB.
[2024-09-05T07:41:32.016Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:41:34.668Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:41:38.329Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:41:40.981Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:41:42.696Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:41:44.416Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:41:46.121Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:41:47.833Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:41:47.833Z] 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.
[2024-09-05T07:41:47.833Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:41:47.833Z] Movies recommended for you:
[2024-09-05T07:41:47.833Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:41:47.833Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:41:47.833Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (18895.024 ms) ======
[2024-09-05T07:41:47.833Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-05T07:41:47.833Z] GC before operation: completed in 82.737 ms, heap usage 66.438 MB -> 53.762 MB.
[2024-09-05T07:41:51.520Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:41:54.178Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:41:56.831Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:41:59.486Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:42:01.206Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:42:02.920Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:42:04.633Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:42:06.349Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:42:06.349Z] 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.
[2024-09-05T07:42:06.349Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:42:06.349Z] Movies recommended for you:
[2024-09-05T07:42:06.349Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:42:06.349Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:42:06.349Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18225.263 ms) ======
[2024-09-05T07:42:06.350Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-05T07:42:06.350Z] GC before operation: completed in 93.143 ms, heap usage 394.838 MB -> 53.911 MB.
[2024-09-05T07:42:09.001Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:42:11.659Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:42:15.326Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:42:17.981Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:42:18.815Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:42:20.528Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:42:22.244Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:42:23.966Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:42:23.966Z] 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.
[2024-09-05T07:42:23.966Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:42:23.966Z] Movies recommended for you:
[2024-09-05T07:42:23.966Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:42:23.966Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:42:23.966Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17814.360 ms) ======
[2024-09-05T07:42:23.966Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-05T07:42:23.966Z] GC before operation: completed in 87.154 ms, heap usage 415.945 MB -> 54.046 MB.
[2024-09-05T07:42:26.627Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:42:29.279Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:42:32.954Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:42:35.255Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:42:36.968Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:42:38.681Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:42:40.396Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:42:41.230Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:42:42.069Z] 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.
[2024-09-05T07:42:42.069Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:42:42.069Z] Movies recommended for you:
[2024-09-05T07:42:42.069Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:42:42.069Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:42:42.069Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17589.895 ms) ======
[2024-09-05T07:42:42.069Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-05T07:42:42.069Z] GC before operation: completed in 100.921 ms, heap usage 69.823 MB -> 53.954 MB.
[2024-09-05T07:42:44.726Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:42:47.389Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:42:50.048Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:42:52.694Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:42:54.415Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:42:55.245Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:42:56.955Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:42:58.665Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:42:59.499Z] 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.
[2024-09-05T07:42:59.499Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:42:59.499Z] Movies recommended for you:
[2024-09-05T07:42:59.499Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:42:59.499Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:42:59.499Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17182.238 ms) ======
[2024-09-05T07:42:59.499Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-05T07:42:59.499Z] GC before operation: completed in 100.814 ms, heap usage 394.458 MB -> 54.156 MB.
[2024-09-05T07:43:02.161Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:43:04.817Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:43:07.476Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:43:10.145Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:43:11.859Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:43:13.575Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:43:15.294Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:43:17.010Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:43:17.010Z] 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.
[2024-09-05T07:43:17.010Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:43:17.010Z] Movies recommended for you:
[2024-09-05T07:43:17.010Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:43:17.010Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:43:17.010Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17994.905 ms) ======
[2024-09-05T07:43:17.010Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-05T07:43:17.010Z] GC before operation: completed in 88.943 ms, heap usage 68.301 MB -> 50.748 MB.
[2024-09-05T07:43:19.658Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:43:23.340Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:43:26.167Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:43:28.814Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:43:30.523Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:43:32.245Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:43:33.967Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:43:35.693Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:43:35.693Z] 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.
[2024-09-05T07:43:35.693Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:43:35.693Z] Movies recommended for you:
[2024-09-05T07:43:35.693Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:43:35.693Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:43:35.693Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (18508.876 ms) ======
[2024-09-05T07:43:35.693Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-05T07:43:35.693Z] GC before operation: completed in 85.423 ms, heap usage 181.279 MB -> 50.700 MB.
[2024-09-05T07:43:38.357Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:43:42.039Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:43:44.698Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:43:47.365Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:43:49.083Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:43:50.488Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:43:52.209Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:43:53.929Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:43:53.929Z] 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.
[2024-09-05T07:43:53.929Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:43:53.929Z] Movies recommended for you:
[2024-09-05T07:43:53.929Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:43:53.929Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:43:53.929Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17895.035 ms) ======
[2024-09-05T07:43:53.929Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-05T07:43:53.929Z] GC before operation: completed in 96.964 ms, heap usage 71.868 MB -> 52.238 MB.
[2024-09-05T07:43:56.586Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:43:59.252Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:44:02.930Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:44:05.582Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:44:07.297Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:44:09.023Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:44:10.737Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:44:12.455Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:44:12.455Z] 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.
[2024-09-05T07:44:12.455Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:44:12.455Z] Movies recommended for you:
[2024-09-05T07:44:12.455Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:44:12.455Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:44:12.455Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18576.657 ms) ======
[2024-09-05T07:44:12.455Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-05T07:44:12.455Z] GC before operation: completed in 77.057 ms, heap usage 420.309 MB -> 54.383 MB.
[2024-09-05T07:44:16.132Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:44:18.793Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:44:21.453Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:44:24.107Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:44:25.820Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:44:27.547Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:44:29.265Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:44:30.980Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:44:30.980Z] 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.
[2024-09-05T07:44:30.980Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:44:30.980Z] Movies recommended for you:
[2024-09-05T07:44:30.980Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:44:30.980Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:44:30.980Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (18723.140 ms) ======
[2024-09-05T07:44:30.980Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-05T07:44:31.811Z] GC before operation: completed in 81.030 ms, heap usage 178.696 MB -> 50.692 MB.
[2024-09-05T07:44:34.474Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:44:37.119Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:44:39.765Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:44:42.414Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:44:44.129Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:44:45.838Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:44:47.551Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:44:49.269Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:44:49.269Z] 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.
[2024-09-05T07:44:49.269Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:44:49.269Z] Movies recommended for you:
[2024-09-05T07:44:49.269Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:44:49.269Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:44:49.269Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (18177.769 ms) ======
[2024-09-05T07:44:49.269Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-05T07:44:50.100Z] GC before operation: completed in 77.723 ms, heap usage 180.727 MB -> 50.936 MB.
[2024-09-05T07:44:52.773Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:44:55.417Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:44:58.072Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:45:00.724Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:45:02.438Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:45:04.740Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:45:06.459Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:45:08.180Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:45:08.180Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-09-05T07:45:08.180Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:45:08.180Z] Movies recommended for you:
[2024-09-05T07:45:08.180Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:45:08.180Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:45:08.180Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18876.156 ms) ======
[2024-09-05T07:45:08.180Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-05T07:45:09.018Z] GC before operation: completed in 80.295 ms, heap usage 150.594 MB -> 50.986 MB.
[2024-09-05T07:45:11.670Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:45:14.332Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:45:17.000Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:45:19.665Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:45:21.373Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:45:23.194Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:45:24.915Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:45:26.631Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:45:27.465Z] 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.
[2024-09-05T07:45:27.465Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:45:27.465Z] Movies recommended for you:
[2024-09-05T07:45:27.465Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:45:27.465Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:45:27.465Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (18579.026 ms) ======
[2024-09-05T07:45:27.465Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-05T07:45:27.465Z] GC before operation: completed in 110.792 ms, heap usage 428.886 MB -> 54.254 MB.
[2024-09-05T07:45:30.126Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:45:33.806Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:45:36.484Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:45:39.143Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:45:41.799Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:45:43.528Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:45:45.244Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:45:46.960Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:45:46.960Z] 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.
[2024-09-05T07:45:46.960Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:45:46.960Z] Movies recommended for you:
[2024-09-05T07:45:46.960Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:45:46.960Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:45:46.961Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19751.442 ms) ======
[2024-09-05T07:45:46.961Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-05T07:45:46.961Z] GC before operation: completed in 102.422 ms, heap usage 416.212 MB -> 54.279 MB.
[2024-09-05T07:45:50.637Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:45:53.291Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:45:55.960Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:45:58.620Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:46:01.281Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:46:02.998Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:46:04.712Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:46:05.546Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:46:06.376Z] 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.
[2024-09-05T07:46:06.376Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:46:06.376Z] Movies recommended for you:
[2024-09-05T07:46:06.376Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:46:06.376Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:46:06.376Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19038.233 ms) ======
[2024-09-05T07:46:06.376Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-05T07:46:06.376Z] GC before operation: completed in 104.143 ms, heap usage 173.140 MB -> 51.090 MB.
[2024-09-05T07:46:09.039Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T07:46:11.696Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T07:46:15.380Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T07:46:17.693Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T07:46:19.403Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T07:46:21.124Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T07:46:22.832Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T07:46:23.663Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T07:46:24.497Z] 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.
[2024-09-05T07:46:24.497Z] The best model improves the baseline by 14.52%.
[2024-09-05T07:46:24.497Z] Movies recommended for you:
[2024-09-05T07:46:24.497Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T07:46:24.497Z] There is no way to check that no silent failure occurred.
[2024-09-05T07:46:24.497Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17917.003 ms) ======
[2024-09-05T07:46:24.497Z] -----------------------------------
[2024-09-05T07:46:24.497Z] renaissance-movie-lens_0_PASSED
[2024-09-05T07:46:24.497Z] -----------------------------------
[2024-09-05T07:46:24.497Z]
[2024-09-05T07:46:24.497Z] TEST TEARDOWN:
[2024-09-05T07:46:24.497Z] Nothing to be done for teardown.
[2024-09-05T07:46:24.497Z] renaissance-movie-lens_0 Finish Time: Thu Sep 5 07:46:24 2024 Epoch Time (ms): 1725522384191