renaissance-movie-lens_0
[2024-10-16T01:10:19.920Z] Running test renaissance-movie-lens_0 ...
[2024-10-16T01:10:19.920Z] ===============================================
[2024-10-16T01:10:19.920Z] renaissance-movie-lens_0 Start Time: Wed Oct 16 01:10:19 2024 Epoch Time (ms): 1729041019296
[2024-10-16T01:10:19.920Z] variation: NoOptions
[2024-10-16T01:10:19.920Z] JVM_OPTIONS:
[2024-10-16T01:10:19.920Z] { \
[2024-10-16T01:10:19.920Z] echo ""; echo "TEST SETUP:"; \
[2024-10-16T01:10:19.920Z] echo "Nothing to be done for setup."; \
[2024-10-16T01:10:19.920Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17290386481295/renaissance-movie-lens_0"; \
[2024-10-16T01:10:19.920Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17290386481295/renaissance-movie-lens_0"; \
[2024-10-16T01:10:19.920Z] echo ""; echo "TESTING:"; \
[2024-10-16T01:10:19.921Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_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_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17290386481295/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-16T01:10:19.921Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17290386481295/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-16T01:10:19.921Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-16T01:10:19.921Z] echo "Nothing to be done for teardown."; \
[2024-10-16T01:10:19.921Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17290386481295/TestTargetResult";
[2024-10-16T01:10:19.921Z]
[2024-10-16T01:10:19.921Z] TEST SETUP:
[2024-10-16T01:10:19.921Z] Nothing to be done for setup.
[2024-10-16T01:10:19.921Z]
[2024-10-16T01:10:19.921Z] TESTING:
[2024-10-16T01:10:31.046Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-16T01:10:39.635Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-10-16T01:10:53.936Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-16T01:10:54.597Z] Training: 60056, validation: 20285, test: 19854
[2024-10-16T01:10:54.598Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-16T01:10:54.598Z] GC before operation: completed in 293.749 ms, heap usage 72.757 MB -> 36.462 MB.
[2024-10-16T01:11:24.413Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:11:38.351Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:11:54.221Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:12:04.084Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:12:09.416Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:12:13.459Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:12:20.416Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:12:28.694Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:12:30.091Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:12:30.091Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:12:30.091Z] Movies recommended for you:
[2024-10-16T01:12:30.091Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:12:30.091Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:12:30.091Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (95380.795 ms) ======
[2024-10-16T01:12:30.091Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-16T01:12:31.014Z] GC before operation: completed in 763.342 ms, heap usage 202.797 MB -> 57.908 MB.
[2024-10-16T01:12:42.466Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:12:54.329Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:13:04.329Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:13:15.970Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:13:24.156Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:13:28.181Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:13:33.540Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:13:39.071Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:13:40.719Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:13:40.719Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:13:41.874Z] Movies recommended for you:
[2024-10-16T01:13:41.874Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:13:41.874Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:13:41.874Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (70523.675 ms) ======
[2024-10-16T01:13:41.874Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-16T01:13:42.677Z] GC before operation: completed in 800.585 ms, heap usage 107.357 MB -> 48.354 MB.
[2024-10-16T01:13:57.955Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:14:09.145Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:14:18.560Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:14:26.264Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:14:33.096Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:14:39.830Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:14:45.802Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:14:54.243Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:14:54.906Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:14:54.906Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:14:54.906Z] Movies recommended for you:
[2024-10-16T01:14:54.906Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:14:54.906Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:14:54.906Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (72613.610 ms) ======
[2024-10-16T01:14:54.906Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-16T01:14:55.632Z] GC before operation: completed in 372.704 ms, heap usage 254.060 MB -> 48.751 MB.
[2024-10-16T01:15:07.149Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:15:18.425Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:15:26.275Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:15:32.703Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:15:39.101Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:15:43.748Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:15:48.957Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:15:52.174Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:15:53.053Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:15:53.054Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:15:53.054Z] Movies recommended for you:
[2024-10-16T01:15:53.054Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:15:53.054Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:15:53.054Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (57840.859 ms) ======
[2024-10-16T01:15:53.054Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-16T01:15:53.834Z] GC before operation: completed in 627.169 ms, heap usage 115.784 MB -> 48.944 MB.
[2024-10-16T01:16:03.386Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:16:09.869Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:16:19.146Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:16:25.964Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:16:32.836Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:16:37.354Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:16:41.431Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:16:45.422Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:16:46.085Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:16:46.085Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:16:46.085Z] Movies recommended for you:
[2024-10-16T01:16:46.085Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:16:46.085Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:16:46.085Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (52482.928 ms) ======
[2024-10-16T01:16:46.085Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-16T01:16:46.797Z] GC before operation: completed in 268.044 ms, heap usage 295.426 MB -> 49.336 MB.
[2024-10-16T01:16:57.750Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:17:05.483Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:17:14.971Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:17:22.737Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:17:25.808Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:17:29.825Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:17:33.895Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:17:37.938Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:17:38.815Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:17:38.815Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:17:38.815Z] Movies recommended for you:
[2024-10-16T01:17:38.815Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:17:38.815Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:17:38.815Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (52015.559 ms) ======
[2024-10-16T01:17:38.815Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-16T01:17:38.815Z] GC before operation: completed in 503.395 ms, heap usage 212.238 MB -> 51.860 MB.
[2024-10-16T01:17:50.189Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:17:59.263Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:18:12.436Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:18:21.432Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:18:29.580Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:18:34.709Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:18:40.569Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:18:47.311Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:18:47.311Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:18:47.311Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:18:47.311Z] Movies recommended for you:
[2024-10-16T01:18:47.311Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:18:47.311Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:18:47.311Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (68270.816 ms) ======
[2024-10-16T01:18:47.311Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-16T01:18:48.075Z] GC before operation: completed in 470.985 ms, heap usage 135.362 MB -> 49.272 MB.
[2024-10-16T01:18:55.908Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:19:07.437Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:19:15.677Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:19:27.206Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:19:31.195Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:19:34.418Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:19:39.691Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:19:44.863Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:19:45.515Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:19:45.515Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:19:45.515Z] Movies recommended for you:
[2024-10-16T01:19:45.515Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:19:45.515Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:19:45.515Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (57513.173 ms) ======
[2024-10-16T01:19:45.515Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-16T01:19:46.261Z] GC before operation: completed in 784.526 ms, heap usage 345.658 MB -> 52.917 MB.
[2024-10-16T01:19:55.505Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:20:05.530Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:20:13.655Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:20:21.585Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:20:25.595Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:20:31.324Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:20:35.271Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:20:40.592Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:20:40.592Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:20:40.592Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:20:41.715Z] Movies recommended for you:
[2024-10-16T01:20:41.715Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:20:41.715Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:20:41.716Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (54843.793 ms) ======
[2024-10-16T01:20:41.716Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-16T01:20:42.466Z] GC before operation: completed in 1286.470 ms, heap usage 74.742 MB -> 54.869 MB.
[2024-10-16T01:20:53.199Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:20:59.285Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:21:12.499Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:21:24.396Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:21:26.512Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:21:30.867Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:21:36.580Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:21:43.062Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:21:43.749Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:21:43.749Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:21:43.749Z] Movies recommended for you:
[2024-10-16T01:21:43.749Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:21:43.749Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:21:43.749Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (61573.783 ms) ======
[2024-10-16T01:21:43.749Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-16T01:21:44.415Z] GC before operation: completed in 693.864 ms, heap usage 236.941 MB -> 49.578 MB.
[2024-10-16T01:21:54.233Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:22:01.784Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:22:11.554Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:22:17.899Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:22:23.427Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:22:30.359Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:22:35.645Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:22:42.875Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:22:43.536Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:22:43.536Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:22:43.536Z] Movies recommended for you:
[2024-10-16T01:22:43.536Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:22:43.536Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:22:43.536Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (59000.362 ms) ======
[2024-10-16T01:22:43.536Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-16T01:22:43.536Z] GC before operation: completed in 303.651 ms, heap usage 169.363 MB -> 49.273 MB.
[2024-10-16T01:22:55.216Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:23:04.865Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:23:18.693Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:23:26.419Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:23:31.645Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:23:36.810Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:23:41.079Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:23:45.275Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:23:45.275Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:23:45.275Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:23:45.275Z] Movies recommended for you:
[2024-10-16T01:23:45.275Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:23:45.275Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:23:45.275Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (61658.464 ms) ======
[2024-10-16T01:23:45.275Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-16T01:23:45.933Z] GC before operation: completed in 353.038 ms, heap usage 73.843 MB -> 52.292 MB.
[2024-10-16T01:23:57.291Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:24:08.892Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:24:16.994Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:24:27.119Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:24:32.622Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:24:38.366Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:24:43.726Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:24:49.557Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:24:50.347Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:24:50.347Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:24:51.029Z] Movies recommended for you:
[2024-10-16T01:24:51.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:24:51.029Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:24:51.029Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (65000.678 ms) ======
[2024-10-16T01:24:51.029Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-16T01:24:52.519Z] GC before operation: completed in 1281.677 ms, heap usage 314.272 MB -> 49.749 MB.
[2024-10-16T01:25:01.803Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:25:06.898Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:25:20.480Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:25:28.015Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:25:31.636Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:25:41.367Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:25:46.945Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:25:51.448Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:25:52.865Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:25:52.865Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:25:52.865Z] Movies recommended for you:
[2024-10-16T01:25:52.865Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:25:52.865Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:25:52.866Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (60802.834 ms) ======
[2024-10-16T01:25:52.866Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-16T01:25:53.594Z] GC before operation: completed in 435.609 ms, heap usage 269.486 MB -> 49.493 MB.
[2024-10-16T01:26:02.907Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:26:12.398Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:26:20.042Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:26:25.892Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:26:30.104Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:26:34.210Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:26:40.476Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:26:44.852Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:26:46.656Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:26:46.656Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:26:46.656Z] Movies recommended for you:
[2024-10-16T01:26:46.656Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:26:46.656Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:26:46.656Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (53550.335 ms) ======
[2024-10-16T01:26:46.656Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-16T01:26:47.269Z] GC before operation: completed in 341.042 ms, heap usage 112.398 MB -> 50.439 MB.
[2024-10-16T01:26:54.751Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:27:05.856Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:27:14.940Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:27:22.796Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:27:27.862Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:27:32.207Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:27:40.505Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:27:45.768Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:27:46.554Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:27:46.554Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:27:47.303Z] Movies recommended for you:
[2024-10-16T01:27:47.304Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:27:47.304Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:27:47.304Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (59692.719 ms) ======
[2024-10-16T01:27:47.304Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-16T01:27:47.304Z] GC before operation: completed in 509.816 ms, heap usage 73.823 MB -> 50.199 MB.
[2024-10-16T01:27:55.199Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:28:03.072Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:28:11.542Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:28:18.091Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:28:22.056Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:28:26.148Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:28:31.363Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:28:35.668Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:28:36.339Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:28:36.339Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:28:36.339Z] Movies recommended for you:
[2024-10-16T01:28:36.339Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:28:36.339Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:28:36.339Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (49048.507 ms) ======
[2024-10-16T01:28:36.339Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-16T01:28:36.993Z] GC before operation: completed in 618.030 ms, heap usage 161.843 MB -> 47.415 MB.
[2024-10-16T01:28:44.808Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:28:52.872Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:29:00.029Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:29:07.495Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:29:12.688Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:29:16.848Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:29:20.209Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:29:24.237Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:29:25.978Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:29:25.978Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:29:25.978Z] Movies recommended for you:
[2024-10-16T01:29:25.978Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:29:25.978Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:29:25.978Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (48644.549 ms) ======
[2024-10-16T01:29:25.978Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-16T01:29:25.978Z] GC before operation: completed in 415.917 ms, heap usage 282.492 MB -> 50.209 MB.
[2024-10-16T01:29:33.906Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:29:40.505Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:29:49.232Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:29:58.749Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:30:03.233Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:30:09.809Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:30:16.395Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:30:21.818Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:30:21.818Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:30:22.520Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:30:22.520Z] Movies recommended for you:
[2024-10-16T01:30:22.520Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:30:22.520Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:30:22.520Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (56319.346 ms) ======
[2024-10-16T01:30:22.520Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-16T01:30:23.276Z] GC before operation: completed in 345.039 ms, heap usage 254.648 MB -> 47.164 MB.
[2024-10-16T01:30:32.714Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-16T01:30:39.403Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-16T01:30:48.753Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-16T01:30:57.125Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-16T01:31:02.718Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-16T01:31:08.428Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-16T01:31:13.493Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-16T01:31:19.520Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-16T01:31:19.520Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-16T01:31:20.205Z] The best model improves the baseline by 14.34%.
[2024-10-16T01:31:20.205Z] Movies recommended for you:
[2024-10-16T01:31:20.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-16T01:31:20.205Z] There is no way to check that no silent failure occurred.
[2024-10-16T01:31:20.205Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (57119.291 ms) ======
[2024-10-16T01:31:20.855Z] -----------------------------------
[2024-10-16T01:31:20.855Z] renaissance-movie-lens_0_PASSED
[2024-10-16T01:31:20.855Z] -----------------------------------
[2024-10-16T01:31:20.855Z]
[2024-10-16T01:31:20.855Z] TEST TEARDOWN:
[2024-10-16T01:31:20.855Z] Nothing to be done for teardown.
[2024-10-16T01:31:20.855Z] renaissance-movie-lens_0 Finish Time: Wed Oct 16 01:31:20 2024 Epoch Time (ms): 1729042280512