renaissance-movie-lens_0
[2024-08-28T23:38:52.470Z] Running test renaissance-movie-lens_0 ...
[2024-08-28T23:38:52.470Z] ===============================================
[2024-08-28T23:38:52.470Z] renaissance-movie-lens_0 Start Time: Wed Aug 28 23:38:51 2024 Epoch Time (ms): 1724888331672
[2024-08-28T23:38:52.470Z] variation: NoOptions
[2024-08-28T23:38:52.470Z] JVM_OPTIONS:
[2024-08-28T23:38:52.470Z] { \
[2024-08-28T23:38:52.470Z] echo ""; echo "TEST SETUP:"; \
[2024-08-28T23:38:52.470Z] echo "Nothing to be done for setup."; \
[2024-08-28T23:38:52.470Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_172488200392/renaissance-movie-lens_0"; \
[2024-08-28T23:38:52.470Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_172488200392/renaissance-movie-lens_0"; \
[2024-08-28T23:38:52.470Z] echo ""; echo "TESTING:"; \
[2024-08-28T23:38:52.470Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-11.0.25+5/bin/..//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_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_172488200392/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-28T23:38:52.470Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_172488200392/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-28T23:38:52.470Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-28T23:38:52.470Z] echo "Nothing to be done for teardown."; \
[2024-08-28T23:38:52.470Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_172488200392/TestTargetResult";
[2024-08-28T23:38:52.470Z]
[2024-08-28T23:38:52.470Z] TEST SETUP:
[2024-08-28T23:38:52.470Z] Nothing to be done for setup.
[2024-08-28T23:38:52.470Z]
[2024-08-28T23:38:52.470Z] TESTING:
[2024-08-28T23:39:04.241Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-28T23:39:18.355Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-08-28T23:39:41.390Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-28T23:39:42.182Z] Training: 60056, validation: 20285, test: 19854
[2024-08-28T23:39:42.182Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-28T23:39:42.965Z] GC before operation: completed in 619.477 ms, heap usage 114.139 MB -> 36.676 MB.
[2024-08-28T23:40:40.561Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:41:07.042Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:41:33.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:41:56.761Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:42:09.006Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:42:21.226Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:42:35.470Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:42:45.608Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:42:48.177Z] 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.
[2024-08-28T23:42:48.177Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:42:48.966Z] Movies recommended for you:
[2024-08-28T23:42:48.966Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:42:48.966Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:42:48.966Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (186423.205 ms) ======
[2024-08-28T23:42:48.966Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-28T23:42:49.818Z] GC before operation: completed in 829.475 ms, heap usage 220.855 MB -> 50.091 MB.
[2024-08-28T23:43:09.006Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:43:29.423Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:43:46.039Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:44:02.678Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:44:12.915Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:44:23.520Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:44:34.181Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:44:42.967Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:44:45.663Z] 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.
[2024-08-28T23:44:45.663Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:44:46.477Z] Movies recommended for you:
[2024-08-28T23:44:46.477Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:44:46.477Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:44:46.477Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (116124.420 ms) ======
[2024-08-28T23:44:46.477Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-28T23:44:46.477Z] GC before operation: completed in 566.348 ms, heap usage 298.108 MB -> 49.250 MB.
[2024-08-28T23:45:04.006Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:45:21.135Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:45:41.022Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:45:57.866Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:46:06.581Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:46:17.013Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:46:28.089Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:46:40.543Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:46:42.273Z] 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.
[2024-08-28T23:46:42.273Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:46:43.103Z] Movies recommended for you:
[2024-08-28T23:46:43.103Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:46:43.103Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:46:43.103Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (116132.782 ms) ======
[2024-08-28T23:46:43.103Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-28T23:46:44.002Z] GC before operation: completed in 561.327 ms, heap usage 90.530 MB -> 49.384 MB.
[2024-08-28T23:47:01.145Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:47:20.879Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:47:37.958Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:47:52.424Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:48:01.783Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:48:14.107Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:48:23.044Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:48:31.979Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:48:33.760Z] 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.
[2024-08-28T23:48:33.760Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:48:34.586Z] Movies recommended for you:
[2024-08-28T23:48:34.586Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:48:34.586Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:48:34.586Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (111162.475 ms) ======
[2024-08-28T23:48:34.586Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-28T23:48:35.426Z] GC before operation: completed in 505.243 ms, heap usage 228.492 MB -> 49.801 MB.
[2024-08-28T23:48:52.295Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:49:04.563Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:49:21.569Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:49:39.255Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:49:49.731Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:50:00.167Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:50:10.741Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:50:19.742Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:50:21.525Z] 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.
[2024-08-28T23:50:22.364Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:50:22.364Z] Movies recommended for you:
[2024-08-28T23:50:22.364Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:50:22.364Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:50:22.364Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (107506.471 ms) ======
[2024-08-28T23:50:22.364Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-28T23:50:23.218Z] GC before operation: completed in 716.509 ms, heap usage 128.865 MB -> 49.909 MB.
[2024-08-28T23:50:43.228Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:50:58.378Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:51:18.611Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:51:35.591Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:51:48.151Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:51:57.051Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:52:07.763Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:52:18.358Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:52:20.111Z] 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.
[2024-08-28T23:52:20.111Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:52:20.963Z] Movies recommended for you:
[2024-08-28T23:52:20.963Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:52:20.963Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:52:20.963Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (117144.458 ms) ======
[2024-08-28T23:52:20.963Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-28T23:52:20.963Z] GC before operation: completed in 550.471 ms, heap usage 298.679 MB -> 49.986 MB.
[2024-08-28T23:52:38.611Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:52:55.493Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:53:15.268Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:53:30.001Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:53:38.919Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:53:47.506Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:53:57.783Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:54:06.989Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:54:08.678Z] 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.
[2024-08-28T23:54:08.678Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:54:09.522Z] Movies recommended for you:
[2024-08-28T23:54:09.522Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:54:09.522Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:54:09.522Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (108347.770 ms) ======
[2024-08-28T23:54:09.522Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-28T23:54:10.335Z] GC before operation: completed in 620.148 ms, heap usage 321.490 MB -> 50.213 MB.
[2024-08-28T23:54:29.755Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:54:41.719Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:54:58.137Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:55:10.146Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:55:18.643Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:55:25.820Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:55:33.749Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:55:42.386Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:55:42.386Z] 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.
[2024-08-28T23:55:43.235Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:55:43.235Z] Movies recommended for you:
[2024-08-28T23:55:43.235Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:55:43.235Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:55:43.235Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (93160.442 ms) ======
[2024-08-28T23:55:43.235Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-28T23:55:44.028Z] GC before operation: completed in 407.637 ms, heap usage 277.112 MB -> 50.416 MB.
[2024-08-28T23:55:58.171Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:56:10.350Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:56:24.439Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:56:36.496Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:56:43.749Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:56:50.994Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:57:00.070Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:57:07.327Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:57:09.051Z] 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.
[2024-08-28T23:57:09.051Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:57:09.896Z] Movies recommended for you:
[2024-08-28T23:57:09.896Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:57:09.896Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:57:09.896Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (86145.616 ms) ======
[2024-08-28T23:57:09.896Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-28T23:57:10.732Z] GC before operation: completed in 600.836 ms, heap usage 225.383 MB -> 50.191 MB.
[2024-08-28T23:57:22.947Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:57:37.399Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:57:54.219Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:58:06.440Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:58:13.751Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:58:22.936Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-28T23:58:31.550Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-28T23:58:40.206Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-28T23:58:41.903Z] 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.
[2024-08-28T23:58:41.903Z] The best model improves the baseline by 14.52%.
[2024-08-28T23:58:41.903Z] Movies recommended for you:
[2024-08-28T23:58:41.903Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-28T23:58:41.903Z] There is no way to check that no silent failure occurred.
[2024-08-28T23:58:41.903Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (91832.570 ms) ======
[2024-08-28T23:58:41.903Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-28T23:58:42.726Z] GC before operation: completed in 462.959 ms, heap usage 199.563 MB -> 50.301 MB.
[2024-08-28T23:58:56.945Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-28T23:59:11.180Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-28T23:59:21.575Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-28T23:59:35.819Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-28T23:59:45.133Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-28T23:59:52.249Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:00:00.512Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:00:10.315Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:00:11.095Z] 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.
[2024-08-29T00:00:11.095Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:00:11.850Z] Movies recommended for you:
[2024-08-29T00:00:11.850Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:00:11.850Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:00:11.850Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (89055.916 ms) ======
[2024-08-29T00:00:11.850Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-29T00:00:12.627Z] GC before operation: completed in 519.657 ms, heap usage 99.472 MB -> 53.212 MB.
[2024-08-29T00:00:26.341Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:00:40.019Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:00:51.715Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:01:05.431Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:01:12.853Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:01:21.152Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:01:29.485Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:01:37.915Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:01:39.491Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-08-29T00:01:39.491Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:01:39.491Z] Movies recommended for you:
[2024-08-29T00:01:39.491Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:01:39.491Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:01:39.491Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (87568.115 ms) ======
[2024-08-29T00:01:39.491Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-29T00:01:40.263Z] GC before operation: completed in 578.170 ms, heap usage 244.622 MB -> 50.283 MB.
[2024-08-29T00:01:53.955Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:02:07.651Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:02:23.705Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:02:35.404Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:02:42.259Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:02:50.565Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:02:58.017Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:03:07.899Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:03:09.515Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-08-29T00:03:09.515Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:03:10.281Z] Movies recommended for you:
[2024-08-29T00:03:10.281Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:03:10.281Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:03:10.281Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (89782.432 ms) ======
[2024-08-29T00:03:10.281Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-29T00:03:11.056Z] GC before operation: completed in 561.908 ms, heap usage 187.955 MB -> 50.384 MB.
[2024-08-29T00:03:24.748Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:03:38.429Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:03:54.534Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:04:08.198Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:04:15.080Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:04:23.506Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:04:33.335Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:04:40.813Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:04:42.411Z] 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.
[2024-08-29T00:04:42.411Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:04:43.187Z] Movies recommended for you:
[2024-08-29T00:04:43.187Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:04:43.187Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:04:43.187Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (92426.602 ms) ======
[2024-08-29T00:04:43.187Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-29T00:04:43.970Z] GC before operation: completed in 639.269 ms, heap usage 181.868 MB -> 47.783 MB.
[2024-08-29T00:04:57.750Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:05:11.496Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:05:25.283Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:05:39.139Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:05:47.423Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:05:54.263Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:06:05.935Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:06:11.437Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:06:12.980Z] 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.
[2024-08-29T00:06:12.980Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:06:13.728Z] Movies recommended for you:
[2024-08-29T00:06:13.728Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:06:13.728Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:06:13.728Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (89830.148 ms) ======
[2024-08-29T00:06:13.728Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-29T00:06:14.482Z] GC before operation: completed in 468.092 ms, heap usage 271.307 MB -> 47.940 MB.
[2024-08-29T00:06:28.686Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:06:44.693Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:06:58.308Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:07:09.890Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:07:18.335Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:07:26.517Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:07:33.347Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:07:41.538Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:07:43.099Z] 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.
[2024-08-29T00:07:43.099Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:07:43.838Z] Movies recommended for you:
[2024-08-29T00:07:43.838Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:07:43.838Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:07:43.838Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (89543.963 ms) ======
[2024-08-29T00:07:43.838Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-29T00:07:43.838Z] GC before operation: completed in 487.201 ms, heap usage 213.533 MB -> 47.826 MB.
[2024-08-29T00:08:00.183Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:08:13.743Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:08:29.748Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:08:41.239Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:08:49.379Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:08:57.483Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:09:05.635Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:09:13.697Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:09:15.230Z] 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.
[2024-08-29T00:09:15.230Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:09:15.971Z] Movies recommended for you:
[2024-08-29T00:09:15.971Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:09:15.971Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:09:15.971Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (91907.115 ms) ======
[2024-08-29T00:09:15.971Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-29T00:09:16.738Z] GC before operation: completed in 539.919 ms, heap usage 138.529 MB -> 48.154 MB.
[2024-08-29T00:09:30.384Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:09:44.535Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:09:58.128Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:10:09.587Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:10:16.382Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:10:23.097Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:10:31.442Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:10:39.677Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:10:41.248Z] 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.
[2024-08-29T00:10:41.248Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:10:41.248Z] Movies recommended for you:
[2024-08-29T00:10:41.248Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:10:41.248Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:10:41.248Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (85004.997 ms) ======
[2024-08-29T00:10:41.248Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-29T00:10:41.995Z] GC before operation: completed in 637.880 ms, heap usage 187.450 MB -> 47.734 MB.
[2024-08-29T00:10:55.588Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:11:09.139Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:11:20.711Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:11:34.549Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:11:41.346Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:11:46.959Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:11:55.122Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:12:01.813Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:12:03.409Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-08-29T00:12:03.409Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:12:04.172Z] Movies recommended for you:
[2024-08-29T00:12:04.172Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:12:04.172Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:12:04.172Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (81649.437 ms) ======
[2024-08-29T00:12:04.172Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-29T00:12:04.172Z] GC before operation: completed in 420.890 ms, heap usage 280.217 MB -> 47.963 MB.
[2024-08-29T00:12:17.680Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T00:12:29.603Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T00:12:41.816Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T00:12:52.264Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T00:12:59.594Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T00:13:06.183Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T00:13:13.497Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T00:13:20.869Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T00:13:22.630Z] 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.
[2024-08-29T00:13:22.630Z] The best model improves the baseline by 14.52%.
[2024-08-29T00:13:23.476Z] Movies recommended for you:
[2024-08-29T00:13:23.477Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T00:13:23.477Z] There is no way to check that no silent failure occurred.
[2024-08-29T00:13:23.477Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (78693.933 ms) ======
[2024-08-29T00:13:25.180Z] -----------------------------------
[2024-08-29T00:13:25.180Z] renaissance-movie-lens_0_PASSED
[2024-08-29T00:13:25.180Z] -----------------------------------
[2024-08-29T00:13:25.180Z]
[2024-08-29T00:13:25.180Z] TEST TEARDOWN:
[2024-08-29T00:13:25.180Z] Nothing to be done for teardown.
[2024-08-29T00:13:25.180Z] renaissance-movie-lens_0 Finish Time: Thu Aug 29 00:13:24 2024 Epoch Time (ms): 1724890404623