renaissance-movie-lens_0
[2025-02-13T00:34:44.913Z] Running test renaissance-movie-lens_0 ...
[2025-02-13T00:34:44.913Z] ===============================================
[2025-02-13T00:34:44.913Z] renaissance-movie-lens_0 Start Time: Thu Feb 13 00:34:44 2025 Epoch Time (ms): 1739406884052
[2025-02-13T00:34:44.913Z] variation: NoOptions
[2025-02-13T00:34:44.913Z] JVM_OPTIONS:
[2025-02-13T00:34:44.913Z] { \
[2025-02-13T00:34:44.913Z] echo ""; echo "TEST SETUP:"; \
[2025-02-13T00:34:44.913Z] echo "Nothing to be done for setup."; \
[2025-02-13T00:34:44.913Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17394024543185/renaissance-movie-lens_0"; \
[2025-02-13T00:34:44.913Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17394024543185/renaissance-movie-lens_0"; \
[2025-02-13T00:34:44.913Z] echo ""; echo "TESTING:"; \
[2025-02-13T00:34:44.913Z] "/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_17394024543185/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-13T00:34:44.913Z] 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_17394024543185/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-13T00:34:44.913Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-13T00:34:44.913Z] echo "Nothing to be done for teardown."; \
[2025-02-13T00:34:44.913Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17394024543185/TestTargetResult";
[2025-02-13T00:34:44.913Z]
[2025-02-13T00:34:44.913Z] TEST SETUP:
[2025-02-13T00:34:44.913Z] Nothing to be done for setup.
[2025-02-13T00:34:44.913Z]
[2025-02-13T00:34:44.913Z] TESTING:
[2025-02-13T00:35:02.424Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-13T00:35:17.586Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-13T00:35:50.313Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-13T00:35:52.143Z] Training: 60056, validation: 20285, test: 19854
[2025-02-13T00:35:52.143Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-13T00:35:52.143Z] GC before operation: completed in 393.554 ms, heap usage 143.578 MB -> 37.157 MB.
[2025-02-13T00:37:02.051Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:37:34.653Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:38:12.501Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:38:44.072Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:39:03.975Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:39:19.309Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:39:34.343Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:39:49.065Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:39:49.956Z] 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.
[2025-02-13T00:39:50.823Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:39:51.702Z] Movies recommended for you:
[2025-02-13T00:39:51.702Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:39:51.702Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:39:51.702Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (239471.219 ms) ======
[2025-02-13T00:39:51.702Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-13T00:39:52.557Z] GC before operation: completed in 743.326 ms, heap usage 279.490 MB -> 52.503 MB.
[2025-02-13T00:40:19.825Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:40:40.036Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:41:03.348Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:41:20.550Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:41:33.049Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:41:46.031Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:41:58.628Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:42:10.912Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:42:11.711Z] 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.
[2025-02-13T00:42:11.711Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:42:12.497Z] Movies recommended for you:
[2025-02-13T00:42:12.497Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:42:12.497Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:42:12.497Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (139991.795 ms) ======
[2025-02-13T00:42:12.497Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-13T00:42:13.303Z] GC before operation: completed in 506.945 ms, heap usage 193.956 MB -> 52.834 MB.
[2025-02-13T00:42:32.635Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:42:55.314Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:43:18.238Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:43:32.667Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:43:42.996Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:43:53.285Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:44:04.294Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:44:14.707Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:44:17.481Z] 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.
[2025-02-13T00:44:17.481Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:44:18.322Z] Movies recommended for you:
[2025-02-13T00:44:18.322Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:44:18.322Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:44:18.322Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (124998.170 ms) ======
[2025-02-13T00:44:18.322Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-13T00:44:19.139Z] GC before operation: completed in 672.198 ms, heap usage 225.820 MB -> 52.153 MB.
[2025-02-13T00:44:38.777Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:44:53.424Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:45:10.087Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:45:24.448Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:45:36.386Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:45:45.007Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:45:54.148Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:46:02.678Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:46:05.484Z] 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.
[2025-02-13T00:46:05.484Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:46:06.297Z] Movies recommended for you:
[2025-02-13T00:46:06.297Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:46:06.297Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:46:06.297Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (107002.050 ms) ======
[2025-02-13T00:46:06.297Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-13T00:46:06.297Z] GC before operation: completed in 429.485 ms, heap usage 188.022 MB -> 50.449 MB.
[2025-02-13T00:46:29.230Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:46:45.883Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:47:03.207Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:47:17.451Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:47:29.659Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:47:38.275Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:47:48.347Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:47:58.505Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:48:01.065Z] 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.
[2025-02-13T00:48:01.065Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:48:02.052Z] Movies recommended for you:
[2025-02-13T00:48:02.052Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:48:02.052Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:48:02.052Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (115527.095 ms) ======
[2025-02-13T00:48:02.052Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-13T00:48:03.247Z] GC before operation: completed in 642.904 ms, heap usage 214.773 MB -> 50.354 MB.
[2025-02-13T00:48:19.867Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:48:36.431Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:48:52.762Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:49:09.248Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:49:18.195Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:49:30.147Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:49:42.102Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:49:52.252Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:49:53.051Z] 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.
[2025-02-13T00:49:53.859Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:49:53.860Z] Movies recommended for you:
[2025-02-13T00:49:53.860Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:49:53.860Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:49:53.860Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (111727.999 ms) ======
[2025-02-13T00:49:53.860Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-13T00:49:54.707Z] GC before operation: completed in 522.114 ms, heap usage 582.046 MB -> 53.826 MB.
[2025-02-13T00:50:14.294Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:50:30.981Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:50:47.303Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:51:03.642Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:51:13.853Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:51:22.252Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:51:34.198Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:51:42.730Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:51:43.546Z] 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.
[2025-02-13T00:51:44.403Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:51:44.403Z] Movies recommended for you:
[2025-02-13T00:51:44.403Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:51:44.403Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:51:44.403Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (109817.555 ms) ======
[2025-02-13T00:51:44.403Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-13T00:51:45.208Z] GC before operation: completed in 506.737 ms, heap usage 239.733 MB -> 50.498 MB.
[2025-02-13T00:52:01.797Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:52:16.018Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:52:30.177Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:52:44.195Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:52:52.804Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:53:01.392Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:53:09.856Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:53:20.712Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:53:22.344Z] 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.
[2025-02-13T00:53:22.344Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:53:23.125Z] Movies recommended for you:
[2025-02-13T00:53:23.125Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:53:23.125Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:53:23.125Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (97762.350 ms) ======
[2025-02-13T00:53:23.125Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-13T00:53:23.125Z] GC before operation: completed in 465.465 ms, heap usage 335.514 MB -> 50.897 MB.
[2025-02-13T00:53:42.360Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:54:01.685Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:54:15.858Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:54:32.135Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:54:40.481Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:54:50.688Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:55:00.702Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:55:09.075Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:55:11.569Z] 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.
[2025-02-13T00:55:11.569Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:55:12.362Z] Movies recommended for you:
[2025-02-13T00:55:12.362Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:55:12.362Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:55:12.362Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (109224.764 ms) ======
[2025-02-13T00:55:12.362Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-13T00:55:13.168Z] GC before operation: completed in 569.968 ms, heap usage 264.446 MB -> 52.912 MB.
[2025-02-13T00:55:32.774Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:55:46.673Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:56:03.110Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:56:19.759Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:56:27.419Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:56:35.928Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:56:46.044Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:56:54.477Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:56:56.142Z] 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.
[2025-02-13T00:56:56.910Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:56:56.910Z] Movies recommended for you:
[2025-02-13T00:56:56.910Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:56:56.910Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:56:56.910Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (104012.783 ms) ======
[2025-02-13T00:56:56.910Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-13T00:56:57.724Z] GC before operation: completed in 604.951 ms, heap usage 269.890 MB -> 54.523 MB.
[2025-02-13T00:57:16.928Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:57:31.420Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:57:47.693Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:58:01.646Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:58:10.014Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:58:18.381Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T00:58:28.837Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T00:58:37.242Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T00:58:38.045Z] 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.
[2025-02-13T00:58:38.045Z] The best model improves the baseline by 14.52%.
[2025-02-13T00:58:38.834Z] Movies recommended for you:
[2025-02-13T00:58:38.835Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T00:58:38.835Z] There is no way to check that no silent failure occurred.
[2025-02-13T00:58:38.835Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (101139.929 ms) ======
[2025-02-13T00:58:38.835Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-13T00:58:39.626Z] GC before operation: completed in 457.581 ms, heap usage 162.245 MB -> 50.499 MB.
[2025-02-13T00:58:55.964Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T00:59:09.865Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T00:59:26.440Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T00:59:39.179Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T00:59:48.002Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T00:59:55.376Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:00:04.489Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:00:15.041Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:00:15.874Z] 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.
[2025-02-13T01:00:15.874Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:00:17.061Z] Movies recommended for you:
[2025-02-13T01:00:17.061Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:00:17.061Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:00:17.061Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (96987.274 ms) ======
[2025-02-13T01:00:17.061Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-13T01:00:17.061Z] GC before operation: completed in 771.692 ms, heap usage 387.901 MB -> 49.001 MB.
[2025-02-13T01:00:33.976Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:00:48.620Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:01:03.264Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:01:17.971Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:01:26.805Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:01:35.904Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:01:46.500Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:01:55.245Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:01:57.001Z] 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.
[2025-02-13T01:01:57.001Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:01:57.825Z] Movies recommended for you:
[2025-02-13T01:01:57.825Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:01:57.825Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:01:57.825Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (100650.397 ms) ======
[2025-02-13T01:01:57.825Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-13T01:01:58.684Z] GC before operation: completed in 710.705 ms, heap usage 268.908 MB -> 48.663 MB.
[2025-02-13T01:02:13.249Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:02:28.161Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:02:43.511Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:02:58.141Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:03:07.063Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:03:17.681Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:03:28.251Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:03:37.902Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:03:38.743Z] 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.
[2025-02-13T01:03:38.743Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:03:39.612Z] Movies recommended for you:
[2025-02-13T01:03:39.612Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:03:39.612Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:03:39.612Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (101374.712 ms) ======
[2025-02-13T01:03:39.612Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-13T01:03:40.466Z] GC before operation: completed in 571.965 ms, heap usage 173.536 MB -> 50.398 MB.
[2025-02-13T01:04:00.540Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:04:15.111Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:04:29.720Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:04:42.567Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:04:50.250Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:05:00.722Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:05:09.530Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:05:16.766Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:05:19.438Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-13T01:05:19.438Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:05:20.292Z] Movies recommended for you:
[2025-02-13T01:05:20.292Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:05:20.292Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:05:20.292Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (99921.291 ms) ======
[2025-02-13T01:05:20.292Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-13T01:05:21.120Z] GC before operation: completed in 530.949 ms, heap usage 159.082 MB -> 48.667 MB.
[2025-02-13T01:05:34.044Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:05:48.749Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:06:01.107Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:06:13.808Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:06:21.393Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:06:28.830Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:06:36.605Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:06:45.574Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:06:46.431Z] 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.
[2025-02-13T01:06:46.431Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:06:47.341Z] Movies recommended for you:
[2025-02-13T01:06:47.341Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:06:47.341Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:06:47.341Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (86375.727 ms) ======
[2025-02-13T01:06:47.341Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-13T01:06:48.196Z] GC before operation: completed in 484.194 ms, heap usage 207.317 MB -> 49.018 MB.
[2025-02-13T01:07:08.113Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:07:20.612Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:07:35.193Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:07:46.528Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:07:53.921Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:08:01.397Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:08:10.144Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:08:19.130Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:08:19.993Z] 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.
[2025-02-13T01:08:19.993Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:08:20.821Z] Movies recommended for you:
[2025-02-13T01:08:20.822Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:08:20.822Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:08:20.822Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (92951.365 ms) ======
[2025-02-13T01:08:20.822Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-13T01:08:21.650Z] GC before operation: completed in 532.738 ms, heap usage 138.239 MB -> 50.083 MB.
[2025-02-13T01:08:38.559Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:08:50.998Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:09:03.365Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:09:16.495Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:09:25.292Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:09:33.996Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:09:41.241Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:09:50.102Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:09:50.959Z] 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.
[2025-02-13T01:09:50.959Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:09:51.847Z] Movies recommended for you:
[2025-02-13T01:09:51.847Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:09:51.847Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:09:51.847Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (90545.660 ms) ======
[2025-02-13T01:09:51.847Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-13T01:09:52.675Z] GC before operation: completed in 529.173 ms, heap usage 314.144 MB -> 48.531 MB.
[2025-02-13T01:10:05.702Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:10:20.951Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:10:35.328Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:10:47.724Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:10:56.493Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:11:05.160Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:11:14.649Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:11:21.965Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:11:23.677Z] 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.
[2025-02-13T01:11:23.677Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:11:24.567Z] Movies recommended for you:
[2025-02-13T01:11:24.567Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:11:24.567Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:11:24.567Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (91757.682 ms) ======
[2025-02-13T01:11:24.567Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-13T01:11:24.567Z] GC before operation: completed in 428.418 ms, heap usage 583.054 MB -> 52.161 MB.
[2025-02-13T01:11:38.973Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:11:53.348Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:12:11.039Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:12:21.540Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:12:28.902Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:12:36.341Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:12:46.798Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:12:55.544Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:12:57.432Z] 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.
[2025-02-13T01:12:58.261Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:12:58.261Z] Movies recommended for you:
[2025-02-13T01:12:58.261Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:12:58.261Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:12:58.261Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (94006.658 ms) ======
[2025-02-13T01:13:00.925Z] -----------------------------------
[2025-02-13T01:13:00.925Z] renaissance-movie-lens_0_PASSED
[2025-02-13T01:13:00.925Z] -----------------------------------
[2025-02-13T01:13:00.925Z]
[2025-02-13T01:13:00.925Z] TEST TEARDOWN:
[2025-02-13T01:13:00.925Z] Nothing to be done for teardown.
[2025-02-13T01:13:00.925Z] renaissance-movie-lens_0 Finish Time: Thu Feb 13 01:13:00 2025 Epoch Time (ms): 1739409180562