renaissance-movie-lens_0
[2025-02-27T12:24:43.370Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T12:24:43.370Z] ===============================================
[2025-02-27T12:24:43.370Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 12:24:42 2025 Epoch Time (ms): 1740659082923
[2025-02-27T12:24:43.370Z] variation: NoOptions
[2025-02-27T12:24:43.370Z] JVM_OPTIONS:
[2025-02-27T12:24:43.370Z] { \
[2025-02-27T12:24:43.370Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T12:24:43.370Z] echo "Nothing to be done for setup."; \
[2025-02-27T12:24:43.370Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406569533119/renaissance-movie-lens_0"; \
[2025-02-27T12:24:43.370Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406569533119/renaissance-movie-lens_0"; \
[2025-02-27T12:24:43.370Z] echo ""; echo "TESTING:"; \
[2025-02-27T12:24:43.370Z] "/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_17406569533119/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T12:24:43.370Z] 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_17406569533119/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T12:24:43.370Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T12:24:43.370Z] echo "Nothing to be done for teardown."; \
[2025-02-27T12:24:43.370Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17406569533119/TestTargetResult";
[2025-02-27T12:24:43.370Z]
[2025-02-27T12:24:43.370Z] TEST SETUP:
[2025-02-27T12:24:43.370Z] Nothing to be done for setup.
[2025-02-27T12:24:43.370Z]
[2025-02-27T12:24:43.370Z] TESTING:
[2025-02-27T12:24:48.967Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T12:24:54.560Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2025-02-27T12:25:04.743Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T12:25:06.345Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T12:25:06.345Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T12:25:06.345Z] GC before operation: completed in 215.414 ms, heap usage 117.965 MB -> 37.067 MB.
[2025-02-27T12:25:28.494Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:25:40.256Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:25:52.037Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:26:01.936Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:26:07.596Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:26:13.314Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:26:20.207Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:26:25.853Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:26:26.637Z] 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.
[2025-02-27T12:26:26.637Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:26:27.420Z] Movies recommended for you:
[2025-02-27T12:26:27.421Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:26:27.421Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:26:27.421Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (81078.543 ms) ======
[2025-02-27T12:26:27.421Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T12:26:27.421Z] GC before operation: completed in 365.799 ms, heap usage 74.048 MB -> 51.028 MB.
[2025-02-27T12:26:35.794Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:26:45.637Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:26:55.962Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:27:02.780Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:27:06.196Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:27:10.667Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:27:16.268Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:27:20.704Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:27:21.466Z] 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.
[2025-02-27T12:27:21.467Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:27:21.467Z] Movies recommended for you:
[2025-02-27T12:27:21.467Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:27:21.467Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:27:21.467Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (53897.034 ms) ======
[2025-02-27T12:27:21.467Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T12:27:22.246Z] GC before operation: completed in 292.581 ms, heap usage 421.576 MB -> 52.966 MB.
[2025-02-27T12:27:29.132Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:27:36.051Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:27:44.338Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:27:51.229Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:27:54.673Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:27:57.145Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:28:00.571Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:28:03.032Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:28:03.803Z] 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.
[2025-02-27T12:28:03.803Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:28:03.803Z] Movies recommended for you:
[2025-02-27T12:28:03.803Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:28:03.803Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:28:03.803Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (42162.221 ms) ======
[2025-02-27T12:28:03.803Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T12:28:04.572Z] GC before operation: completed in 171.611 ms, heap usage 447.941 MB -> 53.381 MB.
[2025-02-27T12:28:09.033Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:28:14.740Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:28:20.935Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:28:26.625Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:28:30.120Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:28:35.277Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:28:39.825Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:28:42.329Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:28:43.109Z] 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.
[2025-02-27T12:28:43.110Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:28:43.110Z] Movies recommended for you:
[2025-02-27T12:28:43.110Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:28:43.110Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:28:43.110Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (39206.362 ms) ======
[2025-02-27T12:28:43.110Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T12:28:43.892Z] GC before operation: completed in 162.922 ms, heap usage 334.646 MB -> 50.454 MB.
[2025-02-27T12:28:48.414Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:28:55.375Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:29:01.092Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:29:06.771Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:29:11.276Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:29:14.731Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:29:19.380Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:29:22.854Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:29:23.639Z] 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.
[2025-02-27T12:29:23.639Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:29:24.418Z] Movies recommended for you:
[2025-02-27T12:29:24.418Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:29:24.418Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:29:24.418Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (40492.775 ms) ======
[2025-02-27T12:29:24.418Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T12:29:24.418Z] GC before operation: completed in 211.101 ms, heap usage 362.335 MB -> 50.694 MB.
[2025-02-27T12:29:30.072Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:29:37.044Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:29:43.985Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:29:49.685Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:29:53.576Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:29:57.115Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:30:00.588Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:30:04.095Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:30:04.877Z] 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.
[2025-02-27T12:30:04.877Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:30:04.877Z] Movies recommended for you:
[2025-02-27T12:30:04.877Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:30:04.877Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:30:04.877Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (40681.470 ms) ======
[2025-02-27T12:30:04.877Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T12:30:04.877Z] GC before operation: completed in 206.887 ms, heap usage 203.697 MB -> 50.380 MB.
[2025-02-27T12:30:10.689Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:30:16.379Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:30:22.116Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:30:27.783Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:30:32.315Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:30:34.825Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:30:39.385Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:30:42.163Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:30:42.939Z] 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.
[2025-02-27T12:30:42.939Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:30:42.939Z] Movies recommended for you:
[2025-02-27T12:30:42.939Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:30:42.939Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:30:42.939Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (38041.414 ms) ======
[2025-02-27T12:30:42.939Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T12:30:43.719Z] GC before operation: completed in 220.890 ms, heap usage 414.288 MB -> 53.981 MB.
[2025-02-27T12:30:49.445Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:30:56.416Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:31:02.145Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:31:07.835Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:31:11.405Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:31:14.887Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:31:18.381Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:31:24.105Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:31:24.105Z] 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.
[2025-02-27T12:31:24.878Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:31:24.878Z] Movies recommended for you:
[2025-02-27T12:31:24.878Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:31:24.878Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:31:24.878Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (41330.135 ms) ======
[2025-02-27T12:31:24.878Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T12:31:24.878Z] GC before operation: completed in 269.194 ms, heap usage 337.492 MB -> 51.101 MB.
[2025-02-27T12:31:31.844Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:31:37.556Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:31:44.673Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:31:50.386Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:31:53.869Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:31:57.366Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:32:01.906Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:32:04.436Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:32:05.226Z] 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.
[2025-02-27T12:32:05.226Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:32:05.226Z] Movies recommended for you:
[2025-02-27T12:32:05.226Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:32:05.226Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:32:05.226Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (40343.566 ms) ======
[2025-02-27T12:32:05.226Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T12:32:05.226Z] GC before operation: completed in 225.814 ms, heap usage 404.109 MB -> 54.121 MB.
[2025-02-27T12:32:12.206Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:32:16.765Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:32:23.740Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:32:29.455Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:32:31.988Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:32:35.481Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:32:38.539Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:32:43.033Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:32:43.033Z] 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.
[2025-02-27T12:32:43.822Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:32:43.822Z] Movies recommended for you:
[2025-02-27T12:32:43.822Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:32:43.822Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:32:43.822Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (38099.200 ms) ======
[2025-02-27T12:32:43.822Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T12:32:43.822Z] GC before operation: completed in 319.571 ms, heap usage 285.101 MB -> 50.857 MB.
[2025-02-27T12:32:52.217Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:32:57.871Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:33:04.823Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:33:10.575Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:33:14.060Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:33:17.559Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:33:21.057Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:33:24.546Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:33:24.546Z] 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.
[2025-02-27T12:33:24.546Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:33:25.326Z] Movies recommended for you:
[2025-02-27T12:33:25.326Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:33:25.326Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:33:25.326Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (41091.851 ms) ======
[2025-02-27T12:33:25.326Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T12:33:25.326Z] GC before operation: completed in 213.517 ms, heap usage 66.993 MB -> 53.834 MB.
[2025-02-27T12:33:31.008Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:33:37.217Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:33:44.177Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:33:48.731Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:33:52.252Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:33:55.758Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:34:00.303Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:34:03.806Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:34:04.593Z] 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.
[2025-02-27T12:34:04.593Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:34:04.593Z] Movies recommended for you:
[2025-02-27T12:34:04.593Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:34:04.593Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:34:04.593Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (39152.738 ms) ======
[2025-02-27T12:34:04.593Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T12:34:04.593Z] GC before operation: completed in 239.461 ms, heap usage 405.789 MB -> 54.103 MB.
[2025-02-27T12:34:10.302Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:34:20.451Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:34:35.243Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:34:40.191Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:34:43.790Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:34:47.244Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:34:51.749Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:34:54.260Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:34:55.040Z] 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.
[2025-02-27T12:34:55.040Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:34:55.040Z] Movies recommended for you:
[2025-02-27T12:34:55.040Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:34:55.040Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:34:55.040Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (50687.682 ms) ======
[2025-02-27T12:34:55.040Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T12:34:55.825Z] GC before operation: completed in 196.737 ms, heap usage 175.360 MB -> 50.875 MB.
[2025-02-27T12:35:02.791Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:35:08.449Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:35:15.473Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:35:21.173Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:35:25.650Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:35:28.146Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:35:32.682Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:35:35.197Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:35:35.987Z] 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.
[2025-02-27T12:35:35.987Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:35:35.987Z] Movies recommended for you:
[2025-02-27T12:35:35.987Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:35:35.987Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:35:35.987Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (40648.329 ms) ======
[2025-02-27T12:35:35.987Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T12:35:37.285Z] GC before operation: completed in 187.338 ms, heap usage 403.644 MB -> 54.015 MB.
[2025-02-27T12:35:41.863Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:35:47.482Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:35:53.159Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:35:58.913Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:36:02.460Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:36:07.080Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:36:11.715Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:36:15.247Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:36:16.046Z] 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.
[2025-02-27T12:36:16.046Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:36:16.046Z] Movies recommended for you:
[2025-02-27T12:36:16.046Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:36:16.046Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:36:16.046Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (39777.757 ms) ======
[2025-02-27T12:36:16.046Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T12:36:16.845Z] GC before operation: completed in 252.676 ms, heap usage 257.447 MB -> 50.894 MB.
[2025-02-27T12:36:22.633Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:36:28.425Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:36:35.547Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:36:42.707Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:36:46.245Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:36:49.804Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:36:54.479Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:36:58.091Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:36:58.092Z] 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.
[2025-02-27T12:36:58.092Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:36:58.890Z] Movies recommended for you:
[2025-02-27T12:36:58.890Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:36:58.890Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:36:58.890Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (42086.799 ms) ======
[2025-02-27T12:36:58.890Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T12:36:58.890Z] GC before operation: completed in 255.120 ms, heap usage 76.232 MB -> 53.500 MB.
[2025-02-27T12:37:04.676Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:37:10.563Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:37:17.622Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:37:23.393Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:37:25.966Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:37:29.507Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:37:33.590Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:37:36.157Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:37:36.975Z] 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.
[2025-02-27T12:37:36.975Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:37:36.975Z] Movies recommended for you:
[2025-02-27T12:37:36.975Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:37:36.975Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:37:36.975Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (38352.054 ms) ======
[2025-02-27T12:37:36.975Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T12:37:37.782Z] GC before operation: completed in 226.381 ms, heap usage 434.753 MB -> 54.129 MB.
[2025-02-27T12:37:42.398Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:37:48.198Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:37:52.817Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:37:57.420Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:38:00.985Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:38:03.578Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:38:07.133Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:38:09.699Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:38:10.498Z] 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.
[2025-02-27T12:38:10.498Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:38:10.498Z] Movies recommended for you:
[2025-02-27T12:38:10.498Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:38:10.498Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:38:10.498Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (33287.314 ms) ======
[2025-02-27T12:38:10.498Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T12:38:11.292Z] GC before operation: completed in 176.932 ms, heap usage 179.313 MB -> 50.823 MB.
[2025-02-27T12:38:17.066Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:38:21.690Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:38:27.462Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:38:32.632Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:38:36.194Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:38:39.765Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:38:43.347Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:38:46.911Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:38:47.717Z] 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.
[2025-02-27T12:38:47.717Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:38:47.717Z] Movies recommended for you:
[2025-02-27T12:38:47.717Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:38:47.717Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:38:47.717Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (36726.261 ms) ======
[2025-02-27T12:38:47.717Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T12:38:47.717Z] GC before operation: completed in 232.110 ms, heap usage 70.297 MB -> 53.455 MB.
[2025-02-27T12:38:53.524Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T12:38:59.324Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T12:39:05.138Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T12:39:10.927Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T12:39:13.499Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T12:39:17.055Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T12:39:20.769Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T12:39:24.339Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T12:39:24.339Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2025-02-27T12:39:24.339Z] The best model improves the baseline by 14.52%.
[2025-02-27T12:39:25.407Z] Movies recommended for you:
[2025-02-27T12:39:25.407Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T12:39:25.407Z] There is no way to check that no silent failure occurred.
[2025-02-27T12:39:25.407Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (37011.137 ms) ======
[2025-02-27T12:39:27.999Z] -----------------------------------
[2025-02-27T12:39:27.999Z] renaissance-movie-lens_0_PASSED
[2025-02-27T12:39:27.999Z] -----------------------------------
[2025-02-27T12:39:27.999Z]
[2025-02-27T12:39:27.999Z] TEST TEARDOWN:
[2025-02-27T12:39:27.999Z] Nothing to be done for teardown.
[2025-02-27T12:39:27.999Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 12:39:25 2025 Epoch Time (ms): 1740659965739