renaissance-movie-lens_0
[2024-12-05T03:18:53.780Z] Running test renaissance-movie-lens_0 ...
[2024-12-05T03:18:53.780Z] ===============================================
[2024-12-05T03:18:54.428Z] renaissance-movie-lens_0 Start Time: Thu Dec 5 03:18:53 2024 Epoch Time (ms): 1733368733763
[2024-12-05T03:18:54.428Z] variation: NoOptions
[2024-12-05T03:18:54.428Z] JVM_OPTIONS:
[2024-12-05T03:18:54.428Z] { \
[2024-12-05T03:18:54.428Z] echo ""; echo "TEST SETUP:"; \
[2024-12-05T03:18:54.428Z] echo "Nothing to be done for setup."; \
[2024-12-05T03:18:54.428Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17333621009543/renaissance-movie-lens_0"; \
[2024-12-05T03:18:54.428Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17333621009543/renaissance-movie-lens_0"; \
[2024-12-05T03:18:54.428Z] echo ""; echo "TESTING:"; \
[2024-12-05T03:18:54.428Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_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_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17333621009543/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-05T03:18:54.428Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17333621009543/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-05T03:18:54.428Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-05T03:18:54.428Z] echo "Nothing to be done for teardown."; \
[2024-12-05T03:18:54.428Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17333621009543/TestTargetResult";
[2024-12-05T03:18:54.428Z]
[2024-12-05T03:18:54.428Z] TEST SETUP:
[2024-12-05T03:18:54.428Z] Nothing to be done for setup.
[2024-12-05T03:18:54.428Z]
[2024-12-05T03:18:54.428Z] TESTING:
[2024-12-05T03:19:09.723Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-05T03:19:27.087Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-12-05T03:20:00.521Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-05T03:20:00.521Z] Training: 60056, validation: 20285, test: 19854
[2024-12-05T03:20:00.521Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-05T03:20:00.521Z] GC before operation: completed in 465.582 ms, heap usage 70.378 MB -> 37.050 MB.
[2024-12-05T03:21:05.371Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:21:44.642Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:22:23.618Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:22:52.296Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:23:12.960Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:23:25.669Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:23:40.723Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:23:53.202Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:23:56.164Z] 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.
[2024-12-05T03:23:56.819Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:23:58.163Z] Movies recommended for you:
[2024-12-05T03:23:58.163Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:23:58.163Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:23:58.163Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (237676.356 ms) ======
[2024-12-05T03:23:58.163Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-05T03:23:58.796Z] GC before operation: completed in 685.077 ms, heap usage 295.282 MB -> 52.528 MB.
[2024-12-05T03:24:22.795Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:24:46.768Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:25:07.331Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:25:27.596Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:25:39.960Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:25:52.228Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:26:05.250Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:26:15.787Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:26:17.935Z] 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.
[2024-12-05T03:26:17.935Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:26:18.639Z] Movies recommended for you:
[2024-12-05T03:26:18.639Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:26:18.639Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:26:18.639Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (139786.407 ms) ======
[2024-12-05T03:26:18.639Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-05T03:26:19.279Z] GC before operation: completed in 690.570 ms, heap usage 424.219 MB -> 52.929 MB.
[2024-12-05T03:26:39.787Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:27:00.365Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:27:20.978Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:27:39.084Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:27:49.755Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:28:00.369Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:28:12.807Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:28:23.424Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:28:24.106Z] 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.
[2024-12-05T03:28:24.768Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:28:25.433Z] Movies recommended for you:
[2024-12-05T03:28:25.433Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:28:25.433Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:28:25.433Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (125760.895 ms) ======
[2024-12-05T03:28:25.433Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-05T03:28:26.109Z] GC before operation: completed in 542.689 ms, heap usage 84.202 MB -> 51.725 MB.
[2024-12-05T03:28:43.894Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:29:01.257Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:29:18.769Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:29:33.661Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:29:46.281Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:29:53.629Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:30:04.852Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:30:15.435Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:30:16.120Z] 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.
[2024-12-05T03:30:16.120Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:30:16.815Z] Movies recommended for you:
[2024-12-05T03:30:16.815Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:30:16.815Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:30:16.815Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (111225.441 ms) ======
[2024-12-05T03:30:16.815Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-05T03:30:17.482Z] GC before operation: completed in 647.504 ms, heap usage 423.031 MB -> 53.716 MB.
[2024-12-05T03:30:34.998Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:30:52.481Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:31:09.836Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:31:30.343Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:31:41.038Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:31:51.633Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:32:04.037Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:32:16.405Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:32:17.054Z] 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.
[2024-12-05T03:32:17.054Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:32:17.722Z] Movies recommended for you:
[2024-12-05T03:32:17.722Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:32:17.722Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:32:17.722Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (120175.370 ms) ======
[2024-12-05T03:32:17.722Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-05T03:32:18.360Z] GC before operation: completed in 642.376 ms, heap usage 150.337 MB -> 50.386 MB.
[2024-12-05T03:32:35.648Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:32:53.103Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:33:17.223Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:33:31.991Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:33:40.750Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:33:53.278Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:34:02.773Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:34:13.595Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:34:15.720Z] 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.
[2024-12-05T03:34:15.720Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:34:16.413Z] Movies recommended for you:
[2024-12-05T03:34:16.414Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:34:16.414Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:34:16.414Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (117900.416 ms) ======
[2024-12-05T03:34:16.414Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-05T03:34:17.077Z] GC before operation: completed in 486.161 ms, heap usage 129.152 MB -> 50.515 MB.
[2024-12-05T03:34:34.540Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:34:49.190Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:35:06.530Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:35:21.787Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:35:34.473Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:35:45.069Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:35:53.945Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:36:04.407Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:36:05.857Z] 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.
[2024-12-05T03:36:06.508Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:36:07.175Z] Movies recommended for you:
[2024-12-05T03:36:07.175Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:36:07.175Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:36:07.175Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (110228.560 ms) ======
[2024-12-05T03:36:07.175Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-05T03:36:07.820Z] GC before operation: completed in 517.239 ms, heap usage 161.534 MB -> 50.693 MB.
[2024-12-05T03:36:25.122Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:36:40.618Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:37:00.982Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:37:15.542Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:37:24.264Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:37:34.670Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:37:45.572Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:37:54.239Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:37:55.558Z] 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.
[2024-12-05T03:37:55.558Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:37:56.195Z] Movies recommended for you:
[2024-12-05T03:37:56.195Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:37:56.195Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:37:56.195Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (108780.508 ms) ======
[2024-12-05T03:37:56.195Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-05T03:37:56.828Z] GC before operation: completed in 591.870 ms, heap usage 101.870 MB -> 50.841 MB.
[2024-12-05T03:38:14.025Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:38:31.484Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:38:48.899Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:39:03.477Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:39:13.024Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:39:23.625Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:39:36.105Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:39:45.103Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:39:47.262Z] 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.
[2024-12-05T03:39:47.262Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:39:47.951Z] Movies recommended for you:
[2024-12-05T03:39:47.951Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:39:47.951Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:39:47.951Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (110742.692 ms) ======
[2024-12-05T03:39:47.951Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-05T03:39:48.608Z] GC before operation: completed in 582.295 ms, heap usage 134.414 MB -> 50.613 MB.
[2024-12-05T03:40:05.898Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:40:21.130Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:40:38.535Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:40:55.730Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:41:07.366Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:41:17.718Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:41:28.301Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:41:38.162Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:41:39.551Z] 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.
[2024-12-05T03:41:40.214Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:41:40.863Z] Movies recommended for you:
[2024-12-05T03:41:40.863Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:41:40.863Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:41:40.863Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (112297.919 ms) ======
[2024-12-05T03:41:40.863Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-05T03:41:41.514Z] GC before operation: completed in 625.401 ms, heap usage 167.756 MB -> 50.804 MB.
[2024-12-05T03:41:58.778Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:42:13.681Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:42:34.168Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:42:49.004Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:42:57.919Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:43:06.841Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:43:15.692Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:43:26.215Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:43:26.860Z] 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.
[2024-12-05T03:43:26.860Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:43:27.543Z] Movies recommended for you:
[2024-12-05T03:43:27.543Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:43:27.543Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:43:27.543Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (106310.539 ms) ======
[2024-12-05T03:43:27.543Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-05T03:43:28.202Z] GC before operation: completed in 510.598 ms, heap usage 134.943 MB -> 50.552 MB.
[2024-12-05T03:43:45.741Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:44:00.440Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:44:18.478Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:44:33.169Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:44:42.064Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:44:50.844Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:45:03.331Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:45:12.217Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:45:12.875Z] 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.
[2024-12-05T03:45:13.520Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:45:14.623Z] Movies recommended for you:
[2024-12-05T03:45:14.623Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:45:14.623Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:45:14.623Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (105797.327 ms) ======
[2024-12-05T03:45:14.623Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-05T03:45:15.277Z] GC before operation: completed in 850.301 ms, heap usage 168.353 MB -> 48.514 MB.
[2024-12-05T03:45:32.714Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:45:50.039Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:46:07.445Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:46:25.039Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:46:36.120Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:46:46.683Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:46:57.319Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:47:08.026Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:47:10.163Z] 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.
[2024-12-05T03:47:10.163Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:47:11.594Z] Movies recommended for you:
[2024-12-05T03:47:11.594Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:47:11.594Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:47:11.594Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (116423.382 ms) ======
[2024-12-05T03:47:11.594Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-05T03:47:11.594Z] GC before operation: completed in 624.244 ms, heap usage 240.579 MB -> 48.672 MB.
[2024-12-05T03:47:29.004Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:47:46.489Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:48:04.360Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:48:18.970Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:48:29.584Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:48:38.496Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:48:47.382Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:48:57.942Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:48:59.321Z] 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.
[2024-12-05T03:48:59.321Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:49:00.031Z] Movies recommended for you:
[2024-12-05T03:49:00.031Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:49:00.031Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:49:00.031Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (108392.361 ms) ======
[2024-12-05T03:49:00.031Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-05T03:49:00.688Z] GC before operation: completed in 639.111 ms, heap usage 203.277 MB -> 48.392 MB.
[2024-12-05T03:49:18.776Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:49:33.766Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:49:51.294Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:50:06.074Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:50:14.795Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:50:24.040Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:50:34.640Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:50:41.933Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:50:44.086Z] 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.
[2024-12-05T03:50:44.086Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:50:44.765Z] Movies recommended for you:
[2024-12-05T03:50:44.765Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:50:44.765Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:50:44.765Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (103523.660 ms) ======
[2024-12-05T03:50:44.765Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-05T03:50:44.765Z] GC before operation: completed in 585.651 ms, heap usage 448.482 MB -> 54.432 MB.
[2024-12-05T03:50:59.553Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:51:14.403Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:51:29.236Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:51:44.486Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:51:53.290Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:52:02.218Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:52:12.791Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:52:21.687Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:52:22.344Z] 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.
[2024-12-05T03:52:22.991Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:52:23.629Z] Movies recommended for you:
[2024-12-05T03:52:23.629Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:52:23.629Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:52:23.629Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (98390.518 ms) ======
[2024-12-05T03:52:23.629Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-05T03:52:23.629Z] GC before operation: completed in 514.002 ms, heap usage 182.579 MB -> 48.237 MB.
[2024-12-05T03:52:38.285Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:52:55.441Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:53:12.670Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:53:27.472Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:53:36.282Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:53:45.019Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:53:56.112Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:54:03.365Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:54:04.741Z] 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.
[2024-12-05T03:54:04.741Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:54:05.395Z] Movies recommended for you:
[2024-12-05T03:54:05.395Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:54:05.395Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:54:05.395Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (101260.053 ms) ======
[2024-12-05T03:54:05.395Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-05T03:54:05.395Z] GC before operation: completed in 483.909 ms, heap usage 202.828 MB -> 48.427 MB.
[2024-12-05T03:54:22.924Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:54:37.759Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:54:52.463Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:55:06.992Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:55:17.335Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:55:26.005Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:55:36.433Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:55:45.100Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:55:47.174Z] 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.
[2024-12-05T03:55:47.174Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:55:47.808Z] Movies recommended for you:
[2024-12-05T03:55:47.808Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:55:47.808Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:55:47.808Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (101974.572 ms) ======
[2024-12-05T03:55:47.808Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-05T03:55:48.443Z] GC before operation: completed in 467.585 ms, heap usage 181.609 MB -> 48.515 MB.
[2024-12-05T03:56:03.374Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:56:18.276Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:56:33.130Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:56:47.993Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:56:59.113Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:57:08.079Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:57:16.993Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:57:25.882Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:57:28.089Z] 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.
[2024-12-05T03:57:28.089Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:57:28.766Z] Movies recommended for you:
[2024-12-05T03:57:28.766Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:57:28.766Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:57:28.767Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (100571.313 ms) ======
[2024-12-05T03:57:28.767Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-05T03:57:29.410Z] GC before operation: completed in 549.363 ms, heap usage 109.593 MB -> 48.616 MB.
[2024-12-05T03:57:44.227Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T03:58:01.664Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T03:58:16.510Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T03:58:31.445Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T03:58:41.875Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T03:58:52.439Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T03:59:02.988Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T03:59:11.920Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T03:59:13.294Z] 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.
[2024-12-05T03:59:13.294Z] The best model improves the baseline by 14.52%.
[2024-12-05T03:59:13.946Z] Movies recommended for you:
[2024-12-05T03:59:13.946Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T03:59:13.946Z] There is no way to check that no silent failure occurred.
[2024-12-05T03:59:13.946Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (104880.657 ms) ======
[2024-12-05T03:59:17.870Z] -----------------------------------
[2024-12-05T03:59:17.870Z] renaissance-movie-lens_0_PASSED
[2024-12-05T03:59:17.870Z] -----------------------------------
[2024-12-05T03:59:17.870Z]
[2024-12-05T03:59:17.870Z] TEST TEARDOWN:
[2024-12-05T03:59:17.870Z] Nothing to be done for teardown.
[2024-12-05T03:59:17.870Z] renaissance-movie-lens_0 Finish Time: Thu Dec 5 03:59:17 2024 Epoch Time (ms): 1733371157587