renaissance-movie-lens_0
[2024-11-14T01:54:39.613Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T01:54:39.613Z] ===============================================
[2024-11-14T01:54:39.613Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 01:54:38 2024 Epoch Time (ms): 1731549278991
[2024-11-14T01:54:39.613Z] variation: NoOptions
[2024-11-14T01:54:39.613Z] JVM_OPTIONS:
[2024-11-14T01:54:39.613Z] { \
[2024-11-14T01:54:39.613Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T01:54:39.613Z] echo "Nothing to be done for setup."; \
[2024-11-14T01:54:39.613Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1731543159347/renaissance-movie-lens_0"; \
[2024-11-14T01:54:39.613Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1731543159347/renaissance-movie-lens_0"; \
[2024-11-14T01:54:39.613Z] echo ""; echo "TESTING:"; \
[2024-11-14T01:54:39.613Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-17.0.14+3/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_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_1731543159347/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T01:54:39.613Z] 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_1731543159347/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T01:54:39.613Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T01:54:39.613Z] echo "Nothing to be done for teardown."; \
[2024-11-14T01:54:39.613Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1731543159347/TestTargetResult";
[2024-11-14T01:54:39.613Z]
[2024-11-14T01:54:39.613Z] TEST SETUP:
[2024-11-14T01:54:39.613Z] Nothing to be done for setup.
[2024-11-14T01:54:39.613Z]
[2024-11-14T01:54:39.613Z] TESTING:
[2024-11-14T01:55:01.710Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T01:55:18.473Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-14T01:55:54.318Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T01:55:56.820Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T01:55:56.820Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T01:55:56.820Z] GC before operation: completed in 585.838 ms, heap usage 101.648 MB -> 37.297 MB.
[2024-11-14T01:57:16.171Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T01:57:51.739Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T01:58:22.599Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T01:58:58.196Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T01:59:17.105Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T01:59:36.357Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T01:59:56.068Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:00:12.304Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:00:14.896Z] 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.
[2024-11-14T02:00:15.720Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:00:17.352Z] Movies recommended for you:
[2024-11-14T02:00:17.352Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:00:17.352Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:00:17.352Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (260577.773 ms) ======
[2024-11-14T02:00:17.352Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T02:00:18.110Z] GC before operation: completed in 826.309 ms, heap usage 71.996 MB -> 53.824 MB.
[2024-11-14T02:00:44.123Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:01:06.325Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:01:32.377Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:01:54.951Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:02:06.933Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:02:18.747Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:02:32.503Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:02:44.214Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:02:45.845Z] 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.
[2024-11-14T02:02:45.845Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:02:46.646Z] Movies recommended for you:
[2024-11-14T02:02:46.646Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:02:46.646Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:02:46.646Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (148567.706 ms) ======
[2024-11-14T02:02:46.646Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T02:02:47.444Z] GC before operation: completed in 689.869 ms, heap usage 379.618 MB -> 53.159 MB.
[2024-11-14T02:03:09.642Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:03:32.686Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:03:55.057Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:04:17.711Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:04:32.104Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:04:44.170Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:05:00.742Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:05:13.384Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:05:16.007Z] 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.
[2024-11-14T02:05:16.007Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:05:17.703Z] Movies recommended for you:
[2024-11-14T02:05:17.703Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:05:17.703Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:05:17.703Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (149597.898 ms) ======
[2024-11-14T02:05:17.703Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T02:05:17.703Z] GC before operation: completed in 614.593 ms, heap usage 308.983 MB -> 50.216 MB.
[2024-11-14T02:05:40.271Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:06:02.676Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:06:24.824Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:06:47.383Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:06:59.773Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:07:11.454Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:07:25.241Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:07:39.038Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:07:40.645Z] 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.
[2024-11-14T02:07:40.645Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:07:41.434Z] Movies recommended for you:
[2024-11-14T02:07:41.434Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:07:41.434Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:07:41.434Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (143958.936 ms) ======
[2024-11-14T02:07:41.434Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T02:07:42.286Z] GC before operation: completed in 767.722 ms, heap usage 204.272 MB -> 50.451 MB.
[2024-11-14T02:08:08.141Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:08:27.136Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:08:50.132Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:09:12.693Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:09:24.825Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:09:38.963Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:09:52.702Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:10:04.469Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:10:06.041Z] 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.
[2024-11-14T02:10:06.041Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:10:06.822Z] Movies recommended for you:
[2024-11-14T02:10:06.822Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:10:06.822Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:10:06.822Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (144548.668 ms) ======
[2024-11-14T02:10:06.822Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T02:10:08.422Z] GC before operation: completed in 832.604 ms, heap usage 225.710 MB -> 50.631 MB.
[2024-11-14T02:10:30.547Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:10:52.657Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:11:11.548Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:11:33.655Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:11:43.754Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:11:57.862Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:12:10.254Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:12:24.145Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:12:24.904Z] 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.
[2024-11-14T02:12:25.660Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:12:26.435Z] Movies recommended for you:
[2024-11-14T02:12:26.435Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:12:26.435Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:12:26.435Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (138565.316 ms) ======
[2024-11-14T02:12:26.435Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T02:12:27.226Z] GC before operation: completed in 754.511 ms, heap usage 398.208 MB -> 53.971 MB.
[2024-11-14T02:12:49.592Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:13:15.718Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:13:34.822Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:13:57.716Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:14:09.414Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:14:21.171Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:14:34.976Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:14:46.941Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:14:49.474Z] 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.
[2024-11-14T02:14:49.474Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:14:50.302Z] Movies recommended for you:
[2024-11-14T02:14:50.302Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:14:50.302Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:14:50.302Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (143110.704 ms) ======
[2024-11-14T02:14:50.302Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T02:14:51.123Z] GC before operation: completed in 600.591 ms, heap usage 196.685 MB -> 50.826 MB.
[2024-11-14T02:15:13.459Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:15:33.546Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:15:56.212Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:16:12.597Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:16:26.455Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:16:38.145Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:16:49.865Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:17:06.096Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:17:06.893Z] 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.
[2024-11-14T02:17:06.893Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:17:07.670Z] Movies recommended for you:
[2024-11-14T02:17:07.670Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:17:07.670Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:17:07.670Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (136980.176 ms) ======
[2024-11-14T02:17:07.670Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T02:17:09.041Z] GC before operation: completed in 895.644 ms, heap usage 141.980 MB -> 50.985 MB.
[2024-11-14T02:17:31.494Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:17:50.602Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:18:09.733Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:18:26.014Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:18:39.763Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:18:50.155Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:19:01.804Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:19:13.758Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:19:16.292Z] 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.
[2024-11-14T02:19:16.292Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:19:17.058Z] Movies recommended for you:
[2024-11-14T02:19:17.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:19:17.058Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:19:17.058Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (128143.851 ms) ======
[2024-11-14T02:19:17.058Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T02:19:17.930Z] GC before operation: completed in 744.937 ms, heap usage 198.471 MB -> 50.896 MB.
[2024-11-14T02:19:40.178Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:19:56.245Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:20:15.287Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:20:31.635Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:20:41.660Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:20:53.393Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:21:03.231Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:21:14.870Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:21:15.631Z] 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.
[2024-11-14T02:21:16.393Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:21:17.154Z] Movies recommended for you:
[2024-11-14T02:21:17.154Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:21:17.154Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:21:17.154Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (119137.116 ms) ======
[2024-11-14T02:21:17.154Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T02:21:17.913Z] GC before operation: completed in 753.395 ms, heap usage 109.208 MB -> 48.710 MB.
[2024-11-14T02:21:36.692Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:21:53.027Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:22:12.202Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:22:25.891Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:22:35.899Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:22:45.968Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:22:57.701Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:23:05.982Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:23:08.433Z] 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.
[2024-11-14T02:23:08.433Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:23:08.433Z] Movies recommended for you:
[2024-11-14T02:23:08.433Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:23:08.433Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:23:08.433Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (111089.753 ms) ======
[2024-11-14T02:23:08.433Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T02:23:10.016Z] GC before operation: completed in 808.733 ms, heap usage 414.061 MB -> 51.854 MB.
[2024-11-14T02:23:26.576Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:23:43.404Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:23:59.949Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:24:16.201Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:24:24.549Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:24:34.404Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:24:46.331Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:24:56.258Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:24:57.827Z] 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.
[2024-11-14T02:24:57.827Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:24:58.593Z] Movies recommended for you:
[2024-11-14T02:24:58.593Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:24:58.593Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:24:58.593Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (109082.862 ms) ======
[2024-11-14T02:24:58.593Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T02:24:59.357Z] GC before operation: completed in 734.779 ms, heap usage 407.540 MB -> 52.005 MB.
[2024-11-14T02:25:18.821Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:25:37.712Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:25:56.613Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:26:12.724Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:26:19.633Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:26:27.913Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:26:38.320Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:26:48.205Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:26:48.968Z] 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.
[2024-11-14T02:26:49.728Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:26:49.728Z] Movies recommended for you:
[2024-11-14T02:26:49.728Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:26:49.728Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:26:49.728Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (110528.006 ms) ======
[2024-11-14T02:26:49.728Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T02:26:50.491Z] GC before operation: completed in 547.460 ms, heap usage 278.721 MB -> 52.441 MB.
[2024-11-14T02:27:06.813Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:27:20.768Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:27:34.687Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:27:50.773Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:27:59.133Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:28:06.086Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:28:16.161Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:28:24.819Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:28:26.460Z] 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.
[2024-11-14T02:28:26.460Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:28:27.330Z] Movies recommended for you:
[2024-11-14T02:28:27.330Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:28:27.330Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:28:27.330Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (96719.030 ms) ======
[2024-11-14T02:28:27.330Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T02:28:28.105Z] GC before operation: completed in 569.639 ms, heap usage 383.086 MB -> 51.310 MB.
[2024-11-14T02:28:42.128Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:28:56.244Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:29:12.596Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:29:24.422Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:29:34.449Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:29:43.317Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:29:53.167Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:30:00.009Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:30:02.591Z] 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.
[2024-11-14T02:30:02.591Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:30:03.433Z] Movies recommended for you:
[2024-11-14T02:30:03.433Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:30:03.433Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:30:03.433Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (95614.021 ms) ======
[2024-11-14T02:30:03.433Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T02:30:04.196Z] GC before operation: completed in 627.435 ms, heap usage 259.333 MB -> 48.085 MB.
[2024-11-14T02:30:17.941Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:30:31.740Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:30:48.160Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:31:01.969Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:31:10.446Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:31:17.346Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:31:27.544Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:31:36.051Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:31:36.831Z] 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.
[2024-11-14T02:31:37.630Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:31:37.630Z] Movies recommended for you:
[2024-11-14T02:31:37.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:31:37.630Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:31:37.630Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (93822.817 ms) ======
[2024-11-14T02:31:37.630Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T02:31:38.427Z] GC before operation: completed in 399.215 ms, heap usage 124.209 MB -> 48.131 MB.
[2024-11-14T02:31:52.474Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:32:11.627Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:32:28.053Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:32:45.107Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:32:55.218Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:33:06.866Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:33:18.841Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:33:29.140Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:33:30.790Z] 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.
[2024-11-14T02:33:30.790Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:33:31.574Z] Movies recommended for you:
[2024-11-14T02:33:31.574Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:33:31.574Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:33:31.574Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (113278.718 ms) ======
[2024-11-14T02:33:31.574Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T02:33:32.373Z] GC before operation: completed in 656.921 ms, heap usage 283.277 MB -> 48.590 MB.
[2024-11-14T02:33:51.405Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:34:10.632Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:34:30.264Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:34:44.234Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:34:54.423Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:35:04.405Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:35:16.051Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:35:27.675Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:35:28.437Z] 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.
[2024-11-14T02:35:29.200Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:35:29.962Z] Movies recommended for you:
[2024-11-14T02:35:29.962Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:35:29.962Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:35:29.962Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (117691.746 ms) ======
[2024-11-14T02:35:29.962Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T02:35:30.723Z] GC before operation: completed in 623.149 ms, heap usage 113.403 MB -> 48.476 MB.
[2024-11-14T02:35:50.144Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:36:08.998Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:36:25.295Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:36:42.191Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:36:53.994Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:37:02.544Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:37:14.631Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:37:24.784Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:37:26.393Z] 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.
[2024-11-14T02:37:26.393Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:37:27.301Z] Movies recommended for you:
[2024-11-14T02:37:27.301Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:37:27.301Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:37:27.301Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (116430.250 ms) ======
[2024-11-14T02:37:27.301Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T02:37:28.086Z] GC before operation: completed in 668.319 ms, heap usage 71.995 MB -> 51.590 MB.
[2024-11-14T02:37:44.533Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T02:38:00.803Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T02:38:19.709Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T02:38:36.183Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T02:38:43.661Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T02:38:53.844Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T02:39:05.728Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T02:39:15.649Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T02:39:18.216Z] 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.
[2024-11-14T02:39:18.216Z] The best model improves the baseline by 14.52%.
[2024-11-14T02:39:18.989Z] Movies recommended for you:
[2024-11-14T02:39:18.989Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T02:39:18.989Z] There is no way to check that no silent failure occurred.
[2024-11-14T02:39:18.989Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (110908.039 ms) ======
[2024-11-14T02:39:22.411Z] -----------------------------------
[2024-11-14T02:39:22.412Z] renaissance-movie-lens_0_PASSED
[2024-11-14T02:39:22.412Z] -----------------------------------
[2024-11-14T02:39:22.412Z]
[2024-11-14T02:39:22.412Z] TEST TEARDOWN:
[2024-11-14T02:39:22.412Z] Nothing to be done for teardown.
[2024-11-14T02:39:23.192Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 02:39:22 2024 Epoch Time (ms): 1731551962404