[2025-03-06T00:44:33.412Z] Running test renaissance-movie-lens_0 ... [2025-03-06T00:44:33.412Z] =============================================== [2025-03-06T00:44:33.412Z] renaissance-movie-lens_0 Start Time: Thu Mar 6 00:44:31 2025 Epoch Time (ms): 1741221871447 [2025-03-06T00:44:33.412Z] variation: NoOptions [2025-03-06T00:44:33.412Z] JVM_OPTIONS: [2025-03-06T00:44:33.412Z] { \ [2025-03-06T00:44:33.412Z] echo ""; echo "TEST SETUP:"; \ [2025-03-06T00:44:33.412Z] echo "Nothing to be done for setup."; \ [2025-03-06T00:44:33.412Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17412166579484/renaissance-movie-lens_0"; \ [2025-03-06T00:44:33.412Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17412166579484/renaissance-movie-lens_0"; \ [2025-03-06T00:44:33.412Z] echo ""; echo "TESTING:"; \ [2025-03-06T00:44:33.412Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17412166579484/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-03-06T00:44:33.412Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17412166579484/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-03-06T00:44:33.412Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-03-06T00:44:33.412Z] echo "Nothing to be done for teardown."; \ [2025-03-06T00:44:33.412Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17412166579484/TestTargetResult"; [2025-03-06T00:44:33.412Z] [2025-03-06T00:44:33.412Z] TEST SETUP: [2025-03-06T00:44:33.412Z] Nothing to be done for setup. [2025-03-06T00:44:33.412Z] [2025-03-06T00:44:33.412Z] TESTING: [2025-03-06T00:44:44.176Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-03-06T00:44:56.796Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-03-06T00:45:24.069Z] Got 100004 ratings from 671 users on 9066 movies. [2025-03-06T00:45:26.825Z] Training: 60056, validation: 20285, test: 19854 [2025-03-06T00:45:26.825Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-03-06T00:45:27.679Z] GC before operation: completed in 615.274 ms, heap usage 66.821 MB -> 36.454 MB. [2025-03-06T00:46:27.487Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:46:59.099Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:47:26.158Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:47:53.331Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:48:08.074Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:48:18.950Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:48:33.715Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:48:44.621Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:48:46.423Z] 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. [2025-03-06T00:48:47.270Z] The best model improves the baseline by 14.52%. [2025-03-06T00:48:48.113Z] Movies recommended for you: [2025-03-06T00:48:48.113Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:48:48.113Z] There is no way to check that no silent failure occurred. [2025-03-06T00:48:48.113Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (201104.807 ms) ====== [2025-03-06T00:48:48.113Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-03-06T00:48:48.956Z] GC before operation: completed in 755.940 ms, heap usage 226.015 MB -> 49.899 MB. [2025-03-06T00:49:12.951Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:49:32.951Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:49:50.386Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:50:07.722Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:50:18.571Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:50:29.493Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:50:40.299Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:50:49.395Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:50:51.209Z] 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. [2025-03-06T00:50:51.209Z] The best model improves the baseline by 14.52%. [2025-03-06T00:50:52.080Z] Movies recommended for you: [2025-03-06T00:50:52.080Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:50:52.080Z] There is no way to check that no silent failure occurred. [2025-03-06T00:50:52.080Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (122594.740 ms) ====== [2025-03-06T00:50:52.081Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-03-06T00:50:52.081Z] GC before operation: completed in 576.753 ms, heap usage 339.795 MB -> 49.190 MB. [2025-03-06T00:51:09.422Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:51:26.856Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:51:42.412Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:51:59.638Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:52:10.505Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:52:21.424Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:52:34.373Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:52:47.095Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:52:48.947Z] 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. [2025-03-06T00:52:48.947Z] The best model improves the baseline by 14.52%. [2025-03-06T00:52:49.841Z] Movies recommended for you: [2025-03-06T00:52:49.841Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:52:49.841Z] There is no way to check that no silent failure occurred. [2025-03-06T00:52:49.841Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (117673.717 ms) ====== [2025-03-06T00:52:49.841Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-03-06T00:52:50.764Z] GC before operation: completed in 721.177 ms, heap usage 93.576 MB -> 49.199 MB. [2025-03-06T00:53:11.664Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:53:29.210Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:53:49.558Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:54:09.750Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:54:25.035Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:54:38.330Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:54:49.144Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:54:59.930Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:55:02.677Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-03-06T00:55:02.677Z] The best model improves the baseline by 14.52%. [2025-03-06T00:55:03.534Z] Movies recommended for you: [2025-03-06T00:55:03.534Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:55:03.534Z] There is no way to check that no silent failure occurred. [2025-03-06T00:55:03.534Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (132671.372 ms) ====== [2025-03-06T00:55:03.534Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-03-06T00:55:04.382Z] GC before operation: completed in 914.046 ms, heap usage 120.284 MB -> 49.533 MB. [2025-03-06T00:55:24.425Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:55:44.683Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:56:04.737Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:56:25.951Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:56:41.032Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:56:55.976Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:57:09.292Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:57:20.908Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:57:22.739Z] 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. [2025-03-06T00:57:22.739Z] The best model improves the baseline by 14.52%. [2025-03-06T00:57:23.630Z] Movies recommended for you: [2025-03-06T00:57:23.631Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:57:23.631Z] There is no way to check that no silent failure occurred. [2025-03-06T00:57:23.631Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (139680.617 ms) ====== [2025-03-06T00:57:23.631Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-03-06T00:57:25.391Z] GC before operation: completed in 1092.552 ms, heap usage 90.782 MB -> 49.711 MB. [2025-03-06T00:57:48.694Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:58:08.672Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:58:25.945Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:58:45.192Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:58:57.733Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:59:08.534Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:59:19.603Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:59:29.869Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:59:32.413Z] 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. [2025-03-06T00:59:32.413Z] The best model improves the baseline by 14.52%. [2025-03-06T00:59:32.413Z] Movies recommended for you: [2025-03-06T00:59:32.413Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:59:32.413Z] There is no way to check that no silent failure occurred. [2025-03-06T00:59:32.413Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (127659.762 ms) ====== [2025-03-06T00:59:32.413Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-03-06T00:59:33.205Z] GC before operation: completed in 809.424 ms, heap usage 63.686 MB -> 49.733 MB. [2025-03-06T00:59:55.560Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:00:18.161Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:00:40.818Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:01:01.856Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:01:16.093Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:01:28.105Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:01:38.288Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:01:50.434Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:01:51.230Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2025-03-06T01:01:51.230Z] The best model improves the baseline by 14.52%. [2025-03-06T01:01:52.087Z] Movies recommended for you: [2025-03-06T01:01:52.087Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:01:52.087Z] There is no way to check that no silent failure occurred. [2025-03-06T01:01:52.087Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (138381.719 ms) ====== [2025-03-06T01:01:52.087Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-03-06T01:01:52.853Z] GC before operation: completed in 765.968 ms, heap usage 170.443 MB -> 49.930 MB. [2025-03-06T01:02:09.344Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:02:25.854Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:02:42.415Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:02:58.918Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:03:09.520Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:03:18.148Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:03:30.205Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:03:38.691Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:03:40.509Z] 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. [2025-03-06T01:03:40.509Z] The best model improves the baseline by 14.52%. [2025-03-06T01:03:41.339Z] Movies recommended for you: [2025-03-06T01:03:41.339Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:03:41.339Z] There is no way to check that no silent failure occurred. [2025-03-06T01:03:41.339Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (108520.939 ms) ====== [2025-03-06T01:03:41.339Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-03-06T01:03:42.169Z] GC before operation: completed in 724.368 ms, heap usage 129.248 MB -> 50.096 MB. [2025-03-06T01:04:01.415Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:04:18.100Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:04:34.685Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:04:54.046Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:05:04.144Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:05:15.947Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:05:26.066Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:05:33.678Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:05:36.366Z] 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. [2025-03-06T01:05:36.366Z] The best model improves the baseline by 14.52%. [2025-03-06T01:05:37.163Z] Movies recommended for you: [2025-03-06T01:05:37.163Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:05:37.163Z] There is no way to check that no silent failure occurred. [2025-03-06T01:05:37.163Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (114980.015 ms) ====== [2025-03-06T01:05:37.163Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-03-06T01:05:37.977Z] GC before operation: completed in 800.582 ms, heap usage 57.613 MB -> 50.135 MB. [2025-03-06T01:05:57.441Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:06:13.741Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:06:30.073Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:06:44.042Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:06:53.111Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:07:03.252Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:07:15.184Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:07:27.217Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:07:29.788Z] 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. [2025-03-06T01:07:29.788Z] The best model improves the baseline by 14.52%. [2025-03-06T01:07:30.590Z] Movies recommended for you: [2025-03-06T01:07:30.590Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:07:30.590Z] There is no way to check that no silent failure occurred. [2025-03-06T01:07:30.590Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (112750.418 ms) ====== [2025-03-06T01:07:30.590Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-03-06T01:07:31.392Z] GC before operation: completed in 896.220 ms, heap usage 133.357 MB -> 50.049 MB. [2025-03-06T01:07:54.106Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:08:13.155Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:08:35.379Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:08:57.402Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:09:08.982Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:09:23.218Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:09:36.854Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:09:50.635Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:09:51.412Z] 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. [2025-03-06T01:09:52.176Z] The best model improves the baseline by 14.52%. [2025-03-06T01:09:52.949Z] Movies recommended for you: [2025-03-06T01:09:52.949Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:09:52.949Z] There is no way to check that no silent failure occurred. [2025-03-06T01:09:52.949Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (141407.141 ms) ====== [2025-03-06T01:09:52.949Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-03-06T01:09:53.699Z] GC before operation: completed in 1103.178 ms, heap usage 133.798 MB -> 47.980 MB. [2025-03-06T01:10:15.768Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:10:37.846Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:10:58.724Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:11:17.515Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:11:31.237Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:11:42.888Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:11:56.697Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:12:09.007Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:12:11.566Z] 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. [2025-03-06T01:12:12.347Z] The best model improves the baseline by 14.52%. [2025-03-06T01:12:13.162Z] Movies recommended for you: [2025-03-06T01:12:13.162Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:12:13.162Z] There is no way to check that no silent failure occurred. [2025-03-06T01:12:13.162Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (138932.246 ms) ====== [2025-03-06T01:12:13.162Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-03-06T01:12:13.952Z] GC before operation: completed in 1066.114 ms, heap usage 198.898 MB -> 47.598 MB. [2025-03-06T01:12:40.034Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:13:02.531Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:13:24.792Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:13:47.440Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:14:01.090Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:14:14.687Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:14:28.386Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:14:42.506Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:14:43.274Z] 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. [2025-03-06T01:14:43.274Z] The best model improves the baseline by 14.52%. [2025-03-06T01:14:44.840Z] Movies recommended for you: [2025-03-06T01:14:44.840Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:14:44.840Z] There is no way to check that no silent failure occurred. [2025-03-06T01:14:44.840Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (150571.877 ms) ====== [2025-03-06T01:14:44.840Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-03-06T01:14:45.611Z] GC before operation: completed in 932.483 ms, heap usage 207.762 MB -> 47.625 MB. [2025-03-06T01:15:07.609Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:15:33.273Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:15:55.481Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:16:14.674Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:16:26.597Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:16:40.500Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:16:54.182Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:17:07.940Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:17:08.758Z] 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. [2025-03-06T01:17:08.758Z] The best model improves the baseline by 14.52%. [2025-03-06T01:17:09.549Z] Movies recommended for you: [2025-03-06T01:17:09.549Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:17:09.549Z] There is no way to check that no silent failure occurred. [2025-03-06T01:17:09.549Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (144118.053 ms) ====== [2025-03-06T01:17:09.549Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-03-06T01:17:10.335Z] GC before operation: completed in 949.420 ms, heap usage 181.476 MB -> 47.034 MB. [2025-03-06T01:17:36.206Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:17:55.364Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:18:21.562Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:18:40.444Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:18:52.768Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:19:04.432Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:19:18.373Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:19:32.277Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:19:33.879Z] 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. [2025-03-06T01:19:33.879Z] The best model improves the baseline by 14.52%. [2025-03-06T01:19:34.648Z] Movies recommended for you: [2025-03-06T01:19:34.648Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:19:34.648Z] There is no way to check that no silent failure occurred. [2025-03-06T01:19:34.648Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (144447.759 ms) ====== [2025-03-06T01:19:34.648Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-03-06T01:19:36.275Z] GC before operation: completed in 1108.819 ms, heap usage 162.284 MB -> 46.947 MB. [2025-03-06T01:19:58.656Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:20:22.460Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:20:48.418Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:21:10.990Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:21:23.082Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:21:35.058Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:21:50.124Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:22:02.445Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:22:03.308Z] 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. [2025-03-06T01:22:04.134Z] The best model improves the baseline by 14.52%. [2025-03-06T01:22:04.946Z] Movies recommended for you: [2025-03-06T01:22:04.946Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:22:04.946Z] There is no way to check that no silent failure occurred. [2025-03-06T01:22:04.946Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (148676.477 ms) ====== [2025-03-06T01:22:04.946Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-03-06T01:22:05.759Z] GC before operation: completed in 926.893 ms, heap usage 178.114 MB -> 47.118 MB. [2025-03-06T01:22:36.374Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:22:59.481Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:23:30.426Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:23:52.976Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:24:07.006Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:24:21.104Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:24:37.423Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:24:50.059Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:24:52.666Z] 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. [2025-03-06T01:24:53.523Z] The best model improves the baseline by 14.52%. [2025-03-06T01:24:54.304Z] Movies recommended for you: [2025-03-06T01:24:54.304Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:24:54.305Z] There is no way to check that no silent failure occurred. [2025-03-06T01:24:54.305Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (168361.409 ms) ====== [2025-03-06T01:24:54.305Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-03-06T01:24:55.099Z] GC before operation: completed in 991.184 ms, heap usage 176.223 MB -> 47.503 MB. [2025-03-06T01:25:17.305Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:25:43.718Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:26:06.832Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:26:33.158Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:26:51.798Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:27:08.451Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:27:25.122Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:27:37.842Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:27:40.571Z] 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. [2025-03-06T01:27:40.571Z] The best model improves the baseline by 14.52%. [2025-03-06T01:27:41.404Z] Movies recommended for you: [2025-03-06T01:27:41.404Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:27:41.404Z] There is no way to check that no silent failure occurred. [2025-03-06T01:27:41.404Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (166306.001 ms) ====== [2025-03-06T01:27:41.404Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-03-06T01:27:42.185Z] GC before operation: completed in 1047.290 ms, heap usage 227.243 MB -> 47.540 MB. [2025-03-06T01:28:08.481Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:28:35.055Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:29:02.370Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:29:21.859Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:29:36.051Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:29:50.392Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:30:06.854Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:30:23.630Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:30:24.442Z] 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. [2025-03-06T01:30:24.442Z] The best model improves the baseline by 14.52%. [2025-03-06T01:30:25.274Z] Movies recommended for you: [2025-03-06T01:30:25.274Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:30:25.274Z] There is no way to check that no silent failure occurred. [2025-03-06T01:30:25.274Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (163097.405 ms) ====== [2025-03-06T01:30:25.274Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-03-06T01:30:26.957Z] GC before operation: completed in 1203.979 ms, heap usage 171.222 MB -> 47.696 MB. [2025-03-06T01:30:53.268Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:31:20.356Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:31:43.764Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:32:07.272Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:32:21.847Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:32:36.652Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:32:51.868Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:33:06.566Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:33:10.356Z] 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. [2025-03-06T01:33:10.356Z] The best model improves the baseline by 14.52%. [2025-03-06T01:33:11.200Z] Movies recommended for you: [2025-03-06T01:33:11.200Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:33:11.200Z] There is no way to check that no silent failure occurred. [2025-03-06T01:33:11.200Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (164536.811 ms) ====== [2025-03-06T01:33:16.934Z] ----------------------------------- [2025-03-06T01:33:16.934Z] renaissance-movie-lens_0_PASSED [2025-03-06T01:33:16.934Z] ----------------------------------- [2025-03-06T01:33:17.789Z] [2025-03-06T01:33:17.789Z] TEST TEARDOWN: [2025-03-06T01:33:17.789Z] Nothing to be done for teardown. [2025-03-06T01:33:17.789Z] renaissance-movie-lens_0 Finish Time: Thu Mar 6 01:33:16 2025 Epoch Time (ms): 1741224796942