[2024-11-17T04:45:08.956Z] Running test renaissance-movie-lens_0 ... [2024-11-17T04:45:08.956Z] =============================================== [2024-11-17T04:45:08.956Z] renaissance-movie-lens_0 Start Time: Sun Nov 17 04:45:08 2024 Epoch Time (ms): 1731818708037 [2024-11-17T04:45:08.956Z] variation: NoOptions [2024-11-17T04:45:08.956Z] JVM_OPTIONS: [2024-11-17T04:45:08.956Z] { \ [2024-11-17T04:45:08.956Z] echo ""; echo "TEST SETUP:"; \ [2024-11-17T04:45:08.956Z] echo "Nothing to be done for setup."; \ [2024-11-17T04:45:08.956Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318151552169/renaissance-movie-lens_0"; \ [2024-11-17T04:45:08.956Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318151552169/renaissance-movie-lens_0"; \ [2024-11-17T04:45:08.956Z] echo ""; echo "TESTING:"; \ [2024-11-17T04:45:08.956Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318151552169/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-11-17T04:45:08.956Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318151552169/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-11-17T04:45:08.956Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-11-17T04:45:08.956Z] echo "Nothing to be done for teardown."; \ [2024-11-17T04:45:08.956Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17318151552169/TestTargetResult"; [2024-11-17T04:45:08.956Z] [2024-11-17T04:45:08.956Z] TEST SETUP: [2024-11-17T04:45:08.956Z] Nothing to be done for setup. [2024-11-17T04:45:08.956Z] [2024-11-17T04:45:08.956Z] TESTING: [2024-11-17T04:45:21.115Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-11-17T04:45:33.304Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-11-17T04:45:52.964Z] Got 100004 ratings from 671 users on 9066 movies. [2024-11-17T04:45:54.709Z] Training: 60056, validation: 20285, test: 19854 [2024-11-17T04:45:54.710Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-11-17T04:45:55.528Z] GC before operation: completed in 331.467 ms, heap usage 118.743 MB -> 37.193 MB. [2024-11-17T04:46:32.203Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:46:54.251Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:47:13.179Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:47:32.125Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:47:42.623Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:47:50.224Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:47:56.978Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:48:03.894Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:48:04.667Z] 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. [2024-11-17T04:48:04.667Z] The best model improves the baseline by 14.52%. [2024-11-17T04:48:05.464Z] Movies recommended for you: [2024-11-17T04:48:05.464Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:48:05.464Z] There is no way to check that no silent failure occurred. [2024-11-17T04:48:05.464Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (130532.289 ms) ====== [2024-11-17T04:48:05.464Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-11-17T04:48:06.228Z] GC before operation: completed in 396.065 ms, heap usage 199.477 MB -> 49.389 MB. [2024-11-17T04:48:16.062Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:48:25.951Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:48:35.939Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:48:43.093Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:48:49.189Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:48:56.264Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:49:00.905Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:49:06.644Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:49:07.448Z] 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. [2024-11-17T04:49:07.448Z] The best model improves the baseline by 14.52%. [2024-11-17T04:49:07.448Z] Movies recommended for you: [2024-11-17T04:49:07.448Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:49:07.448Z] There is no way to check that no silent failure occurred. [2024-11-17T04:49:07.448Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (61588.109 ms) ====== [2024-11-17T04:49:07.448Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-11-17T04:49:08.249Z] GC before operation: completed in 289.809 ms, heap usage 178.770 MB -> 49.689 MB. [2024-11-17T04:49:16.808Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:49:25.424Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:49:35.620Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:49:44.181Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:49:50.593Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:49:56.407Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:50:03.484Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:50:09.244Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:50:09.244Z] 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. [2024-11-17T04:50:10.060Z] The best model improves the baseline by 14.52%. [2024-11-17T04:50:10.061Z] Movies recommended for you: [2024-11-17T04:50:10.061Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:50:10.061Z] There is no way to check that no silent failure occurred. [2024-11-17T04:50:10.061Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (62088.221 ms) ====== [2024-11-17T04:50:10.061Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-11-17T04:50:10.061Z] GC before operation: completed in 314.541 ms, heap usage 408.939 MB -> 53.531 MB. [2024-11-17T04:50:20.238Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:50:28.802Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:50:38.989Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:50:47.624Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:50:53.982Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:50:58.623Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:51:03.295Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:51:07.948Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:51:08.904Z] 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. [2024-11-17T04:51:08.904Z] The best model improves the baseline by 14.52%. [2024-11-17T04:51:09.715Z] Movies recommended for you: [2024-11-17T04:51:09.715Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:51:09.715Z] There is no way to check that no silent failure occurred. [2024-11-17T04:51:09.715Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (59142.985 ms) ====== [2024-11-17T04:51:09.715Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-11-17T04:51:09.715Z] GC before operation: completed in 289.308 ms, heap usage 127.338 MB -> 50.221 MB. [2024-11-17T04:51:18.276Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:51:28.479Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:51:40.539Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:51:49.197Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:51:55.590Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:52:01.436Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:52:07.306Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:52:13.156Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:52:14.005Z] 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. [2024-11-17T04:52:14.005Z] The best model improves the baseline by 14.52%. [2024-11-17T04:52:14.848Z] Movies recommended for you: [2024-11-17T04:52:14.848Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:52:14.848Z] There is no way to check that no silent failure occurred. [2024-11-17T04:52:14.848Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (64656.263 ms) ====== [2024-11-17T04:52:14.848Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-11-17T04:52:14.848Z] GC before operation: completed in 517.397 ms, heap usage 394.781 MB -> 54.029 MB. [2024-11-17T04:52:23.456Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:52:33.581Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:52:42.170Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:52:54.247Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:52:59.385Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:53:05.164Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:53:10.909Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:53:15.513Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:53:16.307Z] 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. [2024-11-17T04:53:16.307Z] The best model improves the baseline by 14.52%. [2024-11-17T04:53:17.102Z] Movies recommended for you: [2024-11-17T04:53:17.102Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:53:17.102Z] There is no way to check that no silent failure occurred. [2024-11-17T04:53:17.102Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (61980.006 ms) ====== [2024-11-17T04:53:17.102Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-11-17T04:53:17.102Z] GC before operation: completed in 321.014 ms, heap usage 103.987 MB -> 50.423 MB. [2024-11-17T04:53:25.666Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:53:35.837Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:53:46.041Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:53:54.685Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:54:00.548Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:54:06.351Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:54:12.245Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:54:17.990Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:54:18.808Z] 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. [2024-11-17T04:54:18.808Z] The best model improves the baseline by 14.52%. [2024-11-17T04:54:18.808Z] Movies recommended for you: [2024-11-17T04:54:18.808Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:54:18.808Z] There is no way to check that no silent failure occurred. [2024-11-17T04:54:18.808Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (61731.115 ms) ====== [2024-11-17T04:54:18.808Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-11-17T04:54:19.607Z] GC before operation: completed in 328.933 ms, heap usage 394.460 MB -> 54.176 MB. [2024-11-17T04:54:28.112Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:54:35.182Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:54:45.380Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:54:52.389Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:54:57.563Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:55:02.225Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:55:08.017Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:55:12.574Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:55:13.370Z] 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. [2024-11-17T04:55:13.370Z] The best model improves the baseline by 14.52%. [2024-11-17T04:55:14.184Z] Movies recommended for you: [2024-11-17T04:55:14.184Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:55:14.184Z] There is no way to check that no silent failure occurred. [2024-11-17T04:55:14.184Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (54745.120 ms) ====== [2024-11-17T04:55:14.184Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-11-17T04:55:14.184Z] GC before operation: completed in 365.025 ms, heap usage 691.024 MB -> 57.417 MB. [2024-11-17T04:55:22.511Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:55:30.710Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:55:40.519Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:55:47.349Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:55:51.829Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:55:56.669Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:56:01.183Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:56:06.835Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:56:08.484Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-11-17T04:56:08.484Z] The best model improves the baseline by 14.52%. [2024-11-17T04:56:08.484Z] Movies recommended for you: [2024-11-17T04:56:08.484Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:56:08.484Z] There is no way to check that no silent failure occurred. [2024-11-17T04:56:08.484Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (54193.824 ms) ====== [2024-11-17T04:56:08.484Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-11-17T04:56:09.411Z] GC before operation: completed in 309.657 ms, heap usage 302.029 MB -> 52.691 MB. [2024-11-17T04:56:17.747Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:56:27.599Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:56:37.499Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:56:44.476Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:56:48.984Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:56:53.771Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:56:59.416Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:57:03.914Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:57:04.694Z] 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. [2024-11-17T04:57:04.694Z] The best model improves the baseline by 14.52%. [2024-11-17T04:57:04.694Z] Movies recommended for you: [2024-11-17T04:57:04.694Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:57:04.694Z] There is no way to check that no silent failure occurred. [2024-11-17T04:57:04.694Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (55656.822 ms) ====== [2024-11-17T04:57:04.694Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-11-17T04:57:04.694Z] GC before operation: completed in 311.104 ms, heap usage 390.721 MB -> 54.287 MB. [2024-11-17T04:57:13.027Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:57:21.312Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:57:33.068Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:57:41.393Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:57:46.964Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:57:51.944Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:57:57.623Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:58:02.119Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:58:02.889Z] 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. [2024-11-17T04:58:03.661Z] The best model improves the baseline by 14.52%. [2024-11-17T04:58:03.661Z] Movies recommended for you: [2024-11-17T04:58:03.661Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:58:03.661Z] There is no way to check that no silent failure occurred. [2024-11-17T04:58:03.661Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (58654.955 ms) ====== [2024-11-17T04:58:03.661Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-11-17T04:58:03.661Z] GC before operation: completed in 294.328 ms, heap usage 401.860 MB -> 54.027 MB. [2024-11-17T04:58:11.977Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:58:20.295Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:58:28.529Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:58:36.749Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:58:42.330Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:58:47.977Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:58:54.860Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T04:58:59.908Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T04:59:00.790Z] 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. [2024-11-17T04:59:00.790Z] The best model improves the baseline by 14.52%. [2024-11-17T04:59:00.790Z] Movies recommended for you: [2024-11-17T04:59:00.790Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T04:59:00.790Z] There is no way to check that no silent failure occurred. [2024-11-17T04:59:00.790Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (57103.236 ms) ====== [2024-11-17T04:59:00.790Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-11-17T04:59:01.670Z] GC before operation: completed in 425.805 ms, heap usage 104.512 MB -> 53.965 MB. [2024-11-17T04:59:14.462Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T04:59:23.644Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T04:59:32.823Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T04:59:41.973Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T04:59:47.038Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T04:59:53.300Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T04:59:58.564Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:00:06.282Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:00:06.282Z] 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. [2024-11-17T05:00:07.155Z] The best model improves the baseline by 14.52%. [2024-11-17T05:00:07.155Z] Movies recommended for you: [2024-11-17T05:00:07.155Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:00:07.155Z] There is no way to check that no silent failure occurred. [2024-11-17T05:00:07.155Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (65545.558 ms) ====== [2024-11-17T05:00:07.155Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-11-17T05:00:07.155Z] GC before operation: completed in 362.879 ms, heap usage 377.089 MB -> 54.862 MB. [2024-11-17T05:00:16.374Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:00:27.281Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:00:38.247Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:00:47.334Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:00:53.560Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:00:58.539Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:01:04.176Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:01:09.133Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:01:10.157Z] 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. [2024-11-17T05:01:11.035Z] The best model improves the baseline by 14.52%. [2024-11-17T05:01:11.035Z] Movies recommended for you: [2024-11-17T05:01:11.035Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:01:11.035Z] There is no way to check that no silent failure occurred. [2024-11-17T05:01:11.035Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (63597.074 ms) ====== [2024-11-17T05:01:11.035Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-11-17T05:01:11.035Z] GC before operation: completed in 347.507 ms, heap usage 150.490 MB -> 54.008 MB. [2024-11-17T05:01:20.095Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:01:27.640Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:01:36.849Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:01:44.552Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:01:48.397Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:01:53.380Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:01:58.318Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:02:02.129Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:02:02.994Z] 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. [2024-11-17T05:02:02.994Z] The best model improves the baseline by 14.52%. [2024-11-17T05:02:03.858Z] Movies recommended for you: [2024-11-17T05:02:03.858Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:02:03.858Z] There is no way to check that no silent failure occurred. [2024-11-17T05:02:03.858Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (52254.468 ms) ====== [2024-11-17T05:02:03.858Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-11-17T05:02:03.858Z] GC before operation: completed in 255.410 ms, heap usage 123.552 MB -> 54.048 MB. [2024-11-17T05:02:12.059Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:02:21.175Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:02:28.800Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:02:36.402Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:02:41.371Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:02:46.377Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:02:51.368Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:02:57.564Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:02:57.564Z] 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. [2024-11-17T05:02:57.564Z] The best model improves the baseline by 14.52%. [2024-11-17T05:02:58.435Z] Movies recommended for you: [2024-11-17T05:02:58.435Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:02:58.435Z] There is no way to check that no silent failure occurred. [2024-11-17T05:02:58.435Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (54295.548 ms) ====== [2024-11-17T05:02:58.435Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-11-17T05:02:58.435Z] GC before operation: completed in 352.610 ms, heap usage 196.337 MB -> 53.297 MB. [2024-11-17T05:03:09.280Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:03:19.061Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:03:26.721Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:03:34.374Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:03:38.274Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:03:43.407Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:03:47.241Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:03:51.070Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:03:52.859Z] 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. [2024-11-17T05:03:52.859Z] The best model improves the baseline by 14.52%. [2024-11-17T05:03:52.859Z] Movies recommended for you: [2024-11-17T05:03:52.859Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:03:52.859Z] There is no way to check that no silent failure occurred. [2024-11-17T05:03:52.859Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (54383.582 ms) ====== [2024-11-17T05:03:52.859Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-11-17T05:03:53.740Z] GC before operation: completed in 402.100 ms, heap usage 557.223 MB -> 54.397 MB. [2024-11-17T05:04:01.354Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:04:08.945Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:04:16.703Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:04:24.288Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:04:29.259Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:04:34.237Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:04:38.099Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:04:43.237Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:04:44.110Z] 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. [2024-11-17T05:04:44.110Z] The best model improves the baseline by 14.52%. [2024-11-17T05:04:44.110Z] Movies recommended for you: [2024-11-17T05:04:44.110Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:04:44.110Z] There is no way to check that no silent failure occurred. [2024-11-17T05:04:44.110Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (51036.257 ms) ====== [2024-11-17T05:04:44.110Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-11-17T05:04:45.014Z] GC before operation: completed in 283.892 ms, heap usage 179.238 MB -> 50.838 MB. [2024-11-17T05:04:52.630Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:05:00.273Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:05:09.433Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:05:16.042Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:05:21.607Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:05:25.550Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:05:30.656Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:05:34.588Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:05:35.480Z] 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. [2024-11-17T05:05:35.480Z] The best model improves the baseline by 14.52%. [2024-11-17T05:05:35.480Z] Movies recommended for you: [2024-11-17T05:05:35.480Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:05:35.480Z] There is no way to check that no silent failure occurred. [2024-11-17T05:05:35.480Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (50940.730 ms) ====== [2024-11-17T05:05:35.480Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-11-17T05:05:35.480Z] GC before operation: completed in 340.114 ms, heap usage 569.464 MB -> 54.612 MB. [2024-11-17T05:05:43.191Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T05:05:50.938Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T05:05:58.662Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T05:06:06.378Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T05:06:10.462Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T05:06:15.532Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T05:06:20.149Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T05:06:24.105Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T05:06:24.998Z] 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. [2024-11-17T05:06:24.998Z] The best model improves the baseline by 14.52%. [2024-11-17T05:06:24.998Z] Movies recommended for you: [2024-11-17T05:06:24.998Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T05:06:24.998Z] There is no way to check that no silent failure occurred. [2024-11-17T05:06:24.998Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (49508.798 ms) ====== [2024-11-17T05:06:26.838Z] ----------------------------------- [2024-11-17T05:06:26.838Z] renaissance-movie-lens_0_PASSED [2024-11-17T05:06:26.838Z] ----------------------------------- [2024-11-17T05:06:26.838Z] [2024-11-17T05:06:26.838Z] TEST TEARDOWN: [2024-11-17T05:06:26.838Z] Nothing to be done for teardown. [2024-11-17T05:06:26.838Z] renaissance-movie-lens_0 Finish Time: Sun Nov 17 05:06:26 2024 Epoch Time (ms): 1731819986585