[2024-09-26T23:59:25.922Z] Running test renaissance-movie-lens_0 ... [2024-09-26T23:59:25.922Z] =============================================== [2024-09-26T23:59:25.922Z] renaissance-movie-lens_0 Start Time: Thu Sep 26 18:59:25 2024 Epoch Time (ms): 1727395165755 [2024-09-26T23:59:25.922Z] variation: NoOptions [2024-09-26T23:59:25.922Z] JVM_OPTIONS: [2024-09-26T23:59:25.922Z] { \ [2024-09-26T23:59:25.922Z] echo ""; echo "TEST SETUP:"; \ [2024-09-26T23:59:25.922Z] echo "Nothing to be done for setup."; \ [2024-09-26T23:59:25.922Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1727394285331/renaissance-movie-lens_0"; \ [2024-09-26T23:59:25.922Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1727394285331/renaissance-movie-lens_0"; \ [2024-09-26T23:59:25.922Z] echo ""; echo "TESTING:"; \ [2024-09-26T23:59:25.922Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk8u432-b05/bin/..//bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1727394285331/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-09-26T23:59:25.922Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1727394285331/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-09-26T23:59:25.922Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-09-26T23:59:25.922Z] echo "Nothing to be done for teardown."; \ [2024-09-26T23:59:25.922Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1727394285331/TestTargetResult"; [2024-09-26T23:59:25.922Z] [2024-09-26T23:59:25.922Z] TEST SETUP: [2024-09-26T23:59:25.922Z] Nothing to be done for setup. [2024-09-26T23:59:25.922Z] [2024-09-26T23:59:25.922Z] TESTING: [2024-09-26T23:59:29.977Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-09-26T23:59:32.201Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2024-09-26T23:59:36.249Z] Got 100004 ratings from 671 users on 9066 movies. [2024-09-26T23:59:36.249Z] Training: 60056, validation: 20285, test: 19854 [2024-09-26T23:59:36.249Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-09-26T23:59:36.249Z] GC before operation: completed in 270.341 ms, heap usage 120.832 MB -> 28.922 MB. [2024-09-26T23:59:42.547Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-26T23:59:45.645Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-26T23:59:47.973Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-26T23:59:51.064Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-26T23:59:52.507Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-26T23:59:53.925Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-26T23:59:56.141Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-26T23:59:57.568Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-26T23:59:57.568Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-26T23:59:57.568Z] The best model improves the baseline by 14.43%. [2024-09-26T23:59:58.264Z] Movies recommended for you: [2024-09-26T23:59:58.264Z] WARNING: This benchmark provides no result that can be validated. [2024-09-26T23:59:58.264Z] There is no way to check that no silent failure occurred. [2024-09-26T23:59:58.264Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21620.784 ms) ====== [2024-09-26T23:59:58.264Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-09-26T23:59:58.264Z] GC before operation: completed in 326.956 ms, heap usage 204.346 MB -> 48.919 MB. [2024-09-27T00:00:01.356Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:00:03.582Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:00:06.668Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:00:08.885Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:00:10.306Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:00:11.731Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:00:13.152Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:00:15.369Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:00:15.369Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:00:15.369Z] The best model improves the baseline by 14.43%. [2024-09-27T00:00:15.369Z] Movies recommended for you: [2024-09-27T00:00:15.369Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:00:15.370Z] There is no way to check that no silent failure occurred. [2024-09-27T00:00:15.370Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17114.231 ms) ====== [2024-09-27T00:00:15.370Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-09-27T00:00:15.370Z] GC before operation: completed in 237.975 ms, heap usage 405.176 MB -> 46.931 MB. [2024-09-27T00:00:18.501Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:00:20.715Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:00:22.932Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:00:25.150Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:00:26.574Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:00:28.789Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:00:30.264Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:00:33.602Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:00:33.602Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:00:33.602Z] The best model improves the baseline by 14.43%. [2024-09-27T00:00:33.602Z] Movies recommended for you: [2024-09-27T00:00:33.602Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:00:33.602Z] There is no way to check that no silent failure occurred. [2024-09-27T00:00:33.602Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16244.894 ms) ====== [2024-09-27T00:00:33.602Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-09-27T00:00:34.617Z] GC before operation: completed in 187.279 ms, heap usage 143.841 MB -> 50.377 MB. [2024-09-27T00:00:34.617Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:00:37.183Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:00:39.413Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:00:41.638Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:00:43.057Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:00:44.477Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:00:45.897Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:00:48.116Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:00:48.116Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:00:48.116Z] The best model improves the baseline by 14.43%. [2024-09-27T00:00:48.116Z] Movies recommended for you: [2024-09-27T00:00:48.116Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:00:48.116Z] There is no way to check that no silent failure occurred. [2024-09-27T00:00:48.116Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16110.035 ms) ====== [2024-09-27T00:00:48.116Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-09-27T00:00:48.116Z] GC before operation: completed in 193.929 ms, heap usage 578.649 MB -> 50.431 MB. [2024-09-27T00:00:50.339Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:00:53.426Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:00:55.643Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:00:57.866Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:00:58.553Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:00:59.980Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:01:01.406Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:01:02.842Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:01:03.552Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:01:03.552Z] The best model improves the baseline by 14.43%. [2024-09-27T00:01:03.552Z] Movies recommended for you: [2024-09-27T00:01:03.552Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:01:03.552Z] There is no way to check that no silent failure occurred. [2024-09-27T00:01:03.552Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15137.813 ms) ====== [2024-09-27T00:01:03.552Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-09-27T00:01:03.552Z] GC before operation: completed in 154.967 ms, heap usage 598.183 MB -> 50.589 MB. [2024-09-27T00:01:05.772Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:01:07.989Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:01:11.086Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:01:13.303Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:01:14.725Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:01:16.147Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:01:17.567Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:01:18.988Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:01:18.988Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:01:18.988Z] The best model improves the baseline by 14.43%. [2024-09-27T00:01:18.988Z] Movies recommended for you: [2024-09-27T00:01:18.988Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:01:18.988Z] There is no way to check that no silent failure occurred. [2024-09-27T00:01:18.988Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15366.027 ms) ====== [2024-09-27T00:01:18.988Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-09-27T00:01:18.988Z] GC before operation: completed in 176.050 ms, heap usage 614.905 MB -> 50.544 MB. [2024-09-27T00:01:21.203Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:01:23.419Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:01:26.503Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:01:28.724Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:01:29.411Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:01:30.839Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:01:32.261Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:01:33.687Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:01:34.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:01:34.374Z] The best model improves the baseline by 14.43%. [2024-09-27T00:01:34.375Z] Movies recommended for you: [2024-09-27T00:01:34.375Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:01:34.375Z] There is no way to check that no silent failure occurred. [2024-09-27T00:01:34.375Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15054.683 ms) ====== [2024-09-27T00:01:34.375Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-09-27T00:01:34.375Z] GC before operation: completed in 203.952 ms, heap usage 247.379 MB -> 48.332 MB. [2024-09-27T00:01:36.595Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:01:38.812Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:01:41.036Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:01:43.252Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:01:44.676Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:01:46.098Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:01:47.522Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:01:48.945Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:01:49.631Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:01:49.631Z] The best model improves the baseline by 14.43%. [2024-09-27T00:01:49.631Z] Movies recommended for you: [2024-09-27T00:01:49.631Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:01:49.631Z] There is no way to check that no silent failure occurred. [2024-09-27T00:01:49.631Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15141.170 ms) ====== [2024-09-27T00:01:49.631Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-09-27T00:01:49.632Z] GC before operation: completed in 237.884 ms, heap usage 426.788 MB -> 50.868 MB. [2024-09-27T00:01:51.849Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:01:54.073Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:01:56.293Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:01:58.510Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:01:59.931Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:02:01.352Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:02:02.786Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:02:04.240Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:02:04.923Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:02:04.924Z] The best model improves the baseline by 14.43%. [2024-09-27T00:02:04.924Z] Movies recommended for you: [2024-09-27T00:02:04.924Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:02:04.924Z] There is no way to check that no silent failure occurred. [2024-09-27T00:02:04.924Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15096.237 ms) ====== [2024-09-27T00:02:04.924Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-09-27T00:02:04.924Z] GC before operation: completed in 185.591 ms, heap usage 671.288 MB -> 52.341 MB. [2024-09-27T00:02:07.138Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:02:09.352Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:02:12.434Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:02:14.652Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:02:15.342Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:02:16.766Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:02:18.189Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:02:19.612Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:02:20.299Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:02:20.299Z] The best model improves the baseline by 14.43%. [2024-09-27T00:02:20.299Z] Movies recommended for you: [2024-09-27T00:02:20.299Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:02:20.299Z] There is no way to check that no silent failure occurred. [2024-09-27T00:02:20.299Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15040.752 ms) ====== [2024-09-27T00:02:20.299Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-09-27T00:02:20.299Z] GC before operation: completed in 146.843 ms, heap usage 123.384 MB -> 48.034 MB. [2024-09-27T00:02:22.517Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:02:24.736Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:02:26.957Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:02:29.176Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:02:31.391Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:02:32.078Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:02:33.498Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:02:34.922Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:02:35.608Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:02:35.608Z] The best model improves the baseline by 14.43%. [2024-09-27T00:02:35.608Z] Movies recommended for you: [2024-09-27T00:02:35.608Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:02:35.608Z] There is no way to check that no silent failure occurred. [2024-09-27T00:02:35.608Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15286.691 ms) ====== [2024-09-27T00:02:35.608Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-09-27T00:02:35.608Z] GC before operation: completed in 146.978 ms, heap usage 318.392 MB -> 50.234 MB. [2024-09-27T00:02:37.828Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:02:40.046Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:02:42.263Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:02:46.409Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:02:46.409Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:02:47.937Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:02:49.367Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:02:50.053Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:02:50.738Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:02:50.738Z] The best model improves the baseline by 14.43%. [2024-09-27T00:02:50.738Z] Movies recommended for you: [2024-09-27T00:02:50.738Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:02:50.738Z] There is no way to check that no silent failure occurred. [2024-09-27T00:02:50.738Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14973.159 ms) ====== [2024-09-27T00:02:50.738Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-09-27T00:02:50.738Z] GC before operation: completed in 119.633 ms, heap usage 311.466 MB -> 46.762 MB. [2024-09-27T00:02:52.957Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:02:55.172Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:02:57.387Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:02:59.609Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:03:01.032Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:03:02.454Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:03:03.881Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:03:05.299Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:03:05.985Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:03:05.985Z] The best model improves the baseline by 14.43%. [2024-09-27T00:03:05.985Z] Movies recommended for you: [2024-09-27T00:03:05.985Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:03:05.985Z] There is no way to check that no silent failure occurred. [2024-09-27T00:03:05.985Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15090.816 ms) ====== [2024-09-27T00:03:05.985Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-09-27T00:03:05.985Z] GC before operation: completed in 152.527 ms, heap usage 201.475 MB -> 46.695 MB. [2024-09-27T00:03:08.203Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:03:10.420Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:03:13.515Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:03:15.728Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:03:17.150Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:03:18.570Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:03:19.257Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:03:20.673Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:03:21.359Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:03:21.359Z] The best model improves the baseline by 14.43%. [2024-09-27T00:03:21.359Z] Movies recommended for you: [2024-09-27T00:03:21.359Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:03:21.359Z] There is no way to check that no silent failure occurred. [2024-09-27T00:03:21.359Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15234.210 ms) ====== [2024-09-27T00:03:21.359Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-09-27T00:03:21.360Z] GC before operation: completed in 131.614 ms, heap usage 389.576 MB -> 49.422 MB. [2024-09-27T00:03:23.572Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:03:25.784Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:03:28.000Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:03:30.216Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:03:31.634Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:03:33.057Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:03:34.485Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:03:35.909Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:03:36.596Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:03:36.596Z] The best model improves the baseline by 14.43%. [2024-09-27T00:03:36.596Z] Movies recommended for you: [2024-09-27T00:03:36.596Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:03:36.596Z] There is no way to check that no silent failure occurred. [2024-09-27T00:03:36.596Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14993.603 ms) ====== [2024-09-27T00:03:36.596Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-09-27T00:03:36.596Z] GC before operation: completed in 148.630 ms, heap usage 420.906 MB -> 47.122 MB. [2024-09-27T00:03:38.810Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:03:41.023Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:03:43.238Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:03:45.457Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:03:46.879Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:03:48.301Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:03:49.718Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:03:51.137Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:03:51.822Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:03:51.822Z] The best model improves the baseline by 14.43%. [2024-09-27T00:03:51.822Z] Movies recommended for you: [2024-09-27T00:03:51.822Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:03:51.822Z] There is no way to check that no silent failure occurred. [2024-09-27T00:03:51.822Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15132.728 ms) ====== [2024-09-27T00:03:51.822Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-09-27T00:03:51.822Z] GC before operation: completed in 142.452 ms, heap usage 268.076 MB -> 49.268 MB. [2024-09-27T00:03:54.034Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:03:56.256Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:03:58.469Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:04:00.682Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:04:02.103Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:04:03.520Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:04:04.936Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:04:06.359Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:04:07.046Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:04:07.046Z] The best model improves the baseline by 14.43%. [2024-09-27T00:04:07.046Z] Movies recommended for you: [2024-09-27T00:04:07.046Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:04:07.046Z] There is no way to check that no silent failure occurred. [2024-09-27T00:04:07.046Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15179.060 ms) ====== [2024-09-27T00:04:07.046Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-09-27T00:04:07.046Z] GC before operation: completed in 132.357 ms, heap usage 430.243 MB -> 49.501 MB. [2024-09-27T00:04:09.267Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:04:11.482Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:04:14.571Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:04:16.783Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:04:17.467Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:04:18.892Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:04:20.310Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:04:21.729Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:04:22.415Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:04:22.415Z] The best model improves the baseline by 14.43%. [2024-09-27T00:04:22.415Z] Movies recommended for you: [2024-09-27T00:04:22.415Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:04:22.415Z] There is no way to check that no silent failure occurred. [2024-09-27T00:04:22.415Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15236.005 ms) ====== [2024-09-27T00:04:22.415Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-09-27T00:04:22.415Z] GC before operation: completed in 215.580 ms, heap usage 344.545 MB -> 49.893 MB. [2024-09-27T00:04:24.628Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:04:26.894Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:04:29.118Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:04:31.386Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:04:32.808Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:04:34.226Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:04:35.652Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:04:37.097Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:04:37.097Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712. [2024-09-27T00:04:37.097Z] The best model improves the baseline by 14.43%. [2024-09-27T00:04:37.784Z] Movies recommended for you: [2024-09-27T00:04:37.784Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:04:37.784Z] There is no way to check that no silent failure occurred. [2024-09-27T00:04:37.784Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14805.644 ms) ====== [2024-09-27T00:04:37.784Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-09-27T00:04:37.784Z] GC before operation: completed in 145.220 ms, heap usage 147.023 MB -> 51.445 MB. [2024-09-27T00:04:40.000Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-09-27T00:04:42.210Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-09-27T00:04:44.426Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-09-27T00:04:46.757Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-09-27T00:04:48.176Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-09-27T00:04:49.597Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-09-27T00:04:51.020Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-09-27T00:04:52.437Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-09-27T00:04:52.437Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711. [2024-09-27T00:04:52.437Z] The best model improves the baseline by 14.43%. [2024-09-27T00:04:52.437Z] Movies recommended for you: [2024-09-27T00:04:52.437Z] WARNING: This benchmark provides no result that can be validated. [2024-09-27T00:04:52.437Z] There is no way to check that no silent failure occurred. [2024-09-27T00:04:52.437Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14959.256 ms) ====== [2024-09-27T00:04:53.861Z] ----------------------------------- [2024-09-27T00:04:53.861Z] renaissance-movie-lens_0_PASSED [2024-09-27T00:04:53.861Z] ----------------------------------- [2024-09-27T00:04:53.861Z] [2024-09-27T00:04:53.861Z] TEST TEARDOWN: [2024-09-27T00:04:53.861Z] Nothing to be done for teardown. [2024-09-27T00:04:53.861Z] renaissance-movie-lens_0 Finish Time: Thu Sep 26 19:04:53 2024 Epoch Time (ms): 1727395493633