[2025-02-27T06:15:41.765Z] Running test renaissance-movie-lens_0 ... [2025-02-27T06:15:41.765Z] =============================================== [2025-02-27T06:15:41.765Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 06:15:41 2025 Epoch Time (ms): 1740636941596 [2025-02-27T06:15:41.765Z] variation: NoOptions [2025-02-27T06:15:41.765Z] JVM_OPTIONS: [2025-02-27T06:15:41.765Z] { \ [2025-02-27T06:15:41.765Z] echo ""; echo "TEST SETUP:"; \ [2025-02-27T06:15:41.765Z] echo "Nothing to be done for setup."; \ [2025-02-27T06:15:41.765Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17406272595157/renaissance-movie-lens_0"; \ [2025-02-27T06:15:41.765Z] cd "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17406272595157/renaissance-movie-lens_0"; \ [2025-02-27T06:15:41.765Z] echo ""; echo "TESTING:"; \ [2025-02-27T06:15:41.765Z] "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/jdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17406272595157/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-02-27T06:15:41.765Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17406272595157/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-02-27T06:15:41.765Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-02-27T06:15:41.765Z] echo "Nothing to be done for teardown."; \ [2025-02-27T06:15:41.765Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17406272595157/TestTargetResult"; [2025-02-27T06:15:41.765Z] [2025-02-27T06:15:41.765Z] TEST SETUP: [2025-02-27T06:15:41.765Z] Nothing to be done for setup. [2025-02-27T06:15:41.765Z] [2025-02-27T06:15:41.765Z] TESTING: [2025-02-27T06:15:45.093Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-02-27T06:15:47.002Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2025-02-27T06:15:49.573Z] Got 100004 ratings from 671 users on 9066 movies. [2025-02-27T06:15:49.966Z] Training: 60056, validation: 20285, test: 19854 [2025-02-27T06:15:49.966Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-02-27T06:15:49.966Z] GC before operation: completed in 44.709 ms, heap usage 55.757 MB -> 38.378 MB. [2025-02-27T06:16:14.039Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:16:34.152Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:16:54.251Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:17:11.015Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:17:20.608Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:17:28.506Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:17:40.086Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:17:47.990Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:17:47.990Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:17:47.990Z] The best model improves the baseline by 14.43%. [2025-02-27T06:17:47.990Z] Movies recommended for you: [2025-02-27T06:17:47.990Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:17:47.990Z] There is no way to check that no silent failure occurred. [2025-02-27T06:17:47.990Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (118091.475 ms) ====== [2025-02-27T06:17:47.990Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-02-27T06:17:48.384Z] GC before operation: completed in 71.668 ms, heap usage 379.605 MB -> 74.832 MB. [2025-02-27T06:18:08.470Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:18:28.580Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:18:48.677Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:19:05.435Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:19:15.025Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:19:22.924Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:19:38.600Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:19:46.502Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:19:46.502Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:19:46.502Z] The best model improves the baseline by 14.43%. [2025-02-27T06:19:46.502Z] Movies recommended for you: [2025-02-27T06:19:46.502Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:19:46.502Z] There is no way to check that no silent failure occurred. [2025-02-27T06:19:46.502Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (118305.848 ms) ====== [2025-02-27T06:19:46.502Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-02-27T06:19:46.502Z] GC before operation: completed in 69.257 ms, heap usage 513.827 MB -> 80.622 MB. [2025-02-27T06:20:06.641Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:20:27.089Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:20:47.189Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:21:01.128Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:21:10.706Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:21:18.596Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:21:30.185Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:21:38.076Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:21:38.076Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:21:38.495Z] The best model improves the baseline by 14.43%. [2025-02-27T06:21:38.495Z] Movies recommended for you: [2025-02-27T06:21:38.495Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:21:38.495Z] There is no way to check that no silent failure occurred. [2025-02-27T06:21:38.495Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (111779.341 ms) ====== [2025-02-27T06:21:38.495Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-02-27T06:21:38.495Z] GC before operation: completed in 75.477 ms, heap usage 751.827 MB -> 80.969 MB. [2025-02-27T06:21:58.580Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:22:19.891Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:22:43.988Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:22:57.938Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:23:05.845Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:23:13.754Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:23:25.344Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:23:33.243Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:23:33.243Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:23:33.243Z] The best model improves the baseline by 14.43%. [2025-02-27T06:23:33.243Z] Movies recommended for you: [2025-02-27T06:23:33.243Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:23:33.243Z] There is no way to check that no silent failure occurred. [2025-02-27T06:23:33.243Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (114775.269 ms) ====== [2025-02-27T06:23:33.243Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-02-27T06:23:33.243Z] GC before operation: completed in 56.990 ms, heap usage 199.301 MB -> 69.096 MB. [2025-02-27T06:23:53.339Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:24:13.556Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:24:33.679Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:24:47.612Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:24:55.515Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:25:03.417Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:25:15.009Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:25:22.929Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:25:22.929Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:25:22.929Z] The best model improves the baseline by 14.43%. [2025-02-27T06:25:22.929Z] Movies recommended for you: [2025-02-27T06:25:22.929Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:25:22.929Z] There is no way to check that no silent failure occurred. [2025-02-27T06:25:22.929Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (109466.435 ms) ====== [2025-02-27T06:25:22.929Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-02-27T06:25:22.929Z] GC before operation: completed in 75.792 ms, heap usage 1.369 GB -> 81.701 MB. [2025-02-27T06:25:43.006Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:25:59.756Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:26:19.899Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:26:36.660Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:26:48.244Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:26:57.844Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:27:09.430Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:27:17.337Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:27:17.337Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:27:17.337Z] The best model improves the baseline by 14.43%. [2025-02-27T06:27:17.337Z] Movies recommended for you: [2025-02-27T06:27:17.337Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:27:17.337Z] There is no way to check that no silent failure occurred. [2025-02-27T06:27:17.337Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (114506.728 ms) ====== [2025-02-27T06:27:17.337Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-02-27T06:27:17.337Z] GC before operation: completed in 69.127 ms, heap usage 204.949 MB -> 81.197 MB. [2025-02-27T06:27:37.438Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:27:57.531Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:28:17.616Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:28:31.563Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:28:41.151Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:28:49.059Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:29:00.645Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:29:08.563Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:29:08.563Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:29:08.563Z] The best model improves the baseline by 14.43%. [2025-02-27T06:29:08.563Z] Movies recommended for you: [2025-02-27T06:29:08.563Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:29:08.563Z] There is no way to check that no silent failure occurred. [2025-02-27T06:29:08.563Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (110503.067 ms) ====== [2025-02-27T06:29:08.563Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-02-27T06:29:08.563Z] GC before operation: completed in 74.053 ms, heap usage 551.084 MB -> 81.657 MB. [2025-02-27T06:29:28.703Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:29:48.822Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:30:08.920Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:30:25.671Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:30:35.274Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:30:43.174Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:30:54.754Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:31:02.662Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:31:02.662Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:31:02.662Z] The best model improves the baseline by 14.43%. [2025-02-27T06:31:02.662Z] Movies recommended for you: [2025-02-27T06:31:02.662Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:31:02.662Z] There is no way to check that no silent failure occurred. [2025-02-27T06:31:02.662Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (114611.761 ms) ====== [2025-02-27T06:31:02.662Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-02-27T06:31:02.662Z] GC before operation: completed in 66.303 ms, heap usage 645.741 MB -> 69.850 MB. [2025-02-27T06:31:22.749Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:31:42.862Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:32:02.960Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:32:16.917Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:32:24.831Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:32:32.742Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:32:44.333Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:32:52.239Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:32:52.239Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:32:52.240Z] The best model improves the baseline by 14.43%. [2025-02-27T06:32:52.240Z] Movies recommended for you: [2025-02-27T06:32:52.240Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:32:52.240Z] There is no way to check that no silent failure occurred. [2025-02-27T06:32:52.240Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (109513.121 ms) ====== [2025-02-27T06:32:52.240Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-02-27T06:32:52.240Z] GC before operation: completed in 62.961 ms, heap usage 787.015 MB -> 69.719 MB. [2025-02-27T06:33:12.340Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:33:32.442Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:33:52.539Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:34:09.309Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:34:18.915Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:34:26.856Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:34:38.446Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:34:48.042Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:34:48.042Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:34:48.042Z] The best model improves the baseline by 14.43%. [2025-02-27T06:34:48.042Z] Movies recommended for you: [2025-02-27T06:34:48.042Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:34:48.042Z] There is no way to check that no silent failure occurred. [2025-02-27T06:34:48.042Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (115474.327 ms) ====== [2025-02-27T06:34:48.042Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-02-27T06:34:48.042Z] GC before operation: completed in 64.198 ms, heap usage 992.171 MB -> 62.907 MB. [2025-02-27T06:35:08.356Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:35:28.462Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:35:48.562Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:36:02.523Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:36:12.116Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:36:20.022Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:36:31.610Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:36:38.091Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:36:38.091Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:36:38.091Z] The best model improves the baseline by 14.43%. [2025-02-27T06:36:38.484Z] Movies recommended for you: [2025-02-27T06:36:38.484Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:36:38.484Z] There is no way to check that no silent failure occurred. [2025-02-27T06:36:38.484Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (110555.865 ms) ====== [2025-02-27T06:36:38.484Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-02-27T06:36:38.484Z] GC before operation: completed in 70.786 ms, heap usage 302.993 MB -> 81.451 MB. [2025-02-27T06:36:58.581Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:37:15.342Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:37:35.449Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:37:49.396Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:37:58.993Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:38:05.469Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:38:17.055Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:38:24.969Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:38:24.969Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:38:24.969Z] The best model improves the baseline by 14.43%. [2025-02-27T06:38:24.969Z] Movies recommended for you: [2025-02-27T06:38:24.969Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:38:24.969Z] There is no way to check that no silent failure occurred. [2025-02-27T06:38:24.969Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (106454.245 ms) ====== [2025-02-27T06:38:24.969Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-02-27T06:38:24.969Z] GC before operation: completed in 71.491 ms, heap usage 266.716 MB -> 81.649 MB. [2025-02-27T06:38:45.069Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:39:05.170Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:39:25.267Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:39:39.249Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:39:48.878Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:39:56.785Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:40:08.368Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:40:16.285Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:40:16.285Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:40:16.285Z] The best model improves the baseline by 14.43%. [2025-02-27T06:40:16.285Z] Movies recommended for you: [2025-02-27T06:40:16.285Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:40:16.285Z] There is no way to check that no silent failure occurred. [2025-02-27T06:40:16.285Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (110789.012 ms) ====== [2025-02-27T06:40:16.285Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-02-27T06:40:16.285Z] GC before operation: completed in 71.443 ms, heap usage 659.020 MB -> 81.898 MB. [2025-02-27T06:40:36.360Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:40:53.114Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:41:17.264Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:41:34.046Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:41:41.941Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:41:49.841Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:42:01.419Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:42:09.315Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:42:09.315Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:42:09.315Z] The best model improves the baseline by 14.43%. [2025-02-27T06:42:09.315Z] Movies recommended for you: [2025-02-27T06:42:09.315Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:42:09.315Z] There is no way to check that no silent failure occurred. [2025-02-27T06:42:09.315Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (113051.411 ms) ====== [2025-02-27T06:42:09.315Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-02-27T06:42:09.315Z] GC before operation: completed in 73.815 ms, heap usage 318.449 MB -> 81.596 MB. [2025-02-27T06:42:29.911Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:42:50.048Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:43:10.181Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:43:24.117Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:43:33.695Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:43:41.589Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:43:53.160Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:43:59.629Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:44:00.023Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:44:00.023Z] The best model improves the baseline by 14.43%. [2025-02-27T06:44:00.023Z] Movies recommended for you: [2025-02-27T06:44:00.023Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:44:00.023Z] There is no way to check that no silent failure occurred. [2025-02-27T06:44:00.023Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (111160.989 ms) ====== [2025-02-27T06:44:00.023Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-02-27T06:44:00.023Z] GC before operation: completed in 74.325 ms, heap usage 693.498 MB -> 81.901 MB. [2025-02-27T06:44:20.116Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:44:45.017Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:45:09.089Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:45:20.653Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:45:30.229Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:45:38.121Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:45:52.059Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:46:05.456Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:46:05.456Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:46:05.456Z] The best model improves the baseline by 14.43%. [2025-02-27T06:46:05.456Z] Movies recommended for you: [2025-02-27T06:46:05.456Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:46:05.456Z] There is no way to check that no silent failure occurred. [2025-02-27T06:46:05.456Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (124359.630 ms) ====== [2025-02-27T06:46:05.456Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-02-27T06:46:05.456Z] GC before operation: completed in 60.184 ms, heap usage 563.221 MB -> 60.791 MB. [2025-02-27T06:46:25.551Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:46:45.744Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:47:05.845Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:47:22.590Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:47:30.489Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:47:38.667Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:47:50.247Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:47:56.724Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:47:57.116Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:47:57.116Z] The best model improves the baseline by 14.43%. [2025-02-27T06:47:57.509Z] Movies recommended for you: [2025-02-27T06:47:57.509Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:47:57.509Z] There is no way to check that no silent failure occurred. [2025-02-27T06:47:57.509Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (112824.924 ms) ====== [2025-02-27T06:47:57.509Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-02-27T06:47:57.509Z] GC before operation: completed in 63.421 ms, heap usage 533.478 MB -> 56.865 MB. [2025-02-27T06:48:17.618Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:48:34.365Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:48:54.454Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:49:08.424Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:49:20.013Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:49:27.927Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:49:39.550Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:49:47.449Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:49:47.844Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:49:47.844Z] The best model improves the baseline by 14.43%. [2025-02-27T06:49:47.844Z] Movies recommended for you: [2025-02-27T06:49:47.844Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:49:47.844Z] There is no way to check that no silent failure occurred. [2025-02-27T06:49:47.844Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (110418.561 ms) ====== [2025-02-27T06:49:47.844Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-02-27T06:49:47.844Z] GC before operation: completed in 70.758 ms, heap usage 216.802 MB -> 81.659 MB. [2025-02-27T06:50:07.946Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:50:24.705Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:50:44.800Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:50:58.745Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:51:08.338Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:51:16.241Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:51:27.826Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:51:34.304Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:51:34.304Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:51:34.714Z] The best model improves the baseline by 14.43%. [2025-02-27T06:51:34.714Z] Movies recommended for you: [2025-02-27T06:51:34.714Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:51:34.714Z] There is no way to check that no silent failure occurred. [2025-02-27T06:51:34.714Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (106676.339 ms) ====== [2025-02-27T06:51:34.714Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-02-27T06:51:34.714Z] GC before operation: completed in 73.334 ms, heap usage 634.474 MB -> 81.983 MB. [2025-02-27T06:51:54.922Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-02-27T06:52:15.020Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-02-27T06:52:39.143Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-02-27T06:52:50.724Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-02-27T06:53:00.322Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-02-27T06:53:08.226Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-02-27T06:53:19.807Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-02-27T06:53:27.707Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-02-27T06:53:27.707Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2025-02-27T06:53:27.707Z] The best model improves the baseline by 14.43%. [2025-02-27T06:53:27.707Z] Movies recommended for you: [2025-02-27T06:53:27.707Z] WARNING: This benchmark provides no result that can be validated. [2025-02-27T06:53:27.707Z] There is no way to check that no silent failure occurred. [2025-02-27T06:53:27.707Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (112487.886 ms) ====== [2025-02-27T06:53:28.539Z] ----------------------------------- [2025-02-27T06:53:28.539Z] renaissance-movie-lens_0_PASSED [2025-02-27T06:53:28.539Z] ----------------------------------- [2025-02-27T06:53:28.539Z] [2025-02-27T06:53:28.539Z] TEST TEARDOWN: [2025-02-27T06:53:28.539Z] Nothing to be done for teardown. [2025-02-27T06:53:28.539Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 06:53:28 2025 Epoch Time (ms): 1740639208350