[2024-11-23T13:42:27.421Z] Running test renaissance-movie-lens_0 ... [2024-11-23T13:42:27.421Z] =============================================== [2024-11-23T13:42:27.421Z] renaissance-movie-lens_0 Start Time: Sat Nov 23 13:42:27 2024 Epoch Time (ms): 1732369347259 [2024-11-23T13:42:27.421Z] variation: NoOptions [2024-11-23T13:42:27.421Z] JVM_OPTIONS: [2024-11-23T13:42:27.421Z] { \ [2024-11-23T13:42:27.421Z] echo ""; echo "TEST SETUP:"; \ [2024-11-23T13:42:27.421Z] echo "Nothing to be done for setup."; \ [2024-11-23T13:42:27.421Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323563552494/renaissance-movie-lens_0"; \ [2024-11-23T13:42:27.421Z] cd "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323563552494/renaissance-movie-lens_0"; \ [2024-11-23T13:42:27.421Z] echo ""; echo "TESTING:"; \ [2024-11-23T13:42:27.421Z] "/Users/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323563552494/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-11-23T13:42:27.421Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323563552494/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-11-23T13:42:27.421Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-11-23T13:42:27.421Z] echo "Nothing to be done for teardown."; \ [2024-11-23T13:42:27.421Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17323563552494/TestTargetResult"; [2024-11-23T13:42:27.421Z] [2024-11-23T13:42:27.421Z] TEST SETUP: [2024-11-23T13:42:27.421Z] Nothing to be done for setup. [2024-11-23T13:42:27.421Z] [2024-11-23T13:42:27.421Z] TESTING: [2024-11-23T13:42:32.407Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-11-23T13:42:34.177Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2024-11-23T13:42:38.157Z] Got 100004 ratings from 671 users on 9066 movies. [2024-11-23T13:42:38.157Z] Training: 60056, validation: 20285, test: 19854 [2024-11-23T13:42:38.157Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-11-23T13:42:38.157Z] GC before operation: completed in 59.794 ms, heap usage 197.000 MB -> 37.282 MB. [2024-11-23T13:43:12.032Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T13:43:40.290Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T13:44:14.175Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T13:44:33.765Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T13:44:47.271Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T13:45:00.806Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T13:45:17.082Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T13:45:28.257Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T13:45:28.616Z] 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. [2024-11-23T13:45:28.616Z] The best model improves the baseline by 14.43%. [2024-11-23T13:45:28.978Z] Movies recommended for you: [2024-11-23T13:45:28.978Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T13:45:28.978Z] There is no way to check that no silent failure occurred. [2024-11-23T13:45:28.978Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (170623.781 ms) ====== [2024-11-23T13:45:28.979Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-11-23T13:45:28.979Z] GC before operation: completed in 239.843 ms, heap usage 930.780 MB -> 66.192 MB. [2024-11-23T13:45:57.236Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T13:46:25.490Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T13:46:53.748Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T13:47:17.271Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T13:47:30.832Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T13:47:41.994Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T13:47:58.283Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T13:48:11.788Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T13:48:11.788Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T13:48:11.788Z] The best model improves the baseline by 14.43%. [2024-11-23T13:48:11.788Z] Movies recommended for you: [2024-11-23T13:48:11.788Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T13:48:11.788Z] There is no way to check that no silent failure occurred. [2024-11-23T13:48:11.788Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (162191.051 ms) ====== [2024-11-23T13:48:11.788Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-11-23T13:48:11.788Z] GC before operation: completed in 247.984 ms, heap usage 1.350 GB -> 57.103 MB. [2024-11-23T13:48:40.025Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T13:49:13.901Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T13:49:47.818Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T13:50:07.390Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T13:50:23.653Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T13:50:37.165Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T13:50:56.735Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T13:51:07.918Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T13:51:07.918Z] 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. [2024-11-23T13:51:07.918Z] The best model improves the baseline by 14.43%. [2024-11-23T13:51:07.918Z] Movies recommended for you: [2024-11-23T13:51:07.918Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T13:51:07.918Z] There is no way to check that no silent failure occurred. [2024-11-23T13:51:07.918Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (176327.418 ms) ====== [2024-11-23T13:51:07.918Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-11-23T13:51:08.277Z] GC before operation: completed in 304.665 ms, heap usage 671.106 MB -> 60.843 MB. [2024-11-23T13:51:42.211Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T13:52:05.750Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T13:52:39.632Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T13:53:07.879Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T13:53:21.400Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T13:53:32.581Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T13:53:48.873Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T13:54:00.052Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T13:54:00.052Z] 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. [2024-11-23T13:54:00.052Z] The best model improves the baseline by 14.43%. [2024-11-23T13:54:00.415Z] Movies recommended for you: [2024-11-23T13:54:00.415Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T13:54:00.415Z] There is no way to check that no silent failure occurred. [2024-11-23T13:54:00.415Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (172126.718 ms) ====== [2024-11-23T13:54:00.415Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-11-23T13:54:00.415Z] GC before operation: completed in 218.280 ms, heap usage 522.068 MB -> 57.609 MB. [2024-11-23T13:54:28.668Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T13:54:52.211Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T13:55:26.098Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T13:55:45.699Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T13:55:59.234Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T13:56:12.756Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T13:56:29.045Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T13:56:40.250Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T13:56:40.250Z] 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. [2024-11-23T13:56:40.610Z] The best model improves the baseline by 14.43%. [2024-11-23T13:56:40.610Z] Movies recommended for you: [2024-11-23T13:56:40.610Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T13:56:40.610Z] There is no way to check that no silent failure occurred. [2024-11-23T13:56:40.610Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (160066.562 ms) ====== [2024-11-23T13:56:40.610Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-11-23T13:56:40.610Z] GC before operation: completed in 175.077 ms, heap usage 658.089 MB -> 57.764 MB. [2024-11-23T13:57:08.857Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T13:57:42.741Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T13:58:10.996Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T13:58:34.528Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T13:58:45.708Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T13:58:56.876Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T13:59:13.163Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T13:59:26.702Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T13:59:26.702Z] 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. [2024-11-23T13:59:26.702Z] The best model improves the baseline by 14.43%. [2024-11-23T13:59:26.702Z] Movies recommended for you: [2024-11-23T13:59:26.702Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T13:59:26.702Z] There is no way to check that no silent failure occurred. [2024-11-23T13:59:26.702Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (164925.521 ms) ====== [2024-11-23T13:59:26.702Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-11-23T13:59:26.702Z] GC before operation: completed in 188.233 ms, heap usage 261.186 MB -> 57.596 MB. [2024-11-23T13:59:54.933Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:00:18.445Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:00:59.102Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:01:18.688Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:01:35.027Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:01:46.210Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:02:05.809Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:02:15.056Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:02:15.412Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-23T14:02:15.412Z] The best model improves the baseline by 14.43%. [2024-11-23T14:02:15.772Z] Movies recommended for you: [2024-11-23T14:02:15.772Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:02:15.772Z] There is no way to check that no silent failure occurred. [2024-11-23T14:02:15.772Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (169830.361 ms) ====== [2024-11-23T14:02:15.772Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-11-23T14:02:15.772Z] GC before operation: completed in 159.730 ms, heap usage 465.382 MB -> 55.425 MB. [2024-11-23T14:02:44.040Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:03:07.578Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:03:41.435Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:04:01.021Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:04:14.528Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:04:28.023Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:04:44.308Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:04:55.487Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:04:55.487Z] 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. [2024-11-23T14:04:55.487Z] The best model improves the baseline by 14.43%. [2024-11-23T14:04:55.847Z] Movies recommended for you: [2024-11-23T14:04:55.847Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:04:55.847Z] There is no way to check that no silent failure occurred. [2024-11-23T14:04:55.847Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (159853.155 ms) ====== [2024-11-23T14:04:55.847Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-11-23T14:04:55.847Z] GC before operation: completed in 159.576 ms, heap usage 649.214 MB -> 59.311 MB. [2024-11-23T14:05:31.710Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:06:07.304Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:06:35.558Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:06:55.153Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:07:08.648Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:07:22.156Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:07:41.760Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:07:52.936Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:07:52.936Z] 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. [2024-11-23T14:07:52.936Z] The best model improves the baseline by 14.43%. [2024-11-23T14:07:52.936Z] Movies recommended for you: [2024-11-23T14:07:52.936Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:07:52.936Z] There is no way to check that no silent failure occurred. [2024-11-23T14:07:52.936Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (176794.968 ms) ====== [2024-11-23T14:07:52.936Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-11-23T14:07:52.936Z] GC before operation: completed in 195.023 ms, heap usage 760.964 MB -> 61.529 MB. [2024-11-23T14:08:21.182Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:08:44.761Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:09:18.651Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:09:42.193Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:09:53.376Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:10:06.968Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:10:23.244Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:10:34.415Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:10:34.786Z] 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. [2024-11-23T14:10:34.786Z] The best model improves the baseline by 14.43%. [2024-11-23T14:10:34.786Z] Movies recommended for you: [2024-11-23T14:10:34.786Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:10:34.786Z] There is no way to check that no silent failure occurred. [2024-11-23T14:10:34.786Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (162010.597 ms) ====== [2024-11-23T14:10:34.786Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-11-23T14:10:35.143Z] GC before operation: completed in 178.265 ms, heap usage 310.702 MB -> 58.014 MB. [2024-11-23T14:11:03.428Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:11:34.735Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:12:08.660Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:12:28.256Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:12:44.546Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:12:55.729Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:13:12.035Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:13:23.216Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:13:23.575Z] 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. [2024-11-23T14:13:23.948Z] The best model improves the baseline by 14.43%. [2024-11-23T14:13:23.948Z] Movies recommended for you: [2024-11-23T14:13:23.948Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:13:23.948Z] There is no way to check that no silent failure occurred. [2024-11-23T14:13:23.948Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (168852.667 ms) ====== [2024-11-23T14:13:23.948Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-11-23T14:13:23.948Z] GC before operation: completed in 158.883 ms, heap usage 583.018 MB -> 57.787 MB. [2024-11-23T14:13:52.305Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:14:20.575Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:14:48.862Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:15:12.372Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:15:32.783Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:15:43.955Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:16:00.228Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:16:13.730Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:16:13.730Z] 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. [2024-11-23T14:16:13.730Z] The best model improves the baseline by 14.43%. [2024-11-23T14:16:13.730Z] Movies recommended for you: [2024-11-23T14:16:13.730Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:16:13.730Z] There is no way to check that no silent failure occurred. [2024-11-23T14:16:13.730Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (168648.817 ms) ====== [2024-11-23T14:16:13.730Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-11-23T14:16:13.730Z] GC before operation: completed in 179.598 ms, heap usage 323.695 MB -> 57.982 MB. [2024-11-23T14:16:42.008Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:17:05.618Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:17:39.506Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:17:59.107Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:18:15.401Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:18:24.639Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:18:44.267Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:18:55.447Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:18:55.807Z] 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. [2024-11-23T14:18:55.807Z] The best model improves the baseline by 14.43%. [2024-11-23T14:18:55.807Z] Movies recommended for you: [2024-11-23T14:18:55.807Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:18:55.807Z] There is no way to check that no silent failure occurred. [2024-11-23T14:18:55.807Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (162957.530 ms) ====== [2024-11-23T14:18:55.807Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-11-23T14:18:56.165Z] GC before operation: completed in 186.087 ms, heap usage 300.888 MB -> 65.071 MB. [2024-11-23T14:19:30.058Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:19:58.319Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:20:26.578Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:20:50.114Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:21:03.627Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:21:17.128Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:21:33.419Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:21:44.621Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:21:44.621Z] 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. [2024-11-23T14:21:44.621Z] The best model improves the baseline by 14.43%. [2024-11-23T14:21:44.621Z] Movies recommended for you: [2024-11-23T14:21:44.621Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:21:44.621Z] There is no way to check that no silent failure occurred. [2024-11-23T14:21:44.621Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (168535.332 ms) ====== [2024-11-23T14:21:44.621Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-11-23T14:21:44.621Z] GC before operation: completed in 174.345 ms, heap usage 699.193 MB -> 57.887 MB. [2024-11-23T14:22:12.855Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:22:41.101Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:23:09.409Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:23:32.946Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:23:49.230Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:24:02.752Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:24:22.329Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:24:33.514Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:24:34.287Z] 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. [2024-11-23T14:24:34.287Z] The best model improves the baseline by 14.43%. [2024-11-23T14:24:34.288Z] Movies recommended for you: [2024-11-23T14:24:34.288Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:24:34.288Z] There is no way to check that no silent failure occurred. [2024-11-23T14:24:34.288Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (169637.469 ms) ====== [2024-11-23T14:24:34.288Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-11-23T14:24:34.647Z] GC before operation: completed in 233.341 ms, heap usage 687.855 MB -> 58.092 MB. [2024-11-23T14:25:08.526Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:25:36.781Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:26:10.677Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:26:30.268Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:26:43.804Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:26:54.993Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:27:11.310Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:27:22.486Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:27:22.848Z] 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. [2024-11-23T14:27:22.848Z] The best model improves the baseline by 14.43%. [2024-11-23T14:27:23.207Z] Movies recommended for you: [2024-11-23T14:27:23.207Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:27:23.207Z] There is no way to check that no silent failure occurred. [2024-11-23T14:27:23.207Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (168488.937 ms) ====== [2024-11-23T14:27:23.207Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-11-23T14:27:23.207Z] GC before operation: completed in 276.734 ms, heap usage 567.119 MB -> 73.619 MB. [2024-11-23T14:27:51.459Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:28:19.718Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:28:47.993Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:29:11.538Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:29:25.058Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:29:38.574Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:29:58.172Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:30:11.684Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:30:11.684Z] 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. [2024-11-23T14:30:11.684Z] The best model improves the baseline by 14.43%. [2024-11-23T14:30:11.684Z] Movies recommended for you: [2024-11-23T14:30:11.684Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:30:11.684Z] There is no way to check that no silent failure occurred. [2024-11-23T14:30:11.684Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (166989.759 ms) ====== [2024-11-23T14:30:11.684Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-11-23T14:30:11.684Z] GC before operation: completed in 169.333 ms, heap usage 294.278 MB -> 57.938 MB. [2024-11-23T14:30:42.703Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:31:10.954Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:31:51.900Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:32:11.490Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:32:25.014Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:32:36.207Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:32:52.491Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:33:03.674Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:33:04.055Z] 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. [2024-11-23T14:33:04.055Z] The best model improves the baseline by 14.43%. [2024-11-23T14:33:04.055Z] Movies recommended for you: [2024-11-23T14:33:04.055Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:33:04.055Z] There is no way to check that no silent failure occurred. [2024-11-23T14:33:04.055Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (173625.167 ms) ====== [2024-11-23T14:33:04.055Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-11-23T14:33:04.412Z] GC before operation: completed in 170.323 ms, heap usage 377.569 MB -> 55.676 MB. [2024-11-23T14:33:32.662Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:33:56.213Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:34:30.102Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:34:49.696Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:35:03.210Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:35:16.707Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:35:36.309Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:35:47.504Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:35:47.504Z] 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. [2024-11-23T14:35:47.504Z] The best model improves the baseline by 14.43%. [2024-11-23T14:35:47.504Z] Movies recommended for you: [2024-11-23T14:35:47.504Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:35:47.504Z] There is no way to check that no silent failure occurred. [2024-11-23T14:35:47.504Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (163274.264 ms) ====== [2024-11-23T14:35:47.504Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-11-23T14:35:47.873Z] GC before operation: completed in 158.918 ms, heap usage 627.208 MB -> 55.874 MB. [2024-11-23T14:36:21.771Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-23T14:36:50.051Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-23T14:37:24.074Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-23T14:37:47.615Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-23T14:37:58.798Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-23T14:38:12.307Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-23T14:38:28.617Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-23T14:38:39.802Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-23T14:38:39.802Z] 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. [2024-11-23T14:38:39.802Z] The best model improves the baseline by 14.43%. [2024-11-23T14:38:39.802Z] Movies recommended for you: [2024-11-23T14:38:39.802Z] WARNING: This benchmark provides no result that can be validated. [2024-11-23T14:38:39.802Z] There is no way to check that no silent failure occurred. [2024-11-23T14:38:39.802Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (171874.452 ms) ====== [2024-11-23T14:38:41.035Z] ----------------------------------- [2024-11-23T14:38:41.035Z] renaissance-movie-lens_0_PASSED [2024-11-23T14:38:41.035Z] ----------------------------------- [2024-11-23T14:38:41.035Z] [2024-11-23T14:38:41.035Z] TEST TEARDOWN: [2024-11-23T14:38:41.035Z] Nothing to be done for teardown. [2024-11-23T14:38:41.035Z] renaissance-movie-lens_0 Finish Time: Sat Nov 23 14:38:40 2024 Epoch Time (ms): 1732372720630