[2025-03-04T21:55:10.489Z] Running test renaissance-movie-lens_0 ... [2025-03-04T21:55:10.489Z] =============================================== [2025-03-04T21:55:10.489Z] renaissance-movie-lens_0 Start Time: Tue Mar 4 21:55:09 2025 Epoch Time (ms): 1741125309472 [2025-03-04T21:55:10.489Z] variation: NoOptions [2025-03-04T21:55:10.489Z] JVM_OPTIONS: [2025-03-04T21:55:10.489Z] { \ [2025-03-04T21:55:10.489Z] echo ""; echo "TEST SETUP:"; \ [2025-03-04T21:55:10.489Z] echo "Nothing to be done for setup."; \ [2025-03-04T21:55:10.489Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17411242973314/renaissance-movie-lens_0"; \ [2025-03-04T21:55:10.489Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17411242973314/renaissance-movie-lens_0"; \ [2025-03-04T21:55:10.489Z] echo ""; echo "TESTING:"; \ [2025-03-04T21:55:10.489Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17411242973314/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-03-04T21:55:10.490Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17411242973314/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-03-04T21:55:10.490Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-03-04T21:55:10.490Z] echo "Nothing to be done for teardown."; \ [2025-03-04T21:55:10.490Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17411242973314/TestTargetResult"; [2025-03-04T21:55:10.490Z] [2025-03-04T21:55:10.490Z] TEST SETUP: [2025-03-04T21:55:10.490Z] Nothing to be done for setup. [2025-03-04T21:55:10.490Z] [2025-03-04T21:55:10.490Z] TESTING: [2025-03-04T21:55:13.499Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-03-04T21:55:16.509Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-03-04T21:55:20.646Z] Got 100004 ratings from 671 users on 9066 movies. [2025-03-04T21:55:21.593Z] Training: 60056, validation: 20285, test: 19854 [2025-03-04T21:55:21.593Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-03-04T21:55:21.593Z] GC before operation: completed in 328.549 ms, heap usage 80.673 MB -> 25.832 MB. [2025-03-04T21:55:28.293Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:55:31.306Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:55:34.314Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:55:37.324Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:55:39.289Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:55:41.250Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:55:43.205Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:55:45.180Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:55:45.180Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:55:45.180Z] The best model improves the baseline by 14.52%. [2025-03-04T21:55:45.180Z] Movies recommended for you: [2025-03-04T21:55:45.180Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:55:45.180Z] There is no way to check that no silent failure occurred. [2025-03-04T21:55:45.180Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23701.188 ms) ====== [2025-03-04T21:55:45.180Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-03-04T21:55:45.180Z] GC before operation: completed in 335.162 ms, heap usage 182.170 MB -> 44.087 MB. [2025-03-04T21:55:48.298Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:55:51.339Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:55:53.326Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:55:56.487Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:55:58.455Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:55:59.432Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:56:01.386Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:56:02.339Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:56:03.294Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:56:03.294Z] The best model improves the baseline by 14.52%. [2025-03-04T21:56:03.294Z] Movies recommended for you: [2025-03-04T21:56:03.294Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:56:03.294Z] There is no way to check that no silent failure occurred. [2025-03-04T21:56:03.294Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17625.010 ms) ====== [2025-03-04T21:56:03.294Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-03-04T21:56:03.294Z] GC before operation: completed in 272.968 ms, heap usage 345.996 MB -> 41.328 MB. [2025-03-04T21:56:06.316Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:56:08.279Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:56:11.295Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:56:13.212Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:56:15.414Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:56:16.365Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:56:18.317Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:56:19.271Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:56:20.221Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:56:20.221Z] The best model improves the baseline by 14.52%. [2025-03-04T21:56:20.221Z] Movies recommended for you: [2025-03-04T21:56:20.221Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:56:20.221Z] There is no way to check that no silent failure occurred. [2025-03-04T21:56:20.221Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16650.454 ms) ====== [2025-03-04T21:56:20.221Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-03-04T21:56:20.221Z] GC before operation: completed in 223.207 ms, heap usage 185.492 MB -> 41.432 MB. [2025-03-04T21:56:23.229Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:56:25.180Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:56:27.129Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:56:30.137Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:56:31.088Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:56:33.060Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:56:35.018Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:56:35.975Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:56:35.975Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:56:35.975Z] The best model improves the baseline by 14.52%. [2025-03-04T21:56:36.929Z] Movies recommended for you: [2025-03-04T21:56:36.929Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:56:36.929Z] There is no way to check that no silent failure occurred. [2025-03-04T21:56:36.929Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16101.324 ms) ====== [2025-03-04T21:56:36.929Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-03-04T21:56:36.929Z] GC before operation: completed in 177.621 ms, heap usage 182.103 MB -> 41.839 MB. [2025-03-04T21:56:38.890Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:56:42.004Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:56:43.952Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:56:45.926Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:56:47.885Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:56:48.833Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:56:50.782Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:56:51.741Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:56:52.690Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:56:52.690Z] The best model improves the baseline by 14.52%. [2025-03-04T21:56:52.690Z] Movies recommended for you: [2025-03-04T21:56:52.690Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:56:52.690Z] There is no way to check that no silent failure occurred. [2025-03-04T21:56:52.690Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15797.154 ms) ====== [2025-03-04T21:56:52.690Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-03-04T21:56:52.690Z] GC before operation: completed in 194.892 ms, heap usage 182.814 MB -> 42.147 MB. [2025-03-04T21:56:54.642Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:56:57.650Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:56:59.655Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:57:01.604Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:57:03.560Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:57:04.510Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:57:06.461Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:57:07.411Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:57:08.363Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:57:08.363Z] The best model improves the baseline by 14.52%. [2025-03-04T21:57:08.363Z] Movies recommended for you: [2025-03-04T21:57:08.363Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:57:08.363Z] There is no way to check that no silent failure occurred. [2025-03-04T21:57:08.363Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15456.263 ms) ====== [2025-03-04T21:57:08.363Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-03-04T21:57:08.363Z] GC before operation: completed in 209.229 ms, heap usage 205.281 MB -> 41.986 MB. [2025-03-04T21:57:10.318Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:57:13.328Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:57:15.278Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:57:17.230Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:57:20.566Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:57:20.566Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:57:21.519Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:57:23.467Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:57:23.467Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:57:23.467Z] The best model improves the baseline by 14.52%. [2025-03-04T21:57:23.467Z] Movies recommended for you: [2025-03-04T21:57:23.467Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:57:23.467Z] There is no way to check that no silent failure occurred. [2025-03-04T21:57:23.467Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15372.297 ms) ====== [2025-03-04T21:57:23.467Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-03-04T21:57:23.467Z] GC before operation: completed in 189.502 ms, heap usage 119.794 MB -> 41.802 MB. [2025-03-04T21:57:26.479Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:57:28.431Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:57:30.401Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:57:33.433Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:57:34.567Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:57:35.530Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:57:37.483Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:57:38.434Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:57:38.434Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:57:39.397Z] The best model improves the baseline by 14.52%. [2025-03-04T21:57:39.398Z] Movies recommended for you: [2025-03-04T21:57:39.398Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:57:39.398Z] There is no way to check that no silent failure occurred. [2025-03-04T21:57:39.398Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15123.742 ms) ====== [2025-03-04T21:57:39.398Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-03-04T21:57:39.398Z] GC before operation: completed in 203.949 ms, heap usage 217.346 MB -> 42.516 MB. [2025-03-04T21:57:41.398Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:57:43.422Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:57:46.430Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:57:48.377Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:57:49.326Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:57:51.322Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:57:52.271Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:57:54.224Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:57:54.224Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:57:54.224Z] The best model improves the baseline by 14.52%. [2025-03-04T21:57:54.224Z] Movies recommended for you: [2025-03-04T21:57:54.224Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:57:54.224Z] There is no way to check that no silent failure occurred. [2025-03-04T21:57:54.224Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14932.821 ms) ====== [2025-03-04T21:57:54.225Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-03-04T21:57:54.225Z] GC before operation: completed in 141.062 ms, heap usage 125.372 MB -> 41.958 MB. [2025-03-04T21:57:56.178Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:57:59.217Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:58:01.165Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:58:03.114Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:58:04.163Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:58:06.112Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:58:07.072Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:58:09.030Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:58:09.030Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:58:09.030Z] The best model improves the baseline by 14.52%. [2025-03-04T21:58:09.030Z] Movies recommended for you: [2025-03-04T21:58:09.030Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:58:09.030Z] There is no way to check that no silent failure occurred. [2025-03-04T21:58:09.030Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14797.288 ms) ====== [2025-03-04T21:58:09.030Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-03-04T21:58:09.030Z] GC before operation: completed in 201.222 ms, heap usage 207.201 MB -> 42.447 MB. [2025-03-04T21:58:10.978Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:58:14.006Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:58:15.953Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:58:17.909Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:58:18.859Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:58:20.510Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:58:22.153Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:58:23.103Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:58:24.054Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:58:24.054Z] The best model improves the baseline by 14.52%. [2025-03-04T21:58:24.054Z] Movies recommended for you: [2025-03-04T21:58:24.054Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:58:24.054Z] There is no way to check that no silent failure occurred. [2025-03-04T21:58:24.054Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14562.966 ms) ====== [2025-03-04T21:58:24.054Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-03-04T21:58:24.054Z] GC before operation: completed in 156.440 ms, heap usage 280.205 MB -> 44.052 MB. [2025-03-04T21:58:26.005Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:58:27.957Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:58:30.970Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:58:32.919Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:58:33.869Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:58:34.819Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:58:36.774Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:58:37.723Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:58:38.673Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:58:38.673Z] The best model improves the baseline by 14.52%. [2025-03-04T21:58:38.673Z] Movies recommended for you: [2025-03-04T21:58:38.673Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:58:38.673Z] There is no way to check that no silent failure occurred. [2025-03-04T21:58:38.673Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14322.234 ms) ====== [2025-03-04T21:58:38.673Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-03-04T21:58:38.674Z] GC before operation: completed in 210.985 ms, heap usage 257.322 MB -> 65.199 MB. [2025-03-04T21:58:40.622Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:58:42.574Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:58:45.586Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:58:47.536Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:58:48.489Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:58:49.440Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:58:51.391Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:58:52.341Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:58:53.292Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:58:53.292Z] The best model improves the baseline by 14.52%. [2025-03-04T21:58:53.292Z] Movies recommended for you: [2025-03-04T21:58:53.292Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:58:53.292Z] There is no way to check that no silent failure occurred. [2025-03-04T21:58:53.292Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14607.110 ms) ====== [2025-03-04T21:58:53.292Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-03-04T21:58:53.292Z] GC before operation: completed in 147.943 ms, heap usage 299.086 MB -> 49.812 MB. [2025-03-04T21:58:55.241Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:58:57.191Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:59:00.206Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:59:02.155Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:59:03.107Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:59:04.166Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:59:06.115Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:59:07.067Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:59:07.067Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:59:07.067Z] The best model improves the baseline by 14.52%. [2025-03-04T21:59:08.018Z] Movies recommended for you: [2025-03-04T21:59:08.018Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:59:08.018Z] There is no way to check that no silent failure occurred. [2025-03-04T21:59:08.018Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14289.980 ms) ====== [2025-03-04T21:59:08.018Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-03-04T21:59:08.018Z] GC before operation: completed in 224.244 ms, heap usage 225.941 MB -> 70.855 MB. [2025-03-04T21:59:09.968Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:59:11.920Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:59:14.932Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:59:16.879Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:59:17.831Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:59:18.782Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:59:20.733Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:59:21.684Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:59:22.639Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:59:22.639Z] The best model improves the baseline by 14.52%. [2025-03-04T21:59:22.639Z] Movies recommended for you: [2025-03-04T21:59:22.639Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:59:22.639Z] There is no way to check that no silent failure occurred. [2025-03-04T21:59:22.639Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14717.232 ms) ====== [2025-03-04T21:59:22.639Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-03-04T21:59:22.639Z] GC before operation: completed in 159.086 ms, heap usage 209.148 MB -> 45.950 MB. [2025-03-04T21:59:24.598Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:59:26.548Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:59:29.725Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:59:31.673Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:59:32.622Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:59:33.574Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:59:35.525Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:59:36.473Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:59:37.425Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:59:37.425Z] The best model improves the baseline by 14.52%. [2025-03-04T21:59:37.425Z] Movies recommended for you: [2025-03-04T21:59:37.425Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:59:37.425Z] There is no way to check that no silent failure occurred. [2025-03-04T21:59:37.425Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14472.057 ms) ====== [2025-03-04T21:59:37.425Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-03-04T21:59:37.425Z] GC before operation: completed in 236.956 ms, heap usage 208.308 MB -> 70.725 MB. [2025-03-04T21:59:39.379Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:59:41.328Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:59:44.333Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T21:59:46.279Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T21:59:47.229Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T21:59:48.182Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T21:59:50.132Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T21:59:51.082Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T21:59:52.032Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T21:59:52.032Z] The best model improves the baseline by 14.52%. [2025-03-04T21:59:52.032Z] Movies recommended for you: [2025-03-04T21:59:52.032Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T21:59:52.032Z] There is no way to check that no silent failure occurred. [2025-03-04T21:59:52.032Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14378.076 ms) ====== [2025-03-04T21:59:52.032Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-03-04T21:59:52.032Z] GC before operation: completed in 254.272 ms, heap usage 259.745 MB -> 70.862 MB. [2025-03-04T21:59:53.982Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T21:59:55.935Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T21:59:58.945Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T22:00:00.897Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T22:00:01.848Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T22:00:03.812Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T22:00:04.769Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T22:00:05.768Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T22:00:06.722Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T22:00:06.722Z] The best model improves the baseline by 14.52%. [2025-03-04T22:00:06.722Z] Movies recommended for you: [2025-03-04T22:00:06.722Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T22:00:06.722Z] There is no way to check that no silent failure occurred. [2025-03-04T22:00:06.722Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14392.776 ms) ====== [2025-03-04T22:00:06.722Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-03-04T22:00:06.722Z] GC before operation: completed in 205.406 ms, heap usage 218.291 MB -> 70.671 MB. [2025-03-04T22:00:08.700Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T22:00:10.649Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T22:00:13.732Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T22:00:15.703Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T22:00:16.658Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T22:00:17.621Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T22:00:19.589Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T22:00:20.573Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T22:00:20.574Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T22:00:20.574Z] The best model improves the baseline by 14.52%. [2025-03-04T22:00:20.574Z] Movies recommended for you: [2025-03-04T22:00:20.574Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T22:00:20.574Z] There is no way to check that no silent failure occurred. [2025-03-04T22:00:20.574Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14225.093 ms) ====== [2025-03-04T22:00:20.574Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-03-04T22:00:21.530Z] GC before operation: completed in 204.730 ms, heap usage 221.706 MB -> 70.927 MB. [2025-03-04T22:00:23.503Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-04T22:00:25.458Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-04T22:00:30.115Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-04T22:00:30.115Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-04T22:00:31.088Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-04T22:00:32.059Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-04T22:00:33.045Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-04T22:00:35.929Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-04T22:00:35.929Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536. [2025-03-04T22:00:35.929Z] The best model improves the baseline by 14.52%. [2025-03-04T22:00:35.929Z] Movies recommended for you: [2025-03-04T22:00:35.929Z] WARNING: This benchmark provides no result that can be validated. [2025-03-04T22:00:35.929Z] There is no way to check that no silent failure occurred. [2025-03-04T22:00:35.929Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14099.816 ms) ====== [2025-03-04T22:00:37.213Z] ----------------------------------- [2025-03-04T22:00:37.213Z] renaissance-movie-lens_0_PASSED [2025-03-04T22:00:37.213Z] ----------------------------------- [2025-03-04T22:00:37.213Z] [2025-03-04T22:00:37.213Z] TEST TEARDOWN: [2025-03-04T22:00:37.213Z] Nothing to be done for teardown. [2025-03-04T22:00:37.213Z] renaissance-movie-lens_0 Finish Time: Tue Mar 4 22:00:35 2025 Epoch Time (ms): 1741125635267