[2024-11-15T23:17:44.160Z] Running test renaissance-movie-lens_0 ... [2024-11-15T23:17:44.160Z] =============================================== [2024-11-15T23:17:44.160Z] renaissance-movie-lens_0 Start Time: Fri Nov 15 23:17:43 2024 Epoch Time (ms): 1731712663374 [2024-11-15T23:17:44.160Z] variation: NoOptions [2024-11-15T23:17:44.160Z] JVM_OPTIONS: [2024-11-15T23:17:44.160Z] { \ [2024-11-15T23:17:44.160Z] echo ""; echo "TEST SETUP:"; \ [2024-11-15T23:17:44.160Z] echo "Nothing to be done for setup."; \ [2024-11-15T23:17:44.160Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317118884967/renaissance-movie-lens_0"; \ [2024-11-15T23:17:44.160Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317118884967/renaissance-movie-lens_0"; \ [2024-11-15T23:17:44.160Z] echo ""; echo "TESTING:"; \ [2024-11-15T23:17:44.160Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317118884967/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-11-15T23:17:44.160Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317118884967/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-11-15T23:17:44.160Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-11-15T23:17:44.160Z] echo "Nothing to be done for teardown."; \ [2024-11-15T23:17:44.160Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17317118884967/TestTargetResult"; [2024-11-15T23:17:44.160Z] [2024-11-15T23:17:44.160Z] TEST SETUP: [2024-11-15T23:17:44.160Z] Nothing to be done for setup. [2024-11-15T23:17:44.160Z] [2024-11-15T23:17:44.160Z] TESTING: [2024-11-15T23:17:47.094Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-11-15T23:17:50.035Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads. [2024-11-15T23:17:54.076Z] Got 100004 ratings from 671 users on 9066 movies. [2024-11-15T23:17:54.076Z] Training: 60056, validation: 20285, test: 19854 [2024-11-15T23:17:54.076Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-11-15T23:17:54.076Z] GC before operation: completed in 63.536 ms, heap usage 76.386 MB -> 39.508 MB. [2024-11-15T23:18:02.033Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:18:06.111Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:18:09.046Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:18:11.980Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:18:13.881Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:18:15.781Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:18:17.687Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:18:19.595Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:18:19.595Z] 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. [2024-11-15T23:18:19.595Z] The best model improves the baseline by 14.43%. [2024-11-15T23:18:19.595Z] Movies recommended for you: [2024-11-15T23:18:19.595Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:18:19.595Z] There is no way to check that no silent failure occurred. [2024-11-15T23:18:19.595Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25509.101 ms) ====== [2024-11-15T23:18:19.595Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-11-15T23:18:19.595Z] GC before operation: completed in 112.633 ms, heap usage 337.014 MB -> 52.541 MB. [2024-11-15T23:18:23.638Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:18:26.576Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:18:29.514Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:18:31.415Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:18:33.432Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:18:35.332Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:18:37.232Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:18:39.008Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:18:39.933Z] 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. [2024-11-15T23:18:39.933Z] The best model improves the baseline by 14.43%. [2024-11-15T23:18:39.933Z] Movies recommended for you: [2024-11-15T23:18:39.933Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:18:39.933Z] There is no way to check that no silent failure occurred. [2024-11-15T23:18:39.933Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19701.475 ms) ====== [2024-11-15T23:18:39.933Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-11-15T23:18:39.933Z] GC before operation: completed in 105.994 ms, heap usage 247.294 MB -> 56.635 MB. [2024-11-15T23:18:42.872Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:18:44.774Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:18:47.719Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:18:50.658Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:18:52.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:18:54.589Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:18:56.488Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:18:58.393Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:18:58.393Z] 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. [2024-11-15T23:18:58.393Z] The best model improves the baseline by 14.43%. [2024-11-15T23:18:58.393Z] Movies recommended for you: [2024-11-15T23:18:58.393Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:18:58.393Z] There is no way to check that no silent failure occurred. [2024-11-15T23:18:58.393Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (18804.889 ms) ====== [2024-11-15T23:18:58.393Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-11-15T23:18:58.393Z] GC before operation: completed in 119.333 ms, heap usage 777.362 MB -> 60.934 MB. [2024-11-15T23:19:01.330Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:19:04.274Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:19:06.173Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:19:09.107Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:19:11.007Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:19:11.932Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:19:13.833Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:19:14.756Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:19:15.686Z] 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. [2024-11-15T23:19:15.686Z] The best model improves the baseline by 14.43%. [2024-11-15T23:19:15.686Z] Movies recommended for you: [2024-11-15T23:19:15.686Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:19:15.686Z] There is no way to check that no silent failure occurred. [2024-11-15T23:19:15.686Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16954.075 ms) ====== [2024-11-15T23:19:15.686Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-11-15T23:19:15.686Z] GC before operation: completed in 107.131 ms, heap usage 1.662 GB -> 60.786 MB. [2024-11-15T23:19:18.633Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:19:20.534Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:19:23.471Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:19:26.404Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:19:27.329Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:19:29.227Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:19:30.151Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:19:32.052Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:19:32.979Z] 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. [2024-11-15T23:19:32.979Z] The best model improves the baseline by 14.43%. [2024-11-15T23:19:32.979Z] Movies recommended for you: [2024-11-15T23:19:32.979Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:19:32.979Z] There is no way to check that no silent failure occurred. [2024-11-15T23:19:32.979Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16914.586 ms) ====== [2024-11-15T23:19:32.979Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-11-15T23:19:32.979Z] GC before operation: completed in 105.894 ms, heap usage 163.848 MB -> 55.365 MB. [2024-11-15T23:19:36.035Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:19:37.936Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:19:40.511Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:19:43.461Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:19:44.385Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:19:46.288Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:19:47.213Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:19:49.113Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:19:49.113Z] 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. [2024-11-15T23:19:49.113Z] The best model improves the baseline by 14.43%. [2024-11-15T23:19:49.113Z] Movies recommended for you: [2024-11-15T23:19:49.113Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:19:49.113Z] There is no way to check that no silent failure occurred. [2024-11-15T23:19:50.040Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16740.080 ms) ====== [2024-11-15T23:19:50.040Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-11-15T23:19:50.040Z] GC before operation: completed in 108.135 ms, heap usage 1.185 GB -> 60.499 MB. [2024-11-15T23:19:52.114Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:19:55.053Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:19:56.956Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:19:58.857Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:20:00.758Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:20:01.682Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:20:03.583Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:20:04.507Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:20:05.496Z] 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. [2024-11-15T23:20:05.496Z] The best model improves the baseline by 14.43%. [2024-11-15T23:20:05.496Z] Movies recommended for you: [2024-11-15T23:20:05.496Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:20:05.496Z] There is no way to check that no silent failure occurred. [2024-11-15T23:20:05.496Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15545.238 ms) ====== [2024-11-15T23:20:05.496Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-11-15T23:20:05.496Z] GC before operation: completed in 109.329 ms, heap usage 973.197 MB -> 62.061 MB. [2024-11-15T23:20:07.396Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:20:10.338Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:20:12.240Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:20:15.181Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:20:16.109Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:20:18.008Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:20:18.933Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:20:20.862Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:20:20.862Z] 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. [2024-11-15T23:20:20.862Z] The best model improves the baseline by 14.43%. [2024-11-15T23:20:20.862Z] Movies recommended for you: [2024-11-15T23:20:20.862Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:20:20.862Z] There is no way to check that no silent failure occurred. [2024-11-15T23:20:20.862Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15776.018 ms) ====== [2024-11-15T23:20:20.862Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-11-15T23:20:20.862Z] GC before operation: completed in 111.371 ms, heap usage 1.216 GB -> 61.042 MB. [2024-11-15T23:20:23.796Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:20:25.694Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:20:28.629Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:20:30.530Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:20:32.431Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:20:33.442Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:20:35.343Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:20:36.267Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:20:37.195Z] 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. [2024-11-15T23:20:37.195Z] The best model improves the baseline by 14.43%. [2024-11-15T23:20:37.195Z] Movies recommended for you: [2024-11-15T23:20:37.195Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:20:37.195Z] There is no way to check that no silent failure occurred. [2024-11-15T23:20:37.195Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15753.325 ms) ====== [2024-11-15T23:20:37.195Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-11-15T23:20:37.195Z] GC before operation: completed in 110.742 ms, heap usage 717.053 MB -> 58.058 MB. [2024-11-15T23:20:40.191Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:20:42.860Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:20:44.762Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:20:47.737Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:20:49.666Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:20:50.590Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:20:52.509Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:20:53.436Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:20:54.362Z] 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. [2024-11-15T23:20:54.362Z] The best model improves the baseline by 14.43%. [2024-11-15T23:20:54.362Z] Movies recommended for you: [2024-11-15T23:20:54.362Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:20:54.362Z] There is no way to check that no silent failure occurred. [2024-11-15T23:20:54.362Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16978.460 ms) ====== [2024-11-15T23:20:54.362Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-11-15T23:20:54.362Z] GC before operation: completed in 108.317 ms, heap usage 166.602 MB -> 54.386 MB. [2024-11-15T23:20:57.301Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:20:59.205Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:21:02.144Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:21:04.045Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:21:05.948Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:21:07.850Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:21:08.776Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:21:10.676Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:21:10.676Z] 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. [2024-11-15T23:21:10.676Z] The best model improves the baseline by 14.43%. [2024-11-15T23:21:10.676Z] Movies recommended for you: [2024-11-15T23:21:10.676Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:21:10.676Z] There is no way to check that no silent failure occurred. [2024-11-15T23:21:10.676Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16721.917 ms) ====== [2024-11-15T23:21:10.676Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-11-15T23:21:10.676Z] GC before operation: completed in 116.025 ms, heap usage 1.747 GB -> 61.550 MB. [2024-11-15T23:21:13.616Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:21:15.518Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:21:18.457Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:21:20.366Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:21:22.269Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:21:23.198Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:21:25.099Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:21:26.025Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:21:26.952Z] 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. [2024-11-15T23:21:26.952Z] The best model improves the baseline by 14.43%. [2024-11-15T23:21:26.952Z] Movies recommended for you: [2024-11-15T23:21:26.952Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:21:26.952Z] There is no way to check that no silent failure occurred. [2024-11-15T23:21:26.952Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15888.414 ms) ====== [2024-11-15T23:21:26.952Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-11-15T23:21:26.952Z] GC before operation: completed in 117.545 ms, heap usage 254.373 MB -> 60.038 MB. [2024-11-15T23:21:28.856Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:21:31.794Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:21:33.760Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:21:36.695Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:21:37.621Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:21:39.525Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:21:40.452Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:21:42.352Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:21:42.352Z] 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. [2024-11-15T23:21:42.352Z] The best model improves the baseline by 14.43%. [2024-11-15T23:21:42.352Z] Movies recommended for you: [2024-11-15T23:21:42.352Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:21:42.352Z] There is no way to check that no silent failure occurred. [2024-11-15T23:21:42.352Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15624.332 ms) ====== [2024-11-15T23:21:42.352Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-11-15T23:21:42.352Z] GC before operation: completed in 117.802 ms, heap usage 1.179 GB -> 60.170 MB. [2024-11-15T23:21:45.051Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:21:47.992Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:21:49.895Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:21:51.799Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:21:53.704Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:21:55.606Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:21:56.533Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:21:58.439Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:21:58.439Z] 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. [2024-11-15T23:21:58.439Z] The best model improves the baseline by 14.43%. [2024-11-15T23:21:58.439Z] Movies recommended for you: [2024-11-15T23:21:58.439Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:21:58.440Z] There is no way to check that no silent failure occurred. [2024-11-15T23:21:58.440Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15717.179 ms) ====== [2024-11-15T23:21:58.440Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-11-15T23:21:58.440Z] GC before operation: completed in 114.051 ms, heap usage 1.940 GB -> 61.716 MB. [2024-11-15T23:22:01.379Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:22:03.282Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:22:06.221Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:22:08.122Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:22:10.023Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:22:10.950Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:22:12.852Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:22:13.779Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:22:14.706Z] 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. [2024-11-15T23:22:14.706Z] The best model improves the baseline by 14.43%. [2024-11-15T23:22:14.706Z] Movies recommended for you: [2024-11-15T23:22:14.706Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:22:14.706Z] There is no way to check that no silent failure occurred. [2024-11-15T23:22:14.706Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16071.048 ms) ====== [2024-11-15T23:22:14.706Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-11-15T23:22:14.706Z] GC before operation: completed in 113.500 ms, heap usage 494.156 MB -> 57.030 MB. [2024-11-15T23:22:17.645Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:22:19.547Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:22:21.455Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:22:24.395Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:22:25.320Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:22:27.221Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:22:28.150Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:22:30.049Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:22:30.049Z] 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. [2024-11-15T23:22:30.049Z] The best model improves the baseline by 14.43%. [2024-11-15T23:22:30.974Z] Movies recommended for you: [2024-11-15T23:22:30.974Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:22:30.974Z] There is no way to check that no silent failure occurred. [2024-11-15T23:22:30.974Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15787.098 ms) ====== [2024-11-15T23:22:30.974Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-11-15T23:22:30.974Z] GC before operation: completed in 121.388 ms, heap usage 1.318 GB -> 59.164 MB. [2024-11-15T23:22:32.875Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:22:35.810Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:22:37.718Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:22:40.654Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:22:41.578Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:22:43.478Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:22:45.380Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:22:46.730Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:22:46.730Z] 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. [2024-11-15T23:22:46.730Z] The best model improves the baseline by 14.43%. [2024-11-15T23:22:46.730Z] Movies recommended for you: [2024-11-15T23:22:46.730Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:22:46.730Z] There is no way to check that no silent failure occurred. [2024-11-15T23:22:46.730Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16331.488 ms) ====== [2024-11-15T23:22:46.730Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-11-15T23:22:46.730Z] GC before operation: completed in 120.716 ms, heap usage 1.814 GB -> 60.510 MB. [2024-11-15T23:22:49.673Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:22:52.610Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:22:54.511Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:22:56.412Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:22:58.315Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:23:00.218Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:23:01.144Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:23:03.046Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:23:03.046Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535. [2024-11-15T23:23:03.046Z] The best model improves the baseline by 14.43%. [2024-11-15T23:23:03.046Z] Movies recommended for you: [2024-11-15T23:23:03.046Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:23:03.046Z] There is no way to check that no silent failure occurred. [2024-11-15T23:23:03.046Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16091.114 ms) ====== [2024-11-15T23:23:03.046Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-11-15T23:23:03.046Z] GC before operation: completed in 122.927 ms, heap usage 1.323 GB -> 61.335 MB. [2024-11-15T23:23:06.012Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:23:07.913Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:23:10.852Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:23:12.762Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:23:14.660Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:23:15.585Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:23:17.484Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:23:18.410Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:23:19.340Z] 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. [2024-11-15T23:23:19.340Z] The best model improves the baseline by 14.43%. [2024-11-15T23:23:19.340Z] Movies recommended for you: [2024-11-15T23:23:19.340Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:23:19.340Z] There is no way to check that no silent failure occurred. [2024-11-15T23:23:19.340Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16020.978 ms) ====== [2024-11-15T23:23:19.340Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-11-15T23:23:19.340Z] GC before operation: completed in 123.087 ms, heap usage 368.194 MB -> 57.241 MB. [2024-11-15T23:23:22.276Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-15T23:23:24.180Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-15T23:23:26.088Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-15T23:23:29.026Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-15T23:23:29.954Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-15T23:23:31.858Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-15T23:23:32.783Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-15T23:23:34.686Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-15T23:23:34.686Z] 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. [2024-11-15T23:23:34.686Z] The best model improves the baseline by 14.43%. [2024-11-15T23:23:34.686Z] Movies recommended for you: [2024-11-15T23:23:34.686Z] WARNING: This benchmark provides no result that can be validated. [2024-11-15T23:23:34.686Z] There is no way to check that no silent failure occurred. [2024-11-15T23:23:34.686Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (15492.794 ms) ====== [2024-11-15T23:23:36.586Z] ----------------------------------- [2024-11-15T23:23:36.586Z] renaissance-movie-lens_0_PASSED [2024-11-15T23:23:36.586Z] ----------------------------------- [2024-11-15T23:23:36.586Z] [2024-11-15T23:23:36.586Z] TEST TEARDOWN: [2024-11-15T23:23:36.586Z] Nothing to be done for teardown. [2024-11-15T23:23:36.586Z] renaissance-movie-lens_0 Finish Time: Fri Nov 15 23:23:36 2024 Epoch Time (ms): 1731713016040