renaissance-movie-lens_0
[2024-11-16T00:27:00.495Z] Running test renaissance-movie-lens_0 ...
[2024-11-16T00:27:00.495Z] ===============================================
[2024-11-16T00:27:00.495Z] renaissance-movie-lens_0 Start Time: Fri Nov 15 18:27:00 2024 Epoch Time (ms): 1731716820057
[2024-11-16T00:27:00.495Z] variation: NoOptions
[2024-11-16T00:27:00.495Z] JVM_OPTIONS:
[2024-11-16T00:27:00.495Z] { \
[2024-11-16T00:27:00.495Z] echo ""; echo "TEST SETUP:"; \
[2024-11-16T00:27:00.495Z] echo "Nothing to be done for setup."; \
[2024-11-16T00:27:00.495Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17317161745105/renaissance-movie-lens_0"; \
[2024-11-16T00:27:00.495Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17317161745105/renaissance-movie-lens_0"; \
[2024-11-16T00:27:00.495Z] echo ""; echo "TESTING:"; \
[2024-11-16T00:27:00.495Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/jdkbinary/j2sdk-image/jdk-17.0.14+3/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 "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17317161745105/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-16T00:27:00.495Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17317161745105/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-16T00:27:00.495Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-16T00:27:00.495Z] echo "Nothing to be done for teardown."; \
[2024-11-16T00:27:00.495Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17317161745105/TestTargetResult";
[2024-11-16T00:27:00.495Z]
[2024-11-16T00:27:00.495Z] TEST SETUP:
[2024-11-16T00:27:00.495Z] Nothing to be done for setup.
[2024-11-16T00:27:00.495Z]
[2024-11-16T00:27:00.495Z] TESTING:
[2024-11-16T00:27:03.551Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-16T00:27:05.740Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-11-16T00:27:08.784Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-16T00:27:09.459Z] Training: 60056, validation: 20285, test: 19854
[2024-11-16T00:27:09.459Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-16T00:27:09.459Z] GC before operation: completed in 98.879 ms, heap usage 95.400 MB -> 37.825 MB.
[2024-11-16T00:27:15.666Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:27:18.721Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:27:21.894Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:27:25.975Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:27:26.737Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:27:28.144Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:27:29.561Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:27:30.973Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:27:30.973Z] 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.
[2024-11-16T00:27:31.655Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:27:31.655Z] Movies recommended for you:
[2024-11-16T00:27:31.655Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:27:31.655Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:27:31.655Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (22143.570 ms) ======
[2024-11-16T00:27:31.655Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-16T00:27:31.655Z] GC before operation: completed in 58.438 ms, heap usage 469.035 MB -> 57.686 MB.
[2024-11-16T00:27:34.706Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:27:36.904Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:27:39.960Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:27:42.156Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:27:43.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:27:44.972Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:27:46.385Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:27:47.792Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:27:48.470Z] 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.
[2024-11-16T00:27:48.470Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:27:48.470Z] Movies recommended for you:
[2024-11-16T00:27:48.470Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:27:48.470Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:27:48.470Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16824.068 ms) ======
[2024-11-16T00:27:48.470Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-16T00:27:48.470Z] GC before operation: completed in 54.948 ms, heap usage 423.880 MB -> 51.811 MB.
[2024-11-16T00:27:51.523Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:27:53.726Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:27:56.776Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:27:58.967Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:28:00.387Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:28:01.794Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:28:03.199Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:28:04.602Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:28:04.602Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-16T00:28:04.602Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:28:05.280Z] Movies recommended for you:
[2024-11-16T00:28:05.280Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:28:05.280Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:28:05.280Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16522.719 ms) ======
[2024-11-16T00:28:05.280Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-16T00:28:05.280Z] GC before operation: completed in 56.897 ms, heap usage 152.763 MB -> 54.433 MB.
[2024-11-16T00:28:07.472Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:28:10.525Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:28:12.718Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:28:14.910Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:28:16.316Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:28:17.724Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:28:19.129Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:28:20.532Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:28:20.532Z] 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.
[2024-11-16T00:28:20.532Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:28:20.532Z] Movies recommended for you:
[2024-11-16T00:28:20.532Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:28:20.532Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:28:20.532Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15614.726 ms) ======
[2024-11-16T00:28:20.532Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-16T00:28:20.532Z] GC before operation: completed in 62.504 ms, heap usage 140.391 MB -> 52.409 MB.
[2024-11-16T00:28:23.591Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:28:25.786Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:28:27.975Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:28:30.165Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:28:31.573Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:28:34.138Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:28:34.922Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:28:35.597Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:28:36.274Z] 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.
[2024-11-16T00:28:36.274Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:28:36.274Z] Movies recommended for you:
[2024-11-16T00:28:36.274Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:28:36.274Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:28:36.274Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15581.469 ms) ======
[2024-11-16T00:28:36.274Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-16T00:28:36.274Z] GC before operation: completed in 60.748 ms, heap usage 148.037 MB -> 55.697 MB.
[2024-11-16T00:28:39.335Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:28:41.524Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:28:43.718Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:28:45.916Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:28:47.324Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:28:48.726Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:28:50.142Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:28:51.547Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:28:51.547Z] 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.
[2024-11-16T00:28:51.547Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:28:51.547Z] Movies recommended for you:
[2024-11-16T00:28:51.547Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:28:51.547Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:28:51.547Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15261.523 ms) ======
[2024-11-16T00:28:51.547Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-16T00:28:51.547Z] GC before operation: completed in 63.516 ms, heap usage 427.547 MB -> 52.662 MB.
[2024-11-16T00:28:54.617Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:28:56.811Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:28:59.004Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:29:01.202Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:29:02.604Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:29:04.006Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:29:05.404Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:29:06.803Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:29:06.804Z] 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.
[2024-11-16T00:29:06.804Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:29:06.804Z] Movies recommended for you:
[2024-11-16T00:29:06.804Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:29:06.804Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:29:06.804Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15427.079 ms) ======
[2024-11-16T00:29:06.804Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-16T00:29:06.804Z] GC before operation: completed in 60.048 ms, heap usage 500.083 MB -> 56.124 MB.
[2024-11-16T00:29:09.851Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:29:12.042Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:29:14.234Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:29:16.428Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:29:17.832Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:29:19.252Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:29:20.659Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:29:22.082Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:29:22.082Z] 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.
[2024-11-16T00:29:22.082Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:29:22.082Z] Movies recommended for you:
[2024-11-16T00:29:22.082Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:29:22.082Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:29:22.082Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (15029.401 ms) ======
[2024-11-16T00:29:22.082Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-16T00:29:22.082Z] GC before operation: completed in 56.957 ms, heap usage 426.922 MB -> 53.053 MB.
[2024-11-16T00:29:25.131Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:29:27.324Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:29:29.518Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:29:31.712Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:29:33.116Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:29:34.523Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:29:35.930Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:29:37.343Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:29:37.343Z] 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.
[2024-11-16T00:29:37.343Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:29:37.343Z] Movies recommended for you:
[2024-11-16T00:29:37.343Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:29:37.343Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:29:37.343Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15271.907 ms) ======
[2024-11-16T00:29:37.343Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-16T00:29:37.343Z] GC before operation: completed in 57.146 ms, heap usage 230.191 MB -> 52.789 MB.
[2024-11-16T00:29:41.948Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:29:42.624Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:29:44.818Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:29:47.008Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:29:48.411Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:29:49.813Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:29:51.216Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:29:52.621Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:29:52.621Z] 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.
[2024-11-16T00:29:52.621Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:29:53.301Z] Movies recommended for you:
[2024-11-16T00:29:53.301Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:29:53.301Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:29:53.301Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15442.908 ms) ======
[2024-11-16T00:29:53.301Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-16T00:29:53.301Z] GC before operation: completed in 79.782 ms, heap usage 331.176 MB -> 52.891 MB.
[2024-11-16T00:29:55.491Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:29:57.686Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:29:59.877Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:30:02.939Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:30:03.614Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:30:05.015Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:30:06.419Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:30:07.824Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:30:08.503Z] 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.
[2024-11-16T00:30:08.503Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:30:08.503Z] Movies recommended for you:
[2024-11-16T00:30:08.503Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:30:08.503Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:30:08.503Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15227.926 ms) ======
[2024-11-16T00:30:08.503Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-16T00:30:08.503Z] GC before operation: completed in 57.254 ms, heap usage 409.673 MB -> 52.767 MB.
[2024-11-16T00:30:10.691Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:30:13.738Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:30:15.928Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:30:18.123Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:30:19.529Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:30:20.979Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:30:22.383Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:30:23.793Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:30:23.793Z] 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.
[2024-11-16T00:30:23.793Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:30:23.793Z] Movies recommended for you:
[2024-11-16T00:30:23.793Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:30:23.794Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:30:23.794Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15649.230 ms) ======
[2024-11-16T00:30:23.794Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-16T00:30:23.794Z] GC before operation: completed in 56.739 ms, heap usage 75.342 MB -> 55.928 MB.
[2024-11-16T00:30:26.843Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:30:29.036Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:30:31.228Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:30:33.418Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:30:34.819Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:30:36.227Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:30:37.630Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:30:39.034Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:30:39.034Z] 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.
[2024-11-16T00:30:39.034Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:30:39.718Z] Movies recommended for you:
[2024-11-16T00:30:39.718Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:30:39.718Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:30:39.719Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15407.185 ms) ======
[2024-11-16T00:30:39.719Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-16T00:30:39.719Z] GC before operation: completed in 66.769 ms, heap usage 156.850 MB -> 52.974 MB.
[2024-11-16T00:30:41.910Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:30:44.960Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:30:46.363Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:30:50.449Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:30:50.449Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:30:51.224Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:30:52.628Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:30:54.031Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:30:54.710Z] 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.
[2024-11-16T00:30:54.710Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:30:54.710Z] Movies recommended for you:
[2024-11-16T00:30:54.710Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:30:54.710Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:30:54.710Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15038.346 ms) ======
[2024-11-16T00:30:54.710Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-16T00:30:54.710Z] GC before operation: completed in 58.756 ms, heap usage 873.025 MB -> 56.856 MB.
[2024-11-16T00:30:57.936Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:31:00.124Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:31:02.317Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:31:04.508Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:31:05.911Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:31:07.317Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:31:08.719Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:31:10.121Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:31:10.121Z] 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.
[2024-11-16T00:31:10.121Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:31:10.805Z] Movies recommended for you:
[2024-11-16T00:31:10.805Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:31:10.805Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:31:10.805Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15914.791 ms) ======
[2024-11-16T00:31:10.805Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-16T00:31:10.805Z] GC before operation: completed in 53.542 ms, heap usage 272.955 MB -> 52.963 MB.
[2024-11-16T00:31:12.992Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:31:15.183Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:31:17.476Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:31:20.524Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:31:21.201Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:31:22.601Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:31:24.003Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:31:25.405Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:31:25.405Z] 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.
[2024-11-16T00:31:25.405Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:31:26.084Z] Movies recommended for you:
[2024-11-16T00:31:26.084Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:31:26.084Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:31:26.084Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (15236.823 ms) ======
[2024-11-16T00:31:26.084Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-16T00:31:26.084Z] GC before operation: completed in 57.143 ms, heap usage 459.102 MB -> 56.439 MB.
[2024-11-16T00:31:29.133Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:31:31.320Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:31:33.507Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:31:35.694Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:31:37.095Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:31:38.495Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:31:39.897Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:31:41.297Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:31:41.976Z] 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.
[2024-11-16T00:31:41.976Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:31:41.976Z] Movies recommended for you:
[2024-11-16T00:31:41.976Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:31:41.976Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:31:41.977Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15932.387 ms) ======
[2024-11-16T00:31:41.977Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-16T00:31:41.977Z] GC before operation: completed in 59.372 ms, heap usage 347.075 MB -> 52.923 MB.
[2024-11-16T00:31:45.025Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:31:47.215Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:31:49.410Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:31:51.609Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:31:53.014Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:31:54.420Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:31:55.819Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:31:59.140Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:31:59.140Z] 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.
[2024-11-16T00:31:59.140Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:31:59.140Z] Movies recommended for you:
[2024-11-16T00:31:59.140Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:31:59.140Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:31:59.140Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15705.126 ms) ======
[2024-11-16T00:31:59.140Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-16T00:31:59.140Z] GC before operation: completed in 60.272 ms, heap usage 617.683 MB -> 56.417 MB.
[2024-11-16T00:32:00.543Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:32:02.756Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:32:04.943Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:32:07.135Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:32:08.536Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:32:09.939Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:32:11.340Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:32:12.745Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:32:12.745Z] 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.
[2024-11-16T00:32:12.745Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:32:13.422Z] Movies recommended for you:
[2024-11-16T00:32:13.422Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:32:13.422Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:32:13.422Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15519.709 ms) ======
[2024-11-16T00:32:13.422Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-16T00:32:13.422Z] GC before operation: completed in 73.523 ms, heap usage 850.395 MB -> 57.086 MB.
[2024-11-16T00:32:15.614Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T00:32:17.806Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T00:32:20.093Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T00:32:22.282Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T00:32:23.684Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T00:32:25.085Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T00:32:26.491Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T00:32:27.896Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T00:32:27.896Z] 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.
[2024-11-16T00:32:27.896Z] The best model improves the baseline by 14.43%.
[2024-11-16T00:32:27.896Z] Movies recommended for you:
[2024-11-16T00:32:27.896Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T00:32:27.896Z] There is no way to check that no silent failure occurred.
[2024-11-16T00:32:27.896Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14886.605 ms) ======
[2024-11-16T00:32:29.306Z] -----------------------------------
[2024-11-16T00:32:29.306Z] renaissance-movie-lens_0_PASSED
[2024-11-16T00:32:29.306Z] -----------------------------------
[2024-11-16T00:32:29.982Z]
[2024-11-16T00:32:29.982Z] TEST TEARDOWN:
[2024-11-16T00:32:29.982Z] Nothing to be done for teardown.
[2024-11-16T00:32:29.982Z] renaissance-movie-lens_0 Finish Time: Fri Nov 15 18:32:29 2024 Epoch Time (ms): 1731717149297