renaissance-movie-lens_0
[2024-11-08T15:19:00.144Z] Running test renaissance-movie-lens_0 ...
[2024-11-08T15:19:00.144Z] ===============================================
[2024-11-08T15:19:00.144Z] renaissance-movie-lens_0 Start Time: Fri Nov 8 15:18:59 2024 Epoch Time (ms): 1731079139383
[2024-11-08T15:19:00.144Z] variation: NoOptions
[2024-11-08T15:19:00.144Z] JVM_OPTIONS:
[2024-11-08T15:19:00.144Z] { \
[2024-11-08T15:19:00.144Z] echo ""; echo "TEST SETUP:"; \
[2024-11-08T15:19:00.144Z] echo "Nothing to be done for setup."; \
[2024-11-08T15:19:00.144Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17310782938852/renaissance-movie-lens_0"; \
[2024-11-08T15:19:00.144Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17310782938852/renaissance-movie-lens_0"; \
[2024-11-08T15:19:00.144Z] echo ""; echo "TESTING:"; \
[2024-11-08T15:19:00.144Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/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_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17310782938852/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-08T15:19:00.144Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17310782938852/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-08T15:19:00.144Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-08T15:19:00.144Z] echo "Nothing to be done for teardown."; \
[2024-11-08T15:19:00.144Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17310782938852/TestTargetResult";
[2024-11-08T15:19:00.144Z]
[2024-11-08T15:19:00.144Z] TEST SETUP:
[2024-11-08T15:19:00.144Z] Nothing to be done for setup.
[2024-11-08T15:19:00.144Z]
[2024-11-08T15:19:00.144Z] TESTING:
[2024-11-08T15:19:04.589Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-08T15:19:06.171Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-08T15:19:09.589Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-08T15:19:09.589Z] Training: 60056, validation: 20285, test: 19854
[2024-11-08T15:19:09.589Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-08T15:19:09.589Z] GC before operation: completed in 55.140 ms, heap usage 188.296 MB -> 37.482 MB.
[2024-11-08T15:19:14.033Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:19:17.441Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:19:19.903Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:19:23.318Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:19:24.084Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:19:25.693Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:19:27.280Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:19:28.861Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:19:29.630Z] 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.
[2024-11-08T15:19:29.630Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:19:29.630Z] Movies recommended for you:
[2024-11-08T15:19:29.630Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:19:29.630Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:19:29.630Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19876.976 ms) ======
[2024-11-08T15:19:29.630Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-08T15:19:29.630Z] GC before operation: completed in 85.173 ms, heap usage 90.617 MB -> 53.062 MB.
[2024-11-08T15:19:32.099Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:19:34.569Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:19:37.976Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:19:40.439Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:19:42.024Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:19:43.612Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:19:45.198Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:19:45.965Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:19:46.731Z] 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.
[2024-11-08T15:19:46.731Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:19:46.731Z] Movies recommended for you:
[2024-11-08T15:19:46.731Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:19:46.731Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:19:46.731Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17049.453 ms) ======
[2024-11-08T15:19:46.731Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-08T15:19:46.731Z] GC before operation: completed in 79.371 ms, heap usage 281.457 MB -> 51.246 MB.
[2024-11-08T15:19:49.188Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:19:51.684Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:19:55.310Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:19:56.892Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:19:58.482Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:20:00.069Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:20:01.652Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:20:03.239Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:20:03.239Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-08T15:20:03.239Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:20:03.239Z] Movies recommended for you:
[2024-11-08T15:20:03.239Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:20:03.239Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:20:03.239Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16737.301 ms) ======
[2024-11-08T15:20:03.239Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-08T15:20:04.005Z] GC before operation: completed in 75.588 ms, heap usage 237.754 MB -> 51.614 MB.
[2024-11-08T15:20:06.475Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:20:08.938Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:20:11.425Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:20:13.888Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:20:15.470Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:20:16.240Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:20:17.819Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:20:19.406Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:20:20.172Z] 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.
[2024-11-08T15:20:20.172Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:20:20.172Z] Movies recommended for you:
[2024-11-08T15:20:20.172Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:20:20.172Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:20:20.172Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16446.939 ms) ======
[2024-11-08T15:20:20.172Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-08T15:20:20.172Z] GC before operation: completed in 84.868 ms, heap usage 2.323 GB -> 56.851 MB.
[2024-11-08T15:20:22.636Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:20:25.114Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:20:27.592Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:20:30.057Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:20:31.639Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:20:33.221Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:20:34.803Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:20:36.404Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:20:36.404Z] 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.
[2024-11-08T15:20:36.404Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:20:36.404Z] Movies recommended for you:
[2024-11-08T15:20:36.404Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:20:36.404Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:20:36.404Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16455.967 ms) ======
[2024-11-08T15:20:36.404Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-08T15:20:36.404Z] GC before operation: completed in 75.813 ms, heap usage 168.353 MB -> 55.261 MB.
[2024-11-08T15:20:39.816Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:20:42.289Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:20:44.747Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:20:47.228Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:20:48.813Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:20:49.590Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:20:51.190Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:20:52.775Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:20:52.775Z] 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.
[2024-11-08T15:20:52.775Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:20:53.550Z] Movies recommended for you:
[2024-11-08T15:20:53.550Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:20:53.550Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:20:53.550Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16520.511 ms) ======
[2024-11-08T15:20:53.550Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-08T15:20:53.550Z] GC before operation: completed in 86.381 ms, heap usage 2.654 GB -> 56.980 MB.
[2024-11-08T15:20:56.007Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:20:58.471Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:21:00.933Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:21:03.405Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:21:04.995Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:21:06.585Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:21:08.192Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:21:08.970Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:21:09.734Z] 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.
[2024-11-08T15:21:09.734Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:21:09.734Z] Movies recommended for you:
[2024-11-08T15:21:09.734Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:21:09.734Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:21:09.734Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16308.749 ms) ======
[2024-11-08T15:21:09.734Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-08T15:21:09.734Z] GC before operation: completed in 77.389 ms, heap usage 95.538 MB -> 55.543 MB.
[2024-11-08T15:21:12.201Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:21:14.667Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:21:17.127Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:21:19.587Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:21:21.180Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:21:22.769Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:21:24.357Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:21:25.141Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:21:25.909Z] 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.
[2024-11-08T15:21:25.909Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:21:25.909Z] Movies recommended for you:
[2024-11-08T15:21:25.909Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:21:25.909Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:21:25.909Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16170.119 ms) ======
[2024-11-08T15:21:25.909Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-08T15:21:25.909Z] GC before operation: completed in 77.740 ms, heap usage 97.767 MB -> 54.576 MB.
[2024-11-08T15:21:28.373Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:21:30.845Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:21:33.339Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:21:35.809Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:21:37.400Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:21:38.985Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:21:40.565Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:21:42.190Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:21:42.190Z] 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.
[2024-11-08T15:21:42.190Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:21:42.190Z] Movies recommended for you:
[2024-11-08T15:21:42.190Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:21:42.190Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:21:42.190Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16410.038 ms) ======
[2024-11-08T15:21:42.190Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-08T15:21:42.190Z] GC before operation: completed in 91.561 ms, heap usage 2.023 GB -> 57.217 MB.
[2024-11-08T15:21:44.656Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:21:47.120Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:21:49.591Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:21:52.061Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:21:53.649Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:21:55.236Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:21:56.824Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:21:58.418Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:21:58.418Z] 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.
[2024-11-08T15:21:58.418Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:21:58.418Z] Movies recommended for you:
[2024-11-08T15:21:58.418Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:21:58.418Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:21:58.418Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16320.918 ms) ======
[2024-11-08T15:21:58.418Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-08T15:21:59.185Z] GC before operation: completed in 101.931 ms, heap usage 3.653 GB -> 62.012 MB.
[2024-11-08T15:22:01.640Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:22:04.119Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:22:06.575Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:22:09.037Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:22:10.647Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:22:11.413Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:22:12.995Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:22:14.596Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:22:15.371Z] 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.
[2024-11-08T15:22:15.371Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:22:15.371Z] Movies recommended for you:
[2024-11-08T15:22:15.371Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:22:15.371Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:22:15.371Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16279.685 ms) ======
[2024-11-08T15:22:15.371Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-08T15:22:15.371Z] GC before operation: completed in 81.275 ms, heap usage 97.494 MB -> 56.469 MB.
[2024-11-08T15:22:17.842Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:22:20.329Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:22:22.789Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:22:25.258Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:22:26.848Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:22:28.432Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:22:30.023Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:22:30.794Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:22:31.563Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-08T15:22:31.563Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:22:31.563Z] Movies recommended for you:
[2024-11-08T15:22:31.563Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:22:31.563Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:22:31.563Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16168.570 ms) ======
[2024-11-08T15:22:31.563Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-08T15:22:31.563Z] GC before operation: completed in 74.165 ms, heap usage 169.566 MB -> 52.242 MB.
[2024-11-08T15:22:34.064Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:22:36.521Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:22:38.981Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:22:41.457Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:22:43.043Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:22:43.809Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:22:45.408Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:22:47.000Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:22:47.768Z] 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.
[2024-11-08T15:22:47.768Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:22:47.768Z] Movies recommended for you:
[2024-11-08T15:22:47.768Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:22:47.768Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:22:47.768Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16083.018 ms) ======
[2024-11-08T15:22:47.768Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-08T15:22:47.768Z] GC before operation: completed in 76.406 ms, heap usage 278.312 MB -> 52.553 MB.
[2024-11-08T15:22:50.228Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:22:52.699Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:22:55.158Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:22:58.291Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:22:59.263Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:23:01.047Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:23:02.311Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:23:03.893Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:23:04.659Z] 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.
[2024-11-08T15:23:04.659Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:23:04.659Z] Movies recommended for you:
[2024-11-08T15:23:04.659Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:23:04.659Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:23:04.659Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16998.200 ms) ======
[2024-11-08T15:23:04.659Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-08T15:23:04.659Z] GC before operation: completed in 71.947 ms, heap usage 170.198 MB -> 52.174 MB.
[2024-11-08T15:23:07.124Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:23:10.545Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:23:13.012Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:23:15.501Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:23:17.085Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:23:18.666Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:23:20.250Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:23:21.836Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:23:21.836Z] 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.
[2024-11-08T15:23:21.836Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:23:21.836Z] Movies recommended for you:
[2024-11-08T15:23:21.836Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:23:21.836Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:23:21.836Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (17351.581 ms) ======
[2024-11-08T15:23:21.836Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-08T15:23:21.836Z] GC before operation: completed in 82.314 ms, heap usage 650.550 MB -> 55.930 MB.
[2024-11-08T15:23:24.300Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:23:27.720Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:23:30.179Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:23:32.643Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:23:34.234Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:23:35.825Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:23:37.410Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:23:39.023Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:23:39.023Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-08T15:23:39.023Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:23:39.023Z] Movies recommended for you:
[2024-11-08T15:23:39.023Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:23:39.023Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:23:39.023Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17242.802 ms) ======
[2024-11-08T15:23:39.023Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-08T15:23:39.796Z] GC before operation: completed in 83.883 ms, heap usage 1.232 GB -> 56.956 MB.
[2024-11-08T15:23:42.262Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:23:44.734Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:23:47.200Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:23:49.677Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:23:51.280Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:23:52.055Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:23:54.523Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:23:55.290Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:23:56.056Z] 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.
[2024-11-08T15:23:56.056Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:23:56.056Z] Movies recommended for you:
[2024-11-08T15:23:56.056Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:23:56.056Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:23:56.056Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16409.131 ms) ======
[2024-11-08T15:23:56.056Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-08T15:23:56.056Z] GC before operation: completed in 93.289 ms, heap usage 1.771 GB -> 58.455 MB.
[2024-11-08T15:23:58.535Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:24:01.042Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:24:03.504Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:24:05.971Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:24:07.556Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:24:09.149Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:24:10.732Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:24:12.314Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:24:12.314Z] 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.
[2024-11-08T15:24:12.314Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:24:12.314Z] Movies recommended for you:
[2024-11-08T15:24:12.314Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:24:12.314Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:24:12.314Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16416.397 ms) ======
[2024-11-08T15:24:12.314Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-08T15:24:12.314Z] GC before operation: completed in 92.666 ms, heap usage 1.366 GB -> 60.296 MB.
[2024-11-08T15:24:14.776Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:24:17.233Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:24:20.651Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:24:23.114Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:24:24.713Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:24:26.300Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:24:27.885Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:24:29.479Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:24:29.479Z] 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.
[2024-11-08T15:24:29.479Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:24:29.479Z] Movies recommended for you:
[2024-11-08T15:24:29.479Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:24:29.479Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:24:29.479Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17101.850 ms) ======
[2024-11-08T15:24:29.479Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-08T15:24:29.479Z] GC before operation: completed in 89.240 ms, heap usage 990.842 MB -> 56.651 MB.
[2024-11-08T15:24:31.946Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-08T15:24:35.362Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-08T15:24:37.822Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-08T15:24:40.347Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-08T15:24:41.930Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-08T15:24:43.518Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-08T15:24:45.102Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-08T15:24:46.684Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-08T15:24:46.684Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-08T15:24:46.684Z] The best model improves the baseline by 14.43%.
[2024-11-08T15:24:46.684Z] Movies recommended for you:
[2024-11-08T15:24:46.684Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-08T15:24:46.684Z] There is no way to check that no silent failure occurred.
[2024-11-08T15:24:46.684Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17097.330 ms) ======
[2024-11-08T15:24:47.451Z] -----------------------------------
[2024-11-08T15:24:47.451Z] renaissance-movie-lens_0_PASSED
[2024-11-08T15:24:47.451Z] -----------------------------------
[2024-11-08T15:24:47.451Z]
[2024-11-08T15:24:47.451Z] TEST TEARDOWN:
[2024-11-08T15:24:47.451Z] Nothing to be done for teardown.
[2024-11-08T15:24:47.451Z] renaissance-movie-lens_0 Finish Time: Fri Nov 8 15:24:46 2024 Epoch Time (ms): 1731079486863