renaissance-movie-lens_0
[2025-01-10T18:57:46.028Z] Running test renaissance-movie-lens_0 ...
[2025-01-10T18:57:46.028Z] ===============================================
[2025-01-10T18:57:46.705Z] renaissance-movie-lens_0 Start Time: Fri Jan 10 12:57:46 2025 Epoch Time (ms): 1736535466100
[2025-01-10T18:57:46.705Z] variation: NoOptions
[2025-01-10T18:57:46.705Z] JVM_OPTIONS:
[2025-01-10T18:57:46.705Z] { \
[2025-01-10T18:57:46.705Z] echo ""; echo "TEST SETUP:"; \
[2025-01-10T18:57:46.705Z] echo "Nothing to be done for setup."; \
[2025-01-10T18:57:46.705Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1736534605520/renaissance-movie-lens_0"; \
[2025-01-10T18:57:46.705Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1736534605520/renaissance-movie-lens_0"; \
[2025-01-10T18:57:46.705Z] echo ""; echo "TESTING:"; \
[2025-01-10T18:57:46.705Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_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_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1736534605520/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-10T18:57:46.705Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1736534605520/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-10T18:57:46.705Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-10T18:57:46.705Z] echo "Nothing to be done for teardown."; \
[2025-01-10T18:57:46.705Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_1736534605520/TestTargetResult";
[2025-01-10T18:57:46.705Z]
[2025-01-10T18:57:46.705Z] TEST SETUP:
[2025-01-10T18:57:46.705Z] Nothing to be done for setup.
[2025-01-10T18:57:46.705Z]
[2025-01-10T18:57:46.705Z] TESTING:
[2025-01-10T18:57:48.909Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-10T18:57:51.101Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-01-10T18:57:54.152Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-10T18:57:54.867Z] Training: 60056, validation: 20285, test: 19854
[2025-01-10T18:57:54.867Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-10T18:57:54.867Z] GC before operation: completed in 53.840 ms, heap usage 96.101 MB -> 37.160 MB.
[2025-01-10T18:58:02.482Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:58:05.553Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:58:09.971Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:58:12.197Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:58:14.487Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:58:15.939Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:58:18.153Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:58:20.351Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:58:20.351Z] 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.
[2025-01-10T18:58:20.351Z] The best model improves the baseline by 14.43%.
[2025-01-10T18:58:20.351Z] Movies recommended for you:
[2025-01-10T18:58:20.351Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:58:20.351Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:58:20.351Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25862.047 ms) ======
[2025-01-10T18:58:20.351Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-10T18:58:21.033Z] GC before operation: completed in 91.925 ms, heap usage 292.982 MB -> 50.567 MB.
[2025-01-10T18:58:24.089Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:58:26.364Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:58:29.523Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:58:32.634Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:58:34.055Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:58:35.460Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:58:36.869Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:58:39.068Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:58:39.750Z] 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.
[2025-01-10T18:58:39.750Z] The best model improves the baseline by 14.43%.
[2025-01-10T18:58:39.750Z] Movies recommended for you:
[2025-01-10T18:58:39.750Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:58:39.750Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:58:39.750Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19041.188 ms) ======
[2025-01-10T18:58:39.750Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-10T18:58:39.750Z] GC before operation: completed in 162.325 ms, heap usage 68.015 MB -> 53.724 MB.
[2025-01-10T18:58:42.818Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:58:45.910Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:58:48.113Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:58:51.175Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:58:52.583Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:58:54.003Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:58:55.408Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:58:56.834Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:58:56.834Z] 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.
[2025-01-10T18:58:56.834Z] The best model improves the baseline by 14.43%.
[2025-01-10T18:58:57.511Z] Movies recommended for you:
[2025-01-10T18:58:57.511Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:58:57.511Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:58:57.511Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17306.653 ms) ======
[2025-01-10T18:58:57.511Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-10T18:58:57.511Z] GC before operation: completed in 89.692 ms, heap usage 201.369 MB -> 51.394 MB.
[2025-01-10T18:58:59.713Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:59:01.914Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:59:05.430Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:59:06.839Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:59:08.260Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:59:09.739Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:59:11.974Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:59:13.379Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:59:13.379Z] 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.
[2025-01-10T18:59:13.379Z] The best model improves the baseline by 14.43%.
[2025-01-10T18:59:13.379Z] Movies recommended for you:
[2025-01-10T18:59:13.379Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:59:13.379Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:59:13.379Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16414.068 ms) ======
[2025-01-10T18:59:13.379Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-10T18:59:14.142Z] GC before operation: completed in 85.949 ms, heap usage 234.696 MB -> 51.707 MB.
[2025-01-10T18:59:16.337Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:59:18.539Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:59:20.765Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:59:22.992Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:59:25.212Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:59:25.901Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:59:28.123Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:59:29.530Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:59:29.530Z] 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.
[2025-01-10T18:59:29.530Z] The best model improves the baseline by 14.43%.
[2025-01-10T18:59:29.530Z] Movies recommended for you:
[2025-01-10T18:59:29.530Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:59:29.530Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:59:29.530Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15792.360 ms) ======
[2025-01-10T18:59:29.530Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-10T18:59:29.530Z] GC before operation: completed in 134.824 ms, heap usage 533.517 MB -> 55.291 MB.
[2025-01-10T18:59:32.633Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:59:34.833Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:59:37.061Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:59:40.129Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:59:41.553Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:59:42.966Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T18:59:44.388Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T18:59:45.804Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T18:59:45.804Z] 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.
[2025-01-10T18:59:45.804Z] The best model improves the baseline by 14.43%.
[2025-01-10T18:59:46.484Z] Movies recommended for you:
[2025-01-10T18:59:46.484Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T18:59:46.484Z] There is no way to check that no silent failure occurred.
[2025-01-10T18:59:46.484Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16418.206 ms) ======
[2025-01-10T18:59:46.484Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-10T18:59:46.484Z] GC before operation: completed in 73.552 ms, heap usage 77.311 MB -> 51.678 MB.
[2025-01-10T18:59:48.680Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T18:59:51.101Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T18:59:53.308Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T18:59:55.516Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T18:59:56.937Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T18:59:59.213Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:00.645Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:02.053Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:02.053Z] 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.
[2025-01-10T19:00:02.053Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:00:02.053Z] Movies recommended for you:
[2025-01-10T19:00:02.053Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:02.053Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:02.053Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16027.288 ms) ======
[2025-01-10T19:00:02.053Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-10T19:00:02.053Z] GC before operation: completed in 83.354 ms, heap usage 258.991 MB -> 51.951 MB.
[2025-01-10T19:00:05.150Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:07.353Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:09.571Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:11.771Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:00:13.985Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:00:15.407Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:16.811Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:18.250Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:18.250Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-01-10T19:00:18.250Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:00:18.927Z] Movies recommended for you:
[2025-01-10T19:00:18.927Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:18.927Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:18.927Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16315.414 ms) ======
[2025-01-10T19:00:18.927Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-10T19:00:18.927Z] GC before operation: completed in 77.625 ms, heap usage 156.336 MB -> 52.135 MB.
[2025-01-10T19:00:21.130Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:23.347Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:26.440Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:28.652Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:00:29.345Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:00:31.583Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:33.000Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:34.431Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:34.431Z] 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.
[2025-01-10T19:00:34.431Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:00:34.431Z] Movies recommended for you:
[2025-01-10T19:00:34.431Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:34.431Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:34.431Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (15780.414 ms) ======
[2025-01-10T19:00:34.431Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-10T19:00:34.431Z] GC before operation: completed in 80.682 ms, heap usage 211.125 MB -> 52.056 MB.
[2025-01-10T19:00:37.939Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:39.355Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:41.552Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:43.790Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:00:45.210Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:00:46.654Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:00:48.068Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:00:49.510Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:00:50.203Z] 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.
[2025-01-10T19:00:50.203Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:00:50.203Z] Movies recommended for you:
[2025-01-10T19:00:50.203Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:00:50.203Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:00:50.203Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (15546.186 ms) ======
[2025-01-10T19:00:50.203Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-10T19:00:50.203Z] GC before operation: completed in 90.127 ms, heap usage 312.823 MB -> 52.210 MB.
[2025-01-10T19:00:52.400Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:00:54.603Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:00:57.707Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:00:59.112Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:01.334Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:02.761Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:04.182Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:05.602Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:05.602Z] 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.
[2025-01-10T19:01:05.602Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:01:05.602Z] Movies recommended for you:
[2025-01-10T19:01:05.602Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:05.602Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:05.602Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15445.219 ms) ======
[2025-01-10T19:01:05.602Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-10T19:01:05.602Z] GC before operation: completed in 93.648 ms, heap usage 249.930 MB -> 51.903 MB.
[2025-01-10T19:01:08.690Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:10.905Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:01:13.150Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:01:15.367Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:16.775Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:18.206Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:19.642Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:21.059Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:21.745Z] 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.
[2025-01-10T19:01:21.745Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:01:21.745Z] Movies recommended for you:
[2025-01-10T19:01:21.745Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:21.745Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:21.745Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (15943.795 ms) ======
[2025-01-10T19:01:21.745Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-10T19:01:21.745Z] GC before operation: completed in 85.544 ms, heap usage 467.778 MB -> 52.236 MB.
[2025-01-10T19:01:23.939Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:27.026Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:01:29.333Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:01:31.575Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:33.009Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:35.253Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:36.680Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:38.093Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:38.093Z] 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.
[2025-01-10T19:01:38.093Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:01:38.093Z] Movies recommended for you:
[2025-01-10T19:01:38.093Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:38.093Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:38.093Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16504.963 ms) ======
[2025-01-10T19:01:38.093Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-10T19:01:38.093Z] GC before operation: completed in 77.871 ms, heap usage 124.741 MB -> 52.141 MB.
[2025-01-10T19:01:41.158Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:43.362Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:01:45.595Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:01:47.809Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:01:49.227Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:01:50.646Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:01:52.073Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:01:53.508Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:01:54.185Z] 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.
[2025-01-10T19:01:54.185Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:01:54.185Z] Movies recommended for you:
[2025-01-10T19:01:54.185Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:01:54.185Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:01:54.185Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15729.698 ms) ======
[2025-01-10T19:01:54.185Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-10T19:01:54.185Z] GC before operation: completed in 82.570 ms, heap usage 346.306 MB -> 52.065 MB.
[2025-01-10T19:01:57.248Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:01:59.466Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:01.656Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:03.859Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:05.270Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:06.679Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:08.104Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:09.525Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:10.227Z] 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.
[2025-01-10T19:02:10.227Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:02:10.227Z] Movies recommended for you:
[2025-01-10T19:02:10.227Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:10.227Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:10.227Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15946.334 ms) ======
[2025-01-10T19:02:10.227Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-10T19:02:10.227Z] GC before operation: completed in 89.391 ms, heap usage 455.938 MB -> 52.317 MB.
[2025-01-10T19:02:13.824Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:02:15.256Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:17.464Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:19.666Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:21.093Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:22.500Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:24.723Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:26.143Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:26.143Z] 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.
[2025-01-10T19:02:26.143Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:02:26.143Z] Movies recommended for you:
[2025-01-10T19:02:26.143Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:26.143Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:26.143Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16118.076 ms) ======
[2025-01-10T19:02:26.143Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-10T19:02:26.143Z] GC before operation: completed in 95.150 ms, heap usage 250.935 MB -> 52.288 MB.
[2025-01-10T19:02:29.231Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:02:31.588Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:33.793Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:35.996Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:37.433Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:38.860Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:40.284Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:41.692Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:42.384Z] 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.
[2025-01-10T19:02:42.384Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:02:42.384Z] Movies recommended for you:
[2025-01-10T19:02:42.384Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:42.384Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:42.384Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15876.658 ms) ======
[2025-01-10T19:02:42.384Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-10T19:02:42.384Z] GC before operation: completed in 96.325 ms, heap usage 398.879 MB -> 52.224 MB.
[2025-01-10T19:02:45.446Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:02:47.680Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:02:49.886Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:02:52.094Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:02:53.525Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:02:54.982Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:02:56.417Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:02:58.621Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:02:58.621Z] 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.
[2025-01-10T19:02:58.621Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:02:58.621Z] Movies recommended for you:
[2025-01-10T19:02:58.621Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:02:58.621Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:02:58.621Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16196.186 ms) ======
[2025-01-10T19:02:58.621Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-10T19:02:58.621Z] GC before operation: completed in 104.697 ms, heap usage 353.032 MB -> 52.184 MB.
[2025-01-10T19:03:01.267Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:03:03.491Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:03:05.700Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:03:07.902Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:03:09.334Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:03:10.752Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:03:12.171Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:03:13.606Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:03:14.303Z] 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.
[2025-01-10T19:03:14.303Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:03:14.303Z] Movies recommended for you:
[2025-01-10T19:03:14.303Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:03:14.303Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:03:14.303Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15501.846 ms) ======
[2025-01-10T19:03:14.303Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-10T19:03:14.303Z] GC before operation: completed in 82.607 ms, heap usage 418.657 MB -> 52.488 MB.
[2025-01-10T19:03:16.508Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-10T19:03:18.706Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-10T19:03:21.786Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-10T19:03:23.987Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-10T19:03:25.407Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-10T19:03:26.836Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-10T19:03:28.362Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-10T19:03:29.770Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-10T19:03:29.770Z] 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.
[2025-01-10T19:03:29.770Z] The best model improves the baseline by 14.43%.
[2025-01-10T19:03:29.770Z] Movies recommended for you:
[2025-01-10T19:03:29.770Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-10T19:03:29.770Z] There is no way to check that no silent failure occurred.
[2025-01-10T19:03:29.770Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (15576.654 ms) ======
[2025-01-10T19:03:31.206Z] -----------------------------------
[2025-01-10T19:03:31.206Z] renaissance-movie-lens_0_PASSED
[2025-01-10T19:03:31.206Z] -----------------------------------
[2025-01-10T19:03:31.206Z]
[2025-01-10T19:03:31.206Z] TEST TEARDOWN:
[2025-01-10T19:03:31.206Z] Nothing to be done for teardown.
[2025-01-10T19:03:31.206Z] renaissance-movie-lens_0 Finish Time: Fri Jan 10 13:03:30 2025 Epoch Time (ms): 1736535810583