renaissance-movie-lens_0
[2024-11-16T09:21:55.959Z] Running test renaissance-movie-lens_0 ...
[2024-11-16T09:21:55.959Z] ===============================================
[2024-11-16T09:21:55.959Z] renaissance-movie-lens_0 Start Time: Sat Nov 16 09:21:55 2024 Epoch Time (ms): 1731748915230
[2024-11-16T09:21:55.959Z] variation: NoOptions
[2024-11-16T09:21:55.959Z] JVM_OPTIONS:
[2024-11-16T09:21:55.959Z] { \
[2024-11-16T09:21:55.959Z] echo ""; echo "TEST SETUP:"; \
[2024-11-16T09:21:55.959Z] echo "Nothing to be done for setup."; \
[2024-11-16T09:21:55.959Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317478759248/renaissance-movie-lens_0"; \
[2024-11-16T09:21:55.959Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317478759248/renaissance-movie-lens_0"; \
[2024-11-16T09:21:55.959Z] echo ""; echo "TESTING:"; \
[2024-11-16T09:21:55.959Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317478759248/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-16T09:21:55.959Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317478759248/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-16T09:21:55.959Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-16T09:21:55.959Z] echo "Nothing to be done for teardown."; \
[2024-11-16T09:21:55.959Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317478759248/TestTargetResult";
[2024-11-16T09:21:55.959Z]
[2024-11-16T09:21:55.959Z] TEST SETUP:
[2024-11-16T09:21:55.959Z] Nothing to be done for setup.
[2024-11-16T09:21:55.959Z]
[2024-11-16T09:21:55.959Z] TESTING:
[2024-11-16T09:22:01.471Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-16T09:22:03.903Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-16T09:22:08.294Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-16T09:22:09.054Z] Training: 60056, validation: 20285, test: 19854
[2024-11-16T09:22:09.054Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-16T09:22:09.054Z] GC before operation: completed in 60.370 ms, heap usage 54.481 MB -> 38.083 MB.
[2024-11-16T09:22:15.822Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:22:19.182Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:22:23.575Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:22:26.963Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:22:29.389Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:22:30.951Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:22:33.398Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:22:35.817Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:22:35.817Z] 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.
[2024-11-16T09:22:35.817Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:22:35.817Z] Movies recommended for you:
[2024-11-16T09:22:35.817Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:22:35.817Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:22:35.817Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (27135.100 ms) ======
[2024-11-16T09:22:35.817Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-16T09:22:35.817Z] GC before operation: completed in 110.884 ms, heap usage 561.198 MB -> 54.189 MB.
[2024-11-16T09:22:40.203Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:22:43.568Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:22:46.930Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:22:50.291Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:22:51.850Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:22:54.287Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:22:55.845Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:22:58.272Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:22:59.026Z] 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.
[2024-11-16T09:22:59.026Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:22:59.026Z] Movies recommended for you:
[2024-11-16T09:22:59.026Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:22:59.026Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:22:59.026Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (22679.263 ms) ======
[2024-11-16T09:22:59.026Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-16T09:22:59.026Z] GC before operation: completed in 120.921 ms, heap usage 270.049 MB -> 51.738 MB.
[2024-11-16T09:23:02.394Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:23:05.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:23:09.118Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:23:12.491Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:23:14.051Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:23:16.477Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:23:18.038Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:23:20.475Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:23:20.475Z] 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.
[2024-11-16T09:23:20.475Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:23:20.475Z] Movies recommended for you:
[2024-11-16T09:23:20.475Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:23:20.475Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:23:20.475Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (21636.026 ms) ======
[2024-11-16T09:23:20.475Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-16T09:23:20.475Z] GC before operation: completed in 117.334 ms, heap usage 411.490 MB -> 52.261 MB.
[2024-11-16T09:23:23.840Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:23:27.237Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:23:30.597Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:23:33.967Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:23:35.525Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:23:37.953Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:23:39.515Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:23:41.945Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:23:41.945Z] 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.
[2024-11-16T09:23:41.945Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:23:41.945Z] Movies recommended for you:
[2024-11-16T09:23:41.945Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:23:41.945Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:23:41.945Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (21317.303 ms) ======
[2024-11-16T09:23:41.945Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-16T09:23:41.945Z] GC before operation: completed in 117.361 ms, heap usage 346.413 MB -> 52.550 MB.
[2024-11-16T09:23:45.305Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:23:48.668Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:23:52.033Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:23:55.401Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:23:56.960Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:23:59.390Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:24:00.947Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:24:03.390Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:24:03.390Z] 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.
[2024-11-16T09:24:03.390Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:24:03.390Z] Movies recommended for you:
[2024-11-16T09:24:03.390Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:24:03.390Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:24:03.390Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21313.000 ms) ======
[2024-11-16T09:24:03.390Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-16T09:24:03.390Z] GC before operation: completed in 124.013 ms, heap usage 494.349 MB -> 56.113 MB.
[2024-11-16T09:24:06.755Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:24:10.121Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:24:13.488Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:24:16.856Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:24:18.414Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:24:20.841Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:24:22.406Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:24:24.837Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:24:24.837Z] 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.
[2024-11-16T09:24:24.837Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:24:24.837Z] Movies recommended for you:
[2024-11-16T09:24:24.837Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:24:24.837Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:24:24.837Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (21390.148 ms) ======
[2024-11-16T09:24:24.837Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-16T09:24:24.837Z] GC before operation: completed in 129.291 ms, heap usage 394.128 MB -> 52.735 MB.
[2024-11-16T09:24:28.192Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:24:31.562Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:24:34.924Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:24:38.458Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:24:40.036Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:24:41.608Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:24:44.044Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:24:45.604Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:24:46.358Z] 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.
[2024-11-16T09:24:46.358Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:24:46.358Z] Movies recommended for you:
[2024-11-16T09:24:46.358Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:24:46.358Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:24:46.358Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (21188.055 ms) ======
[2024-11-16T09:24:46.358Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-16T09:24:46.358Z] GC before operation: completed in 119.717 ms, heap usage 517.379 MB -> 56.216 MB.
[2024-11-16T09:24:49.721Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:24:53.097Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:24:56.466Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:24:58.895Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:25:01.326Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:25:02.883Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:25:05.330Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:25:06.915Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:25:07.669Z] 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.
[2024-11-16T09:25:07.669Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:25:07.669Z] Movies recommended for you:
[2024-11-16T09:25:07.669Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:25:07.669Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:25:07.669Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (21200.134 ms) ======
[2024-11-16T09:25:07.669Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-16T09:25:07.669Z] GC before operation: completed in 123.433 ms, heap usage 242.937 MB -> 53.004 MB.
[2024-11-16T09:25:11.051Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:25:14.419Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:25:17.786Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:25:20.211Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:25:22.641Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:25:24.204Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:25:26.629Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:25:28.195Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:25:28.195Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-16T09:25:28.957Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:25:28.957Z] Movies recommended for you:
[2024-11-16T09:25:28.957Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:25:28.957Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:25:28.957Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20950.320 ms) ======
[2024-11-16T09:25:28.957Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-16T09:25:28.957Z] GC before operation: completed in 127.174 ms, heap usage 262.157 MB -> 52.817 MB.
[2024-11-16T09:25:32.319Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:25:35.688Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:25:39.060Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:25:41.487Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:25:43.925Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:25:45.486Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:25:47.921Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:25:49.481Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:25:49.481Z] 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.
[2024-11-16T09:25:49.482Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:25:50.237Z] Movies recommended for you:
[2024-11-16T09:25:50.237Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:25:50.237Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:25:50.237Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (21044.092 ms) ======
[2024-11-16T09:25:50.237Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-16T09:25:50.237Z] GC before operation: completed in 127.321 ms, heap usage 206.222 MB -> 52.948 MB.
[2024-11-16T09:25:53.598Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:25:56.022Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:25:59.398Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:26:02.757Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:26:04.315Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:26:06.741Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:26:08.313Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:26:10.754Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:26:10.754Z] 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.
[2024-11-16T09:26:10.754Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:26:10.754Z] Movies recommended for you:
[2024-11-16T09:26:10.754Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:26:10.754Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:26:10.754Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20882.253 ms) ======
[2024-11-16T09:26:10.754Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-16T09:26:10.754Z] GC before operation: completed in 126.046 ms, heap usage 265.097 MB -> 52.667 MB.
[2024-11-16T09:26:14.125Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:26:17.493Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:26:20.857Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:26:23.285Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:26:25.711Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:26:27.274Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:26:29.701Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:26:31.265Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:26:32.024Z] 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.
[2024-11-16T09:26:32.024Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:26:32.024Z] Movies recommended for you:
[2024-11-16T09:26:32.024Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:26:32.024Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:26:32.024Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20855.142 ms) ======
[2024-11-16T09:26:32.024Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-16T09:26:32.024Z] GC before operation: completed in 122.464 ms, heap usage 246.577 MB -> 52.872 MB.
[2024-11-16T09:26:35.383Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:26:38.747Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:26:42.115Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:26:44.551Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:26:46.974Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:26:48.539Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:26:50.973Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:26:52.541Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:26:52.541Z] 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.
[2024-11-16T09:26:52.541Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:26:53.810Z] Movies recommended for you:
[2024-11-16T09:26:53.810Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:26:53.810Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:26:53.810Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (21011.598 ms) ======
[2024-11-16T09:26:53.810Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-16T09:26:53.810Z] GC before operation: completed in 122.133 ms, heap usage 261.650 MB -> 53.062 MB.
[2024-11-16T09:26:55.908Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:26:59.674Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:27:03.048Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:27:06.415Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:27:07.982Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:27:09.547Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:27:11.975Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:27:13.558Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:27:14.313Z] 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.
[2024-11-16T09:27:14.313Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:27:14.313Z] Movies recommended for you:
[2024-11-16T09:27:14.313Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:27:14.313Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:27:14.313Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (21094.495 ms) ======
[2024-11-16T09:27:14.313Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-16T09:27:14.313Z] GC before operation: completed in 126.038 ms, heap usage 237.267 MB -> 55.980 MB.
[2024-11-16T09:27:17.692Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:27:20.117Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:27:23.480Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:27:26.839Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:27:28.398Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:27:29.956Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:27:32.388Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:27:33.951Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:27:33.951Z] 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.
[2024-11-16T09:27:33.951Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:27:34.706Z] Movies recommended for you:
[2024-11-16T09:27:34.706Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:27:34.706Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:27:34.706Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20082.890 ms) ======
[2024-11-16T09:27:34.706Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-16T09:27:34.706Z] GC before operation: completed in 123.346 ms, heap usage 393.596 MB -> 53.057 MB.
[2024-11-16T09:27:38.066Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:27:40.500Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:27:43.859Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:27:47.222Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:27:48.787Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:27:50.347Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:27:52.787Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:27:54.362Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:27:54.362Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-16T09:27:54.362Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:27:55.117Z] Movies recommended for you:
[2024-11-16T09:27:55.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:27:55.117Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:27:55.117Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20205.845 ms) ======
[2024-11-16T09:27:55.117Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-16T09:27:55.117Z] GC before operation: completed in 151.399 ms, heap usage 297.417 MB -> 53.065 MB.
[2024-11-16T09:27:57.552Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:28:00.933Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:28:04.297Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:28:07.663Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:28:09.225Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:28:10.790Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:28:12.352Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:28:14.788Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:28:14.788Z] 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.
[2024-11-16T09:28:14.788Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:28:14.788Z] Movies recommended for you:
[2024-11-16T09:28:14.788Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:28:14.788Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:28:14.788Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (20070.693 ms) ======
[2024-11-16T09:28:14.788Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-16T09:28:14.788Z] GC before operation: completed in 117.478 ms, heap usage 472.196 MB -> 53.069 MB.
[2024-11-16T09:28:18.148Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:28:21.531Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:28:24.899Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:28:27.353Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:28:29.788Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:28:31.349Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:28:32.911Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:28:34.477Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:28:35.232Z] 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.
[2024-11-16T09:28:35.232Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:28:35.232Z] Movies recommended for you:
[2024-11-16T09:28:35.232Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:28:35.232Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:28:35.232Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (20246.368 ms) ======
[2024-11-16T09:28:35.232Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-16T09:28:35.232Z] GC before operation: completed in 119.464 ms, heap usage 368.995 MB -> 53.017 MB.
[2024-11-16T09:28:38.598Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:28:41.979Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:28:45.351Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:28:47.785Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:28:49.345Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:28:51.769Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:28:53.334Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:28:54.907Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:28:55.667Z] 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.
[2024-11-16T09:28:55.667Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:28:55.667Z] Movies recommended for you:
[2024-11-16T09:28:55.667Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:28:55.667Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:28:55.667Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (20222.852 ms) ======
[2024-11-16T09:28:55.667Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-16T09:28:55.667Z] GC before operation: completed in 136.880 ms, heap usage 519.534 MB -> 56.599 MB.
[2024-11-16T09:28:59.044Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T09:29:02.441Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T09:29:04.870Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T09:29:08.234Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T09:29:09.810Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T09:29:12.245Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T09:29:13.800Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T09:29:15.360Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T09:29:16.115Z] 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.
[2024-11-16T09:29:16.115Z] The best model improves the baseline by 14.43%.
[2024-11-16T09:29:16.115Z] Movies recommended for you:
[2024-11-16T09:29:16.115Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T09:29:16.115Z] There is no way to check that no silent failure occurred.
[2024-11-16T09:29:16.115Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (20339.462 ms) ======
[2024-11-16T09:29:16.871Z] -----------------------------------
[2024-11-16T09:29:16.871Z] renaissance-movie-lens_0_PASSED
[2024-11-16T09:29:16.871Z] -----------------------------------
[2024-11-16T09:29:16.871Z]
[2024-11-16T09:29:16.871Z] TEST TEARDOWN:
[2024-11-16T09:29:16.871Z] Nothing to be done for teardown.
[2024-11-16T09:29:16.871Z] renaissance-movie-lens_0 Finish Time: Sat Nov 16 09:29:16 2024 Epoch Time (ms): 1731749356509