renaissance-movie-lens_0
[2024-11-15T22:08:49.271Z] Running test renaissance-movie-lens_0 ...
[2024-11-15T22:08:49.271Z] ===============================================
[2024-11-15T22:08:49.271Z] renaissance-movie-lens_0 Start Time: Fri Nov 15 22:08:48 2024 Epoch Time (ms): 1731708528500
[2024-11-15T22:08:49.271Z] variation: NoOptions
[2024-11-15T22:08:49.271Z] JVM_OPTIONS:
[2024-11-15T22:08:49.271Z] { \
[2024-11-15T22:08:49.271Z] echo ""; echo "TEST SETUP:"; \
[2024-11-15T22:08:49.271Z] echo "Nothing to be done for setup."; \
[2024-11-15T22:08:49.271Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317076864849/renaissance-movie-lens_0"; \
[2024-11-15T22:08:49.271Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317076864849/renaissance-movie-lens_0"; \
[2024-11-15T22:08:49.271Z] echo ""; echo "TESTING:"; \
[2024-11-15T22:08:49.271Z] "/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_17317076864849/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-15T22:08:49.271Z] 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_17317076864849/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-15T22:08:49.271Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-15T22:08:49.271Z] echo "Nothing to be done for teardown."; \
[2024-11-15T22:08:49.271Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17317076864849/TestTargetResult";
[2024-11-15T22:08:49.271Z]
[2024-11-15T22:08:49.271Z] TEST SETUP:
[2024-11-15T22:08:49.271Z] Nothing to be done for setup.
[2024-11-15T22:08:49.271Z]
[2024-11-15T22:08:49.271Z] TESTING:
[2024-11-15T22:08:53.729Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-15T22:08:55.311Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-15T22:08:58.770Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-15T22:08:58.771Z] Training: 60056, validation: 20285, test: 19854
[2024-11-15T22:08:58.771Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-15T22:08:58.771Z] GC before operation: completed in 53.062 ms, heap usage 190.883 MB -> 37.463 MB.
[2024-11-15T22:09:04.368Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:09:06.833Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:09:10.269Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:09:12.731Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:09:14.318Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:09:15.903Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:09:17.491Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:09:19.077Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:09:19.845Z] 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.
[2024-11-15T22:09:19.845Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:09:19.845Z] Movies recommended for you:
[2024-11-15T22:09:19.845Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:09:19.845Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:09:19.845Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20787.554 ms) ======
[2024-11-15T22:09:19.845Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-15T22:09:19.845Z] GC before operation: completed in 91.003 ms, heap usage 1.878 GB -> 55.763 MB.
[2024-11-15T22:09:22.312Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:09:25.744Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:09:28.220Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:09:30.687Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:09:32.275Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:09:33.860Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:09:35.450Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:09:37.039Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:09:37.806Z] 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.
[2024-11-15T22:09:37.806Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:09:37.806Z] Movies recommended for you:
[2024-11-15T22:09:37.806Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:09:37.806Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:09:37.806Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17869.565 ms) ======
[2024-11-15T22:09:37.806Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-15T22:09:37.806Z] GC before operation: completed in 73.995 ms, heap usage 234.568 MB -> 51.268 MB.
[2024-11-15T22:09:40.307Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:09:42.781Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:09:46.217Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:09:48.866Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:09:50.456Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:09:52.051Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:09:53.661Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:09:55.249Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:09:55.249Z] 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.
[2024-11-15T22:09:55.249Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:09:55.249Z] Movies recommended for you:
[2024-11-15T22:09:55.249Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:09:55.249Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:09:55.249Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17582.353 ms) ======
[2024-11-15T22:09:55.249Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-15T22:09:55.249Z] GC before operation: completed in 90.918 ms, heap usage 2.566 GB -> 56.648 MB.
[2024-11-15T22:09:57.727Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:10:01.160Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:10:03.625Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:10:06.089Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:10:07.679Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:10:09.261Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:10:10.846Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:10:12.447Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:10:12.447Z] 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.
[2024-11-15T22:10:12.447Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:10:12.447Z] Movies recommended for you:
[2024-11-15T22:10:12.447Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:10:12.447Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:10:12.447Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16927.030 ms) ======
[2024-11-15T22:10:12.447Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-15T22:10:12.447Z] GC before operation: completed in 74.953 ms, heap usage 984.186 MB -> 56.132 MB.
[2024-11-15T22:10:14.933Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:10:17.399Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:10:20.832Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:10:22.421Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:10:24.012Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:10:25.602Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:10:27.199Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:10:28.800Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:10:29.591Z] 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.
[2024-11-15T22:10:29.591Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:10:29.591Z] Movies recommended for you:
[2024-11-15T22:10:29.591Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:10:29.591Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:10:29.591Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16839.382 ms) ======
[2024-11-15T22:10:29.591Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-15T22:10:29.591Z] GC before operation: completed in 89.191 ms, heap usage 1.198 GB -> 56.618 MB.
[2024-11-15T22:10:32.058Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:10:34.872Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:10:37.336Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:10:39.805Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:10:41.407Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:10:43.004Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:10:44.597Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:10:46.188Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:10:46.188Z] 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.
[2024-11-15T22:10:46.188Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:10:46.188Z] Movies recommended for you:
[2024-11-15T22:10:46.188Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:10:46.188Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:10:46.188Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16852.806 ms) ======
[2024-11-15T22:10:46.188Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-15T22:10:46.188Z] GC before operation: completed in 94.000 ms, heap usage 1.029 GB -> 56.297 MB.
[2024-11-15T22:10:48.667Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:10:51.134Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:10:54.568Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:10:56.161Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:10:57.750Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:10:59.336Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:11:00.927Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:11:02.529Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:11:02.529Z] 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.
[2024-11-15T22:11:02.529Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:11:03.295Z] Movies recommended for you:
[2024-11-15T22:11:03.295Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:11:03.296Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:11:03.296Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16633.222 ms) ======
[2024-11-15T22:11:03.296Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-15T22:11:03.296Z] GC before operation: completed in 84.628 ms, heap usage 100.026 MB -> 55.693 MB.
[2024-11-15T22:11:05.757Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:11:08.257Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:11:10.732Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:11:13.205Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:11:14.791Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:11:16.392Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:11:17.980Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:11:19.572Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:11:19.572Z] 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.
[2024-11-15T22:11:19.572Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:11:19.572Z] Movies recommended for you:
[2024-11-15T22:11:19.572Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:11:19.572Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:11:19.572Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16695.242 ms) ======
[2024-11-15T22:11:19.572Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-15T22:11:19.572Z] GC before operation: completed in 89.115 ms, heap usage 100.233 MB -> 53.810 MB.
[2024-11-15T22:11:22.052Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:11:25.478Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:11:27.975Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:11:30.631Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:11:31.397Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:11:32.982Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:11:34.580Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:11:36.171Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:11:36.939Z] 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.
[2024-11-15T22:11:36.939Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:11:36.939Z] Movies recommended for you:
[2024-11-15T22:11:36.939Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:11:36.939Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:11:36.939Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16857.307 ms) ======
[2024-11-15T22:11:36.939Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-15T22:11:36.939Z] GC before operation: completed in 77.637 ms, heap usage 99.794 MB -> 56.389 MB.
[2024-11-15T22:11:39.425Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:11:41.895Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:11:45.331Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:11:46.912Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:11:48.498Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:11:50.086Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:11:51.685Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:11:53.285Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:11:54.054Z] 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.
[2024-11-15T22:11:54.054Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:11:54.054Z] Movies recommended for you:
[2024-11-15T22:11:54.054Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:11:54.054Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:11:54.054Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16989.515 ms) ======
[2024-11-15T22:11:54.054Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-15T22:11:54.054Z] GC before operation: completed in 82.705 ms, heap usage 1.758 GB -> 57.354 MB.
[2024-11-15T22:11:56.514Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:11:58.975Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:12:01.437Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:12:03.909Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:12:05.496Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:12:07.095Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:12:08.690Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:12:10.288Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:12:10.288Z] 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.
[2024-11-15T22:12:10.288Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:12:11.058Z] Movies recommended for you:
[2024-11-15T22:12:11.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:12:11.058Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:12:11.058Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16784.298 ms) ======
[2024-11-15T22:12:11.058Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-15T22:12:11.058Z] GC before operation: completed in 76.931 ms, heap usage 180.202 MB -> 52.179 MB.
[2024-11-15T22:12:13.521Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:12:15.990Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:12:18.464Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:12:20.926Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:12:22.517Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:12:24.106Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:12:25.864Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:12:26.637Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:12:27.406Z] 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.
[2024-11-15T22:12:27.406Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:12:27.406Z] Movies recommended for you:
[2024-11-15T22:12:27.406Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:12:27.406Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:12:27.406Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16640.684 ms) ======
[2024-11-15T22:12:27.406Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-15T22:12:27.406Z] GC before operation: completed in 82.973 ms, heap usage 1.064 GB -> 56.585 MB.
[2024-11-15T22:12:29.893Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:12:32.354Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:12:35.782Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:12:38.263Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:12:39.032Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:12:40.637Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:12:42.226Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:12:43.814Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:12:44.580Z] 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.
[2024-11-15T22:12:44.580Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:12:44.580Z] Movies recommended for you:
[2024-11-15T22:12:44.580Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:12:44.580Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:12:44.580Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16941.804 ms) ======
[2024-11-15T22:12:44.580Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-15T22:12:44.580Z] GC before operation: completed in 84.739 ms, heap usage 150.284 MB -> 52.495 MB.
[2024-11-15T22:12:47.039Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:12:49.507Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:12:52.923Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:12:54.522Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:12:56.114Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:12:57.713Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:12:59.303Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:13:00.911Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:13:00.911Z] 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.
[2024-11-15T22:13:00.911Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:13:00.911Z] Movies recommended for you:
[2024-11-15T22:13:00.911Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:13:00.911Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:13:00.911Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16753.832 ms) ======
[2024-11-15T22:13:00.911Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-15T22:13:01.678Z] GC before operation: completed in 83.688 ms, heap usage 2.776 GB -> 57.334 MB.
[2024-11-15T22:13:04.162Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:13:06.629Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:13:09.096Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:13:11.574Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:13:13.165Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:13:14.753Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:13:16.335Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:13:17.106Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:13:17.880Z] 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.
[2024-11-15T22:13:17.880Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:13:17.880Z] Movies recommended for you:
[2024-11-15T22:13:17.880Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:13:17.880Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:13:17.880Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16504.138 ms) ======
[2024-11-15T22:13:17.880Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-15T22:13:17.880Z] GC before operation: completed in 82.138 ms, heap usage 120.806 MB -> 56.908 MB.
[2024-11-15T22:13:20.347Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:13:23.174Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:13:25.636Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:13:28.096Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:13:29.695Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:13:31.290Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:13:32.971Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:13:33.740Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:13:34.504Z] 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.
[2024-11-15T22:13:34.504Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:13:34.504Z] Movies recommended for you:
[2024-11-15T22:13:34.504Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:13:34.504Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:13:34.504Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16473.954 ms) ======
[2024-11-15T22:13:34.504Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-15T22:13:34.504Z] GC before operation: completed in 87.234 ms, heap usage 1.758 GB -> 57.420 MB.
[2024-11-15T22:13:36.967Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:13:39.428Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:13:41.885Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:13:44.351Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:13:45.944Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:13:47.527Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:13:49.115Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:13:50.699Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:13:50.699Z] 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.
[2024-11-15T22:13:50.699Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:13:50.699Z] Movies recommended for you:
[2024-11-15T22:13:50.699Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:13:50.699Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:13:50.699Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16524.860 ms) ======
[2024-11-15T22:13:50.699Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-15T22:13:51.464Z] GC before operation: completed in 93.061 ms, heap usage 478.275 MB -> 52.605 MB.
[2024-11-15T22:13:53.931Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:13:56.404Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:13:58.877Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:14:01.336Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:14:02.920Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:14:04.512Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:14:06.097Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:14:07.689Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:14:07.689Z] 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.
[2024-11-15T22:14:07.689Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:14:07.689Z] Movies recommended for you:
[2024-11-15T22:14:07.689Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:14:07.689Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:14:07.689Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16622.139 ms) ======
[2024-11-15T22:14:07.689Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-15T22:14:07.689Z] GC before operation: completed in 81.703 ms, heap usage 980.142 MB -> 56.495 MB.
[2024-11-15T22:14:10.162Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:14:12.620Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:14:15.090Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:14:17.575Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:14:19.163Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:14:20.764Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:14:22.347Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:14:23.930Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:14:23.930Z] 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.
[2024-11-15T22:14:23.930Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:14:24.697Z] Movies recommended for you:
[2024-11-15T22:14:24.697Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:14:24.697Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:14:24.697Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16544.805 ms) ======
[2024-11-15T22:14:24.697Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-15T22:14:24.697Z] GC before operation: completed in 84.646 ms, heap usage 807.005 MB -> 56.257 MB.
[2024-11-15T22:14:27.209Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-15T22:14:29.691Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-15T22:14:32.153Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-15T22:14:34.676Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-15T22:14:36.270Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-15T22:14:37.870Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-15T22:14:39.454Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-15T22:14:41.042Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-15T22:14:41.042Z] 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.
[2024-11-15T22:14:41.042Z] The best model improves the baseline by 14.43%.
[2024-11-15T22:14:41.042Z] Movies recommended for you:
[2024-11-15T22:14:41.042Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-15T22:14:41.042Z] There is no way to check that no silent failure occurred.
[2024-11-15T22:14:41.042Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16725.091 ms) ======
[2024-11-15T22:14:41.808Z] -----------------------------------
[2024-11-15T22:14:41.808Z] renaissance-movie-lens_0_PASSED
[2024-11-15T22:14:41.808Z] -----------------------------------
[2024-11-15T22:14:41.808Z]
[2024-11-15T22:14:41.808Z] TEST TEARDOWN:
[2024-11-15T22:14:41.808Z] Nothing to be done for teardown.
[2024-11-15T22:14:41.808Z] renaissance-movie-lens_0 Finish Time: Fri Nov 15 22:14:41 2024 Epoch Time (ms): 1731708881376