renaissance-movie-lens_0
[2025-01-22T00:27:51.678Z] Running test renaissance-movie-lens_0 ...
[2025-01-22T00:27:51.678Z] ===============================================
[2025-01-22T00:27:51.678Z] renaissance-movie-lens_0 Start Time: Tue Jan 21 18:27:51 2025 Epoch Time (ms): 1737505671116
[2025-01-22T00:27:51.678Z] variation: NoOptions
[2025-01-22T00:27:51.678Z] JVM_OPTIONS:
[2025-01-22T00:27:51.678Z] { \
[2025-01-22T00:27:51.678Z] echo ""; echo "TEST SETUP:"; \
[2025-01-22T00:27:51.678Z] echo "Nothing to be done for setup."; \
[2025-01-22T00:27:51.678Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17375050781305/renaissance-movie-lens_0"; \
[2025-01-22T00:27:51.678Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17375050781305/renaissance-movie-lens_0"; \
[2025-01-22T00:27:51.678Z] echo ""; echo "TESTING:"; \
[2025-01-22T00:27:51.678Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17375050781305/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-22T00:27:51.679Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17375050781305/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-22T00:27:51.679Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-22T00:27:51.679Z] echo "Nothing to be done for teardown."; \
[2025-01-22T00:27:51.679Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_17375050781305/TestTargetResult";
[2025-01-22T00:27:51.679Z]
[2025-01-22T00:27:51.679Z] TEST SETUP:
[2025-01-22T00:27:51.679Z] Nothing to be done for setup.
[2025-01-22T00:27:51.679Z]
[2025-01-22T00:27:51.679Z] TESTING:
[2025-01-22T00:27:53.845Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-22T00:27:56.015Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-01-22T00:27:59.045Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-22T00:27:59.045Z] Training: 60056, validation: 20285, test: 19854
[2025-01-22T00:27:59.045Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-22T00:27:59.045Z] GC before operation: completed in 112.774 ms, heap usage 143.326 MB -> 37.842 MB.
[2025-01-22T00:28:06.649Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:28:09.716Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:28:12.776Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:28:15.062Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:28:17.269Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:28:18.678Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:28:20.102Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:28:22.333Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:28:22.333Z] 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.
[2025-01-22T00:28:22.333Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:28:22.333Z] Movies recommended for you:
[2025-01-22T00:28:22.333Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:28:22.333Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:28:22.333Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23120.466 ms) ======
[2025-01-22T00:28:22.333Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-22T00:28:22.333Z] GC before operation: completed in 103.331 ms, heap usage 515.089 MB -> 54.608 MB.
[2025-01-22T00:28:25.373Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:28:28.400Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:28:31.437Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:28:33.632Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:28:35.039Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:28:36.437Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:28:37.836Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:28:40.016Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:28:40.016Z] 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.
[2025-01-22T00:28:40.016Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:28:40.016Z] Movies recommended for you:
[2025-01-22T00:28:40.016Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:28:40.016Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:28:40.016Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17527.642 ms) ======
[2025-01-22T00:28:40.016Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-22T00:28:40.016Z] GC before operation: completed in 69.971 ms, heap usage 362.156 MB -> 54.848 MB.
[2025-01-22T00:28:43.089Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:28:45.707Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:28:47.919Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:28:50.129Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:28:51.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:28:52.968Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:28:54.379Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:28:55.795Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:28:56.466Z] 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.
[2025-01-22T00:28:56.466Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:28:56.466Z] Movies recommended for you:
[2025-01-22T00:28:56.466Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:28:56.466Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:28:56.466Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16270.269 ms) ======
[2025-01-22T00:28:56.466Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-22T00:28:56.466Z] GC before operation: completed in 75.482 ms, heap usage 248.713 MB -> 51.971 MB.
[2025-01-22T00:28:58.665Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:29:01.748Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:29:03.145Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:29:05.329Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:29:06.733Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:29:08.123Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:29:09.528Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:29:10.969Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:29:10.969Z] 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.
[2025-01-22T00:29:10.969Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:29:10.969Z] Movies recommended for you:
[2025-01-22T00:29:10.969Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:29:10.969Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:29:10.969Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14445.922 ms) ======
[2025-01-22T00:29:10.969Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-22T00:29:10.969Z] GC before operation: completed in 52.949 ms, heap usage 276.596 MB -> 52.349 MB.
[2025-01-22T00:29:13.158Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:29:15.352Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:29:17.527Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:29:19.705Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:29:21.109Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:29:22.509Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:29:23.903Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:29:24.595Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:29:25.283Z] 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.
[2025-01-22T00:29:25.283Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:29:25.283Z] Movies recommended for you:
[2025-01-22T00:29:25.283Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:29:25.283Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:29:25.283Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (14214.532 ms) ======
[2025-01-22T00:29:25.283Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-22T00:29:25.283Z] GC before operation: completed in 76.307 ms, heap usage 536.273 MB -> 55.985 MB.
[2025-01-22T00:29:27.491Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:29:29.668Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:29:31.856Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:29:34.036Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:29:35.479Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:29:36.897Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:29:38.294Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:29:39.695Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:29:39.695Z] 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.
[2025-01-22T00:29:39.695Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:29:39.695Z] Movies recommended for you:
[2025-01-22T00:29:39.695Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:29:39.695Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:29:39.695Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14485.773 ms) ======
[2025-01-22T00:29:39.695Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-22T00:29:39.695Z] GC before operation: completed in 50.183 ms, heap usage 410.510 MB -> 52.650 MB.
[2025-01-22T00:29:42.750Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:29:44.143Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:29:46.341Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:29:48.555Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:29:49.962Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:29:51.364Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:29:52.785Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:29:53.467Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:29:54.141Z] 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.
[2025-01-22T00:29:54.141Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:29:54.141Z] Movies recommended for you:
[2025-01-22T00:29:54.141Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:29:54.141Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:29:54.141Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14376.167 ms) ======
[2025-01-22T00:29:54.141Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-22T00:29:54.141Z] GC before operation: completed in 57.155 ms, heap usage 411.062 MB -> 52.757 MB.
[2025-01-22T00:29:56.757Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:29:58.950Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:30:01.201Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:30:02.695Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:30:04.103Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:30:05.515Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:30:06.927Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:30:08.338Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:30:08.338Z] 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.
[2025-01-22T00:30:08.338Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:30:08.338Z] Movies recommended for you:
[2025-01-22T00:30:08.338Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:30:08.338Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:30:08.338Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14147.298 ms) ======
[2025-01-22T00:30:08.338Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-22T00:30:08.338Z] GC before operation: completed in 67.680 ms, heap usage 617.031 MB -> 56.488 MB.
[2025-01-22T00:30:10.541Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:30:12.718Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:30:14.903Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:30:17.097Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:30:18.491Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:30:19.182Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:30:20.583Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:30:21.995Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:30:22.663Z] 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.
[2025-01-22T00:30:22.663Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:30:22.663Z] Movies recommended for you:
[2025-01-22T00:30:22.663Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:30:22.663Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:30:22.663Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14055.336 ms) ======
[2025-01-22T00:30:22.663Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-22T00:30:22.663Z] GC before operation: completed in 52.483 ms, heap usage 107.144 MB -> 55.546 MB.
[2025-01-22T00:30:24.845Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:30:27.023Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:30:29.218Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:30:31.402Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:30:32.081Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:30:33.474Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:30:34.893Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:30:36.310Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:30:36.310Z] 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.
[2025-01-22T00:30:36.310Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:30:36.988Z] Movies recommended for you:
[2025-01-22T00:30:36.989Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:30:36.989Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:30:36.989Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14125.701 ms) ======
[2025-01-22T00:30:36.989Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-22T00:30:36.989Z] GC before operation: completed in 80.629 ms, heap usage 417.334 MB -> 53.040 MB.
[2025-01-22T00:30:39.181Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:30:41.368Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:30:43.578Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:30:45.771Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:30:46.443Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:30:47.850Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:30:49.249Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:30:50.662Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:30:50.662Z] 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.
[2025-01-22T00:30:50.662Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:30:51.332Z] Movies recommended for you:
[2025-01-22T00:30:51.332Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:30:51.332Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:30:51.332Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14232.135 ms) ======
[2025-01-22T00:30:51.332Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-22T00:30:51.332Z] GC before operation: completed in 83.951 ms, heap usage 355.163 MB -> 52.699 MB.
[2025-01-22T00:30:53.532Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:30:55.718Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:30:57.908Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:30:59.301Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:31:00.701Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:31:02.126Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:31:03.533Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:31:04.965Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:31:04.965Z] 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.
[2025-01-22T00:31:04.965Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:31:04.965Z] Movies recommended for you:
[2025-01-22T00:31:04.965Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:31:04.965Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:31:04.965Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14017.425 ms) ======
[2025-01-22T00:31:04.965Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-22T00:31:04.965Z] GC before operation: completed in 53.283 ms, heap usage 624.108 MB -> 56.328 MB.
[2025-01-22T00:31:07.170Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:31:09.374Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:31:12.110Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:31:13.511Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:31:14.935Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:31:16.335Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:31:17.724Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:31:19.123Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:31:19.123Z] 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.
[2025-01-22T00:31:19.123Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:31:19.123Z] Movies recommended for you:
[2025-01-22T00:31:19.123Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:31:19.123Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:31:19.123Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14242.854 ms) ======
[2025-01-22T00:31:19.123Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-22T00:31:19.793Z] GC before operation: completed in 62.631 ms, heap usage 259.545 MB -> 53.022 MB.
[2025-01-22T00:31:21.969Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:31:24.186Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:31:26.394Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:31:27.788Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:31:29.187Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:31:30.593Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:31:31.988Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:31:33.381Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:31:33.381Z] 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.
[2025-01-22T00:31:33.381Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:31:33.381Z] Movies recommended for you:
[2025-01-22T00:31:33.381Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:31:33.381Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:31:33.381Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13871.775 ms) ======
[2025-01-22T00:31:33.381Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-22T00:31:33.381Z] GC before operation: completed in 68.478 ms, heap usage 111.420 MB -> 55.883 MB.
[2025-01-22T00:31:35.573Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:31:37.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:31:39.945Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:31:41.344Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:31:42.749Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:31:44.153Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:31:45.558Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:31:46.967Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:31:46.967Z] 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.
[2025-01-22T00:31:46.967Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:31:46.967Z] Movies recommended for you:
[2025-01-22T00:31:46.967Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:31:46.967Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:31:46.967Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13852.250 ms) ======
[2025-01-22T00:31:46.967Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-22T00:31:47.643Z] GC before operation: completed in 63.048 ms, heap usage 281.078 MB -> 52.941 MB.
[2025-01-22T00:31:49.844Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:31:51.240Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:31:53.443Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:31:55.645Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:31:57.055Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:31:57.739Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:31:59.147Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:32:00.543Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:32:00.543Z] 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.
[2025-01-22T00:32:00.543Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:32:01.282Z] Movies recommended for you:
[2025-01-22T00:32:01.282Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:32:01.282Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:32:01.282Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13623.043 ms) ======
[2025-01-22T00:32:01.282Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-22T00:32:01.282Z] GC before operation: completed in 66.953 ms, heap usage 151.292 MB -> 52.939 MB.
[2025-01-22T00:32:02.707Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:32:05.742Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:32:07.178Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:32:09.366Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:32:10.759Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:32:12.167Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:32:13.584Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:32:14.267Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:32:14.939Z] 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.
[2025-01-22T00:32:14.939Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:32:14.939Z] Movies recommended for you:
[2025-01-22T00:32:14.939Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:32:14.939Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:32:14.939Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13787.459 ms) ======
[2025-01-22T00:32:14.939Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-22T00:32:14.939Z] GC before operation: completed in 62.562 ms, heap usage 80.582 MB -> 57.075 MB.
[2025-01-22T00:32:17.126Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:32:19.305Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:32:21.506Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:32:23.708Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:32:25.108Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:32:26.511Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:32:27.387Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:32:28.784Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:32:29.456Z] 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.
[2025-01-22T00:32:29.456Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:32:29.456Z] Movies recommended for you:
[2025-01-22T00:32:29.456Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:32:29.456Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:32:29.456Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14417.937 ms) ======
[2025-01-22T00:32:29.456Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-22T00:32:29.456Z] GC before operation: completed in 76.422 ms, heap usage 274.910 MB -> 52.896 MB.
[2025-01-22T00:32:31.652Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:32:33.827Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:32:36.010Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:32:37.417Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:32:38.806Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:32:40.199Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:32:41.600Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:32:43.016Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:32:43.016Z] 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.
[2025-01-22T00:32:43.016Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:32:43.016Z] Movies recommended for you:
[2025-01-22T00:32:43.016Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:32:43.016Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:32:43.016Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13741.515 ms) ======
[2025-01-22T00:32:43.016Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-22T00:32:43.016Z] GC before operation: completed in 52.334 ms, heap usage 384.600 MB -> 53.319 MB.
[2025-01-22T00:32:45.201Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T00:32:48.269Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T00:32:49.678Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T00:32:51.868Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T00:32:53.271Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T00:32:54.662Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T00:32:56.062Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T00:32:57.468Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T00:32:57.469Z] 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.
[2025-01-22T00:32:57.469Z] The best model improves the baseline by 14.43%.
[2025-01-22T00:32:57.469Z] Movies recommended for you:
[2025-01-22T00:32:57.469Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T00:32:57.469Z] There is no way to check that no silent failure occurred.
[2025-01-22T00:32:57.469Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14444.588 ms) ======
[2025-01-22T00:32:58.866Z] -----------------------------------
[2025-01-22T00:32:58.866Z] renaissance-movie-lens_0_PASSED
[2025-01-22T00:32:58.866Z] -----------------------------------
[2025-01-22T00:32:58.866Z]
[2025-01-22T00:32:58.866Z] TEST TEARDOWN:
[2025-01-22T00:32:58.866Z] Nothing to be done for teardown.
[2025-01-22T00:32:58.866Z] renaissance-movie-lens_0 Finish Time: Tue Jan 21 18:32:58 2025 Epoch Time (ms): 1737505978444