renaissance-movie-lens_0
[2024-11-16T03:18:49.083Z] Running test renaissance-movie-lens_0 ...
[2024-11-16T03:18:49.083Z] ===============================================
[2024-11-16T03:18:49.083Z] renaissance-movie-lens_0 Start Time: Sat Nov 16 03:18:48 2024 Epoch Time (ms): 1731727128313
[2024-11-16T03:18:49.083Z] variation: NoOptions
[2024-11-16T03:18:49.083Z] JVM_OPTIONS:
[2024-11-16T03:18:49.083Z] { \
[2024-11-16T03:18:49.083Z] echo ""; echo "TEST SETUP:"; \
[2024-11-16T03:18:49.083Z] echo "Nothing to be done for setup."; \
[2024-11-16T03:18:49.083Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17317261082901/renaissance-movie-lens_0"; \
[2024-11-16T03:18:49.083Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17317261082901/renaissance-movie-lens_0"; \
[2024-11-16T03:18:49.083Z] echo ""; echo "TESTING:"; \
[2024-11-16T03:18:49.083Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_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_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17317261082901/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-16T03:18:49.083Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17317261082901/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-16T03:18:49.083Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-16T03:18:49.083Z] echo "Nothing to be done for teardown."; \
[2024-11-16T03:18:49.083Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17317261082901/TestTargetResult";
[2024-11-16T03:18:49.083Z]
[2024-11-16T03:18:49.083Z] TEST SETUP:
[2024-11-16T03:18:49.083Z] Nothing to be done for setup.
[2024-11-16T03:18:49.083Z]
[2024-11-16T03:18:49.083Z] TESTING:
[2024-11-16T03:18:52.015Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-16T03:18:53.346Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-11-16T03:18:56.206Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-16T03:18:56.859Z] Training: 60056, validation: 20285, test: 19854
[2024-11-16T03:18:56.859Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-16T03:18:56.859Z] GC before operation: completed in 85.279 ms, heap usage 86.450 MB -> 37.006 MB.
[2024-11-16T03:19:02.755Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:19:07.701Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:19:11.509Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:19:14.374Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:19:16.466Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:19:18.602Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:19:20.728Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:19:22.799Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:19:22.799Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:19:23.451Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:19:23.451Z] Movies recommended for you:
[2024-11-16T03:19:23.451Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:19:23.451Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:19:23.451Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26424.332 ms) ======
[2024-11-16T03:19:23.451Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-16T03:19:23.451Z] GC before operation: completed in 148.750 ms, heap usage 244.369 MB -> 54.549 MB.
[2024-11-16T03:19:26.738Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:19:29.684Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:19:32.587Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:19:35.442Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:19:36.784Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:19:38.859Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:19:40.940Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:19:42.283Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:19:42.911Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:19:42.911Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:19:42.911Z] Movies recommended for you:
[2024-11-16T03:19:42.911Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:19:42.911Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:19:42.911Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (19605.436 ms) ======
[2024-11-16T03:19:42.911Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-16T03:19:42.911Z] GC before operation: completed in 110.729 ms, heap usage 124.085 MB -> 48.898 MB.
[2024-11-16T03:19:45.819Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:19:49.656Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:19:52.542Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:19:55.464Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:19:56.831Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:19:58.903Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:20:00.967Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:20:02.288Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:20:02.936Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:20:02.936Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:20:02.936Z] Movies recommended for you:
[2024-11-16T03:20:02.936Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:20:02.936Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:20:02.936Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (19695.991 ms) ======
[2024-11-16T03:20:02.936Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-16T03:20:02.936Z] GC before operation: completed in 92.152 ms, heap usage 316.424 MB -> 49.403 MB.
[2024-11-16T03:20:05.845Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:20:08.718Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:20:11.710Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:20:14.177Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:20:16.305Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:20:17.626Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:20:18.932Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:20:20.954Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:20:20.954Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:20:20.954Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:20:20.954Z] Movies recommended for you:
[2024-11-16T03:20:20.954Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:20:20.954Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:20:20.954Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (18035.019 ms) ======
[2024-11-16T03:20:20.954Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-16T03:20:20.954Z] GC before operation: completed in 87.377 ms, heap usage 111.764 MB -> 49.440 MB.
[2024-11-16T03:20:23.835Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:20:26.680Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:20:29.609Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:20:32.496Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:20:33.801Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:20:35.862Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:20:37.201Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:20:39.660Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:20:39.660Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:20:39.660Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:20:39.660Z] Movies recommended for you:
[2024-11-16T03:20:39.660Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:20:39.660Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:20:39.660Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (18265.358 ms) ======
[2024-11-16T03:20:39.660Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-16T03:20:39.660Z] GC before operation: completed in 125.657 ms, heap usage 198.122 MB -> 49.821 MB.
[2024-11-16T03:20:42.525Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:20:44.587Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:20:47.492Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:20:49.548Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:20:51.592Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:20:52.896Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:20:54.947Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:20:56.260Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:20:56.260Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:20:56.260Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:20:56.889Z] Movies recommended for you:
[2024-11-16T03:20:56.889Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:20:56.889Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:20:56.889Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (17081.523 ms) ======
[2024-11-16T03:20:56.889Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-16T03:20:56.889Z] GC before operation: completed in 90.727 ms, heap usage 277.013 MB -> 49.921 MB.
[2024-11-16T03:20:58.940Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:21:01.447Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:21:04.366Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:21:06.468Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:21:08.538Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:21:09.837Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:21:11.943Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:21:13.281Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:21:13.281Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:21:13.281Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:21:13.281Z] Movies recommended for you:
[2024-11-16T03:21:13.281Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:21:13.281Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:21:13.281Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16854.003 ms) ======
[2024-11-16T03:21:13.281Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-16T03:21:13.909Z] GC before operation: completed in 86.404 ms, heap usage 179.907 MB -> 49.979 MB.
[2024-11-16T03:21:15.933Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:21:17.980Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:21:20.894Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:21:23.761Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:21:25.099Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:21:26.397Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:21:28.482Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:21:29.828Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:21:29.828Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:21:29.828Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:21:30.465Z] Movies recommended for you:
[2024-11-16T03:21:30.465Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:21:30.465Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:21:30.465Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16573.594 ms) ======
[2024-11-16T03:21:30.465Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-16T03:21:30.465Z] GC before operation: completed in 100.284 ms, heap usage 222.785 MB -> 50.308 MB.
[2024-11-16T03:21:32.517Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:21:35.398Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:21:38.319Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:21:40.380Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:21:42.448Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:21:43.761Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:21:45.098Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:21:47.192Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:21:47.192Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:21:47.192Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:21:47.192Z] Movies recommended for you:
[2024-11-16T03:21:47.192Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:21:47.192Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:21:47.192Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17022.965 ms) ======
[2024-11-16T03:21:47.192Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-16T03:21:47.192Z] GC before operation: completed in 109.100 ms, heap usage 276.596 MB -> 50.179 MB.
[2024-11-16T03:21:50.062Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:21:52.826Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:21:55.730Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:21:58.606Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:21:59.902Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:22:01.265Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:22:03.331Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:22:04.649Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:22:04.649Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:22:05.276Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:22:05.276Z] Movies recommended for you:
[2024-11-16T03:22:05.276Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:22:05.276Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:22:05.276Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17644.882 ms) ======
[2024-11-16T03:22:05.276Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-16T03:22:05.277Z] GC before operation: completed in 95.949 ms, heap usage 276.900 MB -> 50.335 MB.
[2024-11-16T03:22:07.413Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:22:10.331Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:22:13.231Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:22:16.165Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:22:17.491Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:22:18.811Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:22:20.900Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:22:22.251Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:22:22.879Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:22:22.879Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:22:22.879Z] Movies recommended for you:
[2024-11-16T03:22:22.879Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:22:22.879Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:22:22.879Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17613.967 ms) ======
[2024-11-16T03:22:22.879Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-16T03:22:22.879Z] GC before operation: completed in 112.359 ms, heap usage 85.069 MB -> 49.835 MB.
[2024-11-16T03:22:24.951Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:22:27.855Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:22:30.749Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:22:32.801Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:22:34.526Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:22:35.265Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:22:37.310Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:22:39.053Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:22:39.053Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:22:39.053Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:22:39.053Z] Movies recommended for you:
[2024-11-16T03:22:39.053Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:22:39.053Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:22:39.053Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16231.971 ms) ======
[2024-11-16T03:22:39.053Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-16T03:22:39.053Z] GC before operation: completed in 98.688 ms, heap usage 340.655 MB -> 53.402 MB.
[2024-11-16T03:22:41.905Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:22:43.953Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:22:46.837Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:22:48.909Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:22:50.980Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:22:52.292Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:22:53.600Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:22:54.930Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:22:55.554Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:22:55.554Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:22:55.554Z] Movies recommended for you:
[2024-11-16T03:22:55.554Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:22:55.554Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:22:55.554Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16189.257 ms) ======
[2024-11-16T03:22:55.554Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-16T03:22:55.554Z] GC before operation: completed in 93.775 ms, heap usage 387.041 MB -> 53.598 MB.
[2024-11-16T03:22:57.624Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:23:00.497Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:23:02.542Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:23:05.410Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:23:06.732Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:23:08.080Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:23:09.412Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:23:10.756Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:23:10.756Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:23:10.756Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:23:11.387Z] Movies recommended for you:
[2024-11-16T03:23:11.387Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:23:11.387Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:23:11.387Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (15587.218 ms) ======
[2024-11-16T03:23:11.387Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-16T03:23:11.387Z] GC before operation: completed in 101.991 ms, heap usage 179.183 MB -> 50.005 MB.
[2024-11-16T03:23:13.453Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:23:16.329Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:23:18.374Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:23:20.429Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:23:21.731Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:23:23.066Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:23:25.501Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:23:26.161Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:23:26.161Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:23:26.803Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:23:26.803Z] Movies recommended for you:
[2024-11-16T03:23:26.803Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:23:26.803Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:23:26.803Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15394.530 ms) ======
[2024-11-16T03:23:26.803Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-16T03:23:26.803Z] GC before operation: completed in 100.357 ms, heap usage 109.802 MB -> 50.120 MB.
[2024-11-16T03:23:28.868Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:23:31.850Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:23:34.719Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:23:36.753Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:23:38.838Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:23:40.187Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:23:41.518Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:23:42.862Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:23:42.863Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:23:42.863Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:23:43.498Z] Movies recommended for you:
[2024-11-16T03:23:43.498Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:23:43.498Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:23:43.498Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16595.659 ms) ======
[2024-11-16T03:23:43.498Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-16T03:23:43.498Z] GC before operation: completed in 116.814 ms, heap usage 202.472 MB -> 50.247 MB.
[2024-11-16T03:23:45.580Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:23:48.476Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:23:51.354Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:23:53.414Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:23:54.729Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:23:56.076Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:23:57.400Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:23:59.456Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:23:59.457Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:23:59.457Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:23:59.457Z] Movies recommended for you:
[2024-11-16T03:23:59.457Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:23:59.457Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:23:59.457Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16056.488 ms) ======
[2024-11-16T03:23:59.457Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-16T03:23:59.457Z] GC before operation: completed in 95.880 ms, heap usage 69.838 MB -> 49.987 MB.
[2024-11-16T03:24:01.506Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:24:04.398Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:24:07.291Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:24:09.390Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:24:10.719Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:24:12.264Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:24:13.569Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:24:14.909Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:24:15.534Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:24:15.534Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:24:15.534Z] Movies recommended for you:
[2024-11-16T03:24:15.534Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:24:15.535Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:24:15.535Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16033.479 ms) ======
[2024-11-16T03:24:15.535Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-16T03:24:15.535Z] GC before operation: completed in 97.602 ms, heap usage 126.889 MB -> 50.129 MB.
[2024-11-16T03:24:18.386Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:24:20.451Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:24:23.287Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:24:25.349Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:24:26.670Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:24:27.984Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:24:30.040Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:24:31.380Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:24:31.380Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:24:31.380Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:24:31.380Z] Movies recommended for you:
[2024-11-16T03:24:31.380Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:24:31.380Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:24:31.380Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (15821.776 ms) ======
[2024-11-16T03:24:31.380Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-16T03:24:31.380Z] GC before operation: completed in 134.524 ms, heap usage 227.639 MB -> 50.393 MB.
[2024-11-16T03:24:34.275Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-16T03:24:36.342Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-16T03:24:39.226Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-16T03:24:41.301Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-16T03:24:42.620Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-16T03:24:43.946Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-16T03:24:45.257Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-16T03:24:47.290Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-16T03:24:47.290Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-11-16T03:24:47.290Z] The best model improves the baseline by 14.34%.
[2024-11-16T03:24:47.290Z] Movies recommended for you:
[2024-11-16T03:24:47.290Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-16T03:24:47.290Z] There is no way to check that no silent failure occurred.
[2024-11-16T03:24:47.290Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (15767.131 ms) ======
[2024-11-16T03:24:47.912Z] -----------------------------------
[2024-11-16T03:24:47.912Z] renaissance-movie-lens_0_PASSED
[2024-11-16T03:24:47.912Z] -----------------------------------
[2024-11-16T03:24:47.912Z]
[2024-11-16T03:24:47.912Z] TEST TEARDOWN:
[2024-11-16T03:24:47.912Z] Nothing to be done for teardown.
[2024-11-16T03:24:47.912Z] renaissance-movie-lens_0 Finish Time: Sat Nov 16 03:24:47 2024 Epoch Time (ms): 1731727487460