renaissance-movie-lens_0
[2024-08-01T04:24:11.134Z] Running test renaissance-movie-lens_0 ...
[2024-08-01T04:24:11.134Z] ===============================================
[2024-08-01T04:24:11.134Z] renaissance-movie-lens_0 Start Time: Thu Aug 1 04:24:10 2024 Epoch Time (ms): 1722486250826
[2024-08-01T04:24:11.134Z] variation: NoOptions
[2024-08-01T04:24:11.134Z] JVM_OPTIONS:
[2024-08-01T04:24:11.134Z] { \
[2024-08-01T04:24:11.134Z] echo ""; echo "TEST SETUP:"; \
[2024-08-01T04:24:11.134Z] echo "Nothing to be done for setup."; \
[2024-08-01T04:24:11.134Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17224862502114/renaissance-movie-lens_0"; \
[2024-08-01T04:24:11.134Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17224862502114/renaissance-movie-lens_0"; \
[2024-08-01T04:24:11.134Z] echo ""; echo "TESTING:"; \
[2024-08-01T04:24:11.134Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17224862502114/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-01T04:24:11.134Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17224862502114/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-01T04:24:11.134Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-01T04:24:11.134Z] echo "Nothing to be done for teardown."; \
[2024-08-01T04:24:11.134Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17224862502114/TestTargetResult";
[2024-08-01T04:24:11.134Z]
[2024-08-01T04:24:11.134Z] TEST SETUP:
[2024-08-01T04:24:11.134Z] Nothing to be done for setup.
[2024-08-01T04:24:11.134Z]
[2024-08-01T04:24:11.134Z] TESTING:
[2024-08-01T04:24:15.903Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-01T04:24:18.791Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-08-01T04:24:24.620Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-01T04:24:25.234Z] Training: 60056, validation: 20285, test: 19854
[2024-08-01T04:24:25.234Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-01T04:24:25.234Z] GC before operation: completed in 186.861 ms, heap usage 78.681 MB -> 36.481 MB.
[2024-08-01T04:24:35.523Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:24:41.487Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:24:47.545Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:24:52.464Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:24:54.478Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:24:58.360Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:25:00.388Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:25:03.295Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:25:03.295Z] 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.
[2024-08-01T04:25:03.927Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:25:03.927Z] Movies recommended for you:
[2024-08-01T04:25:03.927Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:25:03.927Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:25:03.927Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (38432.976 ms) ======
[2024-08-01T04:25:03.927Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-01T04:25:03.927Z] GC before operation: completed in 199.257 ms, heap usage 118.291 MB -> 50.263 MB.
[2024-08-01T04:25:08.672Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:25:12.437Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:25:17.212Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:25:20.956Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:25:23.909Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:25:25.972Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:25:27.996Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:25:30.058Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:25:30.695Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-08-01T04:25:30.695Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:25:30.695Z] Movies recommended for you:
[2024-08-01T04:25:30.695Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:25:30.695Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:25:30.695Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (26835.905 ms) ======
[2024-08-01T04:25:30.695Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-01T04:25:31.395Z] GC before operation: completed in 322.950 ms, heap usage 120.981 MB -> 48.405 MB.
[2024-08-01T04:25:35.125Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:25:39.837Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:25:44.674Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:25:48.367Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:25:50.463Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:25:52.602Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:25:55.479Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:25:58.309Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:25:58.310Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-08-01T04:25:58.310Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:25:58.310Z] Movies recommended for you:
[2024-08-01T04:25:58.310Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:25:58.310Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:25:58.310Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (27286.218 ms) ======
[2024-08-01T04:25:58.310Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-01T04:25:58.945Z] GC before operation: completed in 436.577 ms, heap usage 243.072 MB -> 48.766 MB.
[2024-08-01T04:26:04.953Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:26:09.808Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:26:15.685Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:26:18.527Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:26:20.538Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:26:21.801Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:26:24.607Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:26:26.668Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:26:27.303Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-08-01T04:26:27.303Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:26:27.303Z] Movies recommended for you:
[2024-08-01T04:26:27.303Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:26:27.303Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:26:27.303Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (28618.747 ms) ======
[2024-08-01T04:26:27.303Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-01T04:26:27.976Z] GC before operation: completed in 382.956 ms, heap usage 292.796 MB -> 49.168 MB.
[2024-08-01T04:26:32.834Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:26:36.543Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:26:41.362Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:26:45.159Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:26:47.183Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:26:49.242Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:26:51.273Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:26:53.299Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:26:53.899Z] 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.
[2024-08-01T04:26:53.899Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:26:53.899Z] Movies recommended for you:
[2024-08-01T04:26:53.899Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:26:53.899Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:26:53.899Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (26146.389 ms) ======
[2024-08-01T04:26:53.899Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-01T04:26:54.562Z] GC before operation: completed in 233.231 ms, heap usage 267.287 MB -> 49.327 MB.
[2024-08-01T04:26:59.353Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:27:04.071Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:27:08.963Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:27:12.703Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:27:16.610Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:27:18.660Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:27:20.733Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:27:23.654Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:27:23.654Z] 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.
[2024-08-01T04:27:23.654Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:27:23.654Z] Movies recommended for you:
[2024-08-01T04:27:23.654Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:27:23.654Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:27:23.654Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (29447.214 ms) ======
[2024-08-01T04:27:23.654Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-01T04:27:24.258Z] GC before operation: completed in 230.394 ms, heap usage 116.016 MB -> 49.058 MB.
[2024-08-01T04:27:28.038Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:27:31.778Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:27:35.513Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:27:39.325Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:27:41.377Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:27:43.556Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:27:46.468Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:27:48.542Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:27:48.542Z] 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.
[2024-08-01T04:27:48.542Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:27:48.542Z] Movies recommended for you:
[2024-08-01T04:27:48.542Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:27:48.542Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:27:48.542Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (24841.669 ms) ======
[2024-08-01T04:27:48.542Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-01T04:27:49.208Z] GC before operation: completed in 289.409 ms, heap usage 67.640 MB -> 49.192 MB.
[2024-08-01T04:27:54.058Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:27:57.884Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:28:01.666Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:28:05.372Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:28:06.716Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:28:08.804Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:28:11.648Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:28:13.672Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:28:13.672Z] 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.
[2024-08-01T04:28:13.672Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:28:14.324Z] Movies recommended for you:
[2024-08-01T04:28:14.324Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:28:14.324Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:28:14.324Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (24880.687 ms) ======
[2024-08-01T04:28:14.324Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-01T04:28:14.324Z] GC before operation: completed in 284.659 ms, heap usage 248.256 MB -> 49.634 MB.
[2024-08-01T04:28:18.130Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:28:21.863Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:28:26.681Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:28:29.494Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:28:31.561Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:28:34.396Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:28:36.433Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:28:38.461Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:28:39.087Z] 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.
[2024-08-01T04:28:39.087Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:28:39.087Z] Movies recommended for you:
[2024-08-01T04:28:39.087Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:28:39.087Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:28:39.087Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (24674.284 ms) ======
[2024-08-01T04:28:39.087Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-01T04:28:39.087Z] GC before operation: completed in 278.571 ms, heap usage 241.690 MB -> 49.514 MB.
[2024-08-01T04:28:43.813Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:28:47.578Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:28:52.075Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:28:55.953Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:28:58.808Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:29:00.857Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:29:02.983Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:29:05.058Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:29:05.692Z] 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.
[2024-08-01T04:29:05.692Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:29:05.692Z] Movies recommended for you:
[2024-08-01T04:29:05.692Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:29:05.692Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:29:05.692Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (26555.016 ms) ======
[2024-08-01T04:29:05.692Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-01T04:29:05.692Z] GC before operation: completed in 204.121 ms, heap usage 125.234 MB -> 49.463 MB.
[2024-08-01T04:29:10.598Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:29:14.374Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:29:19.326Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:29:23.224Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:29:25.274Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:29:27.318Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:29:30.102Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:29:32.217Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:29:32.902Z] 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.
[2024-08-01T04:29:32.902Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:29:32.902Z] Movies recommended for you:
[2024-08-01T04:29:32.902Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:29:32.902Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:29:32.902Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (26933.611 ms) ======
[2024-08-01T04:29:32.902Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-01T04:29:32.902Z] GC before operation: completed in 252.100 ms, heap usage 152.853 MB -> 49.267 MB.
[2024-08-01T04:29:38.859Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:29:41.737Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:29:46.451Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:29:49.257Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:29:52.256Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:29:54.483Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:29:56.514Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:29:58.594Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:29:59.245Z] 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.
[2024-08-01T04:29:59.245Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:29:59.245Z] Movies recommended for you:
[2024-08-01T04:29:59.245Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:29:59.245Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:29:59.245Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (26222.163 ms) ======
[2024-08-01T04:29:59.245Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-01T04:29:59.859Z] GC before operation: completed in 207.911 ms, heap usage 71.210 MB -> 49.319 MB.
[2024-08-01T04:30:03.729Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:30:10.007Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:30:16.062Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:30:19.955Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:30:22.932Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:30:25.151Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:30:28.064Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:30:30.043Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:30:30.658Z] 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.
[2024-08-01T04:30:30.658Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:30:30.658Z] Movies recommended for you:
[2024-08-01T04:30:30.658Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:30:30.658Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:30:30.658Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (31061.810 ms) ======
[2024-08-01T04:30:30.658Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-01T04:30:30.658Z] GC before operation: completed in 218.688 ms, heap usage 127.304 MB -> 49.562 MB.
[2024-08-01T04:30:35.407Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:30:40.182Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:30:43.877Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:30:48.207Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:30:51.199Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:30:54.166Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:30:56.271Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:30:59.137Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:30:59.137Z] 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.
[2024-08-01T04:30:59.137Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:30:59.137Z] Movies recommended for you:
[2024-08-01T04:30:59.137Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:30:59.137Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:30:59.137Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (28515.342 ms) ======
[2024-08-01T04:30:59.137Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-01T04:30:59.823Z] GC before operation: completed in 322.918 ms, heap usage 148.723 MB -> 49.355 MB.
[2024-08-01T04:31:04.566Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:31:07.433Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:31:12.350Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:31:16.385Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:31:19.194Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:31:20.496Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:31:23.671Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:31:25.732Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:31:26.340Z] 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.
[2024-08-01T04:31:26.340Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:31:26.340Z] Movies recommended for you:
[2024-08-01T04:31:26.340Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:31:26.340Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:31:26.340Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (26903.732 ms) ======
[2024-08-01T04:31:26.340Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-01T04:31:27.003Z] GC before operation: completed in 274.182 ms, heap usage 145.889 MB -> 49.504 MB.
[2024-08-01T04:31:31.791Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:31:35.669Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:31:38.581Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:31:42.445Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:31:44.463Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:31:46.493Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:31:48.528Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:31:50.602Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:31:51.208Z] 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.
[2024-08-01T04:31:51.208Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:31:51.208Z] Movies recommended for you:
[2024-08-01T04:31:51.208Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:31:51.208Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:31:51.208Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (24180.318 ms) ======
[2024-08-01T04:31:51.208Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-01T04:31:51.208Z] GC before operation: completed in 227.307 ms, heap usage 127.748 MB -> 49.583 MB.
[2024-08-01T04:31:56.008Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:31:59.742Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:32:04.669Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:32:08.519Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:32:10.601Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:32:13.618Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:32:15.696Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:32:17.782Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:32:18.387Z] 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.
[2024-08-01T04:32:18.387Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:32:18.387Z] Movies recommended for you:
[2024-08-01T04:32:18.387Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:32:18.387Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:32:18.387Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (27191.039 ms) ======
[2024-08-01T04:32:18.387Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-01T04:32:19.011Z] GC before operation: completed in 240.214 ms, heap usage 238.464 MB -> 49.534 MB.
[2024-08-01T04:32:22.755Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:32:26.565Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:32:31.478Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:32:34.454Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:32:36.556Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:32:39.524Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:32:42.462Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:32:45.263Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:32:45.263Z] 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.
[2024-08-01T04:32:45.263Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:32:45.263Z] Movies recommended for you:
[2024-08-01T04:32:45.263Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:32:45.263Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:32:45.263Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (26504.787 ms) ======
[2024-08-01T04:32:45.263Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-01T04:32:45.889Z] GC before operation: completed in 348.017 ms, heap usage 148.884 MB -> 49.497 MB.
[2024-08-01T04:32:48.737Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:32:52.528Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:32:57.351Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:33:01.102Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:33:03.987Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:33:05.993Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:33:08.052Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:33:11.014Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:33:11.014Z] 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.
[2024-08-01T04:33:11.014Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:33:11.014Z] Movies recommended for you:
[2024-08-01T04:33:11.014Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:33:11.014Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:33:11.014Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (25533.171 ms) ======
[2024-08-01T04:33:11.014Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-01T04:33:11.634Z] GC before operation: completed in 333.681 ms, heap usage 271.446 MB -> 49.846 MB.
[2024-08-01T04:33:16.422Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:33:21.193Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:33:25.135Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:33:27.984Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:33:30.936Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:33:33.062Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:33:36.032Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:33:38.143Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:33:38.144Z] 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.
[2024-08-01T04:33:38.144Z] The best model improves the baseline by 14.34%.
[2024-08-01T04:33:38.776Z] Movies recommended for you:
[2024-08-01T04:33:38.776Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:33:38.776Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:33:38.776Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (27016.548 ms) ======
[2024-08-01T04:33:39.376Z] -----------------------------------
[2024-08-01T04:33:39.376Z] renaissance-movie-lens_0_PASSED
[2024-08-01T04:33:39.376Z] -----------------------------------
[2024-08-01T04:33:39.376Z]
[2024-08-01T04:33:39.376Z] TEST TEARDOWN:
[2024-08-01T04:33:39.376Z] Nothing to be done for teardown.
[2024-08-01T04:33:39.376Z] renaissance-movie-lens_0 Finish Time: Thu Aug 1 04:33:38 2024 Epoch Time (ms): 1722486818888