renaissance-movie-lens_0
[2025-01-22T01:34:09.128Z] Running test renaissance-movie-lens_0 ...
[2025-01-22T01:34:09.128Z] ===============================================
[2025-01-22T01:34:09.128Z] renaissance-movie-lens_0 Start Time: Wed Jan 22 01:34:08 2025 Epoch Time (ms): 1737509648438
[2025-01-22T01:34:09.128Z] variation: NoOptions
[2025-01-22T01:34:09.128Z] JVM_OPTIONS:
[2025-01-22T01:34:09.128Z] { \
[2025-01-22T01:34:09.128Z] echo ""; echo "TEST SETUP:"; \
[2025-01-22T01:34:09.128Z] echo "Nothing to be done for setup."; \
[2025-01-22T01:34:09.128Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17375086968837/renaissance-movie-lens_0"; \
[2025-01-22T01:34:09.128Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17375086968837/renaissance-movie-lens_0"; \
[2025-01-22T01:34:09.128Z] echo ""; echo "TESTING:"; \
[2025-01-22T01:34:09.128Z] "/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_17375086968837/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-22T01:34:09.128Z] 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_17375086968837/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-22T01:34:09.128Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-22T01:34:09.128Z] echo "Nothing to be done for teardown."; \
[2025-01-22T01:34:09.128Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17375086968837/TestTargetResult";
[2025-01-22T01:34:09.128Z]
[2025-01-22T01:34:09.128Z] TEST SETUP:
[2025-01-22T01:34:09.128Z] Nothing to be done for setup.
[2025-01-22T01:34:09.128Z]
[2025-01-22T01:34:09.128Z] TESTING:
[2025-01-22T01:34:12.030Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-22T01:34:13.367Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2025-01-22T01:34:17.215Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-22T01:34:17.216Z] Training: 60056, validation: 20285, test: 19854
[2025-01-22T01:34:17.216Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-22T01:34:17.216Z] GC before operation: completed in 90.740 ms, heap usage 95.084 MB -> 37.074 MB.
[2025-01-22T01:34:24.491Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:34:28.297Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:34:33.225Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:34:36.097Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:34:38.364Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:34:40.479Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:34:42.624Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:34:43.991Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:34:44.630Z] 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.
[2025-01-22T01:34:44.630Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:34:44.630Z] Movies recommended for you:
[2025-01-22T01:34:44.630Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:34:44.630Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:34:44.630Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (27422.773 ms) ======
[2025-01-22T01:34:44.630Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-22T01:34:44.630Z] GC before operation: completed in 125.282 ms, heap usage 164.785 MB -> 57.778 MB.
[2025-01-22T01:34:47.577Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:34:51.435Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:34:54.364Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:34:57.312Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:34:59.407Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:35:01.493Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:35:02.807Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:35:04.883Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:35:04.883Z] 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.
[2025-01-22T01:35:04.883Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:35:04.883Z] Movies recommended for you:
[2025-01-22T01:35:04.883Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:35:04.883Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:35:04.883Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (20331.142 ms) ======
[2025-01-22T01:35:04.883Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-22T01:35:05.541Z] GC before operation: completed in 103.565 ms, heap usage 165.415 MB -> 48.976 MB.
[2025-01-22T01:35:07.618Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:35:10.490Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:35:13.361Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:35:16.250Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:35:17.582Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:35:18.900Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:35:20.977Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:35:22.305Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:35:22.305Z] 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.
[2025-01-22T01:35:22.305Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:35:22.946Z] Movies recommended for you:
[2025-01-22T01:35:22.946Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:35:22.946Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:35:22.946Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17485.146 ms) ======
[2025-01-22T01:35:22.946Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-22T01:35:22.946Z] GC before operation: completed in 91.891 ms, heap usage 113.045 MB -> 49.181 MB.
[2025-01-22T01:35:25.011Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:35:27.942Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:35:30.002Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:35:32.512Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:35:33.848Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:35:35.195Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:35:37.269Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:35:38.596Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:35:38.596Z] 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.
[2025-01-22T01:35:38.596Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:35:38.596Z] Movies recommended for you:
[2025-01-22T01:35:38.596Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:35:38.596Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:35:38.596Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16074.575 ms) ======
[2025-01-22T01:35:38.596Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-22T01:35:39.225Z] GC before operation: completed in 82.001 ms, heap usage 234.570 MB -> 49.639 MB.
[2025-01-22T01:35:41.279Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:35:44.170Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:35:47.066Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:35:50.050Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:35:51.412Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:35:52.735Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:35:54.046Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:35:56.146Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:35:56.146Z] 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.
[2025-01-22T01:35:56.146Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:35:56.146Z] Movies recommended for you:
[2025-01-22T01:35:56.146Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:35:56.146Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:35:56.146Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17149.088 ms) ======
[2025-01-22T01:35:56.146Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-22T01:35:56.146Z] GC before operation: completed in 95.387 ms, heap usage 163.494 MB -> 49.745 MB.
[2025-01-22T01:35:58.210Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:36:01.101Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:36:04.011Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:36:06.973Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:36:09.047Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:36:10.368Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:36:12.471Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:36:13.814Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:36:14.455Z] 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.
[2025-01-22T01:36:14.455Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:36:14.455Z] Movies recommended for you:
[2025-01-22T01:36:14.455Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:36:14.455Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:36:14.455Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (18072.152 ms) ======
[2025-01-22T01:36:14.455Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-22T01:36:14.455Z] GC before operation: completed in 89.143 ms, heap usage 180.012 MB -> 49.751 MB.
[2025-01-22T01:36:16.526Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:36:20.380Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:36:23.350Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:36:25.410Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:36:27.496Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:36:28.821Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:36:30.543Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:36:31.883Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:36:31.883Z] 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.
[2025-01-22T01:36:31.883Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:36:32.526Z] Movies recommended for you:
[2025-01-22T01:36:32.526Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:36:32.526Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:36:32.526Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17823.935 ms) ======
[2025-01-22T01:36:32.526Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-22T01:36:32.526Z] GC before operation: completed in 87.639 ms, heap usage 131.421 MB -> 49.870 MB.
[2025-01-22T01:36:34.601Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:36:37.508Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:36:39.624Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:36:42.556Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:36:43.913Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:36:45.264Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:36:46.582Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:36:48.651Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:36:48.651Z] 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.
[2025-01-22T01:36:48.651Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:36:48.651Z] Movies recommended for you:
[2025-01-22T01:36:48.651Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:36:48.651Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:36:48.651Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16435.253 ms) ======
[2025-01-22T01:36:48.651Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-22T01:36:48.651Z] GC before operation: completed in 115.189 ms, heap usage 267.905 MB -> 50.231 MB.
[2025-01-22T01:36:51.537Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:36:54.436Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:36:57.030Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:36:59.105Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:37:00.427Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:37:02.536Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:37:03.882Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:37:05.198Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:37:05.198Z] 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.
[2025-01-22T01:37:05.846Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:37:05.846Z] Movies recommended for you:
[2025-01-22T01:37:05.846Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:37:05.846Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:37:05.846Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16796.213 ms) ======
[2025-01-22T01:37:05.846Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-22T01:37:05.846Z] GC before operation: completed in 96.205 ms, heap usage 56.596 MB -> 50.160 MB.
[2025-01-22T01:37:07.920Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:37:10.852Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:37:13.725Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:37:15.789Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:37:17.103Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:37:18.411Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:37:19.727Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:37:21.804Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:37:21.804Z] 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.
[2025-01-22T01:37:21.804Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:37:21.804Z] Movies recommended for you:
[2025-01-22T01:37:21.804Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:37:21.804Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:37:21.804Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16116.497 ms) ======
[2025-01-22T01:37:21.804Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-22T01:37:21.804Z] GC before operation: completed in 116.888 ms, heap usage 184.580 MB -> 50.127 MB.
[2025-01-22T01:37:24.719Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:37:26.783Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:37:28.930Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:37:31.821Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:37:32.451Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:37:34.552Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:37:35.857Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:37:37.215Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:37:37.215Z] 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.
[2025-01-22T01:37:37.851Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:37:37.851Z] Movies recommended for you:
[2025-01-22T01:37:37.851Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:37:37.851Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:37:37.851Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (15683.192 ms) ======
[2025-01-22T01:37:37.852Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-22T01:37:37.852Z] GC before operation: completed in 132.549 ms, heap usage 104.407 MB -> 49.861 MB.
[2025-01-22T01:37:40.731Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:37:42.802Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:37:45.762Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:37:47.862Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:37:49.180Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:37:50.501Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:37:52.627Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:37:53.981Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:37:53.981Z] 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.
[2025-01-22T01:37:53.981Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:37:53.981Z] Movies recommended for you:
[2025-01-22T01:37:53.981Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:37:53.981Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:37:53.981Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16380.154 ms) ======
[2025-01-22T01:37:53.981Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-22T01:37:53.981Z] GC before operation: completed in 95.529 ms, heap usage 260.767 MB -> 50.137 MB.
[2025-01-22T01:37:56.895Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:37:58.950Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:38:01.847Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:38:03.908Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:38:06.003Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:38:07.312Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:38:08.667Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:38:09.976Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:38:10.630Z] 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.
[2025-01-22T01:38:10.630Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:38:10.630Z] Movies recommended for you:
[2025-01-22T01:38:10.630Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:38:10.630Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:38:10.630Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16246.325 ms) ======
[2025-01-22T01:38:10.630Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-22T01:38:10.630Z] GC before operation: completed in 108.909 ms, heap usage 200.308 MB -> 50.284 MB.
[2025-01-22T01:38:12.686Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:38:15.630Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:38:18.615Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:38:20.711Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:38:22.045Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:38:23.350Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:38:24.815Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:38:26.911Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:38:26.911Z] 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.
[2025-01-22T01:38:26.911Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:38:26.911Z] Movies recommended for you:
[2025-01-22T01:38:26.911Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:38:26.911Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:38:26.911Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16389.397 ms) ======
[2025-01-22T01:38:26.911Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-22T01:38:26.911Z] GC before operation: completed in 107.887 ms, heap usage 267.527 MB -> 50.106 MB.
[2025-01-22T01:38:29.035Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:38:31.935Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:38:34.847Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:38:36.923Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:38:38.261Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:38:39.598Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:38:41.713Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:38:43.033Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:38:43.033Z] 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.
[2025-01-22T01:38:43.033Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:38:43.033Z] Movies recommended for you:
[2025-01-22T01:38:43.033Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:38:43.033Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:38:43.033Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16096.644 ms) ======
[2025-01-22T01:38:43.033Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-22T01:38:43.033Z] GC before operation: completed in 110.220 ms, heap usage 237.387 MB -> 50.276 MB.
[2025-01-22T01:38:45.992Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:38:48.902Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:38:51.011Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:38:53.915Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:38:55.302Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:38:56.619Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:38:57.969Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:38:59.290Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:38:59.941Z] 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.
[2025-01-22T01:38:59.941Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:38:59.941Z] Movies recommended for you:
[2025-01-22T01:38:59.941Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:38:59.941Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:38:59.941Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16555.578 ms) ======
[2025-01-22T01:38:59.941Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-22T01:38:59.941Z] GC before operation: completed in 104.521 ms, heap usage 113.341 MB -> 50.148 MB.
[2025-01-22T01:39:01.986Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:39:04.897Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:39:07.359Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:39:09.412Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:39:10.768Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:39:12.106Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:39:14.147Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:39:15.450Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:39:15.450Z] 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.
[2025-01-22T01:39:15.450Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:39:15.450Z] Movies recommended for you:
[2025-01-22T01:39:15.450Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:39:15.450Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:39:15.450Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15510.301 ms) ======
[2025-01-22T01:39:15.451Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-22T01:39:15.451Z] GC before operation: completed in 91.834 ms, heap usage 72.292 MB -> 49.932 MB.
[2025-01-22T01:39:17.498Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:39:20.388Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:39:22.467Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:39:24.562Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:39:25.885Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:39:28.000Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:39:29.348Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:39:30.672Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:39:30.672Z] 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.
[2025-01-22T01:39:30.672Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:39:30.672Z] Movies recommended for you:
[2025-01-22T01:39:30.672Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:39:30.672Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:39:30.672Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15270.805 ms) ======
[2025-01-22T01:39:30.672Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-22T01:39:30.672Z] GC before operation: completed in 107.199 ms, heap usage 377.765 MB -> 53.526 MB.
[2025-01-22T01:39:33.605Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:39:35.707Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:39:38.569Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:39:40.682Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:39:42.762Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:39:44.105Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:39:46.579Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:39:47.219Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:39:47.219Z] 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.
[2025-01-22T01:39:47.219Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:39:47.863Z] Movies recommended for you:
[2025-01-22T01:39:47.863Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:39:47.863Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:39:47.863Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16587.706 ms) ======
[2025-01-22T01:39:47.863Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-22T01:39:47.863Z] GC before operation: completed in 88.720 ms, heap usage 146.529 MB -> 50.293 MB.
[2025-01-22T01:39:49.959Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T01:39:52.873Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T01:39:54.950Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T01:39:57.880Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T01:39:59.242Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T01:40:00.571Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T01:40:01.935Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T01:40:03.278Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T01:40:03.912Z] 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.
[2025-01-22T01:40:03.912Z] The best model improves the baseline by 14.34%.
[2025-01-22T01:40:03.912Z] Movies recommended for you:
[2025-01-22T01:40:03.912Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T01:40:03.912Z] There is no way to check that no silent failure occurred.
[2025-01-22T01:40:03.912Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16361.565 ms) ======
[2025-01-22T01:40:04.543Z] -----------------------------------
[2025-01-22T01:40:04.543Z] renaissance-movie-lens_0_PASSED
[2025-01-22T01:40:04.543Z] -----------------------------------
[2025-01-22T01:40:04.543Z]
[2025-01-22T01:40:04.543Z] TEST TEARDOWN:
[2025-01-22T01:40:04.543Z] Nothing to be done for teardown.
[2025-01-22T01:40:04.543Z] renaissance-movie-lens_0 Finish Time: Wed Jan 22 01:40:04 2025 Epoch Time (ms): 1737510004060