renaissance-movie-lens_0
[2025-02-27T06:59:02.199Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T06:59:02.199Z] ===============================================
[2025-02-27T06:59:02.865Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 06:59:02 2025 Epoch Time (ms): 1740639542150
[2025-02-27T06:59:02.865Z] variation: NoOptions
[2025-02-27T06:59:02.865Z] JVM_OPTIONS:
[2025-02-27T06:59:02.865Z] { \
[2025-02-27T06:59:02.865Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T06:59:02.865Z] echo "Nothing to be done for setup."; \
[2025-02-27T06:59:02.865Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406395414393/renaissance-movie-lens_0"; \
[2025-02-27T06:59:02.865Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406395414393/renaissance-movie-lens_0"; \
[2025-02-27T06:59:02.865Z] echo ""; echo "TESTING:"; \
[2025-02-27T06:59:02.865Z] "/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_17406395414393/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T06:59:02.865Z] 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_17406395414393/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T06:59:02.865Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T06:59:02.865Z] echo "Nothing to be done for teardown."; \
[2025-02-27T06:59:02.865Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406395414393/TestTargetResult";
[2025-02-27T06:59:02.865Z]
[2025-02-27T06:59:02.865Z] TEST SETUP:
[2025-02-27T06:59:02.865Z] Nothing to be done for setup.
[2025-02-27T06:59:02.865Z]
[2025-02-27T06:59:02.865Z] TESTING:
[2025-02-27T06:59:06.905Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T06:59:09.985Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2025-02-27T06:59:15.158Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T06:59:16.547Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T06:59:16.547Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T06:59:16.547Z] GC before operation: completed in 130.412 ms, heap usage 56.235 MB -> 36.374 MB.
[2025-02-27T06:59:31.731Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:59:39.296Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:59:46.897Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:59:53.256Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:59:57.188Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:59:59.475Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:00:02.581Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:00:05.563Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:00:06.231Z] 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.
[2025-02-27T07:00:06.231Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:00:06.231Z] Movies recommended for you:
[2025-02-27T07:00:06.231Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:00:06.232Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:00:06.232Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (50175.451 ms) ======
[2025-02-27T07:00:06.232Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T07:00:06.873Z] GC before operation: completed in 195.938 ms, heap usage 142.665 MB -> 48.639 MB.
[2025-02-27T07:00:11.888Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:00:15.815Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:00:19.750Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:00:22.698Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:00:24.785Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:00:26.939Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:00:29.968Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:00:32.074Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:00:32.723Z] 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.
[2025-02-27T07:00:32.723Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:00:32.723Z] Movies recommended for you:
[2025-02-27T07:00:32.723Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:00:32.723Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:00:32.723Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (25947.561 ms) ======
[2025-02-27T07:00:32.723Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T07:00:32.723Z] GC before operation: completed in 161.863 ms, heap usage 149.642 MB -> 48.437 MB.
[2025-02-27T07:00:37.633Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:00:41.607Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:00:45.469Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:00:48.424Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:00:50.547Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:00:51.895Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:00:54.829Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:00:56.154Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:00:56.823Z] 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.
[2025-02-27T07:00:56.823Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:00:56.823Z] Movies recommended for you:
[2025-02-27T07:00:56.823Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:00:56.823Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:00:56.823Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (23898.313 ms) ======
[2025-02-27T07:00:56.823Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T07:00:56.823Z] GC before operation: completed in 127.706 ms, heap usage 61.772 MB -> 48.538 MB.
[2025-02-27T07:01:00.691Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:01:04.588Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:01:08.494Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:01:11.553Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:01:13.685Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:01:15.825Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:01:17.975Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:01:20.137Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:01:20.137Z] 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.
[2025-02-27T07:01:20.137Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:01:20.799Z] Movies recommended for you:
[2025-02-27T07:01:20.799Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:01:20.799Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:01:20.799Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (23822.416 ms) ======
[2025-02-27T07:01:20.799Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T07:01:20.799Z] GC before operation: completed in 158.634 ms, heap usage 124.000 MB -> 49.047 MB.
[2025-02-27T07:01:24.726Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:01:27.700Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:01:31.575Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:01:35.466Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:01:36.808Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:01:39.743Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:01:42.265Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:01:43.638Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:01:43.638Z] 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.
[2025-02-27T07:01:43.638Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:01:44.315Z] Movies recommended for you:
[2025-02-27T07:01:44.315Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:01:44.315Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:01:44.315Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (23209.119 ms) ======
[2025-02-27T07:01:44.315Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T07:01:44.315Z] GC before operation: completed in 147.887 ms, heap usage 249.491 MB -> 49.332 MB.
[2025-02-27T07:01:48.375Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:01:51.465Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:01:55.311Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:01:58.254Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:02:00.371Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:02:01.701Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:02:03.831Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:02:05.907Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:02:05.907Z] 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.
[2025-02-27T07:02:05.907Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:02:05.907Z] Movies recommended for you:
[2025-02-27T07:02:05.907Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:02:05.907Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:02:05.907Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (21964.791 ms) ======
[2025-02-27T07:02:05.907Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T07:02:06.563Z] GC before operation: completed in 162.429 ms, heap usage 138.888 MB -> 49.109 MB.
[2025-02-27T07:02:09.488Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:02:12.408Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:02:16.252Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:02:19.159Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:02:20.480Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:02:22.579Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:02:24.677Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:02:25.988Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:02:25.988Z] 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.
[2025-02-27T07:02:25.988Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:02:26.624Z] Movies recommended for you:
[2025-02-27T07:02:26.625Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:02:26.625Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:02:26.625Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20073.562 ms) ======
[2025-02-27T07:02:26.625Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T07:02:26.625Z] GC before operation: completed in 113.962 ms, heap usage 222.748 MB -> 49.385 MB.
[2025-02-27T07:02:28.698Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:02:31.596Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:02:34.461Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:02:37.389Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:02:38.706Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:02:40.022Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:02:42.174Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:02:43.508Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:02:44.145Z] 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.
[2025-02-27T07:02:44.145Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:02:44.145Z] Movies recommended for you:
[2025-02-27T07:02:44.145Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:02:44.145Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:02:44.145Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17771.833 ms) ======
[2025-02-27T07:02:44.145Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T07:02:44.145Z] GC before operation: completed in 159.507 ms, heap usage 126.200 MB -> 49.493 MB.
[2025-02-27T07:02:48.049Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:02:51.961Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:02:57.016Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:03:00.034Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:03:02.236Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:03:05.177Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:03:08.215Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:03:10.418Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:03:11.111Z] 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.
[2025-02-27T07:03:11.111Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:03:11.111Z] Movies recommended for you:
[2025-02-27T07:03:11.111Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:03:11.111Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:03:11.111Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (26683.075 ms) ======
[2025-02-27T07:03:11.111Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T07:03:11.112Z] GC before operation: completed in 208.000 ms, heap usage 148.021 MB -> 49.406 MB.
[2025-02-27T07:03:15.100Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:03:20.224Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:03:25.231Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:03:29.246Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:03:31.374Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:03:34.394Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:03:36.632Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:03:39.538Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:03:39.538Z] 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.
[2025-02-27T07:03:39.538Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:03:39.538Z] Movies recommended for you:
[2025-02-27T07:03:39.538Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:03:39.538Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:03:39.538Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (28505.531 ms) ======
[2025-02-27T07:03:39.538Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T07:03:40.193Z] GC before operation: completed in 248.240 ms, heap usage 113.207 MB -> 49.453 MB.
[2025-02-27T07:03:45.285Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:03:49.345Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:03:54.356Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:03:58.291Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:04:00.460Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:04:03.569Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:04:06.567Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:04:08.750Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:04:09.405Z] 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.
[2025-02-27T07:04:09.405Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:04:09.405Z] Movies recommended for you:
[2025-02-27T07:04:09.405Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:04:09.405Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:04:09.405Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (29383.884 ms) ======
[2025-02-27T07:04:09.405Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T07:04:09.405Z] GC before operation: completed in 241.977 ms, heap usage 150.967 MB -> 49.367 MB.
[2025-02-27T07:04:13.456Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:04:17.374Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:04:22.396Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:04:25.326Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:04:28.327Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:04:30.485Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:04:33.480Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:04:35.686Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:04:36.365Z] 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.
[2025-02-27T07:04:36.365Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:04:36.365Z] Movies recommended for you:
[2025-02-27T07:04:36.365Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:04:36.365Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:04:36.365Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (26930.892 ms) ======
[2025-02-27T07:04:36.365Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T07:04:37.087Z] GC before operation: completed in 163.366 ms, heap usage 160.933 MB -> 49.439 MB.
[2025-02-27T07:04:41.070Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:04:46.149Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:04:51.191Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:04:55.212Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:04:57.444Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:04:59.620Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:05:02.635Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:05:04.784Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:05:04.784Z] 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.
[2025-02-27T07:05:04.784Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:05:05.496Z] Movies recommended for you:
[2025-02-27T07:05:05.496Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:05:05.496Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:05:05.496Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (28394.814 ms) ======
[2025-02-27T07:05:05.496Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T07:05:05.496Z] GC before operation: completed in 152.733 ms, heap usage 139.848 MB -> 49.550 MB.
[2025-02-27T07:05:10.473Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:05:14.409Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:05:19.414Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:05:22.474Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:05:24.615Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:05:26.727Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:05:30.369Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:05:31.764Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:05:32.451Z] 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.
[2025-02-27T07:05:32.451Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:05:32.451Z] Movies recommended for you:
[2025-02-27T07:05:32.451Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:05:32.451Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:05:32.451Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (27030.924 ms) ======
[2025-02-27T07:05:32.451Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T07:05:32.451Z] GC before operation: completed in 123.327 ms, heap usage 142.465 MB -> 49.353 MB.
[2025-02-27T07:05:36.436Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:05:40.413Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:05:44.389Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:05:48.385Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:05:51.479Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:05:53.745Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:05:56.730Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:05:59.708Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:05:59.708Z] 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.
[2025-02-27T07:05:59.708Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:05:59.708Z] Movies recommended for you:
[2025-02-27T07:05:59.708Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:05:59.708Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:05:59.708Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (27508.543 ms) ======
[2025-02-27T07:05:59.708Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T07:06:00.359Z] GC before operation: completed in 232.810 ms, heap usage 135.860 MB -> 49.485 MB.
[2025-02-27T07:06:04.328Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:06:08.251Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:06:13.273Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:06:16.753Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:06:18.922Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:06:21.909Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:06:24.104Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:06:26.256Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:06:26.901Z] 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.
[2025-02-27T07:06:26.901Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:06:26.901Z] Movies recommended for you:
[2025-02-27T07:06:26.901Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:06:26.901Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:06:26.901Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (26834.021 ms) ======
[2025-02-27T07:06:26.901Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T07:06:26.901Z] GC before operation: completed in 168.817 ms, heap usage 116.365 MB -> 49.533 MB.
[2025-02-27T07:06:31.907Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:06:35.927Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:06:39.947Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:06:43.873Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:06:45.974Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:06:48.160Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:06:51.217Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:06:53.362Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:06:53.362Z] 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.
[2025-02-27T07:06:54.018Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:06:54.018Z] Movies recommended for you:
[2025-02-27T07:06:54.018Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:06:54.018Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:06:54.018Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (26646.972 ms) ======
[2025-02-27T07:06:54.018Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T07:06:54.018Z] GC before operation: completed in 138.113 ms, heap usage 249.664 MB -> 49.501 MB.
[2025-02-27T07:06:59.102Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:07:04.162Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:07:08.568Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:07:12.585Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:07:14.754Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:07:16.905Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:07:19.908Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:07:21.275Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:07:21.924Z] 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.
[2025-02-27T07:07:21.924Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:07:21.924Z] Movies recommended for you:
[2025-02-27T07:07:21.924Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:07:21.924Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:07:21.924Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (27912.401 ms) ======
[2025-02-27T07:07:21.924Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T07:07:21.924Z] GC before operation: completed in 144.437 ms, heap usage 243.581 MB -> 49.611 MB.
[2025-02-27T07:07:25.803Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:07:29.771Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:07:34.810Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:07:37.765Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:07:40.040Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:07:42.232Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:07:44.368Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:07:46.488Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:07:46.488Z] 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.
[2025-02-27T07:07:46.488Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:07:47.172Z] Movies recommended for you:
[2025-02-27T07:07:47.172Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:07:47.172Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:07:47.172Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (24841.436 ms) ======
[2025-02-27T07:07:47.172Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T07:07:47.172Z] GC before operation: completed in 172.630 ms, heap usage 147.700 MB -> 49.741 MB.
[2025-02-27T07:07:51.055Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T07:07:55.693Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T07:07:58.645Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T07:08:01.600Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T07:08:03.746Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T07:08:05.880Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T07:08:08.034Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T07:08:10.180Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T07:08:10.180Z] 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.
[2025-02-27T07:08:10.180Z] The best model improves the baseline by 14.34%.
[2025-02-27T07:08:10.843Z] Movies recommended for you:
[2025-02-27T07:08:10.843Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T07:08:10.843Z] There is no way to check that no silent failure occurred.
[2025-02-27T07:08:10.843Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (23463.116 ms) ======
[2025-02-27T07:08:11.482Z] -----------------------------------
[2025-02-27T07:08:11.482Z] renaissance-movie-lens_0_PASSED
[2025-02-27T07:08:11.482Z] -----------------------------------
[2025-02-27T07:08:11.482Z]
[2025-02-27T07:08:11.482Z] TEST TEARDOWN:
[2025-02-27T07:08:11.482Z] Nothing to be done for teardown.
[2025-02-27T07:08:11.482Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 07:08:10 2025 Epoch Time (ms): 1740640090816