renaissance-movie-lens_0
[2025-02-27T06:45:17.164Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T06:45:17.164Z] ===============================================
[2025-02-27T06:45:17.164Z] renaissance-movie-lens_0 Start Time: Thu Feb 27 06:45:16 2025 Epoch Time (ms): 1740638716510
[2025-02-27T06:45:17.164Z] variation: NoOptions
[2025-02-27T06:45:17.164Z] JVM_OPTIONS:
[2025-02-27T06:45:17.164Z] { \
[2025-02-27T06:45:17.164Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T06:45:17.164Z] echo "Nothing to be done for setup."; \
[2025-02-27T06:45:17.164Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406387169604/renaissance-movie-lens_0"; \
[2025-02-27T06:45:17.164Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406387169604/renaissance-movie-lens_0"; \
[2025-02-27T06:45:17.164Z] echo ""; echo "TESTING:"; \
[2025-02-27T06:45:17.164Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406387169604/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T06:45:17.164Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406387169604/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T06:45:17.164Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T06:45:17.164Z] echo "Nothing to be done for teardown."; \
[2025-02-27T06:45:17.164Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17406387169604/TestTargetResult";
[2025-02-27T06:45:17.164Z]
[2025-02-27T06:45:17.164Z] TEST SETUP:
[2025-02-27T06:45:17.164Z] Nothing to be done for setup.
[2025-02-27T06:45:17.164Z]
[2025-02-27T06:45:17.164Z] TESTING:
[2025-02-27T06:45:21.230Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T06:45:23.363Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2025-02-27T06:45:29.675Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T06:45:29.675Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T06:45:29.675Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T06:45:30.337Z] GC before operation: completed in 201.566 ms, heap usage 143.801 MB -> 37.089 MB.
[2025-02-27T06:45:41.205Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:45:48.931Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:45:56.467Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:46:02.941Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:46:06.259Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:46:10.401Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:46:14.605Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:46:18.659Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:46:19.374Z] 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.
[2025-02-27T06:46:19.374Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:46:20.106Z] Movies recommended for you:
[2025-02-27T06:46:20.106Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:46:20.106Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:46:20.106Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (49751.196 ms) ======
[2025-02-27T06:46:20.106Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T06:46:20.106Z] GC before operation: completed in 175.472 ms, heap usage 229.230 MB -> 55.496 MB.
[2025-02-27T06:46:27.854Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:46:34.147Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:46:40.418Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:46:45.451Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:46:48.477Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:46:52.548Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:46:55.701Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:46:57.955Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:46:58.605Z] 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.
[2025-02-27T06:46:58.605Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:46:59.250Z] Movies recommended for you:
[2025-02-27T06:46:59.250Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:46:59.250Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:46:59.250Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (38957.315 ms) ======
[2025-02-27T06:46:59.250Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T06:46:59.250Z] GC before operation: completed in 204.135 ms, heap usage 71.050 MB -> 48.813 MB.
[2025-02-27T06:47:04.319Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:47:09.482Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:47:15.866Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:47:19.829Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:47:22.889Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:47:25.995Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:47:30.275Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:47:33.363Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:47:33.363Z] 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.
[2025-02-27T06:47:33.363Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:47:33.363Z] Movies recommended for you:
[2025-02-27T06:47:33.363Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:47:33.363Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:47:33.363Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (34188.099 ms) ======
[2025-02-27T06:47:33.363Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T06:47:33.363Z] GC before operation: completed in 139.255 ms, heap usage 389.213 MB -> 52.661 MB.
[2025-02-27T06:47:39.605Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:47:43.719Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:47:48.844Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:47:54.089Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:47:58.166Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:48:00.367Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:48:03.451Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:48:05.711Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:48:07.061Z] 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.
[2025-02-27T06:48:07.061Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:48:07.061Z] Movies recommended for you:
[2025-02-27T06:48:07.061Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:48:07.061Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:48:07.061Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (33060.268 ms) ======
[2025-02-27T06:48:07.061Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T06:48:07.061Z] GC before operation: completed in 109.430 ms, heap usage 97.546 MB -> 49.470 MB.
[2025-02-27T06:48:11.125Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:48:16.138Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:48:21.247Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:48:25.233Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:48:29.235Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:48:31.424Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:48:34.496Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:48:36.685Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:48:37.338Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2025-02-27T06:48:37.338Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:48:37.338Z] Movies recommended for you:
[2025-02-27T06:48:37.338Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:48:37.338Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:48:37.338Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (30763.608 ms) ======
[2025-02-27T06:48:37.338Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T06:48:37.338Z] GC before operation: completed in 150.169 ms, heap usage 134.516 MB -> 49.725 MB.
[2025-02-27T06:48:42.483Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:48:47.523Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:48:52.573Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:48:56.568Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:48:59.618Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:49:01.012Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:49:03.977Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:49:07.050Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:49:07.050Z] 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.
[2025-02-27T06:49:07.050Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:49:07.050Z] Movies recommended for you:
[2025-02-27T06:49:07.050Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:49:07.050Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:49:07.050Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (29690.992 ms) ======
[2025-02-27T06:49:07.050Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T06:49:07.715Z] GC before operation: completed in 158.523 ms, heap usage 65.509 MB -> 49.635 MB.
[2025-02-27T06:49:11.665Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:49:15.604Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:49:21.946Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:49:25.985Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:49:29.007Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:49:31.149Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:49:34.259Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:49:36.497Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:49:37.159Z] 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.
[2025-02-27T06:49:37.159Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:49:37.159Z] Movies recommended for you:
[2025-02-27T06:49:37.159Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:49:37.159Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:49:37.159Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (29771.195 ms) ======
[2025-02-27T06:49:37.159Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T06:49:37.159Z] GC before operation: completed in 156.276 ms, heap usage 139.556 MB -> 50.068 MB.
[2025-02-27T06:49:43.456Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:49:47.598Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:49:51.656Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:49:55.683Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:49:59.127Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:50:01.575Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:50:04.769Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:50:06.918Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:50:07.595Z] 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.
[2025-02-27T06:50:07.595Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:50:07.595Z] Movies recommended for you:
[2025-02-27T06:50:07.595Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:50:07.595Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:50:07.595Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (30316.936 ms) ======
[2025-02-27T06:50:07.595Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T06:50:08.245Z] GC before operation: completed in 223.528 ms, heap usage 259.448 MB -> 50.354 MB.
[2025-02-27T06:50:12.280Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:50:16.396Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:50:20.389Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:50:25.474Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:50:27.661Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:50:30.925Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:50:33.992Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:50:36.183Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:50:36.837Z] 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.
[2025-02-27T06:50:36.837Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:50:36.837Z] Movies recommended for you:
[2025-02-27T06:50:36.837Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:50:36.837Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:50:36.837Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (28928.115 ms) ======
[2025-02-27T06:50:36.837Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T06:50:36.837Z] GC before operation: completed in 82.003 ms, heap usage 111.195 MB -> 50.030 MB.
[2025-02-27T06:50:40.895Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:50:44.839Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:50:48.877Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:50:51.988Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:50:55.036Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:50:57.332Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:51:01.302Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:51:02.705Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:51:03.395Z] 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.
[2025-02-27T06:51:03.395Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:51:03.395Z] Movies recommended for you:
[2025-02-27T06:51:03.395Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:51:03.395Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:51:03.395Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (26502.607 ms) ======
[2025-02-27T06:51:03.395Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T06:51:03.395Z] GC before operation: completed in 123.522 ms, heap usage 289.309 MB -> 50.279 MB.
[2025-02-27T06:51:08.505Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:51:13.609Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:51:18.616Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:51:22.840Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:51:25.101Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:51:28.117Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:51:30.327Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:51:32.534Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:51:33.185Z] 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.
[2025-02-27T06:51:33.185Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:51:33.185Z] Movies recommended for you:
[2025-02-27T06:51:33.185Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:51:33.185Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:51:33.185Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (29903.669 ms) ======
[2025-02-27T06:51:33.185Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T06:51:33.855Z] GC before operation: completed in 124.668 ms, heap usage 163.570 MB -> 49.948 MB.
[2025-02-27T06:51:37.842Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:51:42.001Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:51:47.255Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:51:52.746Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:51:55.404Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:51:58.527Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:52:01.503Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:52:03.691Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:52:03.691Z] 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.
[2025-02-27T06:52:04.406Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:52:04.406Z] Movies recommended for you:
[2025-02-27T06:52:04.406Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:52:04.406Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:52:04.406Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (30603.901 ms) ======
[2025-02-27T06:52:04.406Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T06:52:04.406Z] GC before operation: completed in 134.921 ms, heap usage 233.057 MB -> 50.131 MB.
[2025-02-27T06:52:08.508Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:52:13.520Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:52:16.497Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:52:20.414Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:52:22.563Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:52:24.795Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:52:26.920Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:52:29.107Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:52:29.107Z] 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.
[2025-02-27T06:52:29.107Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:52:29.777Z] Movies recommended for you:
[2025-02-27T06:52:29.777Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:52:29.777Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:52:29.777Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (25127.764 ms) ======
[2025-02-27T06:52:29.777Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T06:52:29.777Z] GC before operation: completed in 130.435 ms, heap usage 107.367 MB -> 50.200 MB.
[2025-02-27T06:52:33.708Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:52:37.790Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:52:41.837Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:52:44.870Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:52:47.972Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:52:50.123Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:52:53.207Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:52:55.348Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:52:56.046Z] 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.
[2025-02-27T06:52:56.046Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:52:56.046Z] Movies recommended for you:
[2025-02-27T06:52:56.046Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:52:56.046Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:52:56.046Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (26324.629 ms) ======
[2025-02-27T06:52:56.046Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T06:52:56.046Z] GC before operation: completed in 96.653 ms, heap usage 237.671 MB -> 50.043 MB.
[2025-02-27T06:53:00.029Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:53:04.078Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:53:08.002Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:53:11.986Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:53:15.066Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:53:16.424Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:53:19.565Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:53:21.717Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:53:22.420Z] 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.
[2025-02-27T06:53:22.420Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:53:22.420Z] Movies recommended for you:
[2025-02-27T06:53:22.420Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:53:22.420Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:53:22.420Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (26542.594 ms) ======
[2025-02-27T06:53:22.420Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T06:53:23.083Z] GC before operation: completed in 190.101 ms, heap usage 262.801 MB -> 50.257 MB.
[2025-02-27T06:53:27.249Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:53:31.233Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:53:35.692Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:53:38.847Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:53:42.044Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:53:44.396Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:53:47.078Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:53:49.261Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:53:49.908Z] 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.
[2025-02-27T06:53:49.908Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:53:49.908Z] Movies recommended for you:
[2025-02-27T06:53:49.908Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:53:49.908Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:53:49.908Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (27289.243 ms) ======
[2025-02-27T06:53:49.908Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T06:53:49.908Z] GC before operation: completed in 168.744 ms, heap usage 251.908 MB -> 50.358 MB.
[2025-02-27T06:53:53.885Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:53:57.817Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:54:01.747Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:54:03.926Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:54:06.110Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:54:08.266Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:54:10.485Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:54:12.647Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:54:12.647Z] 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.
[2025-02-27T06:54:12.647Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:54:12.647Z] Movies recommended for you:
[2025-02-27T06:54:12.647Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:54:12.647Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:54:12.647Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (22698.672 ms) ======
[2025-02-27T06:54:12.647Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T06:54:13.312Z] GC before operation: completed in 96.558 ms, heap usage 261.782 MB -> 50.186 MB.
[2025-02-27T06:54:16.376Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:54:19.358Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:54:23.289Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:54:26.253Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:54:29.231Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:54:30.576Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:54:32.690Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:54:34.046Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:54:34.687Z] 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.
[2025-02-27T06:54:34.687Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:54:34.687Z] Movies recommended for you:
[2025-02-27T06:54:34.687Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:54:34.687Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:54:34.687Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (21599.736 ms) ======
[2025-02-27T06:54:34.687Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T06:54:34.687Z] GC before operation: completed in 112.293 ms, heap usage 261.951 MB -> 50.319 MB.
[2025-02-27T06:54:37.642Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:54:39.742Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:54:42.791Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:54:45.733Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:54:47.105Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:54:48.462Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:54:49.808Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:54:51.138Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:54:51.817Z] 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.
[2025-02-27T06:54:51.817Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:54:51.817Z] Movies recommended for you:
[2025-02-27T06:54:51.817Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:54:51.817Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:54:51.817Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17170.528 ms) ======
[2025-02-27T06:54:51.817Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T06:54:51.817Z] GC before operation: completed in 103.981 ms, heap usage 77.095 MB -> 50.167 MB.
[2025-02-27T06:54:54.733Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T06:54:57.669Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T06:54:59.784Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T06:55:02.754Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T06:55:04.107Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T06:55:06.237Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T06:55:07.602Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T06:55:08.973Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T06:55:08.973Z] 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.
[2025-02-27T06:55:08.973Z] The best model improves the baseline by 14.34%.
[2025-02-27T06:55:09.632Z] Movies recommended for you:
[2025-02-27T06:55:09.632Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T06:55:09.632Z] There is no way to check that no silent failure occurred.
[2025-02-27T06:55:09.632Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (17324.517 ms) ======
[2025-02-27T06:55:09.632Z] -----------------------------------
[2025-02-27T06:55:09.632Z] renaissance-movie-lens_0_PASSED
[2025-02-27T06:55:09.632Z] -----------------------------------
[2025-02-27T06:55:09.632Z]
[2025-02-27T06:55:09.632Z] TEST TEARDOWN:
[2025-02-27T06:55:09.632Z] Nothing to be done for teardown.
[2025-02-27T06:55:09.632Z] renaissance-movie-lens_0 Finish Time: Thu Feb 27 06:55:09 2025 Epoch Time (ms): 1740639309470