renaissance-movie-lens_0
[2024-06-27T08:36:49.135Z] Running test renaissance-movie-lens_0 ...
[2024-06-27T08:36:49.135Z] ===============================================
[2024-06-27T08:36:49.789Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 08:36:49 2024 Epoch Time (ms): 1719477409087
[2024-06-27T08:36:49.789Z] variation: NoOptions
[2024-06-27T08:36:49.789Z] JVM_OPTIONS:
[2024-06-27T08:36:49.789Z] { \
[2024-06-27T08:36:49.789Z] echo ""; echo "TEST SETUP:"; \
[2024-06-27T08:36:49.789Z] echo "Nothing to be done for setup."; \
[2024-06-27T08:36:49.789Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194760022523/renaissance-movie-lens_0"; \
[2024-06-27T08:36:49.789Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194760022523/renaissance-movie-lens_0"; \
[2024-06-27T08:36:49.789Z] echo ""; echo "TESTING:"; \
[2024-06-27T08:36:49.789Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/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_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194760022523/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-27T08:36:49.789Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194760022523/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-27T08:36:49.789Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-27T08:36:49.789Z] echo "Nothing to be done for teardown."; \
[2024-06-27T08:36:49.789Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194760022523/TestTargetResult";
[2024-06-27T08:36:49.789Z]
[2024-06-27T08:36:49.789Z] TEST SETUP:
[2024-06-27T08:36:49.789Z] Nothing to be done for setup.
[2024-06-27T08:36:49.789Z]
[2024-06-27T08:36:49.789Z] TESTING:
[2024-06-27T08:36:53.701Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-27T08:36:56.628Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-06-27T08:37:00.418Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-27T08:37:01.697Z] Training: 60056, validation: 20285, test: 19854
[2024-06-27T08:37:01.697Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-27T08:37:01.697Z] GC before operation: completed in 114.611 ms, heap usage 42.025 MB -> 36.218 MB.
[2024-06-27T08:37:10.518Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:37:16.437Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:37:23.842Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:37:28.762Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:37:32.743Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:37:35.653Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:37:39.479Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:37:42.435Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:37:43.040Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:37:43.040Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:37:43.040Z] Movies recommended for you:
[2024-06-27T08:37:43.040Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:37:43.040Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:37:43.040Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (41722.143 ms) ======
[2024-06-27T08:37:43.040Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-27T08:37:43.660Z] GC before operation: completed in 170.441 ms, heap usage 139.354 MB -> 49.326 MB.
[2024-06-27T08:37:48.456Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:37:53.264Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:37:58.168Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:38:02.957Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:38:05.028Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:38:07.071Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:38:10.194Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:38:12.251Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:38:12.861Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:38:12.861Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:38:13.553Z] Movies recommended for you:
[2024-06-27T08:38:13.553Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:38:13.553Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:38:13.553Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (29753.822 ms) ======
[2024-06-27T08:38:13.553Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-27T08:38:13.553Z] GC before operation: completed in 245.223 ms, heap usage 91.668 MB -> 48.192 MB.
[2024-06-27T08:38:17.376Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:38:22.121Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:38:25.970Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:38:30.831Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:38:32.908Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:38:34.938Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:38:37.825Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:38:39.850Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:38:40.515Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:38:40.515Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:38:40.515Z] Movies recommended for you:
[2024-06-27T08:38:40.515Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:38:40.515Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:38:40.515Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (27053.776 ms) ======
[2024-06-27T08:38:40.515Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-27T08:38:40.515Z] GC before operation: completed in 130.993 ms, heap usage 121.273 MB -> 48.421 MB.
[2024-06-27T08:38:45.249Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:38:48.512Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:38:52.294Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:38:56.090Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:38:58.117Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:39:00.146Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:39:02.972Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:39:05.005Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:39:05.005Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:39:05.005Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:39:05.623Z] Movies recommended for you:
[2024-06-27T08:39:05.623Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:39:05.623Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:39:05.623Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (24812.469 ms) ======
[2024-06-27T08:39:05.623Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-27T08:39:05.623Z] GC before operation: completed in 151.540 ms, heap usage 127.669 MB -> 49.767 MB.
[2024-06-27T08:39:09.417Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:39:13.218Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:39:18.024Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:39:22.899Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:39:24.965Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:39:27.804Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:39:30.257Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:39:32.390Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:39:33.039Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:39:33.039Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:39:33.039Z] Movies recommended for you:
[2024-06-27T08:39:33.039Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:39:33.039Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:39:33.039Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (27450.222 ms) ======
[2024-06-27T08:39:33.039Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-27T08:39:33.039Z] GC before operation: completed in 186.110 ms, heap usage 132.576 MB -> 48.963 MB.
[2024-06-27T08:39:38.979Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:39:42.715Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:39:46.458Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:39:50.306Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:39:53.221Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:39:56.139Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:39:58.220Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:40:00.330Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:40:01.015Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:40:01.015Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:40:01.015Z] Movies recommended for you:
[2024-06-27T08:40:01.015Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:40:01.015Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:40:01.015Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (27789.005 ms) ======
[2024-06-27T08:40:01.015Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-27T08:40:01.015Z] GC before operation: completed in 145.116 ms, heap usage 119.787 MB -> 48.877 MB.
[2024-06-27T08:40:05.851Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:40:10.650Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:40:16.015Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:40:18.943Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:40:20.975Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:40:23.925Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:40:25.970Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:40:28.804Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:40:28.804Z] 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.
[2024-06-27T08:40:28.804Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:40:28.804Z] Movies recommended for you:
[2024-06-27T08:40:28.804Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:40:28.804Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:40:28.804Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (27907.174 ms) ======
[2024-06-27T08:40:28.804Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-27T08:40:29.421Z] GC before operation: completed in 178.087 ms, heap usage 86.358 MB -> 51.436 MB.
[2024-06-27T08:40:34.235Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:40:39.002Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:40:42.856Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:40:46.689Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:40:49.598Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:40:51.710Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:40:54.541Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:40:57.022Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:40:57.022Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:40:57.022Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:40:57.690Z] Movies recommended for you:
[2024-06-27T08:40:57.690Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:40:57.690Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:40:57.690Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (28299.092 ms) ======
[2024-06-27T08:40:57.690Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-27T08:40:57.690Z] GC before operation: completed in 150.896 ms, heap usage 121.965 MB -> 49.341 MB.
[2024-06-27T08:41:02.529Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:41:07.305Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:41:12.131Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:41:15.868Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:41:17.933Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:41:20.884Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:41:22.894Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:41:24.923Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:41:24.923Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:41:25.530Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:41:25.530Z] Movies recommended for you:
[2024-06-27T08:41:25.530Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:41:25.530Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:41:25.530Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (27696.107 ms) ======
[2024-06-27T08:41:25.530Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-27T08:41:25.530Z] GC before operation: completed in 222.444 ms, heap usage 152.859 MB -> 49.243 MB.
[2024-06-27T08:41:30.282Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:41:34.049Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:41:38.081Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:41:41.864Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:41:44.686Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:41:47.613Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:41:50.558Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:41:53.393Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:41:54.058Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:41:54.058Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:41:54.058Z] Movies recommended for you:
[2024-06-27T08:41:54.058Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:41:54.058Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:41:54.058Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (28657.065 ms) ======
[2024-06-27T08:41:54.058Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-27T08:41:54.697Z] GC before operation: completed in 217.842 ms, heap usage 117.091 MB -> 49.338 MB.
[2024-06-27T08:41:59.520Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:42:04.483Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:42:08.359Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:42:13.189Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:42:15.222Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:42:18.139Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:42:22.093Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:42:24.958Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:42:24.958Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:42:24.958Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:42:24.958Z] Movies recommended for you:
[2024-06-27T08:42:24.958Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:42:24.958Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:42:24.958Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (30525.950 ms) ======
[2024-06-27T08:42:24.958Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-27T08:42:24.958Z] GC before operation: completed in 146.473 ms, heap usage 119.729 MB -> 49.012 MB.
[2024-06-27T08:42:28.728Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:42:32.442Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:42:37.305Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:42:42.079Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:42:44.164Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:42:46.213Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:42:49.125Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:42:51.970Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:42:52.611Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:42:52.611Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:42:52.611Z] Movies recommended for you:
[2024-06-27T08:42:52.611Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:42:52.611Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:42:52.611Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (27387.230 ms) ======
[2024-06-27T08:42:52.611Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-27T08:42:52.611Z] GC before operation: completed in 301.403 ms, heap usage 135.991 MB -> 49.195 MB.
[2024-06-27T08:42:57.569Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:43:01.371Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:43:06.725Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:43:10.615Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:43:13.564Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:43:15.612Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:43:19.469Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:43:21.534Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:43:22.195Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:43:22.195Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:43:22.195Z] Movies recommended for you:
[2024-06-27T08:43:22.195Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:43:22.195Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:43:22.195Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (29430.708 ms) ======
[2024-06-27T08:43:22.195Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-27T08:43:22.195Z] GC before operation: completed in 122.725 ms, heap usage 138.154 MB -> 49.352 MB.
[2024-06-27T08:43:27.073Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:43:31.851Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:43:35.549Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:43:38.441Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:43:41.230Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:43:44.040Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:43:46.454Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:43:49.438Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:43:49.438Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:43:49.438Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:43:50.089Z] Movies recommended for you:
[2024-06-27T08:43:50.089Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:43:50.089Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:43:50.089Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (27399.492 ms) ======
[2024-06-27T08:43:50.089Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-27T08:43:50.089Z] GC before operation: completed in 145.535 ms, heap usage 129.924 MB -> 49.079 MB.
[2024-06-27T08:43:54.802Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:43:58.472Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:44:03.331Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:44:07.120Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:44:10.064Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:44:12.135Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:44:14.979Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:44:17.067Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:44:17.727Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:44:17.727Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:44:17.727Z] Movies recommended for you:
[2024-06-27T08:44:17.727Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:44:17.727Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:44:17.727Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (27729.052 ms) ======
[2024-06-27T08:44:17.727Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-27T08:44:17.727Z] GC before operation: completed in 120.535 ms, heap usage 149.656 MB -> 49.314 MB.
[2024-06-27T08:44:21.531Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:44:26.465Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:44:30.363Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:44:34.193Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:44:36.321Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:44:39.308Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:44:42.136Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:44:44.328Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:44:44.967Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:44:44.967Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:44:45.618Z] Movies recommended for you:
[2024-06-27T08:44:45.618Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:44:45.618Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:44:45.618Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (27537.308 ms) ======
[2024-06-27T08:44:45.618Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-27T08:44:45.618Z] GC before operation: completed in 160.202 ms, heap usage 141.259 MB -> 49.388 MB.
[2024-06-27T08:44:50.359Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:44:55.109Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:44:58.957Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:45:03.887Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:45:06.019Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:45:08.969Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:45:11.047Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:45:14.455Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:45:14.455Z] 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.
[2024-06-27T08:45:14.455Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:45:15.076Z] Movies recommended for you:
[2024-06-27T08:45:15.076Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:45:15.076Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:45:15.077Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (29372.138 ms) ======
[2024-06-27T08:45:15.077Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-27T08:45:15.077Z] GC before operation: completed in 154.586 ms, heap usage 143.302 MB -> 49.218 MB.
[2024-06-27T08:45:18.943Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:45:23.845Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:45:28.785Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:45:33.650Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:45:36.538Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:45:39.395Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:45:41.450Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:45:44.312Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:45:44.980Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:45:44.980Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:45:44.980Z] Movies recommended for you:
[2024-06-27T08:45:44.980Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:45:44.980Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:45:44.980Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (29831.672 ms) ======
[2024-06-27T08:45:44.980Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-27T08:45:44.980Z] GC before operation: completed in 177.307 ms, heap usage 122.443 MB -> 49.261 MB.
[2024-06-27T08:45:49.719Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:45:53.522Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:45:57.811Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:46:00.730Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:46:03.698Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:46:05.013Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:46:07.948Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:46:10.012Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:46:10.012Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:46:10.012Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:46:10.667Z] Movies recommended for you:
[2024-06-27T08:46:10.667Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:46:10.667Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:46:10.667Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (25397.309 ms) ======
[2024-06-27T08:46:10.667Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-27T08:46:10.667Z] GC before operation: completed in 208.664 ms, heap usage 129.575 MB -> 49.435 MB.
[2024-06-27T08:46:14.440Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T08:46:18.147Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T08:46:22.900Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T08:46:25.770Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T08:46:27.117Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T08:46:29.169Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T08:46:31.169Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T08:46:33.211Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T08:46:33.847Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T08:46:33.847Z] The best model improves the baseline by 14.34%.
[2024-06-27T08:46:33.847Z] Movies recommended for you:
[2024-06-27T08:46:33.847Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T08:46:33.847Z] There is no way to check that no silent failure occurred.
[2024-06-27T08:46:33.847Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (23280.102 ms) ======
[2024-06-27T08:46:34.919Z] -----------------------------------
[2024-06-27T08:46:34.919Z] renaissance-movie-lens_0_PASSED
[2024-06-27T08:46:34.919Z] -----------------------------------
[2024-06-27T08:46:34.919Z]
[2024-06-27T08:46:34.919Z] TEST TEARDOWN:
[2024-06-27T08:46:34.919Z] Nothing to be done for teardown.
[2024-06-27T08:46:34.919Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 08:46:34 2024 Epoch Time (ms): 1719477994137