renaissance-movie-lens_0
[2024-06-27T04:23:30.272Z] Running test renaissance-movie-lens_0 ...
[2024-06-27T04:23:30.272Z] ===============================================
[2024-06-27T04:23:30.272Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 00:23:29 2024 Epoch Time (ms): 1719462209727
[2024-06-27T04:23:30.272Z] variation: NoOptions
[2024-06-27T04:23:30.272Z] JVM_OPTIONS:
[2024-06-27T04:23:30.272Z] { \
[2024-06-27T04:23:30.272Z] echo ""; echo "TEST SETUP:"; \
[2024-06-27T04:23:30.272Z] echo "Nothing to be done for setup."; \
[2024-06-27T04:23:30.272Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194609237698/renaissance-movie-lens_0"; \
[2024-06-27T04:23:30.272Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194609237698/renaissance-movie-lens_0"; \
[2024-06-27T04:23:30.272Z] echo ""; echo "TESTING:"; \
[2024-06-27T04:23:30.272Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194609237698/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-27T04:23:30.272Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194609237698/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-27T04:23:30.272Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-27T04:23:30.272Z] echo "Nothing to be done for teardown."; \
[2024-06-27T04:23:30.272Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_testList_0/aqa-tests/TKG/../TKG/output_17194609237698/TestTargetResult";
[2024-06-27T04:23:30.272Z]
[2024-06-27T04:23:30.272Z] TEST SETUP:
[2024-06-27T04:23:30.272Z] Nothing to be done for setup.
[2024-06-27T04:23:30.272Z]
[2024-06-27T04:23:30.272Z] TESTING:
[2024-06-27T04:23:34.006Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-27T04:23:35.323Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-06-27T04:23:40.255Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-27T04:23:40.919Z] Training: 60056, validation: 20285, test: 19854
[2024-06-27T04:23:40.919Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-27T04:23:40.919Z] GC before operation: completed in 96.638 ms, heap usage 126.927 MB -> 37.056 MB.
[2024-06-27T04:23:48.149Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:23:53.097Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:23:59.318Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:24:03.120Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:24:05.222Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:24:08.349Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:24:11.313Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:24:13.549Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:24:14.194Z] 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.
[2024-06-27T04:24:14.816Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:24:14.816Z] Movies recommended for you:
[2024-06-27T04:24:14.816Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:24:14.816Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:24:14.816Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (34068.951 ms) ======
[2024-06-27T04:24:14.816Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-27T04:24:14.816Z] GC before operation: completed in 200.871 ms, heap usage 144.071 MB -> 55.975 MB.
[2024-06-27T04:24:18.799Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:24:22.702Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:24:26.504Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:24:30.351Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:24:32.449Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:24:34.581Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:24:36.693Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:24:38.829Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:24:38.829Z] 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.
[2024-06-27T04:24:38.829Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:24:38.829Z] Movies recommended for you:
[2024-06-27T04:24:38.829Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:24:38.829Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:24:38.829Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (24174.025 ms) ======
[2024-06-27T04:24:38.829Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-27T04:24:39.488Z] GC before operation: completed in 131.598 ms, heap usage 249.405 MB -> 49.106 MB.
[2024-06-27T04:24:42.338Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:24:45.467Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:24:49.350Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:24:52.181Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:24:54.264Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:24:56.385Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:24:58.474Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:25:00.622Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:25:00.622Z] 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.
[2024-06-27T04:25:01.257Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:25:01.257Z] Movies recommended for you:
[2024-06-27T04:25:01.257Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:25:01.257Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:25:01.257Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (21923.613 ms) ======
[2024-06-27T04:25:01.257Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-27T04:25:01.257Z] GC before operation: completed in 125.557 ms, heap usage 223.012 MB -> 49.377 MB.
[2024-06-27T04:25:05.155Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:25:10.254Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:25:14.171Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:25:18.191Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:25:21.239Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:25:23.398Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:25:25.589Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:25:27.779Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:25:28.442Z] 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.
[2024-06-27T04:25:28.442Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:25:28.442Z] Movies recommended for you:
[2024-06-27T04:25:28.442Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:25:28.442Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:25:28.442Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (27261.450 ms) ======
[2024-06-27T04:25:28.442Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-27T04:25:28.442Z] GC before operation: completed in 141.993 ms, heap usage 129.355 MB -> 50.232 MB.
[2024-06-27T04:25:31.360Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:25:34.654Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:25:38.493Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:25:41.410Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:25:43.572Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:25:45.649Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:25:47.744Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:25:49.048Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:25:49.048Z] 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.
[2024-06-27T04:25:49.661Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:25:49.661Z] Movies recommended for you:
[2024-06-27T04:25:49.661Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:25:49.661Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:25:49.661Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20793.362 ms) ======
[2024-06-27T04:25:49.661Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-27T04:25:49.661Z] GC before operation: completed in 90.775 ms, heap usage 226.819 MB -> 49.906 MB.
[2024-06-27T04:25:52.500Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:25:55.412Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:25:59.298Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:26:01.340Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:26:03.468Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:26:04.762Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:26:06.879Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:26:08.983Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:26:08.983Z] 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.
[2024-06-27T04:26:08.983Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:26:09.607Z] Movies recommended for you:
[2024-06-27T04:26:09.607Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:26:09.607Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:26:09.607Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (19777.883 ms) ======
[2024-06-27T04:26:09.607Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-27T04:26:09.607Z] GC before operation: completed in 128.895 ms, heap usage 163.897 MB -> 49.780 MB.
[2024-06-27T04:26:12.543Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:26:15.701Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:26:18.630Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:26:21.560Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:26:24.099Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:26:26.237Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:26:28.374Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:26:30.538Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:26:30.538Z] 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.
[2024-06-27T04:26:30.538Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:26:30.538Z] Movies recommended for you:
[2024-06-27T04:26:30.538Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:26:30.538Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:26:30.538Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (21222.288 ms) ======
[2024-06-27T04:26:30.538Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-27T04:26:30.538Z] GC before operation: completed in 103.807 ms, heap usage 169.665 MB -> 50.024 MB.
[2024-06-27T04:26:33.497Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:26:37.318Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:26:40.228Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:26:43.164Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:26:45.265Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:26:47.355Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:26:49.498Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:26:51.600Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:26:52.252Z] 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.
[2024-06-27T04:26:52.252Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:26:52.252Z] Movies recommended for you:
[2024-06-27T04:26:52.252Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:26:52.252Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:26:52.252Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (21496.177 ms) ======
[2024-06-27T04:26:52.252Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-27T04:26:52.252Z] GC before operation: completed in 117.858 ms, heap usage 180.170 MB -> 50.279 MB.
[2024-06-27T04:26:55.163Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:26:58.125Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:27:01.898Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:27:03.944Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:27:05.361Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:27:06.641Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:27:08.719Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:27:10.789Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:27:10.789Z] 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.
[2024-06-27T04:27:10.789Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:27:10.789Z] Movies recommended for you:
[2024-06-27T04:27:10.789Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:27:10.789Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:27:10.789Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (18500.258 ms) ======
[2024-06-27T04:27:10.789Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-27T04:27:10.789Z] GC before operation: completed in 136.339 ms, heap usage 160.500 MB -> 50.120 MB.
[2024-06-27T04:27:14.074Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:27:16.157Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:27:19.035Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:27:21.157Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:27:23.189Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:27:24.484Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:27:26.554Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:27:27.880Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:27:28.494Z] 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.
[2024-06-27T04:27:28.494Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:27:28.494Z] Movies recommended for you:
[2024-06-27T04:27:28.494Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:27:28.494Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:27:28.494Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17214.913 ms) ======
[2024-06-27T04:27:28.494Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-27T04:27:28.494Z] GC before operation: completed in 106.540 ms, heap usage 271.816 MB -> 50.295 MB.
[2024-06-27T04:27:31.443Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:27:33.492Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:27:36.390Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:27:39.335Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:27:40.639Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:27:41.955Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:27:44.040Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:27:45.322Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:27:45.322Z] 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.
[2024-06-27T04:27:45.322Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:27:45.322Z] Movies recommended for you:
[2024-06-27T04:27:45.322Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:27:45.322Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:27:45.322Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17103.515 ms) ======
[2024-06-27T04:27:45.322Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-27T04:27:45.322Z] GC before operation: completed in 104.241 ms, heap usage 164.122 MB -> 49.995 MB.
[2024-06-27T04:27:48.150Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:27:50.998Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:27:53.827Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:27:55.883Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:27:57.591Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:27:59.639Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:28:01.761Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:28:03.108Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:28:03.108Z] 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.
[2024-06-27T04:28:03.108Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:28:03.108Z] Movies recommended for you:
[2024-06-27T04:28:03.108Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:28:03.108Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:28:03.108Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17647.709 ms) ======
[2024-06-27T04:28:03.108Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-27T04:28:03.108Z] GC before operation: completed in 110.912 ms, heap usage 159.180 MB -> 50.052 MB.
[2024-06-27T04:28:05.947Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:28:08.810Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:28:11.768Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:28:14.686Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:28:15.999Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:28:18.053Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:28:19.400Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:28:21.446Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:28:21.446Z] 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.
[2024-06-27T04:28:21.446Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:28:21.446Z] Movies recommended for you:
[2024-06-27T04:28:21.446Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:28:21.446Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:28:21.446Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18292.311 ms) ======
[2024-06-27T04:28:21.446Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-27T04:28:21.446Z] GC before operation: completed in 87.418 ms, heap usage 232.447 MB -> 50.380 MB.
[2024-06-27T04:28:24.300Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:28:27.214Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:28:30.089Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:28:32.209Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:28:34.263Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:28:35.580Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:28:36.872Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:28:38.913Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:28:38.913Z] 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.
[2024-06-27T04:28:38.913Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:28:38.913Z] Movies recommended for you:
[2024-06-27T04:28:38.913Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:28:38.913Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:28:38.913Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17244.907 ms) ======
[2024-06-27T04:28:38.913Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-27T04:28:38.913Z] GC before operation: completed in 170.718 ms, heap usage 159.321 MB -> 50.061 MB.
[2024-06-27T04:28:41.827Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:28:44.831Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:28:47.704Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:28:50.578Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:28:52.668Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:28:53.965Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:28:55.255Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:28:56.585Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:28:57.226Z] 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.
[2024-06-27T04:28:57.226Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:28:57.226Z] Movies recommended for you:
[2024-06-27T04:28:57.226Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:28:57.226Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:28:57.226Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (18148.667 ms) ======
[2024-06-27T04:28:57.226Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-27T04:28:57.226Z] GC before operation: completed in 100.331 ms, heap usage 317.891 MB -> 50.323 MB.
[2024-06-27T04:29:00.057Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:29:02.919Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:29:05.778Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:29:07.838Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:29:09.208Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:29:11.310Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:29:13.419Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:29:15.596Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:29:15.596Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-06-27T04:29:15.596Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:29:15.596Z] Movies recommended for you:
[2024-06-27T04:29:15.596Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:29:15.596Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:29:15.596Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18182.086 ms) ======
[2024-06-27T04:29:15.596Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-27T04:29:15.596Z] GC before operation: completed in 97.457 ms, heap usage 91.634 MB -> 50.199 MB.
[2024-06-27T04:29:18.437Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:29:21.283Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:29:25.247Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:29:27.293Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:29:29.473Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:29:31.196Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:29:33.279Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:29:34.613Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:29:35.268Z] 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.
[2024-06-27T04:29:35.268Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:29:35.268Z] Movies recommended for you:
[2024-06-27T04:29:35.268Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:29:35.268Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:29:35.268Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19739.701 ms) ======
[2024-06-27T04:29:35.268Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-27T04:29:35.268Z] GC before operation: completed in 119.857 ms, heap usage 228.334 MB -> 50.139 MB.
[2024-06-27T04:29:38.120Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:29:41.034Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:29:43.888Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:29:46.785Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:29:48.113Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:29:49.438Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:29:51.508Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:29:52.818Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:29:53.440Z] 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.
[2024-06-27T04:29:53.440Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:29:53.440Z] Movies recommended for you:
[2024-06-27T04:29:53.440Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:29:53.440Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:29:53.440Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (17776.001 ms) ======
[2024-06-27T04:29:53.440Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-27T04:29:53.440Z] GC before operation: completed in 101.918 ms, heap usage 283.931 MB -> 50.296 MB.
[2024-06-27T04:29:56.351Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:30:00.329Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:30:05.418Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:30:09.406Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:30:10.765Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:30:12.933Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:30:15.031Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:30:16.519Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:30:17.147Z] 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.
[2024-06-27T04:30:17.147Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:30:17.148Z] Movies recommended for you:
[2024-06-27T04:30:17.148Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:30:17.148Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:30:17.148Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (23535.662 ms) ======
[2024-06-27T04:30:17.148Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-27T04:30:17.148Z] GC before operation: completed in 94.710 ms, heap usage 135.963 MB -> 50.357 MB.
[2024-06-27T04:30:19.717Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:30:22.682Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:30:25.658Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:30:28.530Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:30:29.858Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:30:31.165Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:30:33.299Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:30:34.693Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:30:35.375Z] 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.
[2024-06-27T04:30:35.375Z] The best model improves the baseline by 14.34%.
[2024-06-27T04:30:35.375Z] Movies recommended for you:
[2024-06-27T04:30:35.375Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:30:35.375Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:30:35.375Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (18420.281 ms) ======
[2024-06-27T04:30:36.060Z] -----------------------------------
[2024-06-27T04:30:36.060Z] renaissance-movie-lens_0_PASSED
[2024-06-27T04:30:36.060Z] -----------------------------------
[2024-06-27T04:30:36.060Z]
[2024-06-27T04:30:36.060Z] TEST TEARDOWN:
[2024-06-27T04:30:36.060Z] Nothing to be done for teardown.
[2024-06-27T04:30:36.060Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 00:30:35 2024 Epoch Time (ms): 1719462635725