[2024-06-08T04:03:08.090Z] Running test renaissance-movie-lens_0 ... [2024-06-08T04:03:08.090Z] =============================================== [2024-06-08T04:03:08.462Z] renaissance-movie-lens_0 Start Time: Sat Jun 8 04:03:08 2024 Epoch Time (ms): 1717819388077 [2024-06-08T04:03:08.462Z] variation: NoOptions [2024-06-08T04:03:08.462Z] JVM_OPTIONS: [2024-06-08T04:03:08.462Z] { \ [2024-06-08T04:03:08.462Z] echo ""; echo "TEST SETUP:"; \ [2024-06-08T04:03:08.462Z] echo "Nothing to be done for setup."; \ [2024-06-08T04:03:08.462Z] mkdir -p "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17178181187604\\renaissance-movie-lens_0"; \ [2024-06-08T04:03:08.462Z] cd "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17178181187604\\renaissance-movie-lens_0"; \ [2024-06-08T04:03:08.462Z] echo ""; echo "TESTING:"; \ [2024-06-08T04:03:08.462Z] "c:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_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 "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17178181187604\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2024-06-08T04:03:08.462Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17178181187604\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-06-08T04:03:08.462Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-06-08T04:03:08.462Z] echo "Nothing to be done for teardown."; \ [2024-06-08T04:03:08.462Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows_testList_0/aqa-tests/\\TKG\\output_17178181187604\\TestTargetResult"; [2024-06-08T04:03:08.462Z] [2024-06-08T04:03:08.462Z] TEST SETUP: [2024-06-08T04:03:08.462Z] Nothing to be done for setup. [2024-06-08T04:03:08.463Z] [2024-06-08T04:03:08.463Z] TESTING: [2024-06-08T04:03:19.183Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-06-08T04:03:20.827Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-06-08T04:03:23.805Z] Got 100004 ratings from 671 users on 9066 movies. [2024-06-08T04:03:24.145Z] Training: 60056, validation: 20285, test: 19854 [2024-06-08T04:03:24.145Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-06-08T04:03:24.145Z] GC before operation: completed in 55.186 ms, heap usage 85.865 MB -> 36.929 MB. [2024-06-08T04:03:37.298Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:03:44.520Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:03:51.711Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:03:58.899Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:04:02.662Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:04:07.358Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:04:11.111Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:04:14.863Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:04:15.209Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:04:15.209Z] The best model improves the baseline by 14.52%. [2024-06-08T04:04:15.617Z] Movies recommended for you: [2024-06-08T04:04:15.617Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:04:15.617Z] There is no way to check that no silent failure occurred. [2024-06-08T04:04:15.617Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (51343.390 ms) ====== [2024-06-08T04:04:15.617Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-06-08T04:04:15.617Z] GC before operation: completed in 110.172 ms, heap usage 202.817 MB -> 50.930 MB. [2024-06-08T04:04:22.813Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:04:30.038Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:04:37.246Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:04:44.473Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:04:47.426Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:04:51.184Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:04:55.872Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:04:59.622Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:04:59.622Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:04:59.953Z] The best model improves the baseline by 14.52%. [2024-06-08T04:04:59.953Z] Movies recommended for you: [2024-06-08T04:04:59.953Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:04:59.953Z] There is no way to check that no silent failure occurred. [2024-06-08T04:04:59.953Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (44258.282 ms) ====== [2024-06-08T04:04:59.953Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-06-08T04:04:59.953Z] GC before operation: completed in 84.716 ms, heap usage 217.843 MB -> 52.826 MB. [2024-06-08T04:05:07.196Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:05:14.393Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:05:21.617Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:05:27.464Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:05:31.201Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:05:35.905Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:05:39.658Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:05:42.605Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:05:43.316Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:05:43.316Z] The best model improves the baseline by 14.52%. [2024-06-08T04:05:43.316Z] Movies recommended for you: [2024-06-08T04:05:43.316Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:05:43.316Z] There is no way to check that no silent failure occurred. [2024-06-08T04:05:43.316Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (43294.045 ms) ====== [2024-06-08T04:05:43.316Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-06-08T04:05:43.316Z] GC before operation: completed in 86.885 ms, heap usage 135.462 MB -> 53.022 MB. [2024-06-08T04:05:50.520Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:05:56.373Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:06:03.584Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:06:10.856Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:06:14.585Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:06:18.441Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:06:22.179Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:06:25.100Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:06:25.970Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:06:25.970Z] The best model improves the baseline by 14.52%. [2024-06-08T04:06:25.970Z] Movies recommended for you: [2024-06-08T04:06:25.970Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:06:25.970Z] There is no way to check that no silent failure occurred. [2024-06-08T04:06:25.970Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (42466.380 ms) ====== [2024-06-08T04:06:25.970Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-06-08T04:06:26.301Z] GC before operation: completed in 114.557 ms, heap usage 233.857 MB -> 53.386 MB. [2024-06-08T04:06:33.510Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:06:39.350Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:06:46.537Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:06:53.837Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:06:56.765Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:07:00.515Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:07:05.269Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:07:09.015Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:07:09.015Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:07:09.015Z] The best model improves the baseline by 14.52%. [2024-06-08T04:07:09.015Z] Movies recommended for you: [2024-06-08T04:07:09.015Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:07:09.015Z] There is no way to check that no silent failure occurred. [2024-06-08T04:07:09.015Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (43119.661 ms) ====== [2024-06-08T04:07:09.015Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-06-08T04:07:09.344Z] GC before operation: completed in 82.366 ms, heap usage 96.968 MB -> 50.275 MB. [2024-06-08T04:07:16.546Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:07:22.404Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:07:29.638Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:07:35.477Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:07:40.164Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:07:43.892Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:07:47.635Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:07:51.373Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:07:51.707Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:07:51.707Z] The best model improves the baseline by 14.52%. [2024-06-08T04:07:51.707Z] Movies recommended for you: [2024-06-08T04:07:51.707Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:07:51.707Z] There is no way to check that no silent failure occurred. [2024-06-08T04:07:51.707Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (42591.210 ms) ====== [2024-06-08T04:07:51.707Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-06-08T04:07:51.707Z] GC before operation: completed in 86.458 ms, heap usage 111.702 MB -> 50.229 MB. [2024-06-08T04:07:58.931Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:08:04.796Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:08:12.011Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:08:17.834Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:08:22.537Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:08:26.263Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:08:30.033Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:08:33.750Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:08:34.113Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:08:34.113Z] The best model improves the baseline by 14.52%. [2024-06-08T04:08:34.113Z] Movies recommended for you: [2024-06-08T04:08:34.113Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:08:34.113Z] There is no way to check that no silent failure occurred. [2024-06-08T04:08:34.113Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (42316.708 ms) ====== [2024-06-08T04:08:34.113Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-06-08T04:08:34.447Z] GC before operation: completed in 83.557 ms, heap usage 162.865 MB -> 53.630 MB. [2024-06-08T04:08:40.275Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:08:47.457Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:08:54.695Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:09:00.550Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:09:05.248Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:09:08.153Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:09:12.866Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:09:15.800Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:09:16.509Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:09:16.509Z] The best model improves the baseline by 14.52%. [2024-06-08T04:09:16.509Z] Movies recommended for you: [2024-06-08T04:09:16.509Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:09:16.509Z] There is no way to check that no silent failure occurred. [2024-06-08T04:09:16.509Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (42215.905 ms) ====== [2024-06-08T04:09:16.509Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-06-08T04:09:16.509Z] GC before operation: completed in 81.496 ms, heap usage 162.628 MB -> 50.693 MB. [2024-06-08T04:09:23.714Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:09:30.934Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:09:36.799Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:09:44.019Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:09:46.933Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:09:50.736Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:09:55.436Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:09:58.357Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:09:58.738Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:09:58.739Z] The best model improves the baseline by 14.52%. [2024-06-08T04:09:59.078Z] Movies recommended for you: [2024-06-08T04:09:59.078Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:09:59.078Z] There is no way to check that no silent failure occurred. [2024-06-08T04:09:59.078Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (42383.434 ms) ====== [2024-06-08T04:09:59.078Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-06-08T04:09:59.078Z] GC before operation: completed in 86.540 ms, heap usage 131.445 MB -> 50.484 MB. [2024-06-08T04:10:06.243Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:10:13.465Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:10:19.314Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:10:26.541Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:10:29.481Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:10:33.203Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:10:37.904Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:10:40.815Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:10:41.522Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:10:41.522Z] The best model improves the baseline by 14.52%. [2024-06-08T04:10:41.522Z] Movies recommended for you: [2024-06-08T04:10:41.522Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:10:41.522Z] There is no way to check that no silent failure occurred. [2024-06-08T04:10:41.522Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (42557.882 ms) ====== [2024-06-08T04:10:41.522Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-06-08T04:10:41.847Z] GC before operation: completed in 85.944 ms, heap usage 83.837 MB -> 50.541 MB. [2024-06-08T04:10:49.045Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:10:54.887Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:11:02.108Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:11:09.284Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:11:12.211Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:11:15.946Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:11:20.636Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:11:23.543Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:11:24.253Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:11:24.253Z] The best model improves the baseline by 14.52%. [2024-06-08T04:11:24.253Z] Movies recommended for you: [2024-06-08T04:11:24.253Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:11:24.253Z] There is no way to check that no silent failure occurred. [2024-06-08T04:11:24.253Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (42643.003 ms) ====== [2024-06-08T04:11:24.253Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-06-08T04:11:24.253Z] GC before operation: completed in 83.402 ms, heap usage 73.946 MB -> 50.241 MB. [2024-06-08T04:11:31.502Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:11:37.378Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:11:44.601Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:11:50.432Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:11:54.151Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:11:57.948Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:12:01.708Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:12:05.437Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:12:05.803Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:12:06.134Z] The best model improves the baseline by 14.52%. [2024-06-08T04:12:06.134Z] Movies recommended for you: [2024-06-08T04:12:06.134Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:12:06.134Z] There is no way to check that no silent failure occurred. [2024-06-08T04:12:06.134Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (41685.887 ms) ====== [2024-06-08T04:12:06.134Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-06-08T04:12:06.134Z] GC before operation: completed in 91.017 ms, heap usage 114.080 MB -> 50.493 MB. [2024-06-08T04:12:13.338Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:12:20.546Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:12:27.746Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:12:33.598Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:12:37.340Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:12:41.090Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:12:44.830Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:12:48.597Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:12:48.926Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:12:48.926Z] The best model improves the baseline by 14.52%. [2024-06-08T04:12:49.258Z] Movies recommended for you: [2024-06-08T04:12:49.258Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:12:49.258Z] There is no way to check that no silent failure occurred. [2024-06-08T04:12:49.258Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (42942.837 ms) ====== [2024-06-08T04:12:49.258Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-06-08T04:12:49.258Z] GC before operation: completed in 83.861 ms, heap usage 124.677 MB -> 50.669 MB. [2024-06-08T04:12:56.516Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:13:02.368Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:13:09.585Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:13:16.769Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:13:19.681Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:13:23.421Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:13:27.190Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:13:30.933Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:13:31.663Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:13:31.663Z] The best model improves the baseline by 14.52%. [2024-06-08T04:13:31.663Z] Movies recommended for you: [2024-06-08T04:13:31.663Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:13:31.663Z] There is no way to check that no silent failure occurred. [2024-06-08T04:13:31.663Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (42421.347 ms) ====== [2024-06-08T04:13:31.663Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-06-08T04:13:31.663Z] GC before operation: completed in 109.686 ms, heap usage 132.725 MB -> 50.459 MB. [2024-06-08T04:13:38.868Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:13:44.712Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:13:51.953Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:13:59.142Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:14:02.093Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:14:05.832Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:14:10.527Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:14:13.436Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:14:14.155Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:14:14.155Z] The best model improves the baseline by 14.52%. [2024-06-08T04:14:14.155Z] Movies recommended for you: [2024-06-08T04:14:14.155Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:14:14.155Z] There is no way to check that no silent failure occurred. [2024-06-08T04:14:14.155Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (42486.148 ms) ====== [2024-06-08T04:14:14.155Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-06-08T04:14:14.515Z] GC before operation: completed in 84.859 ms, heap usage 172.321 MB -> 50.661 MB. [2024-06-08T04:14:21.751Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:14:27.596Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:14:34.827Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:14:40.677Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:14:44.407Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:14:48.135Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:14:52.894Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:14:56.662Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:14:56.662Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:14:56.662Z] The best model improves the baseline by 14.52%. [2024-06-08T04:14:56.662Z] Movies recommended for you: [2024-06-08T04:14:56.662Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:14:56.662Z] There is no way to check that no silent failure occurred. [2024-06-08T04:14:56.662Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (42416.078 ms) ====== [2024-06-08T04:14:56.662Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-06-08T04:14:56.996Z] GC before operation: completed in 95.380 ms, heap usage 152.698 MB -> 50.711 MB. [2024-06-08T04:15:04.234Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:15:11.462Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:15:18.674Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:15:25.881Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:15:28.814Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:15:32.589Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:15:37.281Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:15:41.050Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:15:41.050Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:15:41.050Z] The best model improves the baseline by 14.52%. [2024-06-08T04:15:41.050Z] Movies recommended for you: [2024-06-08T04:15:41.050Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:15:41.050Z] There is no way to check that no silent failure occurred. [2024-06-08T04:15:41.050Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (44209.071 ms) ====== [2024-06-08T04:15:41.050Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-06-08T04:15:41.050Z] GC before operation: completed in 84.857 ms, heap usage 92.825 MB -> 50.509 MB. [2024-06-08T04:15:48.256Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:15:55.441Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:16:02.626Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:16:08.465Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:16:12.199Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:16:15.959Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:16:19.709Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:16:23.483Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:16:23.823Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:16:23.823Z] The best model improves the baseline by 14.52%. [2024-06-08T04:16:24.157Z] Movies recommended for you: [2024-06-08T04:16:24.157Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:16:24.157Z] There is no way to check that no silent failure occurred. [2024-06-08T04:16:24.157Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (42958.869 ms) ====== [2024-06-08T04:16:24.157Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-06-08T04:16:24.157Z] GC before operation: completed in 94.640 ms, heap usage 141.939 MB -> 50.596 MB. [2024-06-08T04:16:31.355Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:16:38.566Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:16:45.785Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:16:51.610Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:16:55.370Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:16:59.134Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:17:03.913Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:17:07.646Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:17:07.647Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:17:07.647Z] The best model improves the baseline by 14.52%. [2024-06-08T04:17:07.647Z] Movies recommended for you: [2024-06-08T04:17:07.647Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:17:07.647Z] There is no way to check that no silent failure occurred. [2024-06-08T04:17:07.647Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (43543.516 ms) ====== [2024-06-08T04:17:07.647Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-06-08T04:17:07.981Z] GC before operation: completed in 87.244 ms, heap usage 84.630 MB -> 50.690 MB. [2024-06-08T04:17:15.195Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-06-08T04:17:21.056Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-06-08T04:17:28.256Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-06-08T04:17:35.460Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-06-08T04:17:38.386Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-06-08T04:17:42.149Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-06-08T04:17:46.910Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-06-08T04:17:50.632Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-06-08T04:17:50.632Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-06-08T04:17:50.632Z] The best model improves the baseline by 14.52%. [2024-06-08T04:17:50.632Z] Movies recommended for you: [2024-06-08T04:17:50.632Z] WARNING: This benchmark provides no result that can be validated. [2024-06-08T04:17:50.632Z] There is no way to check that no silent failure occurred. [2024-06-08T04:17:50.632Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (42899.581 ms) ====== [2024-06-08T04:17:51.336Z] ----------------------------------- [2024-06-08T04:17:51.337Z] renaissance-movie-lens_0_PASSED [2024-06-08T04:17:51.337Z] ----------------------------------- [2024-06-08T04:17:51.664Z] [2024-06-08T04:17:51.664Z] TEST TEARDOWN: [2024-06-08T04:17:51.664Z] Nothing to be done for teardown. [2024-06-08T04:17:51.664Z] renaissance-movie-lens_0 Finish Time: Sat Jun 8 04:17:51 2024 Epoch Time (ms): 1717820271504