Test Results Summary Service

renaissance-movie-lens_0

[2024-11-17T22:23:08.340Z] Running test renaissance-movie-lens_0 ... [2024-11-17T22:23:08.340Z] =============================================== [2024-11-17T22:23:08.340Z] renaissance-movie-lens_0 Start Time: Sun Nov 17 22:23:07 2024 Epoch Time (ms): 1731882187253 [2024-11-17T22:23:08.340Z] variation: NoOptions [2024-11-17T22:23:08.340Z] JVM_OPTIONS: [2024-11-17T22:23:08.340Z] { \ [2024-11-17T22:23:08.340Z] echo ""; echo "TEST SETUP:"; \ [2024-11-17T22:23:08.340Z] echo "Nothing to be done for setup."; \ [2024-11-17T22:23:08.340Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17318812223698/renaissance-movie-lens_0"; \ [2024-11-17T22:23:08.340Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17318812223698/renaissance-movie-lens_0"; \ [2024-11-17T22:23:08.340Z] echo ""; echo "TESTING:"; \ [2024-11-17T22:23:08.340Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/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_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17318812223698/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-11-17T22:23:08.340Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17318812223698/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-11-17T22:23:08.340Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-11-17T22:23:08.340Z] echo "Nothing to be done for teardown."; \ [2024-11-17T22:23:08.340Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17318812223698/TestTargetResult"; [2024-11-17T22:23:08.340Z] [2024-11-17T22:23:08.340Z] TEST SETUP: [2024-11-17T22:23:08.340Z] Nothing to be done for setup. [2024-11-17T22:23:08.340Z] [2024-11-17T22:23:08.340Z] TESTING: [2024-11-17T22:23:11.307Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-11-17T22:23:13.230Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-11-17T22:23:17.382Z] Got 100004 ratings from 671 users on 9066 movies. [2024-11-17T22:23:17.383Z] Training: 60056, validation: 20285, test: 19854 [2024-11-17T22:23:17.383Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-11-17T22:23:17.383Z] GC before operation: completed in 76.679 ms, heap usage 94.426 MB -> 36.415 MB. [2024-11-17T22:23:22.852Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:23:26.948Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:23:29.919Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:23:32.895Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:23:34.820Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:23:35.757Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:23:37.679Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:23:39.603Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:23:39.603Z] 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. [2024-11-17T22:23:39.603Z] The best model improves the baseline by 14.52%. [2024-11-17T22:23:39.603Z] Movies recommended for you: [2024-11-17T22:23:39.603Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:23:39.603Z] There is no way to check that no silent failure occurred. [2024-11-17T22:23:39.603Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (23059.529 ms) ====== [2024-11-17T22:23:39.603Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-11-17T22:23:39.603Z] GC before operation: completed in 98.918 ms, heap usage 267.977 MB -> 46.949 MB. [2024-11-17T22:23:42.630Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:23:45.603Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:23:48.589Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:23:50.517Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:23:52.441Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:23:54.367Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:23:55.304Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:23:57.233Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:23:57.233Z] 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. [2024-11-17T22:23:57.233Z] The best model improves the baseline by 14.52%. [2024-11-17T22:23:57.233Z] Movies recommended for you: [2024-11-17T22:23:57.233Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:23:57.233Z] There is no way to check that no silent failure occurred. [2024-11-17T22:23:57.233Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17563.378 ms) ====== [2024-11-17T22:23:57.233Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-11-17T22:23:57.233Z] GC before operation: completed in 99.944 ms, heap usage 169.057 MB -> 49.070 MB. [2024-11-17T22:24:00.209Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:24:03.180Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:24:05.105Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:24:08.081Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:24:09.018Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:24:10.943Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:24:11.880Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:24:13.806Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:24:13.806Z] 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. [2024-11-17T22:24:13.806Z] The best model improves the baseline by 14.52%. [2024-11-17T22:24:13.806Z] Movies recommended for you: [2024-11-17T22:24:13.806Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:24:13.806Z] There is no way to check that no silent failure occurred. [2024-11-17T22:24:13.806Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16485.094 ms) ====== [2024-11-17T22:24:13.806Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-11-17T22:24:13.806Z] GC before operation: completed in 93.742 ms, heap usage 144.041 MB -> 49.265 MB. [2024-11-17T22:24:18.178Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:24:19.118Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:24:21.043Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:24:24.021Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:24:24.958Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:24:26.883Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:24:28.808Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:24:29.746Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:24:29.746Z] 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. [2024-11-17T22:24:29.746Z] The best model improves the baseline by 14.52%. [2024-11-17T22:24:29.746Z] Movies recommended for you: [2024-11-17T22:24:29.746Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:24:29.746Z] There is no way to check that no silent failure occurred. [2024-11-17T22:24:29.746Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16011.551 ms) ====== [2024-11-17T22:24:29.746Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-11-17T22:24:30.683Z] GC before operation: completed in 99.468 ms, heap usage 107.422 MB -> 49.599 MB. [2024-11-17T22:24:32.612Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:24:35.585Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:24:37.521Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:24:40.494Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:24:41.435Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:24:43.360Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:24:44.297Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:24:46.221Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:24:46.221Z] 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. [2024-11-17T22:24:46.221Z] The best model improves the baseline by 14.52%. [2024-11-17T22:24:46.221Z] Movies recommended for you: [2024-11-17T22:24:46.221Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:24:46.221Z] There is no way to check that no silent failure occurred. [2024-11-17T22:24:46.221Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15882.103 ms) ====== [2024-11-17T22:24:46.221Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-11-17T22:24:46.221Z] GC before operation: completed in 88.655 ms, heap usage 173.801 MB -> 49.844 MB. [2024-11-17T22:24:49.228Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:24:51.152Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:24:53.080Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:24:56.049Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:24:56.989Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:24:57.932Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:24:59.867Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:25:01.790Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:25:01.790Z] 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. [2024-11-17T22:25:01.790Z] The best model improves the baseline by 14.52%. [2024-11-17T22:25:01.790Z] Movies recommended for you: [2024-11-17T22:25:01.790Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:25:01.790Z] There is no way to check that no silent failure occurred. [2024-11-17T22:25:01.790Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (15562.011 ms) ====== [2024-11-17T22:25:01.790Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-11-17T22:25:01.790Z] GC before operation: completed in 103.835 ms, heap usage 82.861 MB -> 49.675 MB. [2024-11-17T22:25:04.760Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:25:06.714Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:25:08.640Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:25:11.622Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:25:12.559Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:25:13.498Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:25:15.422Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:25:16.358Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:25:18.325Z] 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. [2024-11-17T22:25:18.325Z] The best model improves the baseline by 14.52%. [2024-11-17T22:25:18.325Z] Movies recommended for you: [2024-11-17T22:25:18.325Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:25:18.325Z] There is no way to check that no silent failure occurred. [2024-11-17T22:25:18.325Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (15292.123 ms) ====== [2024-11-17T22:25:18.325Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-11-17T22:25:18.325Z] GC before operation: completed in 96.346 ms, heap usage 189.442 MB -> 49.953 MB. [2024-11-17T22:25:19.435Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:25:21.357Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:25:24.327Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:25:26.248Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:25:27.185Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:25:29.106Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:25:30.041Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:25:31.969Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:25:31.969Z] 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. [2024-11-17T22:25:31.969Z] The best model improves the baseline by 14.52%. [2024-11-17T22:25:31.969Z] Movies recommended for you: [2024-11-17T22:25:31.969Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:25:31.969Z] There is no way to check that no silent failure occurred. [2024-11-17T22:25:31.969Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14909.579 ms) ====== [2024-11-17T22:25:31.969Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-11-17T22:25:31.969Z] GC before operation: completed in 94.551 ms, heap usage 311.983 MB -> 50.380 MB. [2024-11-17T22:25:34.941Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:25:36.869Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:25:38.805Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:25:41.781Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:25:42.717Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:25:43.652Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:25:45.573Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:25:46.510Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:25:47.530Z] 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. [2024-11-17T22:25:47.530Z] The best model improves the baseline by 14.52%. [2024-11-17T22:25:47.530Z] Movies recommended for you: [2024-11-17T22:25:47.530Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:25:47.530Z] There is no way to check that no silent failure occurred. [2024-11-17T22:25:47.530Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (14918.553 ms) ====== [2024-11-17T22:25:47.530Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-11-17T22:25:47.530Z] GC before operation: completed in 103.991 ms, heap usage 197.991 MB -> 50.055 MB. [2024-11-17T22:25:49.452Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:25:51.372Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:25:54.341Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:25:56.262Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:25:57.198Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:25:59.120Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:26:00.062Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:26:01.983Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:26:01.983Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-11-17T22:26:01.983Z] The best model improves the baseline by 14.52%. [2024-11-17T22:26:01.983Z] Movies recommended for you: [2024-11-17T22:26:01.983Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:26:01.983Z] There is no way to check that no silent failure occurred. [2024-11-17T22:26:01.983Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (14672.657 ms) ====== [2024-11-17T22:26:01.983Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-11-17T22:26:01.983Z] GC before operation: completed in 92.574 ms, heap usage 79.574 MB -> 50.039 MB. [2024-11-17T22:26:04.948Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:26:06.869Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:26:08.802Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:26:10.725Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:26:12.648Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:26:13.583Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:26:15.506Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:26:16.442Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:26:16.442Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-11-17T22:26:16.442Z] The best model improves the baseline by 14.52%. [2024-11-17T22:26:18.247Z] Movies recommended for you: [2024-11-17T22:26:18.247Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:26:18.247Z] There is no way to check that no silent failure occurred. [2024-11-17T22:26:18.247Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (14869.826 ms) ====== [2024-11-17T22:26:18.247Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-11-17T22:26:18.247Z] GC before operation: completed in 99.503 ms, heap usage 177.965 MB -> 49.888 MB. [2024-11-17T22:26:19.514Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:26:21.442Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:26:23.368Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:26:26.344Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:26:27.280Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:26:29.201Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:26:30.137Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:26:31.074Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:26:32.009Z] 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. [2024-11-17T22:26:32.009Z] The best model improves the baseline by 14.52%. [2024-11-17T22:26:32.009Z] Movies recommended for you: [2024-11-17T22:26:32.009Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:26:32.009Z] There is no way to check that no silent failure occurred. [2024-11-17T22:26:32.009Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (14794.104 ms) ====== [2024-11-17T22:26:32.009Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-11-17T22:26:32.009Z] GC before operation: completed in 99.587 ms, heap usage 188.133 MB -> 50.106 MB. [2024-11-17T22:26:33.934Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:26:36.906Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:26:38.827Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:26:40.750Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:26:42.672Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:26:43.607Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:26:45.527Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:26:46.462Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:26:47.527Z] 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. [2024-11-17T22:26:47.527Z] The best model improves the baseline by 14.52%. [2024-11-17T22:26:47.527Z] Movies recommended for you: [2024-11-17T22:26:47.527Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:26:47.527Z] There is no way to check that no silent failure occurred. [2024-11-17T22:26:47.527Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (15098.843 ms) ====== [2024-11-17T22:26:47.527Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-11-17T22:26:47.527Z] GC before operation: completed in 93.682 ms, heap usage 131.929 MB -> 50.162 MB. [2024-11-17T22:26:49.460Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:26:51.380Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:26:54.346Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:26:56.268Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:26:57.206Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:26:59.127Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:27:00.063Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:27:01.989Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:27:01.989Z] 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. [2024-11-17T22:27:01.989Z] The best model improves the baseline by 14.52%. [2024-11-17T22:27:01.989Z] Movies recommended for you: [2024-11-17T22:27:01.989Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:27:01.989Z] There is no way to check that no silent failure occurred. [2024-11-17T22:27:01.989Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14853.588 ms) ====== [2024-11-17T22:27:01.989Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-11-17T22:27:01.989Z] GC before operation: completed in 94.438 ms, heap usage 249.087 MB -> 50.080 MB. [2024-11-17T22:27:04.959Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:27:07.054Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:27:08.978Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:27:10.903Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:27:11.846Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:27:13.769Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:27:14.706Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:27:16.629Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:27:16.629Z] 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. [2024-11-17T22:27:16.629Z] The best model improves the baseline by 14.52%. [2024-11-17T22:27:16.629Z] Movies recommended for you: [2024-11-17T22:27:16.629Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:27:16.629Z] There is no way to check that no silent failure occurred. [2024-11-17T22:27:16.629Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (14737.673 ms) ====== [2024-11-17T22:27:16.629Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-11-17T22:27:16.629Z] GC before operation: completed in 94.375 ms, heap usage 105.571 MB -> 50.060 MB. [2024-11-17T22:27:19.748Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:27:21.675Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:27:23.604Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:27:25.557Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:27:27.517Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:27:28.453Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:27:30.374Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:27:31.310Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:27:32.245Z] 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. [2024-11-17T22:27:32.245Z] The best model improves the baseline by 14.52%. [2024-11-17T22:27:32.245Z] Movies recommended for you: [2024-11-17T22:27:32.245Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:27:32.245Z] There is no way to check that no silent failure occurred. [2024-11-17T22:27:32.245Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (14947.967 ms) ====== [2024-11-17T22:27:32.245Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-11-17T22:27:32.245Z] GC before operation: completed in 94.749 ms, heap usage 274.028 MB -> 50.340 MB. [2024-11-17T22:27:34.172Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:27:36.093Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:27:39.061Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:27:41.126Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:27:42.062Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:27:43.987Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:27:44.924Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:27:45.862Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:27:46.798Z] 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. [2024-11-17T22:27:46.798Z] The best model improves the baseline by 14.52%. [2024-11-17T22:27:46.798Z] Movies recommended for you: [2024-11-17T22:27:46.798Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:27:46.798Z] There is no way to check that no silent failure occurred. [2024-11-17T22:27:46.799Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (14724.628 ms) ====== [2024-11-17T22:27:46.799Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-11-17T22:27:46.799Z] GC before operation: completed in 106.102 ms, heap usage 198.391 MB -> 50.084 MB. [2024-11-17T22:27:48.724Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:27:51.701Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:27:53.624Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:27:55.548Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:27:57.471Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:27:58.412Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:27:59.348Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:28:01.275Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:28:01.275Z] 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. [2024-11-17T22:28:01.275Z] The best model improves the baseline by 14.52%. [2024-11-17T22:28:01.275Z] Movies recommended for you: [2024-11-17T22:28:01.275Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:28:01.275Z] There is no way to check that no silent failure occurred. [2024-11-17T22:28:01.275Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (14650.452 ms) ====== [2024-11-17T22:28:01.275Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-11-17T22:28:01.275Z] GC before operation: completed in 91.730 ms, heap usage 298.597 MB -> 50.248 MB. [2024-11-17T22:28:04.245Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:28:06.167Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:28:08.090Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:28:10.046Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:28:11.969Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:28:13.718Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:28:14.655Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:28:15.592Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:28:16.529Z] 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. [2024-11-17T22:28:16.529Z] The best model improves the baseline by 14.52%. [2024-11-17T22:28:16.529Z] Movies recommended for you: [2024-11-17T22:28:16.529Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:28:16.529Z] There is no way to check that no silent failure occurred. [2024-11-17T22:28:16.529Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (14651.330 ms) ====== [2024-11-17T22:28:16.529Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-11-17T22:28:16.529Z] GC before operation: completed in 93.427 ms, heap usage 151.105 MB -> 50.264 MB. [2024-11-17T22:28:18.452Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-11-17T22:28:20.373Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-11-17T22:28:23.338Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-11-17T22:28:25.261Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-11-17T22:28:26.197Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-11-17T22:28:28.122Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-11-17T22:28:29.059Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-11-17T22:28:30.982Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-11-17T22:28:30.982Z] 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. [2024-11-17T22:28:30.982Z] The best model improves the baseline by 14.52%. [2024-11-17T22:28:30.982Z] Movies recommended for you: [2024-11-17T22:28:30.982Z] WARNING: This benchmark provides no result that can be validated. [2024-11-17T22:28:30.983Z] There is no way to check that no silent failure occurred. [2024-11-17T22:28:30.983Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14769.591 ms) ====== [2024-11-17T22:28:31.920Z] ----------------------------------- [2024-11-17T22:28:31.920Z] renaissance-movie-lens_0_PASSED [2024-11-17T22:28:31.920Z] ----------------------------------- [2024-11-17T22:28:31.920Z] [2024-11-17T22:28:31.920Z] TEST TEARDOWN: [2024-11-17T22:28:31.920Z] Nothing to be done for teardown. [2024-11-17T22:28:31.920Z] renaissance-movie-lens_0 Finish Time: Sun Nov 17 22:28:31 2024 Epoch Time (ms): 1731882511165