[2025-03-06T00:55:28.094Z] Running test renaissance-movie-lens_0 ... [2025-03-06T00:55:28.094Z] =============================================== [2025-03-06T00:55:28.094Z] renaissance-movie-lens_0 Start Time: Thu Mar 6 00:55:27 2025 Epoch Time (ms): 1741222527038 [2025-03-06T00:55:28.094Z] variation: NoOptions [2025-03-06T00:55:28.094Z] JVM_OPTIONS: [2025-03-06T00:55:28.094Z] { \ [2025-03-06T00:55:28.094Z] echo ""; echo "TEST SETUP:"; \ [2025-03-06T00:55:28.094Z] echo "Nothing to be done for setup."; \ [2025-03-06T00:55:28.094Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17412198715947/renaissance-movie-lens_0"; \ [2025-03-06T00:55:28.094Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17412198715947/renaissance-movie-lens_0"; \ [2025-03-06T00:55:28.094Z] echo ""; echo "TESTING:"; \ [2025-03-06T00:55:28.094Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_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_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17412198715947/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2025-03-06T00:55:28.094Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17412198715947/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-03-06T00:55:28.094Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-03-06T00:55:28.094Z] echo "Nothing to be done for teardown."; \ [2025-03-06T00:55:28.094Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17412198715947/TestTargetResult"; [2025-03-06T00:55:28.094Z] [2025-03-06T00:55:28.094Z] TEST SETUP: [2025-03-06T00:55:28.094Z] Nothing to be done for setup. [2025-03-06T00:55:28.094Z] [2025-03-06T00:55:28.094Z] TESTING: [2025-03-06T00:55:32.117Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-03-06T00:55:35.199Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads. [2025-03-06T00:55:43.005Z] Got 100004 ratings from 671 users on 9066 movies. [2025-03-06T00:55:43.709Z] Training: 60056, validation: 20285, test: 19854 [2025-03-06T00:55:43.709Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-03-06T00:55:43.709Z] GC before operation: completed in 244.595 ms, heap usage 52.492 MB -> 36.362 MB. [2025-03-06T00:55:58.811Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:56:07.920Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:56:17.015Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:56:25.099Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:56:30.589Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:56:34.760Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:56:39.019Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:56:43.240Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:56:44.698Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T00:56:44.699Z] The best model improves the baseline by 14.34%. [2025-03-06T00:56:45.419Z] Movies recommended for you: [2025-03-06T00:56:45.419Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:56:45.419Z] There is no way to check that no silent failure occurred. [2025-03-06T00:56:45.419Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (61219.531 ms) ====== [2025-03-06T00:56:45.419Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-03-06T00:56:45.419Z] GC before operation: completed in 150.839 ms, heap usage 144.041 MB -> 47.853 MB. [2025-03-06T00:56:51.645Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:56:58.083Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:57:06.050Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:57:12.807Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:57:17.960Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:57:21.204Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:57:24.778Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:57:26.748Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:57:27.404Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T00:57:27.404Z] The best model improves the baseline by 14.34%. [2025-03-06T00:57:27.404Z] Movies recommended for you: [2025-03-06T00:57:27.404Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:57:27.404Z] There is no way to check that no silent failure occurred. [2025-03-06T00:57:27.404Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (42255.173 ms) ====== [2025-03-06T00:57:27.404Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-03-06T00:57:28.088Z] GC before operation: completed in 235.607 ms, heap usage 341.826 MB -> 51.830 MB. [2025-03-06T00:57:34.268Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:57:41.828Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:57:48.014Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:57:52.396Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:57:55.407Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:57:58.458Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:58:02.623Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:58:05.676Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:58:06.546Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T00:58:07.340Z] The best model improves the baseline by 14.34%. [2025-03-06T00:58:07.340Z] Movies recommended for you: [2025-03-06T00:58:07.340Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:58:07.340Z] There is no way to check that no silent failure occurred. [2025-03-06T00:58:07.340Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (39589.174 ms) ====== [2025-03-06T00:58:07.340Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-03-06T00:58:07.984Z] GC before operation: completed in 319.064 ms, heap usage 91.738 MB -> 50.908 MB. [2025-03-06T00:58:14.621Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:58:23.141Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:58:29.618Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:58:34.769Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:58:38.921Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:58:43.326Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:58:49.880Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:58:54.443Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:58:54.443Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T00:58:54.443Z] The best model improves the baseline by 14.34%. [2025-03-06T00:58:54.443Z] Movies recommended for you: [2025-03-06T00:58:54.443Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:58:54.443Z] There is no way to check that no silent failure occurred. [2025-03-06T00:58:54.443Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (46873.444 ms) ====== [2025-03-06T00:58:54.443Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-03-06T00:58:55.237Z] GC before operation: completed in 291.321 ms, heap usage 95.020 MB -> 48.923 MB. [2025-03-06T00:59:04.038Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:59:11.894Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:59:20.640Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:59:24.605Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:59:28.637Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:59:31.729Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:59:34.851Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:59:37.019Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:59:37.789Z] 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. [2025-03-06T00:59:37.789Z] The best model improves the baseline by 14.34%. [2025-03-06T00:59:38.480Z] Movies recommended for you: [2025-03-06T00:59:38.480Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:59:38.480Z] There is no way to check that no silent failure occurred. [2025-03-06T00:59:38.480Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (43391.967 ms) ====== [2025-03-06T00:59:38.480Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-03-06T00:59:38.480Z] GC before operation: completed in 242.910 ms, heap usage 113.131 MB -> 49.100 MB. [2025-03-06T00:59:46.903Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:59:54.705Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:59:59.628Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:00:03.514Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:00:08.824Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:00:11.207Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:00:15.630Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:00:17.779Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:00:17.779Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:00:18.422Z] The best model improves the baseline by 14.34%. [2025-03-06T01:00:18.422Z] Movies recommended for you: [2025-03-06T01:00:18.422Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:00:18.422Z] There is no way to check that no silent failure occurred. [2025-03-06T01:00:18.422Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (39731.492 ms) ====== [2025-03-06T01:00:18.422Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-03-06T01:00:18.422Z] GC before operation: completed in 319.878 ms, heap usage 247.786 MB -> 49.242 MB. [2025-03-06T01:00:29.194Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:00:34.036Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:00:39.804Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:00:45.299Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:00:49.890Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:00:52.992Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:00:57.196Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:01:00.363Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:01:02.433Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:01:03.121Z] The best model improves the baseline by 14.34%. [2025-03-06T01:01:03.122Z] Movies recommended for you: [2025-03-06T01:01:03.122Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:01:03.122Z] There is no way to check that no silent failure occurred. [2025-03-06T01:01:03.122Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (44730.669 ms) ====== [2025-03-06T01:01:03.122Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-03-06T01:01:03.801Z] GC before operation: completed in 257.540 ms, heap usage 214.431 MB -> 49.361 MB. [2025-03-06T01:01:10.261Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:01:20.498Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:01:25.733Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:01:31.926Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:01:34.119Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:01:37.153Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:01:41.277Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:01:44.436Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:01:45.102Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:01:45.102Z] The best model improves the baseline by 14.34%. [2025-03-06T01:01:45.839Z] Movies recommended for you: [2025-03-06T01:01:45.839Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:01:45.839Z] There is no way to check that no silent failure occurred. [2025-03-06T01:01:45.839Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (41862.865 ms) ====== [2025-03-06T01:01:45.839Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-03-06T01:01:45.839Z] GC before operation: completed in 426.326 ms, heap usage 138.470 MB -> 49.541 MB. [2025-03-06T01:01:54.207Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:01:59.439Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:02:07.649Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:02:12.632Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:02:16.598Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:02:19.654Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:02:23.774Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:02:26.952Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:02:27.615Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:02:27.615Z] The best model improves the baseline by 14.34%. [2025-03-06T01:02:27.615Z] Movies recommended for you: [2025-03-06T01:02:27.615Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:02:27.615Z] There is no way to check that no silent failure occurred. [2025-03-06T01:02:27.615Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (41936.168 ms) ====== [2025-03-06T01:02:27.615Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-03-06T01:02:28.298Z] GC before operation: completed in 163.031 ms, heap usage 315.866 MB -> 52.740 MB. [2025-03-06T01:02:34.580Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:02:39.647Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:02:47.554Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:02:56.896Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:03:02.115Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:03:07.271Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:03:10.245Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:03:14.199Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:03:14.199Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:03:14.199Z] The best model improves the baseline by 14.34%. [2025-03-06T01:03:14.199Z] Movies recommended for you: [2025-03-06T01:03:14.199Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:03:14.199Z] There is no way to check that no silent failure occurred. [2025-03-06T01:03:14.199Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (46466.219 ms) ====== [2025-03-06T01:03:14.199Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-03-06T01:03:14.862Z] GC before operation: completed in 277.696 ms, heap usage 229.449 MB -> 51.744 MB. [2025-03-06T01:03:22.594Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:03:30.834Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:03:36.973Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:03:42.337Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:03:45.429Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:03:49.366Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:03:54.123Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:03:56.295Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:03:56.967Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:03:56.967Z] The best model improves the baseline by 14.34%. [2025-03-06T01:03:56.967Z] Movies recommended for you: [2025-03-06T01:03:56.967Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:03:56.967Z] There is no way to check that no silent failure occurred. [2025-03-06T01:03:56.967Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (42413.391 ms) ====== [2025-03-06T01:03:56.967Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-03-06T01:03:57.618Z] GC before operation: completed in 211.168 ms, heap usage 229.242 MB -> 49.315 MB. [2025-03-06T01:04:02.943Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:04:08.015Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:04:15.909Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:04:23.540Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:04:26.789Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:04:29.837Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:04:33.878Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:04:36.939Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:04:36.939Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:04:36.939Z] The best model improves the baseline by 14.34%. [2025-03-06T01:04:36.939Z] Movies recommended for you: [2025-03-06T01:04:36.939Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:04:36.939Z] There is no way to check that no silent failure occurred. [2025-03-06T01:04:36.939Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (39897.369 ms) ====== [2025-03-06T01:04:36.939Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-03-06T01:04:37.606Z] GC before operation: completed in 185.531 ms, heap usage 208.045 MB -> 49.478 MB. [2025-03-06T01:04:43.141Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:04:48.203Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:04:53.431Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:04:59.939Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:05:04.714Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:05:10.215Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:05:14.294Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:05:17.356Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:05:19.080Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:05:19.080Z] The best model improves the baseline by 14.34%. [2025-03-06T01:05:19.908Z] Movies recommended for you: [2025-03-06T01:05:19.908Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:05:19.908Z] There is no way to check that no silent failure occurred. [2025-03-06T01:05:19.908Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (42625.468 ms) ====== [2025-03-06T01:05:19.908Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-03-06T01:05:21.591Z] GC before operation: completed in 1173.375 ms, heap usage 116.450 MB -> 49.499 MB. [2025-03-06T01:05:27.870Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:05:37.560Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:05:45.693Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:05:48.779Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:05:53.946Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:05:58.139Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:06:02.320Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:06:05.333Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:06:05.333Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:06:05.333Z] The best model improves the baseline by 14.34%. [2025-03-06T01:06:05.999Z] Movies recommended for you: [2025-03-06T01:06:05.999Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:06:05.999Z] There is no way to check that no silent failure occurred. [2025-03-06T01:06:05.999Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (44540.665 ms) ====== [2025-03-06T01:06:05.999Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-03-06T01:06:05.999Z] GC before operation: completed in 179.848 ms, heap usage 244.553 MB -> 51.645 MB. [2025-03-06T01:06:12.368Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:06:17.683Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:06:26.144Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:06:32.981Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:06:38.894Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:06:42.093Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:06:45.081Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:06:47.543Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:06:48.298Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:06:48.298Z] The best model improves the baseline by 14.34%. [2025-03-06T01:06:48.298Z] Movies recommended for you: [2025-03-06T01:06:48.298Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:06:48.298Z] There is no way to check that no silent failure occurred. [2025-03-06T01:06:48.298Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (42635.091 ms) ====== [2025-03-06T01:06:48.298Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-03-06T01:06:49.002Z] GC before operation: completed in 141.195 ms, heap usage 102.786 MB -> 49.411 MB. [2025-03-06T01:06:55.364Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:07:03.432Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:07:11.765Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:07:21.705Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:07:25.792Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:07:28.791Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:07:34.866Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:07:37.100Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:07:37.811Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:07:37.811Z] The best model improves the baseline by 14.34%. [2025-03-06T01:07:38.504Z] Movies recommended for you: [2025-03-06T01:07:38.504Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:07:38.504Z] There is no way to check that no silent failure occurred. [2025-03-06T01:07:38.504Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (49627.168 ms) ====== [2025-03-06T01:07:38.504Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-03-06T01:07:38.504Z] GC before operation: completed in 408.903 ms, heap usage 147.042 MB -> 49.549 MB. [2025-03-06T01:07:47.666Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:07:53.109Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:08:05.131Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:08:12.029Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:08:15.109Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:08:17.344Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:08:21.938Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:08:24.251Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:08:24.993Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:08:25.845Z] The best model improves the baseline by 14.34%. [2025-03-06T01:08:25.845Z] Movies recommended for you: [2025-03-06T01:08:25.845Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:08:25.845Z] There is no way to check that no silent failure occurred. [2025-03-06T01:08:25.845Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (47046.000 ms) ====== [2025-03-06T01:08:25.845Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-03-06T01:08:26.708Z] GC before operation: completed in 1023.340 ms, heap usage 342.377 MB -> 52.785 MB. [2025-03-06T01:08:34.910Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:08:41.309Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:08:51.108Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:08:58.597Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:09:02.032Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:09:07.179Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:09:10.278Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:09:13.676Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:09:14.370Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:09:14.370Z] The best model improves the baseline by 14.34%. [2025-03-06T01:09:14.370Z] Movies recommended for you: [2025-03-06T01:09:14.370Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:09:14.370Z] There is no way to check that no silent failure occurred. [2025-03-06T01:09:14.370Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (47355.621 ms) ====== [2025-03-06T01:09:14.370Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-03-06T01:09:14.370Z] GC before operation: completed in 222.946 ms, heap usage 296.107 MB -> 49.756 MB. [2025-03-06T01:09:27.668Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:09:37.565Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:09:42.970Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:09:48.109Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:09:50.242Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:09:54.278Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:09:57.310Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:10:00.476Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:10:00.476Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:10:00.476Z] The best model improves the baseline by 14.34%. [2025-03-06T01:10:01.329Z] Movies recommended for you: [2025-03-06T01:10:01.329Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:10:01.329Z] There is no way to check that no silent failure occurred. [2025-03-06T01:10:01.329Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (46778.692 ms) ====== [2025-03-06T01:10:01.329Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-03-06T01:10:02.052Z] GC before operation: completed in 980.358 ms, heap usage 374.153 MB -> 53.072 MB. [2025-03-06T01:10:08.278Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:10:14.930Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:10:25.050Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:10:30.142Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:10:33.356Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:10:36.505Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:10:41.912Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:10:45.319Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:10:46.003Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572. [2025-03-06T01:10:46.003Z] The best model improves the baseline by 14.34%. [2025-03-06T01:10:46.003Z] Movies recommended for you: [2025-03-06T01:10:46.003Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:10:46.003Z] There is no way to check that no silent failure occurred. [2025-03-06T01:10:46.003Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (43834.174 ms) ====== [2025-03-06T01:10:47.664Z] ----------------------------------- [2025-03-06T01:10:47.664Z] renaissance-movie-lens_0_PASSED [2025-03-06T01:10:47.664Z] ----------------------------------- [2025-03-06T01:10:47.664Z] [2025-03-06T01:10:47.664Z] TEST TEARDOWN: [2025-03-06T01:10:47.664Z] Nothing to be done for teardown. [2025-03-06T01:10:47.664Z] renaissance-movie-lens_0 Finish Time: Thu Mar 6 01:10:47 2025 Epoch Time (ms): 1741223447508