[2025-03-06T00:51:46.678Z] Running test renaissance-movie-lens_0 ... [2025-03-06T00:51:46.678Z] =============================================== [2025-03-06T00:51:47.016Z] renaissance-movie-lens_0 Start Time: Thu Mar 6 00:51:46 2025 Epoch Time (ms): 1741222306718 [2025-03-06T00:51:47.016Z] variation: NoOptions [2025-03-06T00:51:47.016Z] JVM_OPTIONS: [2025-03-06T00:51:47.016Z] { \ [2025-03-06T00:51:47.016Z] echo ""; echo "TEST SETUP:"; \ [2025-03-06T00:51:47.016Z] echo "Nothing to be done for setup."; \ [2025-03-06T00:51:47.016Z] mkdir -p "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17412209919842\\renaissance-movie-lens_0"; \ [2025-03-06T00:51:47.016Z] cd "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17412209919842\\renaissance-movie-lens_0"; \ [2025-03-06T00:51:47.016Z] echo ""; echo "TESTING:"; \ [2025-03-06T00:51:47.016Z] "c:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/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/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17412209919842\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2025-03-06T00:51:47.016Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17412209919842\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2025-03-06T00:51:47.016Z] echo ""; echo "TEST TEARDOWN:"; \ [2025-03-06T00:51:47.016Z] echo "Nothing to be done for teardown."; \ [2025-03-06T00:51:47.016Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17412209919842\\TestTargetResult"; [2025-03-06T00:51:47.334Z] [2025-03-06T00:51:47.334Z] TEST SETUP: [2025-03-06T00:51:47.334Z] Nothing to be done for setup. [2025-03-06T00:51:47.334Z] [2025-03-06T00:51:47.334Z] TESTING: [2025-03-06T00:52:00.300Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2025-03-06T00:52:01.938Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2025-03-06T00:52:05.777Z] Got 100004 ratings from 671 users on 9066 movies. [2025-03-06T00:52:05.777Z] Training: 60056, validation: 20285, test: 19854 [2025-03-06T00:52:05.777Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2025-03-06T00:52:05.777Z] GC before operation: completed in 69.190 ms, heap usage 51.795 MB -> 36.914 MB. [2025-03-06T00:52:18.961Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:52:27.878Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:52:36.787Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:52:45.573Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:52:49.323Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:52:54.038Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:52:58.760Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:53:03.481Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:53:03.481Z] 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. [2025-03-06T00:53:03.481Z] The best model improves the baseline by 14.52%. [2025-03-06T00:53:03.812Z] Movies recommended for you: [2025-03-06T00:53:03.812Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:53:03.812Z] There is no way to check that no silent failure occurred. [2025-03-06T00:53:03.812Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (58241.986 ms) ====== [2025-03-06T00:53:03.812Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2025-03-06T00:53:03.812Z] GC before operation: completed in 104.339 ms, heap usage 143.019 MB -> 48.556 MB. [2025-03-06T00:53:11.071Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:53:19.852Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:53:27.035Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:53:34.206Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:53:38.876Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:53:42.594Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:53:47.298Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:53:51.030Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:53:51.748Z] 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. [2025-03-06T00:53:51.748Z] The best model improves the baseline by 14.52%. [2025-03-06T00:53:51.748Z] Movies recommended for you: [2025-03-06T00:53:51.748Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:53:51.748Z] There is no way to check that no silent failure occurred. [2025-03-06T00:53:51.748Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (47973.860 ms) ====== [2025-03-06T00:53:51.748Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2025-03-06T00:53:52.077Z] GC before operation: completed in 93.617 ms, heap usage 205.313 MB -> 52.771 MB. [2025-03-06T00:53:59.257Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:54:06.456Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:54:15.328Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:54:21.137Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:54:25.809Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:54:29.638Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:54:33.373Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:54:38.050Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:54:38.050Z] 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. [2025-03-06T00:54:38.050Z] The best model improves the baseline by 14.52%. [2025-03-06T00:54:38.050Z] Movies recommended for you: [2025-03-06T00:54:38.050Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:54:38.051Z] There is no way to check that no silent failure occurred. [2025-03-06T00:54:38.051Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (46149.191 ms) ====== [2025-03-06T00:54:38.051Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2025-03-06T00:54:38.051Z] GC before operation: completed in 98.473 ms, heap usage 224.729 MB -> 53.049 MB. [2025-03-06T00:54:45.228Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:54:52.427Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:55:01.282Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:55:08.473Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:55:12.245Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:55:15.978Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:55:20.673Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:55:25.399Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:55:25.399Z] 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. [2025-03-06T00:55:25.399Z] The best model improves the baseline by 14.52%. [2025-03-06T00:55:25.749Z] Movies recommended for you: [2025-03-06T00:55:25.749Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:55:25.749Z] There is no way to check that no silent failure occurred. [2025-03-06T00:55:25.749Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (47375.482 ms) ====== [2025-03-06T00:55:25.749Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2025-03-06T00:55:25.749Z] GC before operation: completed in 102.818 ms, heap usage 134.255 MB -> 50.055 MB. [2025-03-06T00:55:32.913Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:55:40.079Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:55:48.905Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:55:54.785Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:55:59.481Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:56:03.210Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:56:07.911Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:56:11.637Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:56:12.354Z] 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. [2025-03-06T00:56:12.354Z] The best model improves the baseline by 14.52%. [2025-03-06T00:56:12.354Z] Movies recommended for you: [2025-03-06T00:56:12.354Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:56:12.354Z] There is no way to check that no silent failure occurred. [2025-03-06T00:56:12.354Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (46669.699 ms) ====== [2025-03-06T00:56:12.354Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2025-03-06T00:56:12.354Z] GC before operation: completed in 86.375 ms, heap usage 154.254 MB -> 50.276 MB. [2025-03-06T00:56:19.544Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:56:26.709Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:56:33.887Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:56:41.066Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:56:44.801Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:56:48.520Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:56:53.199Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:56:57.873Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:56:57.873Z] 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. [2025-03-06T00:56:57.873Z] The best model improves the baseline by 14.52%. [2025-03-06T00:56:57.873Z] Movies recommended for you: [2025-03-06T00:56:57.873Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:56:57.873Z] There is no way to check that no silent failure occurred. [2025-03-06T00:56:57.873Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (45472.322 ms) ====== [2025-03-06T00:56:57.873Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2025-03-06T00:56:57.873Z] GC before operation: completed in 92.133 ms, heap usage 118.271 MB -> 50.172 MB. [2025-03-06T00:57:05.060Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:57:12.238Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:57:21.012Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:57:26.847Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:57:31.525Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:57:35.299Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:57:39.991Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:57:43.717Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:57:44.047Z] 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. [2025-03-06T00:57:44.047Z] The best model improves the baseline by 14.52%. [2025-03-06T00:57:44.398Z] Movies recommended for you: [2025-03-06T00:57:44.398Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:57:44.398Z] There is no way to check that no silent failure occurred. [2025-03-06T00:57:44.398Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (46294.422 ms) ====== [2025-03-06T00:57:44.398Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2025-03-06T00:57:44.398Z] GC before operation: completed in 95.748 ms, heap usage 71.148 MB -> 51.444 MB. [2025-03-06T00:57:51.593Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:58:00.398Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:58:07.563Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:58:14.730Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:58:18.463Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:58:22.184Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:58:26.849Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:58:30.599Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:58:30.937Z] 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. [2025-03-06T00:58:30.937Z] The best model improves the baseline by 14.52%. [2025-03-06T00:58:30.937Z] Movies recommended for you: [2025-03-06T00:58:30.937Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:58:30.937Z] There is no way to check that no silent failure occurred. [2025-03-06T00:58:30.937Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (46650.008 ms) ====== [2025-03-06T00:58:30.937Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2025-03-06T00:58:31.278Z] GC before operation: completed in 100.016 ms, heap usage 200.882 MB -> 50.671 MB. [2025-03-06T00:58:38.422Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:58:47.214Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:58:54.369Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:59:01.570Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:59:04.487Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:59:09.171Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:59:12.876Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T00:59:16.601Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T00:59:17.364Z] 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. [2025-03-06T00:59:17.364Z] The best model improves the baseline by 14.52%. [2025-03-06T00:59:17.364Z] Movies recommended for you: [2025-03-06T00:59:17.364Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T00:59:17.364Z] There is no way to check that no silent failure occurred. [2025-03-06T00:59:17.364Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (46210.293 ms) ====== [2025-03-06T00:59:17.364Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2025-03-06T00:59:17.364Z] GC before operation: completed in 101.423 ms, heap usage 261.356 MB -> 53.843 MB. [2025-03-06T00:59:24.529Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T00:59:33.345Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T00:59:40.601Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T00:59:47.826Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T00:59:51.568Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T00:59:55.332Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T00:59:59.984Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:00:03.727Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:00:04.060Z] 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. [2025-03-06T01:00:04.060Z] The best model improves the baseline by 14.52%. [2025-03-06T01:00:04.060Z] Movies recommended for you: [2025-03-06T01:00:04.060Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:00:04.060Z] There is no way to check that no silent failure occurred. [2025-03-06T01:00:04.061Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (46703.695 ms) ====== [2025-03-06T01:00:04.061Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2025-03-06T01:00:04.406Z] GC before operation: completed in 101.460 ms, heap usage 108.492 MB -> 54.988 MB. [2025-03-06T01:00:11.645Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:00:18.821Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:00:27.611Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:00:33.423Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:00:37.144Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:00:41.900Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:00:46.599Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:00:50.350Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:00:50.350Z] 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. [2025-03-06T01:00:50.350Z] The best model improves the baseline by 14.52%. [2025-03-06T01:00:50.350Z] Movies recommended for you: [2025-03-06T01:00:50.350Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:00:50.350Z] There is no way to check that no silent failure occurred. [2025-03-06T01:00:50.350Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (46182.953 ms) ====== [2025-03-06T01:00:50.350Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2025-03-06T01:00:50.694Z] GC before operation: completed in 98.322 ms, heap usage 102.591 MB -> 53.523 MB. [2025-03-06T01:00:58.048Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:01:05.248Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:01:12.414Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:01:19.585Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:01:23.295Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:01:27.198Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:01:30.916Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:01:35.599Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:01:35.599Z] 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. [2025-03-06T01:01:35.599Z] The best model improves the baseline by 14.52%. [2025-03-06T01:01:35.599Z] Movies recommended for you: [2025-03-06T01:01:35.599Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:01:35.599Z] There is no way to check that no silent failure occurred. [2025-03-06T01:01:35.599Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (45100.659 ms) ====== [2025-03-06T01:01:35.599Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2025-03-06T01:01:35.599Z] GC before operation: completed in 98.661 ms, heap usage 211.369 MB -> 53.725 MB. [2025-03-06T01:01:42.779Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:01:50.169Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:01:57.335Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:02:04.551Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:02:08.287Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:02:12.027Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:02:16.709Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:02:20.423Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:02:20.780Z] 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. [2025-03-06T01:02:20.780Z] The best model improves the baseline by 14.52%. [2025-03-06T01:02:20.780Z] Movies recommended for you: [2025-03-06T01:02:20.780Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:02:20.780Z] There is no way to check that no silent failure occurred. [2025-03-06T01:02:20.780Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (45122.445 ms) ====== [2025-03-06T01:02:20.780Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2025-03-06T01:02:21.121Z] GC before operation: completed in 109.167 ms, heap usage 199.782 MB -> 53.938 MB. [2025-03-06T01:02:28.285Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:02:35.459Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:02:44.246Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:02:51.430Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:02:55.149Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:02:58.877Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:03:03.583Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:03:07.308Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:03:08.020Z] 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. [2025-03-06T01:03:08.020Z] The best model improves the baseline by 14.52%. [2025-03-06T01:03:08.020Z] Movies recommended for you: [2025-03-06T01:03:08.020Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:03:08.020Z] There is no way to check that no silent failure occurred. [2025-03-06T01:03:08.020Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (47138.934 ms) ====== [2025-03-06T01:03:08.020Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2025-03-06T01:03:08.434Z] GC before operation: completed in 97.024 ms, heap usage 102.585 MB -> 50.372 MB. [2025-03-06T01:03:15.619Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:03:22.784Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:03:29.983Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:03:37.155Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:03:40.881Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:03:44.639Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:03:49.350Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:03:53.125Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:03:53.460Z] 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. [2025-03-06T01:03:53.460Z] The best model improves the baseline by 14.52%. [2025-03-06T01:03:53.460Z] Movies recommended for you: [2025-03-06T01:03:53.460Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:03:53.460Z] There is no way to check that no silent failure occurred. [2025-03-06T01:03:53.460Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (45336.200 ms) ====== [2025-03-06T01:03:53.460Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2025-03-06T01:03:53.791Z] GC before operation: completed in 103.124 ms, heap usage 121.508 MB -> 52.739 MB. [2025-03-06T01:04:00.958Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:04:08.158Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:04:16.988Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:04:22.825Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:04:26.618Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:04:31.308Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:04:35.048Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:04:39.758Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:04:39.758Z] 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. [2025-03-06T01:04:39.758Z] The best model improves the baseline by 14.52%. [2025-03-06T01:04:39.758Z] Movies recommended for you: [2025-03-06T01:04:39.758Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:04:39.758Z] There is no way to check that no silent failure occurred. [2025-03-06T01:04:39.758Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (46191.752 ms) ====== [2025-03-06T01:04:39.758Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2025-03-06T01:04:40.096Z] GC before operation: completed in 104.788 ms, heap usage 196.570 MB -> 50.696 MB. [2025-03-06T01:04:47.285Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:04:54.571Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:05:01.811Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:05:08.984Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:05:12.712Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:05:16.451Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:05:21.132Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:05:24.838Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:05:25.207Z] 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. [2025-03-06T01:05:25.207Z] The best model improves the baseline by 14.52%. [2025-03-06T01:05:25.207Z] Movies recommended for you: [2025-03-06T01:05:25.207Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:05:25.207Z] There is no way to check that no silent failure occurred. [2025-03-06T01:05:25.207Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (45354.192 ms) ====== [2025-03-06T01:05:25.207Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2025-03-06T01:05:25.535Z] GC before operation: completed in 108.536 ms, heap usage 109.022 MB -> 53.704 MB. [2025-03-06T01:05:32.802Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:05:39.999Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:05:47.211Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:05:54.388Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:05:58.164Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:06:01.876Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:06:06.577Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:06:10.295Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:06:10.991Z] 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. [2025-03-06T01:06:10.991Z] The best model improves the baseline by 14.52%. [2025-03-06T01:06:10.991Z] Movies recommended for you: [2025-03-06T01:06:10.991Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:06:10.991Z] There is no way to check that no silent failure occurred. [2025-03-06T01:06:10.991Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (45569.509 ms) ====== [2025-03-06T01:06:10.991Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2025-03-06T01:06:10.991Z] GC before operation: completed in 93.534 ms, heap usage 98.754 MB -> 52.289 MB. [2025-03-06T01:06:18.218Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:06:25.445Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:06:34.265Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:06:40.088Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:06:43.797Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:06:47.543Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:06:52.256Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:06:55.974Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:06:56.306Z] 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. [2025-03-06T01:06:56.306Z] The best model improves the baseline by 14.52%. [2025-03-06T01:06:56.646Z] Movies recommended for you: [2025-03-06T01:06:56.646Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:06:56.646Z] There is no way to check that no silent failure occurred. [2025-03-06T01:06:56.646Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (45534.424 ms) ====== [2025-03-06T01:06:56.646Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2025-03-06T01:06:56.646Z] GC before operation: completed in 95.107 ms, heap usage 84.033 MB -> 50.658 MB. [2025-03-06T01:07:03.840Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2025-03-06T01:07:11.031Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2025-03-06T01:07:18.216Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2025-03-06T01:07:25.421Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2025-03-06T01:07:29.135Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2025-03-06T01:07:32.853Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2025-03-06T01:07:37.531Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2025-03-06T01:07:41.290Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2025-03-06T01:07:41.290Z] 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. [2025-03-06T01:07:41.290Z] The best model improves the baseline by 14.52%. [2025-03-06T01:07:41.290Z] Movies recommended for you: [2025-03-06T01:07:41.290Z] WARNING: This benchmark provides no result that can be validated. [2025-03-06T01:07:41.290Z] There is no way to check that no silent failure occurred. [2025-03-06T01:07:41.290Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (44706.129 ms) ====== [2025-03-06T01:07:41.992Z] ----------------------------------- [2025-03-06T01:07:41.992Z] renaissance-movie-lens_0_PASSED [2025-03-06T01:07:41.992Z] ----------------------------------- [2025-03-06T01:07:42.676Z] [2025-03-06T01:07:42.676Z] TEST TEARDOWN: [2025-03-06T01:07:42.676Z] Nothing to be done for teardown. [2025-03-06T01:07:42.676Z] renaissance-movie-lens_0 Finish Time: Thu Mar 6 01:07:42 2025 Epoch Time (ms): 1741223262514