[2024-10-31T03:09:52.499Z] Running test renaissance-movie-lens_0 ... [2024-10-31T03:09:52.499Z] =============================================== [2024-10-31T03:09:52.499Z] renaissance-movie-lens_0 Start Time: Thu Oct 31 03:09:52 2024 Epoch Time (ms): 1730344192349 [2024-10-31T03:09:52.499Z] variation: NoOptions [2024-10-31T03:09:52.824Z] JVM_OPTIONS: [2024-10-31T03:09:52.824Z] { \ [2024-10-31T03:09:52.824Z] echo ""; echo "TEST SETUP:"; \ [2024-10-31T03:09:52.824Z] echo "Nothing to be done for setup."; \ [2024-10-31T03:09:52.824Z] mkdir -p "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17303432483900\\renaissance-movie-lens_0"; \ [2024-10-31T03:09:52.824Z] cd "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17303432483900\\renaissance-movie-lens_0"; \ [2024-10-31T03:09:52.824Z] echo ""; echo "TESTING:"; \ [2024-10-31T03:09:52.824Z] "c:/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17303432483900\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \ [2024-10-31T03:09:52.824Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/; rm -f -r "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17303432483900\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-10-31T03:09:52.824Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-10-31T03:09:52.824Z] echo "Nothing to be done for teardown."; \ [2024-10-31T03:09:52.824Z] } 2>&1 | tee -a "C:/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_17303432483900\\TestTargetResult"; [2024-10-31T03:09:52.824Z] [2024-10-31T03:09:52.824Z] TEST SETUP: [2024-10-31T03:09:52.824Z] Nothing to be done for setup. [2024-10-31T03:09:52.824Z] [2024-10-31T03:09:52.824Z] TESTING: [2024-10-31T03:10:03.369Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-10-31T03:10:04.042Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-10-31T03:10:07.024Z] Got 100004 ratings from 671 users on 9066 movies. [2024-10-31T03:10:07.354Z] Training: 60056, validation: 20285, test: 19854 [2024-10-31T03:10:07.354Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-10-31T03:10:07.354Z] GC before operation: completed in 49.111 ms, heap usage 81.004 MB -> 37.370 MB. [2024-10-31T03:10:17.990Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:10:26.663Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:10:33.760Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:10:40.812Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:10:44.473Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:10:48.134Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:10:51.816Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:10:55.459Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:10:55.796Z] 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. [2024-10-31T03:10:55.797Z] The best model improves the baseline by 14.52%. [2024-10-31T03:10:56.117Z] Movies recommended for you: [2024-10-31T03:10:56.117Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:10:56.117Z] There is no way to check that no silent failure occurred. [2024-10-31T03:10:56.117Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (48684.089 ms) ====== [2024-10-31T03:10:56.117Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-10-31T03:10:56.117Z] GC before operation: completed in 81.178 ms, heap usage 246.034 MB -> 58.967 MB. [2024-10-31T03:11:03.302Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:11:09.003Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:11:16.058Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:11:23.109Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:11:25.934Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:11:29.650Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:11:33.276Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:11:36.907Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:11:36.907Z] 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. [2024-10-31T03:11:36.907Z] The best model improves the baseline by 14.52%. [2024-10-31T03:11:37.226Z] Movies recommended for you: [2024-10-31T03:11:37.226Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:11:37.226Z] There is no way to check that no silent failure occurred. [2024-10-31T03:11:37.226Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (41056.106 ms) ====== [2024-10-31T03:11:37.226Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-10-31T03:11:37.226Z] GC before operation: completed in 57.846 ms, heap usage 132.442 MB -> 49.902 MB. [2024-10-31T03:11:44.284Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:11:49.991Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:11:57.046Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:12:02.753Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:12:06.387Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:12:10.029Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:12:13.652Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:12:17.292Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:12:17.606Z] 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. [2024-10-31T03:12:17.606Z] The best model improves the baseline by 14.52%. [2024-10-31T03:12:17.606Z] Movies recommended for you: [2024-10-31T03:12:17.606Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:12:17.606Z] There is no way to check that no silent failure occurred. [2024-10-31T03:12:17.606Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (40357.693 ms) ====== [2024-10-31T03:12:17.606Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-10-31T03:12:17.606Z] GC before operation: completed in 59.055 ms, heap usage 207.074 MB -> 50.239 MB. [2024-10-31T03:12:24.642Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:12:30.357Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:12:37.411Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:12:43.131Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:12:46.769Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:12:49.622Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:12:53.297Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:12:57.096Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:12:57.096Z] 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. [2024-10-31T03:12:57.096Z] The best model improves the baseline by 14.52%. [2024-10-31T03:12:57.096Z] Movies recommended for you: [2024-10-31T03:12:57.096Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:12:57.096Z] There is no way to check that no silent failure occurred. [2024-10-31T03:12:57.096Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (39499.255 ms) ====== [2024-10-31T03:12:57.096Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-10-31T03:12:57.415Z] GC before operation: completed in 57.894 ms, heap usage 238.912 MB -> 50.651 MB. [2024-10-31T03:13:03.129Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:13:10.156Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:13:15.852Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:13:22.901Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:13:25.738Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:13:29.408Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:13:33.047Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:13:35.880Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:13:36.560Z] 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. [2024-10-31T03:13:36.560Z] The best model improves the baseline by 14.52%. [2024-10-31T03:13:36.560Z] Movies recommended for you: [2024-10-31T03:13:36.560Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:13:36.560Z] There is no way to check that no silent failure occurred. [2024-10-31T03:13:36.560Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (39370.900 ms) ====== [2024-10-31T03:13:36.560Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-10-31T03:13:36.560Z] GC before operation: completed in 57.302 ms, heap usage 73.819 MB -> 50.871 MB. [2024-10-31T03:13:43.603Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:13:49.307Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:13:56.359Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:14:02.043Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:14:04.882Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:14:08.538Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:14:12.206Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:14:15.827Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:14:16.497Z] 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. [2024-10-31T03:14:16.497Z] The best model improves the baseline by 14.52%. [2024-10-31T03:14:16.497Z] Movies recommended for you: [2024-10-31T03:14:16.497Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:14:16.497Z] There is no way to check that no silent failure occurred. [2024-10-31T03:14:16.497Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (39820.271 ms) ====== [2024-10-31T03:14:16.497Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-10-31T03:14:16.497Z] GC before operation: completed in 59.731 ms, heap usage 171.740 MB -> 50.734 MB. [2024-10-31T03:14:23.544Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:14:29.246Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:14:34.627Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:14:41.674Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:14:44.527Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:14:47.356Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:14:51.008Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:14:54.639Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:14:54.968Z] 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. [2024-10-31T03:14:54.968Z] The best model improves the baseline by 14.52%. [2024-10-31T03:14:54.968Z] Movies recommended for you: [2024-10-31T03:14:54.968Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:14:54.968Z] There is no way to check that no silent failure occurred. [2024-10-31T03:14:54.968Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (38468.145 ms) ====== [2024-10-31T03:14:54.968Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-10-31T03:14:54.968Z] GC before operation: completed in 56.351 ms, heap usage 199.992 MB -> 50.939 MB. [2024-10-31T03:15:02.011Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:15:07.713Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:15:13.428Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:15:20.477Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:15:23.305Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:15:26.933Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:15:30.605Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:15:33.448Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:15:34.120Z] 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. [2024-10-31T03:15:34.120Z] The best model improves the baseline by 14.52%. [2024-10-31T03:15:34.120Z] Movies recommended for you: [2024-10-31T03:15:34.120Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:15:34.120Z] There is no way to check that no silent failure occurred. [2024-10-31T03:15:34.120Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (39081.747 ms) ====== [2024-10-31T03:15:34.120Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-10-31T03:15:34.120Z] GC before operation: completed in 56.724 ms, heap usage 147.692 MB -> 51.125 MB. [2024-10-31T03:15:41.172Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:15:46.896Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:15:52.598Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:15:58.299Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:16:01.931Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:16:05.550Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:16:09.226Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:16:12.868Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:16:12.868Z] 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. [2024-10-31T03:16:12.868Z] The best model improves the baseline by 14.52%. [2024-10-31T03:16:12.868Z] Movies recommended for you: [2024-10-31T03:16:12.868Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:16:12.868Z] There is no way to check that no silent failure occurred. [2024-10-31T03:16:12.868Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (38639.424 ms) ====== [2024-10-31T03:16:12.868Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-10-31T03:16:12.868Z] GC before operation: completed in 60.579 ms, heap usage 131.941 MB -> 50.950 MB. [2024-10-31T03:16:19.919Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:16:25.614Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:16:31.325Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:16:37.024Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:16:40.652Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:16:44.285Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:16:47.902Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:16:51.561Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:16:51.561Z] 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. [2024-10-31T03:16:51.877Z] The best model improves the baseline by 14.52%. [2024-10-31T03:16:51.877Z] Movies recommended for you: [2024-10-31T03:16:51.877Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:16:51.877Z] There is no way to check that no silent failure occurred. [2024-10-31T03:16:51.877Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (38934.711 ms) ====== [2024-10-31T03:16:51.877Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-10-31T03:16:51.877Z] GC before operation: completed in 61.140 ms, heap usage 336.128 MB -> 51.320 MB. [2024-10-31T03:16:57.596Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:17:04.630Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:17:10.334Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:17:16.033Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:17:19.673Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:17:23.300Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:17:26.921Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:17:29.788Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:17:30.459Z] 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. [2024-10-31T03:17:30.459Z] The best model improves the baseline by 14.52%. [2024-10-31T03:17:30.459Z] Movies recommended for you: [2024-10-31T03:17:30.460Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:17:30.460Z] There is no way to check that no silent failure occurred. [2024-10-31T03:17:30.460Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (38561.542 ms) ====== [2024-10-31T03:17:30.460Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-10-31T03:17:30.460Z] GC before operation: completed in 57.013 ms, heap usage 196.071 MB -> 50.864 MB. [2024-10-31T03:17:36.163Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:17:43.213Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:17:48.953Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:17:54.659Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:17:58.279Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:18:01.923Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:18:05.560Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:18:09.228Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:18:09.228Z] 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. [2024-10-31T03:18:09.228Z] The best model improves the baseline by 14.52%. [2024-10-31T03:18:09.228Z] Movies recommended for you: [2024-10-31T03:18:09.228Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:18:09.228Z] There is no way to check that no silent failure occurred. [2024-10-31T03:18:09.228Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (38673.763 ms) ====== [2024-10-31T03:18:09.228Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-10-31T03:18:09.228Z] GC before operation: completed in 62.075 ms, heap usage 237.270 MB -> 51.061 MB. [2024-10-31T03:18:16.272Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:18:21.991Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:18:27.680Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:18:33.387Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:18:37.011Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:18:40.637Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:18:44.264Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:18:47.907Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:18:47.907Z] 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. [2024-10-31T03:18:47.907Z] The best model improves the baseline by 14.52%. [2024-10-31T03:18:47.907Z] Movies recommended for you: [2024-10-31T03:18:47.907Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:18:47.907Z] There is no way to check that no silent failure occurred. [2024-10-31T03:18:47.907Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (38661.302 ms) ====== [2024-10-31T03:18:47.907Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-10-31T03:18:47.907Z] GC before operation: completed in 57.443 ms, heap usage 238.010 MB -> 51.269 MB. [2024-10-31T03:18:54.964Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:19:00.672Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:19:06.377Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:19:12.110Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:19:15.728Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:19:19.360Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:19:22.988Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:19:26.649Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:19:26.649Z] 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. [2024-10-31T03:19:26.964Z] The best model improves the baseline by 14.52%. [2024-10-31T03:19:26.964Z] Movies recommended for you: [2024-10-31T03:19:26.964Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:19:26.964Z] There is no way to check that no silent failure occurred. [2024-10-31T03:19:26.964Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (38960.702 ms) ====== [2024-10-31T03:19:26.964Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-10-31T03:19:26.964Z] GC before operation: completed in 60.706 ms, heap usage 131.126 MB -> 50.934 MB. [2024-10-31T03:19:32.671Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:19:39.732Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:19:45.432Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:19:51.136Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:19:54.749Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:19:57.585Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:20:01.220Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:20:04.890Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:20:04.890Z] 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. [2024-10-31T03:20:04.890Z] The best model improves the baseline by 14.52%. [2024-10-31T03:20:05.217Z] Movies recommended for you: [2024-10-31T03:20:05.217Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:20:05.217Z] There is no way to check that no silent failure occurred. [2024-10-31T03:20:05.217Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (38084.238 ms) ====== [2024-10-31T03:20:05.217Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-10-31T03:20:05.217Z] GC before operation: completed in 60.763 ms, heap usage 73.839 MB -> 51.087 MB. [2024-10-31T03:20:10.921Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:20:17.966Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:20:23.750Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:20:29.460Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:20:33.091Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:20:36.716Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:20:40.379Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:20:43.211Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:20:43.903Z] 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. [2024-10-31T03:20:43.903Z] The best model improves the baseline by 14.52%. [2024-10-31T03:20:43.903Z] Movies recommended for you: [2024-10-31T03:20:43.903Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:20:43.903Z] There is no way to check that no silent failure occurred. [2024-10-31T03:20:43.903Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (38734.560 ms) ====== [2024-10-31T03:20:43.903Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-10-31T03:20:43.903Z] GC before operation: completed in 59.761 ms, heap usage 199.258 MB -> 51.320 MB. [2024-10-31T03:20:49.643Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:20:56.678Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:21:02.393Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:21:08.098Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:21:11.714Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:21:14.536Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:21:19.114Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:21:21.948Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:21:22.261Z] 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. [2024-10-31T03:21:22.261Z] The best model improves the baseline by 14.52%. [2024-10-31T03:21:22.581Z] Movies recommended for you: [2024-10-31T03:21:22.581Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:21:22.581Z] There is no way to check that no silent failure occurred. [2024-10-31T03:21:22.581Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (38521.790 ms) ====== [2024-10-31T03:21:22.581Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-10-31T03:21:22.581Z] GC before operation: completed in 62.923 ms, heap usage 204.024 MB -> 51.075 MB. [2024-10-31T03:21:29.630Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:21:35.334Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:21:41.038Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:21:46.740Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:21:50.357Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:21:53.986Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:21:57.609Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:22:00.447Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:22:00.801Z] 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. [2024-10-31T03:22:00.801Z] The best model improves the baseline by 14.52%. [2024-10-31T03:22:00.801Z] Movies recommended for you: [2024-10-31T03:22:00.801Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:22:00.801Z] There is no way to check that no silent failure occurred. [2024-10-31T03:22:00.801Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (38369.251 ms) ====== [2024-10-31T03:22:00.801Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-10-31T03:22:01.141Z] GC before operation: completed in 59.113 ms, heap usage 195.909 MB -> 51.131 MB. [2024-10-31T03:22:06.854Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:22:13.897Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:22:19.601Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:22:25.316Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:22:28.937Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:22:32.567Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:22:36.218Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:22:39.079Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:22:39.789Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-10-31T03:22:39.789Z] The best model improves the baseline by 14.52%. [2024-10-31T03:22:39.789Z] Movies recommended for you: [2024-10-31T03:22:39.789Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:22:39.789Z] There is no way to check that no silent failure occurred. [2024-10-31T03:22:39.789Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (38754.663 ms) ====== [2024-10-31T03:22:39.789Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-10-31T03:22:39.789Z] GC before operation: completed in 62.070 ms, heap usage 343.005 MB -> 51.446 MB. [2024-10-31T03:22:45.503Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-10-31T03:22:52.571Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-10-31T03:22:58.282Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-10-31T03:23:04.025Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-10-31T03:23:06.857Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-10-31T03:23:10.491Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-10-31T03:23:14.123Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-10-31T03:23:17.779Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-10-31T03:23:17.779Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-10-31T03:23:17.779Z] The best model improves the baseline by 14.52%. [2024-10-31T03:23:17.779Z] Movies recommended for you: [2024-10-31T03:23:17.779Z] WARNING: This benchmark provides no result that can be validated. [2024-10-31T03:23:17.779Z] There is no way to check that no silent failure occurred. [2024-10-31T03:23:17.779Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (38098.796 ms) ====== [2024-10-31T03:23:18.466Z] ----------------------------------- [2024-10-31T03:23:18.466Z] renaissance-movie-lens_0_PASSED [2024-10-31T03:23:18.466Z] ----------------------------------- [2024-10-31T03:23:18.772Z] [2024-10-31T03:23:18.772Z] TEST TEARDOWN: [2024-10-31T03:23:18.772Z] Nothing to be done for teardown. [2024-10-31T03:23:19.089Z] renaissance-movie-lens_0 Finish Time: Thu Oct 31 03:23:18 2024 Epoch Time (ms): 1730344998763