renaissance-movie-lens_0
[2024-12-04T21:41:12.168Z] Running test renaissance-movie-lens_0 ...
[2024-12-04T21:41:12.168Z] ===============================================
[2024-12-04T21:41:12.168Z] renaissance-movie-lens_0 Start Time: Wed Dec 4 21:41:11 2024 Epoch Time (ms): 1733348471114
[2024-12-04T21:41:12.168Z] variation: NoOptions
[2024-12-04T21:41:12.168Z] JVM_OPTIONS:
[2024-12-04T21:41:12.168Z] { \
[2024-12-04T21:41:12.168Z] echo ""; echo "TEST SETUP:"; \
[2024-12-04T21:41:12.168Z] echo "Nothing to be done for setup."; \
[2024-12-04T21:41:12.168Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17333476264305/renaissance-movie-lens_0"; \
[2024-12-04T21:41:12.168Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17333476264305/renaissance-movie-lens_0"; \
[2024-12-04T21:41:12.168Z] echo ""; echo "TESTING:"; \
[2024-12-04T21:41:12.168Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17333476264305/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-04T21:41:12.168Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17333476264305/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-04T21:41:12.168Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-04T21:41:12.168Z] echo "Nothing to be done for teardown."; \
[2024-12-04T21:41:12.168Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17333476264305/TestTargetResult";
[2024-12-04T21:41:12.168Z]
[2024-12-04T21:41:12.168Z] TEST SETUP:
[2024-12-04T21:41:12.168Z] Nothing to be done for setup.
[2024-12-04T21:41:12.168Z]
[2024-12-04T21:41:12.168Z] TESTING:
[2024-12-04T21:41:15.172Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-04T21:41:16.120Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-12-04T21:41:19.125Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-04T21:41:19.125Z] Training: 60056, validation: 20285, test: 19854
[2024-12-04T21:41:19.125Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-04T21:41:19.125Z] GC before operation: completed in 49.458 ms, heap usage 228.552 MB -> 37.340 MB.
[2024-12-04T21:41:24.476Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:41:27.487Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:41:30.502Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:41:33.511Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:41:34.464Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:41:36.418Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:41:37.368Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:41:39.324Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:41:39.324Z] 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.
[2024-12-04T21:41:39.324Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:41:39.324Z] Movies recommended for you:
[2024-12-04T21:41:39.324Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:41:39.324Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:41:39.324Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (19798.258 ms) ======
[2024-12-04T21:41:39.324Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-04T21:41:39.324Z] GC before operation: completed in 63.626 ms, heap usage 169.495 MB -> 53.425 MB.
[2024-12-04T21:41:42.332Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:41:44.277Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:41:46.225Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:41:49.246Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:41:50.195Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:41:52.141Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:41:53.090Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:41:55.036Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:41:55.036Z] 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.
[2024-12-04T21:41:55.036Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:41:55.036Z] Movies recommended for you:
[2024-12-04T21:41:55.036Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:41:55.036Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:41:55.036Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15578.039 ms) ======
[2024-12-04T21:41:55.036Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-04T21:41:55.036Z] GC before operation: completed in 70.374 ms, heap usage 96.480 MB -> 49.663 MB.
[2024-12-04T21:41:56.989Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:41:58.936Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:42:01.939Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:42:03.883Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:42:04.831Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:42:05.780Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:42:07.730Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:42:08.688Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:42:08.688Z] 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.
[2024-12-04T21:42:08.688Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:42:08.688Z] Movies recommended for you:
[2024-12-04T21:42:08.688Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:42:08.688Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:42:08.688Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (13741.120 ms) ======
[2024-12-04T21:42:08.688Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-04T21:42:08.688Z] GC before operation: completed in 59.071 ms, heap usage 134.782 MB -> 50.069 MB.
[2024-12-04T21:42:11.520Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:42:12.803Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:42:14.753Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:42:16.747Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:42:17.697Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:42:19.643Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:42:20.592Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:42:21.542Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:42:21.542Z] 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.
[2024-12-04T21:42:21.542Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:42:21.542Z] Movies recommended for you:
[2024-12-04T21:42:21.542Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:42:21.542Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:42:21.542Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13173.494 ms) ======
[2024-12-04T21:42:21.542Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-04T21:42:22.490Z] GC before operation: completed in 60.239 ms, heap usage 205.518 MB -> 50.494 MB.
[2024-12-04T21:42:24.440Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:42:26.387Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:42:28.332Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:42:30.280Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:42:31.228Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:42:32.177Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:42:34.120Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:42:35.111Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:42:35.111Z] 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.
[2024-12-04T21:42:35.111Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:42:35.111Z] Movies recommended for you:
[2024-12-04T21:42:35.111Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:42:35.111Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:42:35.111Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13196.206 ms) ======
[2024-12-04T21:42:35.111Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-04T21:42:35.111Z] GC before operation: completed in 58.338 ms, heap usage 115.617 MB -> 50.565 MB.
[2024-12-04T21:42:37.054Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:42:39.003Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:42:40.947Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:42:42.894Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:42:43.841Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:42:44.788Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:42:45.737Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:42:47.690Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:42:47.690Z] 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.
[2024-12-04T21:42:47.690Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:42:47.690Z] Movies recommended for you:
[2024-12-04T21:42:47.690Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:42:47.690Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:42:47.690Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (12258.578 ms) ======
[2024-12-04T21:42:47.690Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-04T21:42:47.690Z] GC before operation: completed in 73.271 ms, heap usage 123.284 MB -> 50.565 MB.
[2024-12-04T21:42:49.655Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:42:51.600Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:42:53.554Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:42:55.503Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:42:56.450Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:42:57.479Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:42:58.430Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:42:59.377Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:43:00.327Z] 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.
[2024-12-04T21:43:00.327Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:43:00.327Z] Movies recommended for you:
[2024-12-04T21:43:00.327Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:43:00.327Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:43:00.327Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12440.844 ms) ======
[2024-12-04T21:43:00.327Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-04T21:43:00.327Z] GC before operation: completed in 74.848 ms, heap usage 115.704 MB -> 50.752 MB.
[2024-12-04T21:43:02.278Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:43:04.224Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:43:06.168Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:43:08.113Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:43:09.061Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:43:10.009Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:43:11.746Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:43:12.698Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:43:12.698Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-04T21:43:12.698Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:43:12.698Z] Movies recommended for you:
[2024-12-04T21:43:12.698Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:43:12.699Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:43:12.699Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12415.690 ms) ======
[2024-12-04T21:43:12.699Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-04T21:43:12.699Z] GC before operation: completed in 108.902 ms, heap usage 440.789 MB -> 54.543 MB.
[2024-12-04T21:43:14.650Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:43:16.600Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:43:18.547Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:43:19.495Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:43:21.443Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:43:22.391Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:43:23.339Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:43:24.288Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:43:24.288Z] 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.
[2024-12-04T21:43:24.288Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:43:25.239Z] Movies recommended for you:
[2024-12-04T21:43:25.239Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:43:25.239Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:43:25.239Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12108.669 ms) ======
[2024-12-04T21:43:25.239Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-04T21:43:25.239Z] GC before operation: completed in 75.033 ms, heap usage 254.547 MB -> 53.354 MB.
[2024-12-04T21:43:27.188Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:43:28.138Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:43:30.091Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:43:33.096Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:43:34.045Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:43:34.994Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:43:35.944Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:43:37.893Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:43:37.893Z] 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.
[2024-12-04T21:43:37.893Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:43:37.893Z] Movies recommended for you:
[2024-12-04T21:43:37.893Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:43:37.893Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:43:37.893Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13020.072 ms) ======
[2024-12-04T21:43:37.893Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-04T21:43:37.893Z] GC before operation: completed in 61.940 ms, heap usage 133.579 MB -> 54.244 MB.
[2024-12-04T21:43:39.840Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:43:41.794Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:43:43.758Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:43:45.716Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:43:46.667Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:43:48.620Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:43:49.570Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:43:50.524Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:43:51.474Z] 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.
[2024-12-04T21:43:51.474Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:43:51.474Z] Movies recommended for you:
[2024-12-04T21:43:51.474Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:43:51.474Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:43:51.474Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13238.042 ms) ======
[2024-12-04T21:43:51.474Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-04T21:43:51.474Z] GC before operation: completed in 61.538 ms, heap usage 378.679 MB -> 53.216 MB.
[2024-12-04T21:43:53.422Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:43:56.432Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:43:58.383Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:43:59.337Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:44:01.292Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:44:02.242Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:44:03.192Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:44:04.146Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:44:04.146Z] 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.
[2024-12-04T21:44:04.146Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:44:04.146Z] Movies recommended for you:
[2024-12-04T21:44:04.146Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:44:04.146Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:44:04.146Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13311.867 ms) ======
[2024-12-04T21:44:04.146Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-04T21:44:05.100Z] GC before operation: completed in 64.896 ms, heap usage 774.239 MB -> 57.441 MB.
[2024-12-04T21:44:07.048Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:44:09.016Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:44:11.726Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:44:12.675Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:44:13.622Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:44:14.570Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:44:16.618Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:44:17.565Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:44:17.565Z] 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.
[2024-12-04T21:44:17.565Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:44:18.513Z] Movies recommended for you:
[2024-12-04T21:44:18.513Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:44:18.513Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:44:18.513Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13396.939 ms) ======
[2024-12-04T21:44:18.513Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-04T21:44:18.513Z] GC before operation: completed in 64.500 ms, heap usage 182.538 MB -> 53.290 MB.
[2024-12-04T21:44:20.463Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:44:22.407Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:44:24.352Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:44:26.298Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:44:27.247Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:44:29.195Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:44:30.144Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:44:31.093Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:44:31.093Z] 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.
[2024-12-04T21:44:31.093Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:44:32.042Z] Movies recommended for you:
[2024-12-04T21:44:32.042Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:44:32.042Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:44:32.042Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13468.548 ms) ======
[2024-12-04T21:44:32.042Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-04T21:44:32.042Z] GC before operation: completed in 68.728 ms, heap usage 684.328 MB -> 57.006 MB.
[2024-12-04T21:44:33.989Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:44:34.938Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:44:36.937Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:44:38.887Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:44:40.835Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:44:41.783Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:44:42.732Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:44:43.684Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:44:44.633Z] 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.
[2024-12-04T21:44:44.633Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:44:44.633Z] Movies recommended for you:
[2024-12-04T21:44:44.633Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:44:44.633Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:44:44.633Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12732.510 ms) ======
[2024-12-04T21:44:44.633Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-04T21:44:44.633Z] GC before operation: completed in 61.837 ms, heap usage 393.982 MB -> 54.413 MB.
[2024-12-04T21:44:46.580Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:44:48.538Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:44:50.489Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:44:52.439Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:44:53.388Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:44:54.338Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:44:56.287Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:44:57.373Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:44:57.373Z] 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.
[2024-12-04T21:44:57.373Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:44:57.373Z] Movies recommended for you:
[2024-12-04T21:44:57.373Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:44:57.373Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:44:57.373Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12983.755 ms) ======
[2024-12-04T21:44:57.373Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-04T21:44:57.373Z] GC before operation: completed in 61.901 ms, heap usage 378.194 MB -> 51.259 MB.
[2024-12-04T21:45:00.390Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:45:02.369Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:45:04.316Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:45:06.267Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:45:07.214Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:45:08.161Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:45:11.410Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:45:11.410Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:45:11.410Z] 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.
[2024-12-04T21:45:11.410Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:45:11.410Z] Movies recommended for you:
[2024-12-04T21:45:11.410Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:45:11.410Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:45:11.410Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13642.856 ms) ======
[2024-12-04T21:45:11.410Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-04T21:45:11.410Z] GC before operation: completed in 64.377 ms, heap usage 84.632 MB -> 53.680 MB.
[2024-12-04T21:45:13.372Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:45:15.321Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:45:17.440Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:45:19.390Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:45:21.351Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:45:22.300Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:45:23.253Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:45:24.202Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:45:25.152Z] 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.
[2024-12-04T21:45:25.152Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:45:25.152Z] Movies recommended for you:
[2024-12-04T21:45:25.152Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:45:25.152Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:45:25.152Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13787.374 ms) ======
[2024-12-04T21:45:25.152Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-04T21:45:25.152Z] GC before operation: completed in 62.645 ms, heap usage 477.380 MB -> 54.461 MB.
[2024-12-04T21:45:27.099Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:45:29.046Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:45:30.995Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:45:32.941Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:45:33.899Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:45:34.851Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:45:36.799Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:45:37.748Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:45:37.748Z] 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.
[2024-12-04T21:45:37.748Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:45:37.748Z] Movies recommended for you:
[2024-12-04T21:45:37.748Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:45:37.748Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:45:37.748Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12808.652 ms) ======
[2024-12-04T21:45:37.748Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-04T21:45:37.748Z] GC before operation: completed in 62.477 ms, heap usage 172.315 MB -> 56.671 MB.
[2024-12-04T21:45:39.696Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:45:41.649Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:45:43.594Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:45:45.545Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:45:46.493Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:45:47.442Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:45:49.389Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:45:50.344Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:45:50.344Z] 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.
[2024-12-04T21:45:50.344Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:45:50.344Z] Movies recommended for you:
[2024-12-04T21:45:50.344Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:45:50.344Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:45:50.344Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12400.795 ms) ======
[2024-12-04T21:45:50.344Z] -----------------------------------
[2024-12-04T21:45:50.344Z] renaissance-movie-lens_0_PASSED
[2024-12-04T21:45:50.344Z] -----------------------------------
[2024-12-04T21:45:51.292Z]
[2024-12-04T21:45:51.292Z] TEST TEARDOWN:
[2024-12-04T21:45:51.292Z] Nothing to be done for teardown.
[2024-12-04T21:45:51.292Z] renaissance-movie-lens_0 Finish Time: Wed Dec 4 21:45:50 2024 Epoch Time (ms): 1733348750294