renaissance-movie-lens_0
[2024-11-28T03:34:53.313Z] Running test renaissance-movie-lens_0 ...
[2024-11-28T03:34:53.313Z] ===============================================
[2024-11-28T03:34:53.313Z] renaissance-movie-lens_0 Start Time: Thu Nov 28 03:34:53 2024 Epoch Time (ms): 1732764893115
[2024-11-28T03:34:53.313Z] variation: NoOptions
[2024-11-28T03:34:53.313Z] JVM_OPTIONS:
[2024-11-28T03:34:53.313Z] { \
[2024-11-28T03:34:53.313Z] echo ""; echo "TEST SETUP:"; \
[2024-11-28T03:34:53.313Z] echo "Nothing to be done for setup."; \
[2024-11-28T03:34:53.313Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17327592569882/renaissance-movie-lens_0"; \
[2024-11-28T03:34:53.313Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17327592569882/renaissance-movie-lens_0"; \
[2024-11-28T03:34:53.313Z] echo ""; echo "TESTING:"; \
[2024-11-28T03:34:53.313Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-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_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17327592569882/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-28T03:34:53.313Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17327592569882/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-28T03:34:53.313Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-28T03:34:53.313Z] echo "Nothing to be done for teardown."; \
[2024-11-28T03:34:53.313Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17327592569882/TestTargetResult";
[2024-11-28T03:34:53.313Z]
[2024-11-28T03:34:53.313Z] TEST SETUP:
[2024-11-28T03:34:53.313Z] Nothing to be done for setup.
[2024-11-28T03:34:53.313Z]
[2024-11-28T03:34:53.313Z] TESTING:
[2024-11-28T03:35:16.247Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-28T03:35:39.304Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-11-28T03:36:22.056Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-28T03:36:22.857Z] Training: 60056, validation: 20285, test: 19854
[2024-11-28T03:36:22.857Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-28T03:36:22.857Z] GC before operation: completed in 627.190 ms, heap usage 85.183 MB -> 37.139 MB.
[2024-11-28T03:37:42.697Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:38:19.075Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:38:55.325Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:39:26.525Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:39:46.874Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:40:01.276Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:40:15.626Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:40:27.918Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:40:29.675Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:40:30.516Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:40:31.350Z] Movies recommended for you:
[2024-11-28T03:40:31.350Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:40:31.350Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:40:31.350Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (248118.726 ms) ======
[2024-11-28T03:40:31.350Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-28T03:40:32.199Z] GC before operation: completed in 617.246 ms, heap usage 449.548 MB -> 57.645 MB.
[2024-11-28T03:40:51.900Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:41:09.154Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:41:28.845Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:41:48.303Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:41:58.703Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:42:13.604Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:42:25.542Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:42:41.248Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:42:42.074Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:42:42.074Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:42:42.904Z] Movies recommended for you:
[2024-11-28T03:42:42.904Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:42:42.904Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:42:42.904Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (130822.009 ms) ======
[2024-11-28T03:42:42.905Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-28T03:42:42.905Z] GC before operation: completed in 498.216 ms, heap usage 309.623 MB -> 52.993 MB.
[2024-11-28T03:43:02.253Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:43:22.026Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:43:41.256Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:44:00.912Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:44:09.689Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:44:19.906Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:44:31.790Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:44:41.973Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:44:44.063Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:44:44.063Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:44:45.097Z] Movies recommended for you:
[2024-11-28T03:44:45.097Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:44:45.097Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:44:45.097Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (121767.759 ms) ======
[2024-11-28T03:44:45.097Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-28T03:44:46.093Z] GC before operation: completed in 756.255 ms, heap usage 101.062 MB -> 54.845 MB.
[2024-11-28T03:45:07.995Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:45:24.341Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:45:43.472Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:46:02.290Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:46:10.799Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:46:20.969Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:46:32.911Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:46:43.067Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:46:45.062Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:46:45.062Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:46:45.062Z] Movies recommended for you:
[2024-11-28T03:46:45.062Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:46:45.062Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:46:45.062Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (119414.791 ms) ======
[2024-11-28T03:46:45.062Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-28T03:46:46.028Z] GC before operation: completed in 493.286 ms, heap usage 463.221 MB -> 56.094 MB.
[2024-11-28T03:47:02.845Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:47:18.947Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:47:35.225Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:47:48.989Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:48:00.876Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:48:09.569Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:48:19.741Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:48:28.158Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:48:29.182Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:48:29.182Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:48:30.202Z] Movies recommended for you:
[2024-11-28T03:48:30.202Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:48:30.202Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:48:30.202Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (104418.594 ms) ======
[2024-11-28T03:48:30.202Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-28T03:48:31.237Z] GC before operation: completed in 531.568 ms, heap usage 182.662 MB -> 52.695 MB.
[2024-11-28T03:48:45.098Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:49:03.871Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:49:20.721Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:49:34.770Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:49:43.137Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:49:53.460Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:50:03.487Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:50:13.591Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:50:14.583Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:50:14.583Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:50:15.575Z] Movies recommended for you:
[2024-11-28T03:50:15.575Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:50:15.575Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:50:15.575Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (104456.730 ms) ======
[2024-11-28T03:50:15.575Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-28T03:50:15.575Z] GC before operation: completed in 492.714 ms, heap usage 200.239 MB -> 50.410 MB.
[2024-11-28T03:50:29.566Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:50:46.066Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:51:02.605Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:51:16.727Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:51:27.216Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:51:34.325Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:51:42.907Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:51:50.024Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:51:52.697Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:51:52.697Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:51:52.697Z] Movies recommended for you:
[2024-11-28T03:51:52.697Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:51:52.697Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:51:52.697Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (97428.046 ms) ======
[2024-11-28T03:51:52.697Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-28T03:51:53.483Z] GC before operation: completed in 519.211 ms, heap usage 205.855 MB -> 50.594 MB.
[2024-11-28T03:52:07.704Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:52:24.809Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:52:41.322Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:52:53.428Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:53:01.834Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:53:10.406Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:53:18.966Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:53:27.454Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:53:28.266Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:53:29.052Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:53:29.885Z] Movies recommended for you:
[2024-11-28T03:53:29.885Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:53:29.885Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:53:29.885Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (96030.775 ms) ======
[2024-11-28T03:53:29.885Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-28T03:53:29.886Z] GC before operation: completed in 608.821 ms, heap usage 80.678 MB -> 52.441 MB.
[2024-11-28T03:53:43.981Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:53:55.984Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:54:07.891Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:54:20.259Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:54:27.850Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:54:34.061Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:54:43.174Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:54:48.144Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:54:49.957Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:54:49.957Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:54:50.857Z] Movies recommended for you:
[2024-11-28T03:54:50.857Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:54:50.857Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:54:50.857Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (80148.851 ms) ======
[2024-11-28T03:54:50.857Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-28T03:54:50.857Z] GC before operation: completed in 538.897 ms, heap usage 382.363 MB -> 54.207 MB.
[2024-11-28T03:55:08.389Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:55:23.980Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:55:36.802Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:55:47.681Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:55:56.874Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:56:03.266Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:56:12.462Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:56:19.451Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:56:21.284Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:56:21.284Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:56:21.284Z] Movies recommended for you:
[2024-11-28T03:56:21.284Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:56:21.284Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:56:21.284Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (90691.541 ms) ======
[2024-11-28T03:56:21.284Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-28T03:56:22.189Z] GC before operation: completed in 401.689 ms, heap usage 407.056 MB -> 54.186 MB.
[2024-11-28T03:56:39.588Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:56:50.620Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:57:03.336Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:57:16.086Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:57:22.995Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:57:30.616Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:57:39.810Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:57:46.001Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:57:46.866Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:57:46.866Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:57:47.740Z] Movies recommended for you:
[2024-11-28T03:57:47.741Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:57:47.741Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:57:47.741Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (85351.588 ms) ======
[2024-11-28T03:57:47.741Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-28T03:57:47.741Z] GC before operation: completed in 454.817 ms, heap usage 556.864 MB -> 56.827 MB.
[2024-11-28T03:58:00.449Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:58:13.142Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T03:58:26.623Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T03:58:39.678Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T03:58:49.038Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T03:58:56.837Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T03:59:06.220Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T03:59:14.024Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T03:59:15.856Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T03:59:15.856Z] The best model improves the baseline by 14.52%.
[2024-11-28T03:59:15.856Z] Movies recommended for you:
[2024-11-28T03:59:15.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T03:59:15.856Z] There is no way to check that no silent failure occurred.
[2024-11-28T03:59:15.856Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (88305.253 ms) ======
[2024-11-28T03:59:15.856Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-28T03:59:16.772Z] GC before operation: completed in 502.280 ms, heap usage 411.446 MB -> 54.089 MB.
[2024-11-28T03:59:34.887Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T03:59:47.591Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:00:02.550Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:00:17.182Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:00:24.811Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:00:32.396Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:00:41.284Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:00:51.844Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:00:51.844Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:00:52.686Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:00:52.686Z] Movies recommended for you:
[2024-11-28T04:00:52.686Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:00:52.686Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:00:52.686Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (96240.923 ms) ======
[2024-11-28T04:00:52.686Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-28T04:00:53.521Z] GC before operation: completed in 636.570 ms, heap usage 113.115 MB -> 52.096 MB.
[2024-11-28T04:01:10.554Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:01:23.226Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:01:34.444Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:01:49.338Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:01:58.482Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:02:09.212Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:02:16.642Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:02:24.793Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:02:24.793Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:02:25.658Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:02:25.658Z] Movies recommended for you:
[2024-11-28T04:02:25.658Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:02:25.658Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:02:25.658Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (92033.302 ms) ======
[2024-11-28T04:02:25.658Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-28T04:02:26.525Z] GC before operation: completed in 637.494 ms, heap usage 159.318 MB -> 51.609 MB.
[2024-11-28T04:02:41.423Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:02:53.850Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:03:08.448Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:03:22.894Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:03:31.039Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:03:37.241Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:03:43.444Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:03:51.005Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:03:51.878Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:03:51.878Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:03:52.730Z] Movies recommended for you:
[2024-11-28T04:03:52.730Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:03:52.730Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:03:52.730Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (86464.077 ms) ======
[2024-11-28T04:03:52.730Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-28T04:03:53.605Z] GC before operation: completed in 453.305 ms, heap usage 230.452 MB -> 50.878 MB.
[2024-11-28T04:04:06.064Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:04:18.863Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:04:31.989Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:04:46.674Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:04:54.196Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:05:03.049Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:05:12.132Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:05:18.336Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:05:20.123Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:05:20.123Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:05:20.123Z] Movies recommended for you:
[2024-11-28T04:05:20.123Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:05:20.123Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:05:20.123Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (87230.481 ms) ======
[2024-11-28T04:05:20.123Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-28T04:05:20.996Z] GC before operation: completed in 424.181 ms, heap usage 140.054 MB -> 50.868 MB.
[2024-11-28T04:05:38.698Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:05:53.609Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:06:06.409Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:06:21.544Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:06:29.254Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:06:37.574Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:06:45.606Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:06:53.230Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:06:54.108Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:06:54.108Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:06:55.057Z] Movies recommended for you:
[2024-11-28T04:06:55.057Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:06:55.057Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:06:55.057Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (93757.635 ms) ======
[2024-11-28T04:06:55.057Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-28T04:06:55.057Z] GC before operation: completed in 669.693 ms, heap usage 550.074 MB -> 54.909 MB.
[2024-11-28T04:07:10.100Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:07:23.034Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:07:36.084Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:07:49.978Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:07:56.429Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:08:04.292Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:08:12.031Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:08:19.792Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:08:20.748Z] 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.
[2024-11-28T04:08:20.748Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:08:20.748Z] Movies recommended for you:
[2024-11-28T04:08:20.748Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:08:20.748Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:08:20.748Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (85722.067 ms) ======
[2024-11-28T04:08:20.748Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-28T04:08:21.628Z] GC before operation: completed in 481.058 ms, heap usage 597.199 MB -> 54.575 MB.
[2024-11-28T04:08:34.576Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:08:47.399Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:09:02.489Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:09:13.060Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:09:19.545Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:09:28.303Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:09:35.431Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:09:42.632Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:09:44.346Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:09:44.346Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:09:45.189Z] Movies recommended for you:
[2024-11-28T04:09:45.189Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:09:45.189Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:09:45.189Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (83620.208 ms) ======
[2024-11-28T04:09:45.189Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-28T04:09:46.021Z] GC before operation: completed in 472.412 ms, heap usage 266.337 MB -> 51.129 MB.
[2024-11-28T04:09:58.216Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T04:10:08.880Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T04:10:21.165Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T04:10:33.346Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T04:10:39.301Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T04:10:46.557Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T04:10:53.699Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T04:11:02.848Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T04:11:03.660Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-11-28T04:11:04.460Z] The best model improves the baseline by 14.52%.
[2024-11-28T04:11:04.460Z] Movies recommended for you:
[2024-11-28T04:11:04.460Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T04:11:04.460Z] There is no way to check that no silent failure occurred.
[2024-11-28T04:11:04.460Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (79114.071 ms) ======
[2024-11-28T04:11:07.070Z] -----------------------------------
[2024-11-28T04:11:07.070Z] renaissance-movie-lens_0_PASSED
[2024-11-28T04:11:07.070Z] -----------------------------------
[2024-11-28T04:11:07.070Z]
[2024-11-28T04:11:07.070Z] TEST TEARDOWN:
[2024-11-28T04:11:07.070Z] Nothing to be done for teardown.
[2024-11-28T04:11:07.070Z] renaissance-movie-lens_0 Finish Time: Thu Nov 28 04:11:06 2024 Epoch Time (ms): 1732767066879