renaissance-movie-lens_0
[2025-02-05T21:27:45.104Z] Running test renaissance-movie-lens_0 ...
[2025-02-05T21:27:45.104Z] ===============================================
[2025-02-05T21:27:45.104Z] renaissance-movie-lens_0 Start Time: Wed Feb 5 16:27:44 2025 Epoch Time (ms): 1738790864966
[2025-02-05T21:27:45.104Z] variation: NoOptions
[2025-02-05T21:27:45.104Z] JVM_OPTIONS:
[2025-02-05T21:27:45.104Z] { \
[2025-02-05T21:27:45.104Z] echo ""; echo "TEST SETUP:"; \
[2025-02-05T21:27:45.104Z] echo "Nothing to be done for setup."; \
[2025-02-05T21:27:45.104Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17387905668889/renaissance-movie-lens_0"; \
[2025-02-05T21:27:45.104Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17387905668889/renaissance-movie-lens_0"; \
[2025-02-05T21:27:45.104Z] echo ""; echo "TESTING:"; \
[2025-02-05T21:27:45.104Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/jdkbinary/j2sdk-image/Contents/Home/bin/..//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 "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17387905668889/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-05T21:27:45.104Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17387905668889/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-05T21:27:45.104Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-05T21:27:45.104Z] echo "Nothing to be done for teardown."; \
[2025-02-05T21:27:45.104Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17387905668889/TestTargetResult";
[2025-02-05T21:27:45.104Z]
[2025-02-05T21:27:45.104Z] TEST SETUP:
[2025-02-05T21:27:45.104Z] Nothing to be done for setup.
[2025-02-05T21:27:45.104Z]
[2025-02-05T21:27:45.104Z] TESTING:
[2025-02-05T21:27:47.686Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-05T21:27:48.127Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-02-05T21:27:49.488Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-05T21:27:49.880Z] Training: 60056, validation: 20285, test: 19854
[2025-02-05T21:27:49.880Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-05T21:27:49.880Z] GC before operation: completed in 21.674 ms, heap usage 47.024 MB -> 37.247 MB.
[2025-02-05T21:27:53.258Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:27:54.602Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:27:56.559Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:27:58.511Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:27:59.380Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:00.278Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:01.645Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:02.536Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:02.536Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:02.536Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:02.536Z] Movies recommended for you:
[2025-02-05T21:28:02.536Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:02.536Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:02.536Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (12878.631 ms) ======
[2025-02-05T21:28:02.536Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-05T21:28:02.941Z] GC before operation: completed in 42.319 ms, heap usage 193.192 MB -> 53.317 MB.
[2025-02-05T21:28:04.468Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:06.414Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:07.759Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:09.109Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:09.972Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:10.823Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:11.671Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:12.513Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:12.955Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:12.955Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:12.955Z] Movies recommended for you:
[2025-02-05T21:28:12.955Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:12.955Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:12.955Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (10095.098 ms) ======
[2025-02-05T21:28:12.955Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-05T21:28:12.955Z] GC before operation: completed in 33.290 ms, heap usage 315.972 MB -> 49.768 MB.
[2025-02-05T21:28:14.927Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:16.267Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:17.629Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:18.992Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:20.373Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:20.777Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:22.125Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:22.996Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:22.996Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:22.996Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:22.996Z] Movies recommended for you:
[2025-02-05T21:28:22.996Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:22.996Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:22.996Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (10140.671 ms) ======
[2025-02-05T21:28:22.996Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-05T21:28:22.996Z] GC before operation: completed in 36.874 ms, heap usage 195.969 MB -> 49.851 MB.
[2025-02-05T21:28:24.338Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:25.712Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:27.055Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:29.007Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:29.863Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:30.734Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:31.169Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:32.022Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:32.022Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:32.022Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:32.424Z] Movies recommended for you:
[2025-02-05T21:28:32.424Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:32.424Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:32.424Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (9203.979 ms) ======
[2025-02-05T21:28:32.424Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-05T21:28:32.424Z] GC before operation: completed in 29.034 ms, heap usage 148.223 MB -> 50.198 MB.
[2025-02-05T21:28:33.278Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:34.625Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:36.585Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:37.430Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:38.282Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:38.679Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:39.521Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:40.362Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:40.362Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:40.362Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:40.362Z] Movies recommended for you:
[2025-02-05T21:28:40.362Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:40.362Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:40.362Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (8189.465 ms) ======
[2025-02-05T21:28:40.362Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-05T21:28:40.362Z] GC before operation: completed in 32.786 ms, heap usage 150.882 MB -> 50.353 MB.
[2025-02-05T21:28:41.743Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:42.590Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:43.950Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:44.783Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:45.189Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:46.077Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:46.468Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:47.328Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:47.328Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:47.328Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:47.328Z] Movies recommended for you:
[2025-02-05T21:28:47.328Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:47.328Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:47.328Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (6762.785 ms) ======
[2025-02-05T21:28:47.328Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-05T21:28:47.328Z] GC before operation: completed in 28.256 ms, heap usage 153.913 MB -> 50.296 MB.
[2025-02-05T21:28:48.677Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:49.514Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:50.365Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:51.202Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:52.048Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:52.445Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:52.838Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:53.670Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:53.670Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:53.670Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:53.670Z] Movies recommended for you:
[2025-02-05T21:28:53.670Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:53.670Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:53.670Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (6294.681 ms) ======
[2025-02-05T21:28:53.670Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-05T21:28:53.670Z] GC before operation: completed in 29.469 ms, heap usage 211.442 MB -> 50.640 MB.
[2025-02-05T21:28:54.517Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:28:55.362Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:28:56.242Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:28:57.590Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:28:57.986Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:28:58.379Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:28:59.242Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:28:59.637Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:28:59.637Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:28:59.637Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:28:59.637Z] Movies recommended for you:
[2025-02-05T21:28:59.637Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:28:59.637Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:28:59.637Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (6006.361 ms) ======
[2025-02-05T21:28:59.637Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-05T21:28:59.637Z] GC before operation: completed in 29.078 ms, heap usage 98.808 MB -> 52.388 MB.
[2025-02-05T21:29:00.484Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:01.364Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:02.215Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:03.093Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:03.955Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:04.350Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:05.230Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:05.628Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:05.628Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:05.628Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:05.628Z] Movies recommended for you:
[2025-02-05T21:29:05.628Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:05.628Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:05.628Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (6149.596 ms) ======
[2025-02-05T21:29:05.628Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-05T21:29:06.025Z] GC before operation: completed in 34.740 ms, heap usage 398.066 MB -> 54.073 MB.
[2025-02-05T21:29:06.866Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:07.708Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:09.054Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:09.947Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:10.341Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:10.749Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:11.595Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:12.008Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:12.400Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:12.400Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:12.400Z] Movies recommended for you:
[2025-02-05T21:29:12.400Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:12.400Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:12.400Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6418.502 ms) ======
[2025-02-05T21:29:12.400Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-05T21:29:12.400Z] GC before operation: completed in 29.182 ms, heap usage 93.198 MB -> 50.766 MB.
[2025-02-05T21:29:13.247Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:14.082Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:15.429Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:16.345Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:16.734Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:17.601Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:17.995Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:18.863Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:18.863Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:18.863Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:18.863Z] Movies recommended for you:
[2025-02-05T21:29:18.863Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:18.863Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:18.863Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (6638.422 ms) ======
[2025-02-05T21:29:18.863Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-05T21:29:18.863Z] GC before operation: completed in 28.799 ms, heap usage 196.226 MB -> 50.537 MB.
[2025-02-05T21:29:20.225Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:21.076Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:21.917Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:22.767Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:23.607Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:24.001Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:24.856Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:25.250Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:25.250Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:25.250Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:25.645Z] Movies recommended for you:
[2025-02-05T21:29:25.645Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:25.645Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:25.645Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (6498.072 ms) ======
[2025-02-05T21:29:25.645Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-05T21:29:25.645Z] GC before operation: completed in 29.137 ms, heap usage 127.604 MB -> 50.644 MB.
[2025-02-05T21:29:26.486Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:27.325Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:28.158Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:29.529Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:29.920Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:30.332Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:31.173Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:31.577Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:31.967Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:31.967Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:31.967Z] Movies recommended for you:
[2025-02-05T21:29:31.967Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:31.967Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:31.967Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (6344.358 ms) ======
[2025-02-05T21:29:31.967Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-05T21:29:31.967Z] GC before operation: completed in 29.932 ms, heap usage 66.016 MB -> 51.030 MB.
[2025-02-05T21:29:32.805Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:33.639Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:34.481Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:35.840Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:36.235Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:36.634Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:37.476Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:37.884Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:37.884Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:37.884Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:37.884Z] Movies recommended for you:
[2025-02-05T21:29:38.277Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:38.277Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:38.277Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (6228.853 ms) ======
[2025-02-05T21:29:38.277Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-05T21:29:38.277Z] GC before operation: completed in 29.317 ms, heap usage 270.049 MB -> 50.722 MB.
[2025-02-05T21:29:39.117Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:39.966Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:41.330Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:42.171Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:43.013Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:43.425Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:44.263Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:44.656Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:44.656Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:45.077Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:45.077Z] Movies recommended for you:
[2025-02-05T21:29:45.077Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:45.077Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:45.077Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (6787.982 ms) ======
[2025-02-05T21:29:45.077Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-05T21:29:45.077Z] GC before operation: completed in 28.683 ms, heap usage 128.759 MB -> 50.767 MB.
[2025-02-05T21:29:45.929Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:46.791Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:47.624Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:48.967Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:49.356Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:49.765Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:50.595Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:51.005Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:51.005Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:51.005Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:51.396Z] Movies recommended for you:
[2025-02-05T21:29:51.396Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:51.396Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:51.396Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (6285.993 ms) ======
[2025-02-05T21:29:51.396Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-05T21:29:51.396Z] GC before operation: completed in 28.962 ms, heap usage 146.712 MB -> 50.988 MB.
[2025-02-05T21:29:52.229Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:53.064Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:29:54.408Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:29:55.260Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:29:56.101Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:29:56.498Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:29:57.344Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:29:57.749Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:29:58.145Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:29:58.145Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:29:58.145Z] Movies recommended for you:
[2025-02-05T21:29:58.145Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:29:58.145Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:29:58.145Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (6748.230 ms) ======
[2025-02-05T21:29:58.145Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-05T21:29:58.145Z] GC before operation: completed in 28.756 ms, heap usage 76.867 MB -> 50.598 MB.
[2025-02-05T21:29:59.015Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:29:59.851Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:30:00.716Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:30:01.565Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:30:01.982Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:30:02.874Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:30:03.291Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:30:04.159Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:30:04.159Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:30:04.159Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:30:04.159Z] Movies recommended for you:
[2025-02-05T21:30:04.159Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:30:04.159Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:30:04.159Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (6204.578 ms) ======
[2025-02-05T21:30:04.159Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-05T21:30:04.159Z] GC before operation: completed in 40.127 ms, heap usage 309.570 MB -> 50.879 MB.
[2025-02-05T21:30:05.515Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:30:06.879Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:30:07.717Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:30:08.562Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:30:09.404Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:30:09.812Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:30:10.658Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:30:11.510Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:30:11.510Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:30:11.510Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:30:11.510Z] Movies recommended for you:
[2025-02-05T21:30:11.510Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:30:11.510Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:30:11.510Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (7195.058 ms) ======
[2025-02-05T21:30:11.510Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-05T21:30:11.510Z] GC before operation: completed in 31.245 ms, heap usage 150.435 MB -> 51.019 MB.
[2025-02-05T21:30:12.348Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-05T21:30:13.685Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-05T21:30:14.527Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-05T21:30:15.359Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-05T21:30:15.770Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-05T21:30:16.603Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-05T21:30:17.020Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-05T21:30:17.905Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-05T21:30:17.905Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-05T21:30:17.905Z] The best model improves the baseline by 14.52%.
[2025-02-05T21:30:17.905Z] Movies recommended for you:
[2025-02-05T21:30:17.905Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-05T21:30:17.905Z] There is no way to check that no silent failure occurred.
[2025-02-05T21:30:17.905Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (6397.236 ms) ======
[2025-02-05T21:30:17.905Z] -----------------------------------
[2025-02-05T21:30:17.905Z] renaissance-movie-lens_0_PASSED
[2025-02-05T21:30:17.905Z] -----------------------------------
[2025-02-05T21:30:17.905Z]
[2025-02-05T21:30:17.905Z] TEST TEARDOWN:
[2025-02-05T21:30:17.905Z] Nothing to be done for teardown.
[2025-02-05T21:30:18.299Z] renaissance-movie-lens_0 Finish Time: Wed Feb 5 16:30:17 2025 Epoch Time (ms): 1738791017796