renaissance-movie-lens_0
[2024-10-29T23:06:52.279Z] Running test renaissance-movie-lens_0 ...
[2024-10-29T23:06:52.279Z] ===============================================
[2024-10-29T23:06:52.279Z] renaissance-movie-lens_0 Start Time: Tue Oct 29 23:06:49 2024 Epoch Time (ms): 1730243209952
[2024-10-29T23:06:52.279Z] variation: NoOptions
[2024-10-29T23:06:52.279Z] JVM_OPTIONS:
[2024-10-29T23:06:52.279Z] { \
[2024-10-29T23:06:52.279Z] echo ""; echo "TEST SETUP:"; \
[2024-10-29T23:06:52.279Z] echo "Nothing to be done for setup."; \
[2024-10-29T23:06:52.279Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17302398921698/renaissance-movie-lens_0"; \
[2024-10-29T23:06:52.279Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17302398921698/renaissance-movie-lens_0"; \
[2024-10-29T23:06:52.279Z] echo ""; echo "TESTING:"; \
[2024-10-29T23:06:52.279Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/jdkbinary/j2sdk-image/bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17302398921698/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-29T23:06:52.279Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17302398921698/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-29T23:06:52.279Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-29T23:06:52.279Z] echo "Nothing to be done for teardown."; \
[2024-10-29T23:06:52.279Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17302398921698/TestTargetResult";
[2024-10-29T23:06:52.279Z]
[2024-10-29T23:06:52.279Z] TEST SETUP:
[2024-10-29T23:06:52.279Z] Nothing to be done for setup.
[2024-10-29T23:06:52.279Z]
[2024-10-29T23:06:52.279Z] TESTING:
[2024-10-29T23:07:01.948Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-29T23:07:13.494Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-29T23:07:32.113Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-29T23:07:32.854Z] Training: 60056, validation: 20285, test: 19854
[2024-10-29T23:07:32.854Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-29T23:07:34.392Z] GC before operation: completed in 1341.254 ms, heap usage 70.568 MB -> 26.933 MB.
[2024-10-29T23:08:09.752Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:08:28.504Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:08:58.577Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:09:17.360Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:09:31.058Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:09:45.395Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:09:59.051Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:10:08.827Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:10:10.405Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:10:10.406Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:10:11.960Z] Movies recommended for you:
[2024-10-29T23:10:11.960Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:10:11.960Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:10:11.960Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (157181.494 ms) ======
[2024-10-29T23:10:11.960Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-29T23:10:13.538Z] GC before operation: completed in 1908.718 ms, heap usage 419.386 MB -> 45.330 MB.
[2024-10-29T23:10:35.557Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:10:54.298Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:11:10.733Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:11:26.673Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:11:36.553Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:11:46.420Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:11:56.359Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:12:04.560Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:12:05.318Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:12:05.318Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:12:06.066Z] Movies recommended for you:
[2024-10-29T23:12:06.066Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:12:06.066Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:12:06.066Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (112380.596 ms) ======
[2024-10-29T23:12:06.066Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-29T23:12:06.843Z] GC before operation: completed in 1113.571 ms, heap usage 317.545 MB -> 42.429 MB.
[2024-10-29T23:12:23.426Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:12:39.366Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:12:55.349Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:13:08.868Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:13:15.554Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:13:22.301Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:13:29.081Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:13:37.728Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:13:37.728Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:13:37.728Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:13:37.728Z] Movies recommended for you:
[2024-10-29T23:13:37.728Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:13:37.728Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:13:37.728Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (91099.213 ms) ======
[2024-10-29T23:13:37.728Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-29T23:13:39.288Z] GC before operation: completed in 915.851 ms, heap usage 520.977 MB -> 46.531 MB.
[2024-10-29T23:13:50.771Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:14:02.329Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:14:15.902Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:14:29.477Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:14:37.638Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:14:44.341Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:14:51.556Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:14:58.295Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:14:59.059Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:14:59.059Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:14:59.806Z] Movies recommended for you:
[2024-10-29T23:14:59.806Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:14:59.806Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:14:59.806Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (80542.666 ms) ======
[2024-10-29T23:14:59.806Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-29T23:14:59.806Z] GC before operation: completed in 483.812 ms, heap usage 489.997 MB -> 47.220 MB.
[2024-10-29T23:15:11.257Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:15:24.763Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:15:36.277Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:15:46.015Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:15:54.116Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:16:02.275Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:16:09.521Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:16:16.325Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:16:17.082Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:16:17.082Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:16:17.841Z] Movies recommended for you:
[2024-10-29T23:16:17.841Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:16:17.841Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:16:17.841Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (77738.359 ms) ======
[2024-10-29T23:16:17.841Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-29T23:16:18.593Z] GC before operation: completed in 626.114 ms, heap usage 424.526 MB -> 48.688 MB.
[2024-10-29T23:16:30.117Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:16:39.809Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:16:53.473Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:17:03.135Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:17:09.859Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:17:15.934Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:17:22.664Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:17:28.163Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:17:28.955Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:17:28.956Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:17:29.697Z] Movies recommended for you:
[2024-10-29T23:17:29.697Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:17:29.697Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:17:29.697Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (71137.993 ms) ======
[2024-10-29T23:17:29.697Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-29T23:17:30.453Z] GC before operation: completed in 701.471 ms, heap usage 469.167 MB -> 47.625 MB.
[2024-10-29T23:17:40.126Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:17:51.630Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:18:03.128Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:18:14.625Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:18:20.115Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:18:26.928Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:18:32.401Z] RMSE (validation) = 0.9227419497655964 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:18:39.132Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:18:39.132Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:18:39.132Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:18:39.132Z] Movies recommended for you:
[2024-10-29T23:18:39.132Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:18:39.132Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:18:39.132Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (69263.644 ms) ======
[2024-10-29T23:18:39.132Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-29T23:18:39.870Z] GC before operation: completed in 471.382 ms, heap usage 505.102 MB -> 47.184 MB.
[2024-10-29T23:18:51.274Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:19:02.739Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:19:16.305Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:19:25.954Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:19:32.644Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:19:39.482Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:19:46.256Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:19:53.075Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:19:53.822Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:19:53.822Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:19:53.822Z] Movies recommended for you:
[2024-10-29T23:19:53.822Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:19:53.822Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:19:53.822Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (73845.237 ms) ======
[2024-10-29T23:19:53.822Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-29T23:19:54.588Z] GC before operation: completed in 593.394 ms, heap usage 425.746 MB -> 47.402 MB.
[2024-10-29T23:20:06.115Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:20:15.801Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:20:27.387Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:20:35.544Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:20:43.861Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:20:49.867Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:20:55.376Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:21:02.115Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:21:02.115Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:21:02.892Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:21:02.892Z] Movies recommended for you:
[2024-10-29T23:21:02.892Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:21:02.892Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:21:02.892Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (68364.235 ms) ======
[2024-10-29T23:21:02.892Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-29T23:21:02.892Z] GC before operation: completed in 388.409 ms, heap usage 435.019 MB -> 47.218 MB.
[2024-10-29T23:21:14.463Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:21:25.998Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:21:35.709Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:21:45.457Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:21:52.366Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:21:58.414Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:22:05.169Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:22:10.687Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:22:11.447Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:22:12.201Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:22:12.201Z] Movies recommended for you:
[2024-10-29T23:22:12.201Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:22:12.201Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:22:12.201Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (68952.498 ms) ======
[2024-10-29T23:22:12.201Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-29T23:22:12.955Z] GC before operation: completed in 681.664 ms, heap usage 438.213 MB -> 50.167 MB.
[2024-10-29T23:22:22.730Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:22:34.335Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:22:45.917Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:22:57.488Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:23:03.027Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:23:08.819Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:23:14.336Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:23:21.105Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:23:21.105Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:23:21.105Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:23:21.105Z] Movies recommended for you:
[2024-10-29T23:23:21.105Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:23:21.105Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:23:21.105Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (68625.914 ms) ======
[2024-10-29T23:23:21.105Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-29T23:23:21.867Z] GC before operation: completed in 414.645 ms, heap usage 428.486 MB -> 47.624 MB.
[2024-10-29T23:23:31.648Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:23:41.373Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:23:52.930Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:24:02.756Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:24:09.577Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:24:16.963Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:24:25.158Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:24:31.974Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:24:32.730Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:24:32.730Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:24:33.505Z] Movies recommended for you:
[2024-10-29T23:24:33.505Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:24:33.505Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:24:33.505Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (71399.568 ms) ======
[2024-10-29T23:24:33.505Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-29T23:24:33.506Z] GC before operation: completed in 474.361 ms, heap usage 437.330 MB -> 47.196 MB.
[2024-10-29T23:24:45.073Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:24:58.726Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:25:12.312Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:25:25.926Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:25:32.010Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:25:38.758Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:25:45.488Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:25:53.659Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:25:55.216Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:25:55.216Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:25:55.216Z] Movies recommended for you:
[2024-10-29T23:25:55.216Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:25:55.216Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:25:55.216Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (81573.208 ms) ======
[2024-10-29T23:25:55.216Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-29T23:25:55.951Z] GC before operation: completed in 588.891 ms, heap usage 457.018 MB -> 47.597 MB.
[2024-10-29T23:26:07.344Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:26:20.890Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:26:32.366Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:26:44.391Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:26:51.213Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:26:56.641Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:27:04.741Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:27:10.189Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:27:11.725Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:27:11.725Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:27:11.725Z] Movies recommended for you:
[2024-10-29T23:27:11.725Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:27:11.725Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:27:11.725Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (76144.566 ms) ======
[2024-10-29T23:27:11.725Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-29T23:27:12.462Z] GC before operation: completed in 724.430 ms, heap usage 537.564 MB -> 47.282 MB.
[2024-10-29T23:27:23.897Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:27:37.418Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:27:49.327Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:28:02.833Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:28:09.650Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:28:16.412Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:28:24.581Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:28:31.253Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:28:31.999Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:28:31.999Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:28:31.999Z] Movies recommended for you:
[2024-10-29T23:28:31.999Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:28:31.999Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:28:31.999Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (79558.454 ms) ======
[2024-10-29T23:28:31.999Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-29T23:28:32.740Z] GC before operation: completed in 621.707 ms, heap usage 165.427 MB -> 48.961 MB.
[2024-10-29T23:28:46.299Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:28:57.816Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:29:09.810Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:29:21.348Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:29:28.109Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:29:36.262Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:29:43.035Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:29:49.777Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:29:50.541Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:29:50.541Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:29:50.541Z] Movies recommended for you:
[2024-10-29T23:29:50.541Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:29:50.541Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:29:50.541Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (77745.437 ms) ======
[2024-10-29T23:29:50.541Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-29T23:29:51.299Z] GC before operation: completed in 621.311 ms, heap usage 387.977 MB -> 47.482 MB.
[2024-10-29T23:30:02.766Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:30:14.874Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:30:28.401Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:30:39.881Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:30:48.018Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:30:53.526Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:31:00.248Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:31:07.072Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:31:07.818Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:31:07.818Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:31:08.555Z] Movies recommended for you:
[2024-10-29T23:31:08.555Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:31:08.555Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:31:08.555Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (77074.970 ms) ======
[2024-10-29T23:31:08.555Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-29T23:31:09.296Z] GC before operation: completed in 571.397 ms, heap usage 485.961 MB -> 47.379 MB.
[2024-10-29T23:31:21.369Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:31:34.903Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:31:48.372Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:31:59.992Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:32:08.185Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:32:13.657Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:32:21.745Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:32:29.016Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:32:29.016Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:32:29.016Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:32:29.016Z] Movies recommended for you:
[2024-10-29T23:32:29.016Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:32:29.016Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:32:29.016Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (80382.491 ms) ======
[2024-10-29T23:32:29.016Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-29T23:32:30.576Z] GC before operation: completed in 1022.310 ms, heap usage 458.868 MB -> 44.256 MB.
[2024-10-29T23:32:40.313Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:32:51.867Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:33:05.493Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:33:15.118Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:33:21.856Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:33:28.508Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:33:35.204Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:33:40.893Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:33:41.636Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:33:41.636Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:33:41.636Z] Movies recommended for you:
[2024-10-29T23:33:41.636Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:33:41.636Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:33:41.636Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (71630.755 ms) ======
[2024-10-29T23:33:41.636Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-29T23:33:43.173Z] GC before operation: completed in 953.422 ms, heap usage 455.728 MB -> 44.392 MB.
[2024-10-29T23:33:54.653Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-29T23:34:08.175Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-29T23:34:19.651Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-29T23:34:33.215Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-29T23:34:38.791Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-29T23:34:45.606Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-29T23:34:54.262Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-29T23:35:01.130Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-29T23:35:01.881Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2024-10-29T23:35:01.881Z] The best model improves the baseline by 14.52%.
[2024-10-29T23:35:01.881Z] Movies recommended for you:
[2024-10-29T23:35:01.881Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-29T23:35:01.881Z] There is no way to check that no silent failure occurred.
[2024-10-29T23:35:01.881Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (79065.056 ms) ======
[2024-10-29T23:35:03.537Z] -----------------------------------
[2024-10-29T23:35:03.537Z] renaissance-movie-lens_0_PASSED
[2024-10-29T23:35:03.537Z] -----------------------------------
[2024-10-29T23:35:03.537Z]
[2024-10-29T23:35:03.537Z] TEST TEARDOWN:
[2024-10-29T23:35:03.537Z] Nothing to be done for teardown.
[2024-10-29T23:35:03.537Z] renaissance-movie-lens_0 Finish Time: Tue Oct 29 23:35:03 2024 Epoch Time (ms): 1730244903362