renaissance-movie-lens_0
[2024-08-01T04:05:57.482Z] Running test renaissance-movie-lens_0 ...
[2024-08-01T04:05:57.482Z] ===============================================
[2024-08-01T04:05:57.482Z] renaissance-movie-lens_0 Start Time: Wed Jul 31 21:05:56 2024 Epoch Time (ms): 1722485156042
[2024-08-01T04:05:57.482Z] variation: NoOptions
[2024-08-01T04:05:57.482Z] JVM_OPTIONS:
[2024-08-01T04:05:57.482Z] { \
[2024-08-01T04:05:57.482Z] echo ""; echo "TEST SETUP:"; \
[2024-08-01T04:05:57.482Z] echo "Nothing to be done for setup."; \
[2024-08-01T04:05:57.482Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17224838837882/renaissance-movie-lens_0"; \
[2024-08-01T04:05:57.482Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17224838837882/renaissance-movie-lens_0"; \
[2024-08-01T04:05:57.482Z] echo ""; echo "TESTING:"; \
[2024-08-01T04:05:57.482Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/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_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17224838837882/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-01T04:05:57.482Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17224838837882/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-01T04:05:57.482Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-01T04:05:57.482Z] echo "Nothing to be done for teardown."; \
[2024-08-01T04:05:57.482Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17224838837882/TestTargetResult";
[2024-08-01T04:05:57.482Z]
[2024-08-01T04:05:57.482Z] TEST SETUP:
[2024-08-01T04:05:57.482Z] Nothing to be done for setup.
[2024-08-01T04:05:57.482Z]
[2024-08-01T04:05:57.482Z] TESTING:
[2024-08-01T04:06:04.613Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-01T04:06:07.445Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-08-01T04:06:14.705Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-01T04:06:15.195Z] Training: 60056, validation: 20285, test: 19854
[2024-08-01T04:06:15.195Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-01T04:06:15.714Z] GC before operation: completed in 198.638 ms, heap usage 187.611 MB -> 37.552 MB.
[2024-08-01T04:06:33.879Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:06:44.600Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:06:55.438Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:07:04.289Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:07:09.106Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:07:13.901Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:07:18.915Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:07:23.580Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:07:24.092Z] 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.
[2024-08-01T04:07:24.092Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:07:24.530Z] Movies recommended for you:
[2024-08-01T04:07:24.530Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:07:24.530Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:07:24.530Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (68884.684 ms) ======
[2024-08-01T04:07:24.530Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-01T04:07:24.530Z] GC before operation: completed in 157.319 ms, heap usage 435.692 MB -> 53.227 MB.
[2024-08-01T04:07:33.304Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:07:43.578Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:07:50.894Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:07:59.242Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:08:04.908Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:08:09.445Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:08:15.087Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:08:19.707Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:08:19.707Z] 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.
[2024-08-01T04:08:20.282Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:08:20.282Z] Movies recommended for you:
[2024-08-01T04:08:20.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:08:20.282Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:08:20.282Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (55774.172 ms) ======
[2024-08-01T04:08:20.282Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-01T04:08:20.750Z] GC before operation: completed in 262.425 ms, heap usage 153.709 MB -> 49.779 MB.
[2024-08-01T04:08:30.888Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:08:39.322Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:08:49.544Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:08:57.873Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:09:02.539Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:09:07.063Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:09:11.584Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:09:17.614Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:09:17.614Z] 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.
[2024-08-01T04:09:17.614Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:09:17.614Z] Movies recommended for you:
[2024-08-01T04:09:17.614Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:09:17.614Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:09:17.614Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (57193.393 ms) ======
[2024-08-01T04:09:17.614Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-01T04:09:18.037Z] GC before operation: completed in 207.932 ms, heap usage 404.463 MB -> 53.529 MB.
[2024-08-01T04:09:28.300Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:09:35.295Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:09:45.222Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:09:53.609Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:09:57.211Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:10:03.126Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:10:07.816Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:10:13.650Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:10:13.650Z] 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.
[2024-08-01T04:10:13.650Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:10:14.044Z] Movies recommended for you:
[2024-08-01T04:10:14.044Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:10:14.044Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:10:14.044Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (55984.339 ms) ======
[2024-08-01T04:10:14.044Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-01T04:10:14.044Z] GC before operation: completed in 126.854 ms, heap usage 204.039 MB -> 50.408 MB.
[2024-08-01T04:10:24.228Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:10:34.324Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:10:46.699Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:10:56.944Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:11:04.042Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:11:12.406Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:11:18.733Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:11:25.192Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:11:26.254Z] 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.
[2024-08-01T04:11:26.254Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:11:26.254Z] Movies recommended for you:
[2024-08-01T04:11:26.254Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:11:26.254Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:11:26.254Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (72290.536 ms) ======
[2024-08-01T04:11:26.254Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-01T04:11:26.816Z] GC before operation: completed in 215.687 ms, heap usage 257.999 MB -> 50.653 MB.
[2024-08-01T04:11:39.202Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:11:49.414Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:12:00.105Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:12:12.584Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:12:16.411Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:12:22.508Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:12:29.386Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:12:35.293Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:12:35.766Z] 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.
[2024-08-01T04:12:35.766Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:12:36.409Z] Movies recommended for you:
[2024-08-01T04:12:36.409Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:12:36.409Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:12:36.409Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (69793.073 ms) ======
[2024-08-01T04:12:36.409Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-01T04:12:36.908Z] GC before operation: completed in 304.886 ms, heap usage 262.355 MB -> 50.587 MB.
[2024-08-01T04:12:49.371Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:12:59.810Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:13:10.182Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:13:20.822Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:13:25.620Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:13:31.670Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:13:37.570Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:13:43.408Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:13:43.912Z] 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.
[2024-08-01T04:13:43.912Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:13:43.912Z] Movies recommended for you:
[2024-08-01T04:13:43.912Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:13:43.912Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:13:43.912Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (67270.249 ms) ======
[2024-08-01T04:13:43.912Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-01T04:13:44.359Z] GC before operation: completed in 283.548 ms, heap usage 341.634 MB -> 50.857 MB.
[2024-08-01T04:13:54.713Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:14:05.057Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:14:15.871Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:14:26.745Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:14:34.376Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:14:38.980Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:14:44.939Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:14:52.239Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:14:52.754Z] 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.
[2024-08-01T04:14:52.754Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:14:53.168Z] Movies recommended for you:
[2024-08-01T04:14:53.168Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:14:53.168Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:14:53.168Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (69127.368 ms) ======
[2024-08-01T04:14:53.168Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-01T04:14:53.713Z] GC before operation: completed in 146.315 ms, heap usage 222.754 MB -> 50.981 MB.
[2024-08-01T04:15:03.858Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:15:14.465Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:15:22.885Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:15:33.111Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:15:37.987Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:15:44.106Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:15:50.932Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:15:55.564Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:15:56.571Z] 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.
[2024-08-01T04:15:56.571Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:15:57.137Z] Movies recommended for you:
[2024-08-01T04:15:57.137Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:15:57.137Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:15:57.137Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (63472.877 ms) ======
[2024-08-01T04:15:57.137Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-01T04:15:57.137Z] GC before operation: completed in 171.498 ms, heap usage 324.541 MB -> 50.984 MB.
[2024-08-01T04:16:09.556Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:16:18.076Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:16:30.316Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:16:38.789Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:16:44.454Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:16:49.088Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:16:54.929Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:17:00.899Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:17:01.401Z] 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.
[2024-08-01T04:17:01.937Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:17:01.937Z] Movies recommended for you:
[2024-08-01T04:17:01.937Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:17:01.937Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:17:01.937Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (64605.300 ms) ======
[2024-08-01T04:17:01.937Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-01T04:17:01.937Z] GC before operation: completed in 204.827 ms, heap usage 693.524 MB -> 55.057 MB.
[2024-08-01T04:17:14.278Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:17:26.414Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:17:35.293Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:17:43.956Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:17:50.042Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:17:56.166Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:18:00.960Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:18:04.905Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:18:05.469Z] 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.
[2024-08-01T04:18:05.469Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:18:05.469Z] Movies recommended for you:
[2024-08-01T04:18:05.469Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:18:05.469Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:18:05.469Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (63669.522 ms) ======
[2024-08-01T04:18:05.469Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-01T04:18:05.899Z] GC before operation: completed in 156.757 ms, heap usage 69.013 MB -> 54.860 MB.
[2024-08-01T04:18:18.041Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:18:26.387Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:18:37.148Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:18:49.694Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:18:55.874Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:19:01.818Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:19:07.711Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:19:14.997Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:19:14.997Z] 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.
[2024-08-01T04:19:14.997Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:19:15.549Z] Movies recommended for you:
[2024-08-01T04:19:15.549Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:19:15.549Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:19:15.549Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (69551.992 ms) ======
[2024-08-01T04:19:15.549Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-01T04:19:15.549Z] GC before operation: completed in 191.853 ms, heap usage 907.780 MB -> 55.652 MB.
[2024-08-01T04:19:26.305Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:19:37.324Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:19:50.069Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:19:59.115Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:20:05.221Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:20:09.940Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:20:15.846Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:20:21.646Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:20:22.213Z] 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.
[2024-08-01T04:20:22.213Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:20:22.697Z] Movies recommended for you:
[2024-08-01T04:20:22.697Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:20:22.697Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:20:22.697Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (67128.569 ms) ======
[2024-08-01T04:20:22.697Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-01T04:20:22.697Z] GC before operation: completed in 177.374 ms, heap usage 444.376 MB -> 54.421 MB.
[2024-08-01T04:20:35.389Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:20:45.759Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:20:57.910Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:21:06.811Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:21:13.860Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:21:17.796Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:21:24.666Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:21:29.232Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:21:30.286Z] 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.
[2024-08-01T04:21:30.286Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:21:30.286Z] Movies recommended for you:
[2024-08-01T04:21:30.286Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:21:30.286Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:21:30.286Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (67328.611 ms) ======
[2024-08-01T04:21:30.286Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-01T04:21:30.775Z] GC before operation: completed in 261.864 ms, heap usage 495.963 MB -> 54.255 MB.
[2024-08-01T04:21:40.984Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:21:51.472Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:22:04.456Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:22:13.023Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:22:19.957Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:22:25.699Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:22:32.743Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:22:38.443Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:22:40.348Z] 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.
[2024-08-01T04:22:40.348Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:22:40.348Z] Movies recommended for you:
[2024-08-01T04:22:40.348Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:22:40.348Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:22:40.348Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (69977.738 ms) ======
[2024-08-01T04:22:40.348Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-01T04:22:40.815Z] GC before operation: completed in 286.606 ms, heap usage 296.839 MB -> 51.068 MB.
[2024-08-01T04:22:53.836Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:23:04.326Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:23:14.715Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:23:25.364Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:23:30.079Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:23:36.918Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:23:43.791Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:23:49.800Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:23:49.800Z] 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.
[2024-08-01T04:23:49.800Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:23:50.256Z] Movies recommended for you:
[2024-08-01T04:23:50.256Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:23:50.256Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:23:50.256Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (69404.403 ms) ======
[2024-08-01T04:23:50.256Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-01T04:23:50.256Z] GC before operation: completed in 139.986 ms, heap usage 204.218 MB -> 51.034 MB.
[2024-08-01T04:24:00.763Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:24:11.346Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:24:23.691Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:24:32.392Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:24:38.445Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:24:44.182Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:24:50.072Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:24:55.901Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:24:56.884Z] 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.
[2024-08-01T04:24:56.885Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:24:57.311Z] Movies recommended for you:
[2024-08-01T04:24:57.311Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:24:57.311Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:24:57.311Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (66771.866 ms) ======
[2024-08-01T04:24:57.311Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-01T04:24:57.311Z] GC before operation: completed in 170.178 ms, heap usage 408.537 MB -> 54.316 MB.
[2024-08-01T04:25:07.531Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:25:19.620Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:25:32.756Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:25:43.289Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:25:47.558Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:25:53.759Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:25:59.586Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:26:06.643Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:26:07.625Z] 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.
[2024-08-01T04:26:07.625Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:26:08.110Z] Movies recommended for you:
[2024-08-01T04:26:08.110Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:26:08.110Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:26:08.110Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (70552.648 ms) ======
[2024-08-01T04:26:08.110Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-01T04:26:08.110Z] GC before operation: completed in 167.956 ms, heap usage 237.963 MB -> 50.997 MB.
[2024-08-01T04:26:20.511Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:26:29.541Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:26:42.088Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:26:51.013Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:26:56.158Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:27:02.012Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:27:08.067Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:27:13.944Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:27:14.461Z] 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.
[2024-08-01T04:27:14.461Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:27:14.946Z] Movies recommended for you:
[2024-08-01T04:27:14.946Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:27:14.946Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:27:14.946Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (66928.134 ms) ======
[2024-08-01T04:27:14.946Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-01T04:27:14.946Z] GC before operation: completed in 240.695 ms, heap usage 175.926 MB -> 51.106 MB.
[2024-08-01T04:27:24.964Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-01T04:27:35.320Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-01T04:27:45.793Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-01T04:27:54.776Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-01T04:28:00.578Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-01T04:28:05.125Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-01T04:28:10.934Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-01T04:28:16.995Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-01T04:28:17.454Z] 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.
[2024-08-01T04:28:17.454Z] The best model improves the baseline by 14.52%.
[2024-08-01T04:28:18.037Z] Movies recommended for you:
[2024-08-01T04:28:18.037Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-01T04:28:18.037Z] There is no way to check that no silent failure occurred.
[2024-08-01T04:28:18.037Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (62750.327 ms) ======
[2024-08-01T04:28:20.752Z] -----------------------------------
[2024-08-01T04:28:20.752Z] renaissance-movie-lens_0_PASSED
[2024-08-01T04:28:20.752Z] -----------------------------------
[2024-08-01T04:28:20.752Z]
[2024-08-01T04:28:20.752Z] TEST TEARDOWN:
[2024-08-01T04:28:20.752Z] Nothing to be done for teardown.
[2024-08-01T04:28:20.752Z] renaissance-movie-lens_0 Finish Time: Wed Jul 31 21:28:19 2024 Epoch Time (ms): 1722486499173