renaissance-movie-lens_0
[2024-08-22T00:05:22.501Z] Running test renaissance-movie-lens_0 ...
[2024-08-22T00:05:22.501Z] ===============================================
[2024-08-22T00:05:23.011Z] renaissance-movie-lens_0 Start Time: Wed Aug 21 17:05:22 2024 Epoch Time (ms): 1724285122129
[2024-08-22T00:05:23.011Z] variation: NoOptions
[2024-08-22T00:05:23.011Z] JVM_OPTIONS:
[2024-08-22T00:05:23.011Z] { \
[2024-08-22T00:05:23.011Z] echo ""; echo "TEST SETUP:"; \
[2024-08-22T00:05:23.011Z] echo "Nothing to be done for setup."; \
[2024-08-22T00:05:23.011Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17242818951253/renaissance-movie-lens_0"; \
[2024-08-22T00:05:23.011Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17242818951253/renaissance-movie-lens_0"; \
[2024-08-22T00:05:23.011Z] echo ""; echo "TESTING:"; \
[2024-08-22T00:05:23.011Z] "/Users/admin/workspace/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17242818951253/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-22T00:05:23.011Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17242818951253/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-22T00:05:23.011Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-22T00:05:23.011Z] echo "Nothing to be done for teardown."; \
[2024-08-22T00:05:23.011Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17242818951253/TestTargetResult";
[2024-08-22T00:05:23.011Z]
[2024-08-22T00:05:23.011Z] TEST SETUP:
[2024-08-22T00:05:23.011Z] Nothing to be done for setup.
[2024-08-22T00:05:23.011Z]
[2024-08-22T00:05:23.011Z] TESTING:
[2024-08-22T00:05:39.394Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-22T00:05:46.446Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-08-22T00:06:05.718Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-22T00:06:07.073Z] Training: 60056, validation: 20285, test: 19854
[2024-08-22T00:06:07.073Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-22T00:06:07.073Z] GC before operation: completed in 383.693 ms, heap usage 186.750 MB -> 36.670 MB.
[2024-08-22T00:06:52.252Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:07:18.896Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:07:41.421Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:08:04.551Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:08:16.187Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:08:30.515Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:08:43.970Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:08:57.453Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:08:57.453Z] 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.
[2024-08-22T00:08:57.997Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:08:59.018Z] Movies recommended for you:
[2024-08-22T00:08:59.018Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:08:59.018Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:08:59.018Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (171613.302 ms) ======
[2024-08-22T00:08:59.018Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-22T00:08:59.447Z] GC before operation: completed in 542.052 ms, heap usage 253.504 MB -> 46.876 MB.
[2024-08-22T00:09:22.125Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:09:45.547Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:10:05.440Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:10:25.062Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:10:34.538Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:10:44.147Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:10:55.996Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:11:05.853Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:11:07.869Z] 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.
[2024-08-22T00:11:07.870Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:11:08.574Z] Movies recommended for you:
[2024-08-22T00:11:08.574Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:11:08.574Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:11:08.574Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (128734.358 ms) ======
[2024-08-22T00:11:08.574Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-22T00:11:08.574Z] GC before operation: completed in 263.282 ms, heap usage 360.950 MB -> 49.014 MB.
[2024-08-22T00:11:28.998Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:11:48.646Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:12:11.314Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:12:24.126Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:12:35.518Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:12:45.384Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:12:55.803Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:13:05.439Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:13:05.439Z] 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.
[2024-08-22T00:13:05.439Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:13:06.856Z] Movies recommended for you:
[2024-08-22T00:13:06.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:13:06.857Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:13:06.857Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (118448.390 ms) ======
[2024-08-22T00:13:06.857Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-22T00:13:07.370Z] GC before operation: completed in 273.123 ms, heap usage 95.270 MB -> 50.424 MB.
[2024-08-22T00:13:27.025Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:13:49.483Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:14:05.620Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:14:25.306Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:14:35.218Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:14:44.406Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:14:55.569Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:15:05.404Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:15:06.541Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-22T00:15:06.542Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:15:08.050Z] Movies recommended for you:
[2024-08-22T00:15:08.050Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:15:08.050Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:15:08.050Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (121030.685 ms) ======
[2024-08-22T00:15:08.050Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-22T00:15:08.571Z] GC before operation: completed in 372.642 ms, heap usage 486.499 MB -> 52.859 MB.
[2024-08-22T00:15:27.444Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:15:46.543Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:16:03.058Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:16:19.827Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:16:30.910Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:16:42.643Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:16:52.318Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:17:00.878Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:17:01.415Z] 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.
[2024-08-22T00:17:01.415Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:17:01.415Z] Movies recommended for you:
[2024-08-22T00:17:01.415Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:17:01.415Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:17:01.415Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (113022.780 ms) ======
[2024-08-22T00:17:01.415Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-22T00:17:01.930Z] GC before operation: completed in 308.095 ms, heap usage 279.010 MB -> 49.714 MB.
[2024-08-22T00:17:25.096Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:17:41.161Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:17:54.471Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:18:11.013Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:18:19.315Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:18:35.678Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:18:43.845Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:18:54.340Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:18:58.713Z] 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.
[2024-08-22T00:18:58.713Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:18:59.293Z] Movies recommended for you:
[2024-08-22T00:18:59.293Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:18:59.293Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:18:59.293Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (117142.673 ms) ======
[2024-08-22T00:18:59.293Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-22T00:19:02.710Z] GC before operation: completed in 2142.400 ms, heap usage 154.120 MB -> 49.535 MB.
[2024-08-22T00:19:21.116Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:19:48.635Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:20:08.757Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:20:27.580Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:20:39.492Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:20:50.280Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:21:00.518Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:21:11.729Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:21:13.538Z] 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.
[2024-08-22T00:21:13.538Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:21:13.538Z] Movies recommended for you:
[2024-08-22T00:21:13.538Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:21:13.538Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:21:13.538Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (132620.785 ms) ======
[2024-08-22T00:21:13.538Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-22T00:21:14.088Z] GC before operation: completed in 451.722 ms, heap usage 425.037 MB -> 53.106 MB.
[2024-08-22T00:21:36.268Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:21:59.588Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:22:18.621Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:22:42.146Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:22:50.167Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:23:02.458Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:23:13.916Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:23:24.938Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:23:26.024Z] 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.
[2024-08-22T00:23:26.024Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:23:26.871Z] Movies recommended for you:
[2024-08-22T00:23:26.871Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:23:26.871Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:23:26.872Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (132115.433 ms) ======
[2024-08-22T00:23:26.872Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-22T00:23:27.998Z] GC before operation: completed in 1455.359 ms, heap usage 217.430 MB -> 50.015 MB.
[2024-08-22T00:23:47.418Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:24:06.968Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:24:25.961Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:24:45.108Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:24:56.563Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:25:07.476Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:25:19.207Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:25:28.540Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:25:29.639Z] 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.
[2024-08-22T00:25:30.109Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:25:30.109Z] Movies recommended for you:
[2024-08-22T00:25:30.109Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:25:30.109Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:25:30.109Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (122393.594 ms) ======
[2024-08-22T00:25:30.109Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-22T00:25:32.088Z] GC before operation: completed in 1499.499 ms, heap usage 389.874 MB -> 53.206 MB.
[2024-08-22T00:25:55.191Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:26:14.028Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:26:32.862Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:26:48.813Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:27:02.002Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:27:11.771Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:27:25.276Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:27:37.085Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:27:37.828Z] 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.
[2024-08-22T00:27:37.829Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:27:38.299Z] Movies recommended for you:
[2024-08-22T00:27:38.299Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:27:38.299Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:27:38.299Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (126779.428 ms) ======
[2024-08-22T00:27:38.299Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-22T00:27:38.774Z] GC before operation: completed in 268.971 ms, heap usage 64.193 MB -> 53.262 MB.
[2024-08-22T00:28:01.150Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:28:17.287Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:28:36.256Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:28:55.361Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:29:09.007Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:29:17.804Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:29:27.605Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:29:39.267Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:29:39.769Z] 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.
[2024-08-22T00:29:39.769Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:29:41.133Z] Movies recommended for you:
[2024-08-22T00:29:41.133Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:29:41.133Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:29:41.133Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (122015.983 ms) ======
[2024-08-22T00:29:41.133Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-22T00:29:41.627Z] GC before operation: completed in 688.590 ms, heap usage 73.036 MB -> 52.529 MB.
[2024-08-22T00:30:08.967Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:30:28.350Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:30:46.503Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:31:05.657Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:31:14.748Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:31:27.968Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:31:38.919Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:31:49.364Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:31:50.677Z] 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.
[2024-08-22T00:31:50.677Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:31:51.124Z] Movies recommended for you:
[2024-08-22T00:31:51.124Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:31:51.124Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:31:51.124Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (129703.424 ms) ======
[2024-08-22T00:31:51.124Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-22T00:31:51.809Z] GC before operation: completed in 293.634 ms, heap usage 207.463 MB -> 49.873 MB.
[2024-08-22T00:32:14.294Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:32:33.704Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:32:52.126Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:33:07.904Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:33:18.993Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:33:30.050Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:33:40.681Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:33:51.335Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:33:53.049Z] 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.
[2024-08-22T00:33:53.542Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:33:53.542Z] Movies recommended for you:
[2024-08-22T00:33:53.542Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:33:53.542Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:33:53.542Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (122069.168 ms) ======
[2024-08-22T00:33:53.542Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-22T00:33:54.128Z] GC before operation: completed in 348.922 ms, heap usage 326.367 MB -> 50.171 MB.
[2024-08-22T00:34:13.052Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:34:32.176Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:34:51.020Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:35:13.622Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:35:23.304Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:35:36.860Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:35:44.946Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:35:58.659Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:35:59.112Z] 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.
[2024-08-22T00:35:59.112Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:35:59.563Z] Movies recommended for you:
[2024-08-22T00:35:59.563Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:35:59.563Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:35:59.563Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (125735.652 ms) ======
[2024-08-22T00:35:59.563Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-22T00:36:00.117Z] GC before operation: completed in 371.357 ms, heap usage 204.256 MB -> 50.958 MB.
[2024-08-22T00:36:22.853Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:36:41.809Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:37:04.379Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:37:20.825Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:37:29.944Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:37:41.249Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:37:52.948Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:38:03.133Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:38:04.357Z] 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.
[2024-08-22T00:38:04.357Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:38:04.783Z] Movies recommended for you:
[2024-08-22T00:38:04.783Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:38:04.783Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:38:04.783Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (124653.033 ms) ======
[2024-08-22T00:38:04.783Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-22T00:38:04.783Z] GC before operation: completed in 250.759 ms, heap usage 383.003 MB -> 53.319 MB.
[2024-08-22T00:38:26.882Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:38:46.191Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:39:05.361Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:39:21.708Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:39:30.548Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:39:39.859Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:39:51.335Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:40:01.214Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:40:01.214Z] 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.
[2024-08-22T00:40:01.214Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:40:02.537Z] Movies recommended for you:
[2024-08-22T00:40:02.537Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:40:02.537Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:40:02.537Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (117148.727 ms) ======
[2024-08-22T00:40:02.537Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-22T00:40:02.537Z] GC before operation: completed in 370.422 ms, heap usage 401.389 MB -> 53.768 MB.
[2024-08-22T00:40:24.921Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:40:40.969Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:40:56.432Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:41:15.800Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:41:23.562Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:41:36.902Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:41:46.511Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:41:54.415Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:41:57.026Z] 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.
[2024-08-22T00:41:57.026Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:41:57.592Z] Movies recommended for you:
[2024-08-22T00:41:57.593Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:41:57.593Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:41:57.593Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (114996.036 ms) ======
[2024-08-22T00:41:57.593Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-22T00:41:57.593Z] GC before operation: completed in 316.712 ms, heap usage 211.219 MB -> 49.884 MB.
[2024-08-22T00:42:19.352Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:42:37.849Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:42:53.358Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:43:12.308Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:43:23.400Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:43:32.836Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:43:42.596Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:43:55.902Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:43:55.902Z] 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.
[2024-08-22T00:43:55.902Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:43:55.902Z] Movies recommended for you:
[2024-08-22T00:43:55.902Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:43:55.902Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:43:55.902Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (117877.266 ms) ======
[2024-08-22T00:43:55.902Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-22T00:43:55.902Z] GC before operation: completed in 316.651 ms, heap usage 315.962 MB -> 50.078 MB.
[2024-08-22T00:44:18.879Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:44:38.147Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:44:54.078Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:45:10.592Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:45:18.072Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:45:29.450Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:45:39.936Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:45:47.602Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:45:50.524Z] 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.
[2024-08-22T00:45:50.524Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:45:50.524Z] Movies recommended for you:
[2024-08-22T00:45:50.524Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:45:50.524Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:45:50.524Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (114652.002 ms) ======
[2024-08-22T00:45:50.524Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-22T00:45:50.909Z] GC before operation: completed in 354.418 ms, heap usage 439.653 MB -> 55.806 MB.
[2024-08-22T00:46:13.822Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-22T00:46:30.265Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-22T00:46:46.610Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-22T00:47:08.505Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-22T00:47:15.946Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-22T00:47:23.810Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-22T00:47:35.700Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-22T00:47:47.091Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-22T00:47:49.399Z] 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.
[2024-08-22T00:47:49.399Z] The best model improves the baseline by 14.52%.
[2024-08-22T00:47:50.449Z] Movies recommended for you:
[2024-08-22T00:47:50.449Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-22T00:47:50.449Z] There is no way to check that no silent failure occurred.
[2024-08-22T00:47:50.449Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (119302.195 ms) ======
[2024-08-22T00:47:56.911Z] -----------------------------------
[2024-08-22T00:47:56.911Z] renaissance-movie-lens_0_PASSED
[2024-08-22T00:47:56.911Z] -----------------------------------
[2024-08-22T00:47:56.911Z]
[2024-08-22T00:47:56.911Z] TEST TEARDOWN:
[2024-08-22T00:47:56.911Z] Nothing to be done for teardown.
[2024-08-22T00:47:56.911Z] renaissance-movie-lens_0 Finish Time: Wed Aug 21 17:47:56 2024 Epoch Time (ms): 1724287676101