renaissance-movie-lens_0
[2024-06-08T00:51:10.675Z] Running test renaissance-movie-lens_0 ...
[2024-06-08T00:51:10.675Z] ===============================================
[2024-06-08T00:51:10.675Z] renaissance-movie-lens_0 Start Time: Sat Jun 8 00:51:10 2024 Epoch Time (ms): 1717807870211
[2024-06-08T00:51:10.675Z] variation: NoOptions
[2024-06-08T00:51:10.675Z] JVM_OPTIONS:
[2024-06-08T00:51:10.675Z] { \
[2024-06-08T00:51:10.675Z] echo ""; echo "TEST SETUP:"; \
[2024-06-08T00:51:10.675Z] echo "Nothing to be done for setup."; \
[2024-06-08T00:51:10.675Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17178068352419/renaissance-movie-lens_0"; \
[2024-06-08T00:51:10.675Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17178068352419/renaissance-movie-lens_0"; \
[2024-06-08T00:51:10.675Z] echo ""; echo "TESTING:"; \
[2024-06-08T00:51:10.675Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17178068352419/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-08T00:51:10.675Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17178068352419/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-08T00:51:10.675Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-08T00:51:10.675Z] echo "Nothing to be done for teardown."; \
[2024-06-08T00:51:10.675Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17178068352419/TestTargetResult";
[2024-06-08T00:51:10.675Z]
[2024-06-08T00:51:10.675Z] TEST SETUP:
[2024-06-08T00:51:10.675Z] Nothing to be done for setup.
[2024-06-08T00:51:10.675Z]
[2024-06-08T00:51:10.676Z] TESTING:
[2024-06-08T00:51:16.197Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-08T00:51:18.629Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-06-08T00:51:22.006Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-08T00:51:22.765Z] Training: 60056, validation: 20285, test: 19854
[2024-06-08T00:51:22.765Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-08T00:51:22.765Z] GC before operation: completed in 57.089 ms, heap usage 125.597 MB -> 37.977 MB.
[2024-06-08T00:51:29.557Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:51:32.926Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:51:36.536Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:51:39.927Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:51:42.492Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:51:44.064Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:51:46.493Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:51:48.060Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:51:48.823Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:51:48.823Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:51:48.823Z] Movies recommended for you:
[2024-06-08T00:51:48.823Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:51:48.823Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:51:48.823Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26316.094 ms) ======
[2024-06-08T00:51:48.823Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-08T00:51:49.585Z] GC before operation: completed in 127.450 ms, heap usage 240.487 MB -> 51.310 MB.
[2024-06-08T00:51:52.965Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:51:56.495Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:51:59.866Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:52:03.235Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:52:04.812Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:52:06.382Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:52:08.814Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:52:11.250Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:52:11.251Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:52:11.251Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:52:11.251Z] Movies recommended for you:
[2024-06-08T00:52:11.251Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:52:11.251Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:52:11.251Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (22129.960 ms) ======
[2024-06-08T00:52:11.251Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-08T00:52:11.251Z] GC before operation: completed in 118.799 ms, heap usage 476.283 MB -> 51.933 MB.
[2024-06-08T00:52:14.627Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:52:18.000Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:52:21.372Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:52:24.743Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:52:26.307Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:52:27.872Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:52:30.321Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:52:31.902Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:52:32.660Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:52:32.660Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:52:32.660Z] Movies recommended for you:
[2024-06-08T00:52:32.660Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:52:32.660Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:52:32.660Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20977.833 ms) ======
[2024-06-08T00:52:32.660Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-08T00:52:32.660Z] GC before operation: completed in 118.487 ms, heap usage 488.451 MB -> 55.607 MB.
[2024-06-08T00:52:36.033Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:52:39.051Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:52:42.424Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:52:44.857Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:52:47.294Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:52:48.869Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:52:51.301Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:52:52.873Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:52:52.873Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:52:52.873Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:52:53.629Z] Movies recommended for you:
[2024-06-08T00:52:53.629Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:52:53.629Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:52:53.629Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20721.856 ms) ======
[2024-06-08T00:52:53.629Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-08T00:52:53.629Z] GC before operation: completed in 125.823 ms, heap usage 242.071 MB -> 55.704 MB.
[2024-06-08T00:52:57.007Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:52:59.438Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:53:02.810Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:53:06.193Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:53:07.755Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:53:10.190Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:53:11.760Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:53:13.332Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:53:14.089Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:53:14.089Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:53:14.089Z] Movies recommended for you:
[2024-06-08T00:53:14.089Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:53:14.089Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:53:14.089Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20772.896 ms) ======
[2024-06-08T00:53:14.089Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-08T00:53:14.089Z] GC before operation: completed in 124.997 ms, heap usage 424.906 MB -> 52.749 MB.
[2024-06-08T00:53:17.457Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:53:20.832Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:53:24.202Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:53:26.633Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:53:29.071Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:53:30.638Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:53:32.208Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:53:34.651Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:53:34.651Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:53:34.651Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:53:34.651Z] Movies recommended for you:
[2024-06-08T00:53:34.651Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:53:34.651Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:53:34.651Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20624.212 ms) ======
[2024-06-08T00:53:34.651Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-08T00:53:35.409Z] GC before operation: completed in 112.890 ms, heap usage 277.649 MB -> 52.562 MB.
[2024-06-08T00:53:37.855Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:53:41.222Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:53:44.987Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:53:47.424Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:53:49.868Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:53:51.449Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:53:53.023Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:53:55.499Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:53:55.499Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:53:55.499Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:53:55.499Z] Movies recommended for you:
[2024-06-08T00:53:55.499Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:53:55.499Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:53:55.499Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20607.427 ms) ======
[2024-06-08T00:53:55.499Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-08T00:53:55.499Z] GC before operation: completed in 115.920 ms, heap usage 343.815 MB -> 52.842 MB.
[2024-06-08T00:53:59.092Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:54:02.470Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:54:05.852Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:54:08.305Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:54:10.751Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:54:12.320Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:54:13.887Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:54:16.321Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:54:16.321Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:54:16.321Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:54:16.321Z] Movies recommended for you:
[2024-06-08T00:54:16.321Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:54:16.321Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:54:16.321Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20603.672 ms) ======
[2024-06-08T00:54:16.321Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-08T00:54:16.321Z] GC before operation: completed in 117.122 ms, heap usage 299.529 MB -> 53.133 MB.
[2024-06-08T00:54:19.703Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:54:23.082Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:54:26.458Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:54:28.890Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:54:30.470Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:54:32.917Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:54:34.480Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:54:36.918Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:54:36.918Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:54:36.918Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:54:36.918Z] Movies recommended for you:
[2024-06-08T00:54:36.918Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:54:36.918Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:54:36.918Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20546.476 ms) ======
[2024-06-08T00:54:36.918Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-08T00:54:36.918Z] GC before operation: completed in 120.298 ms, heap usage 511.437 MB -> 56.329 MB.
[2024-06-08T00:54:40.297Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:54:43.690Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:54:47.066Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:54:49.714Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:54:51.320Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:54:53.770Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:54:55.332Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:54:57.777Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:54:57.777Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:54:57.777Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:54:57.777Z] Movies recommended for you:
[2024-06-08T00:54:57.777Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:54:57.777Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:54:57.777Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20658.823 ms) ======
[2024-06-08T00:54:57.777Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-08T00:54:57.777Z] GC before operation: completed in 128.421 ms, heap usage 423.432 MB -> 53.077 MB.
[2024-06-08T00:55:01.158Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:55:04.551Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:55:06.988Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:55:10.355Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:55:11.940Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:55:14.387Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:55:15.956Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:55:17.538Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:55:18.307Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:55:18.307Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:55:18.307Z] Movies recommended for you:
[2024-06-08T00:55:18.307Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:55:18.307Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:55:18.307Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20460.009 ms) ======
[2024-06-08T00:55:18.307Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-08T00:55:18.307Z] GC before operation: completed in 131.759 ms, heap usage 621.873 MB -> 56.173 MB.
[2024-06-08T00:55:21.695Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:55:25.078Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:55:28.467Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:55:30.904Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:55:33.336Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:55:34.905Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:55:37.336Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:55:38.910Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:55:38.910Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:55:38.910Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:55:39.666Z] Movies recommended for you:
[2024-06-08T00:55:39.666Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:55:39.666Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:55:39.666Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20812.555 ms) ======
[2024-06-08T00:55:39.666Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-08T00:55:39.666Z] GC before operation: completed in 119.866 ms, heap usage 502.265 MB -> 56.318 MB.
[2024-06-08T00:55:43.048Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:55:45.484Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:55:48.856Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:55:52.225Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:55:53.790Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:55:55.576Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:55:57.306Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:55:59.746Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:55:59.746Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:55:59.747Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:55:59.747Z] Movies recommended for you:
[2024-06-08T00:55:59.747Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:55:59.747Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:55:59.747Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20479.531 ms) ======
[2024-06-08T00:55:59.747Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-08T00:55:59.747Z] GC before operation: completed in 127.119 ms, heap usage 260.555 MB -> 53.067 MB.
[2024-06-08T00:56:03.135Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:56:06.502Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:56:09.873Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:56:12.310Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:56:14.753Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:56:16.317Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:56:18.755Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:56:20.327Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:56:20.327Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:56:20.327Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:56:21.082Z] Movies recommended for you:
[2024-06-08T00:56:21.082Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:56:21.082Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:56:21.082Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20681.582 ms) ======
[2024-06-08T00:56:21.082Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-08T00:56:21.082Z] GC before operation: completed in 119.076 ms, heap usage 624.638 MB -> 56.297 MB.
[2024-06-08T00:56:24.463Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:56:26.894Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:56:30.291Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:56:33.665Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:56:35.225Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:56:37.666Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:56:39.232Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:56:41.685Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:56:41.685Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:56:41.685Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:56:41.685Z] Movies recommended for you:
[2024-06-08T00:56:41.685Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:56:41.685Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:56:41.685Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20854.483 ms) ======
[2024-06-08T00:56:41.685Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-08T00:56:41.685Z] GC before operation: completed in 119.519 ms, heap usage 302.241 MB -> 53.049 MB.
[2024-06-08T00:56:45.064Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:56:48.448Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:56:51.852Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:56:54.290Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:56:55.855Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:56:58.321Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:56:59.883Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:57:02.315Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:57:02.315Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:57:02.315Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:57:02.315Z] Movies recommended for you:
[2024-06-08T00:57:02.315Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:57:02.315Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:57:02.315Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20513.071 ms) ======
[2024-06-08T00:57:02.315Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-08T00:57:02.315Z] GC before operation: completed in 141.587 ms, heap usage 373.546 MB -> 53.124 MB.
[2024-06-08T00:57:05.693Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:57:09.075Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:57:12.454Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:57:15.223Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:57:16.803Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:57:18.365Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:57:20.811Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:57:22.373Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:57:22.373Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:57:22.373Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:57:23.130Z] Movies recommended for you:
[2024-06-08T00:57:23.130Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:57:23.130Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:57:23.130Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (20338.723 ms) ======
[2024-06-08T00:57:23.130Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-08T00:57:23.130Z] GC before operation: completed in 119.059 ms, heap usage 302.255 MB -> 52.968 MB.
[2024-06-08T00:57:26.500Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:57:28.933Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:57:32.308Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:57:35.685Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:57:37.253Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:57:38.817Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:57:41.251Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:57:42.815Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:57:42.815Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:57:42.815Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:57:43.573Z] Movies recommended for you:
[2024-06-08T00:57:43.573Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:57:43.573Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:57:43.573Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (20264.729 ms) ======
[2024-06-08T00:57:43.573Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-08T00:57:43.573Z] GC before operation: completed in 119.910 ms, heap usage 403.873 MB -> 53.084 MB.
[2024-06-08T00:57:46.011Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:57:49.388Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:57:52.754Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:57:56.136Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:57:57.701Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:57:59.266Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:58:01.705Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:58:03.271Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:58:03.271Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:58:03.271Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:58:04.033Z] Movies recommended for you:
[2024-06-08T00:58:04.034Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:58:04.034Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:58:04.034Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (20343.740 ms) ======
[2024-06-08T00:58:04.034Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-08T00:58:04.034Z] GC before operation: completed in 123.239 ms, heap usage 268.908 MB -> 53.162 MB.
[2024-06-08T00:58:06.481Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-08T00:58:09.859Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-08T00:58:13.245Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-08T00:58:16.660Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-08T00:58:18.230Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-08T00:58:19.800Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-08T00:58:22.246Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-08T00:58:23.810Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-08T00:58:23.810Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-06-08T00:58:24.564Z] The best model improves the baseline by 14.43%.
[2024-06-08T00:58:24.564Z] Movies recommended for you:
[2024-06-08T00:58:24.565Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-08T00:58:24.565Z] There is no way to check that no silent failure occurred.
[2024-06-08T00:58:24.565Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (20483.444 ms) ======
[2024-06-08T00:58:25.321Z] -----------------------------------
[2024-06-08T00:58:25.321Z] renaissance-movie-lens_0_PASSED
[2024-06-08T00:58:25.321Z] -----------------------------------
[2024-06-08T00:58:25.321Z]
[2024-06-08T00:58:25.321Z] TEST TEARDOWN:
[2024-06-08T00:58:25.321Z] Nothing to be done for teardown.
[2024-06-08T00:58:25.321Z] renaissance-movie-lens_0 Finish Time: Sat Jun 8 00:58:24 2024 Epoch Time (ms): 1717808304544