renaissance-movie-lens_0
[2024-09-05T03:03:44.362Z] Running test renaissance-movie-lens_0 ...
[2024-09-05T03:03:44.362Z] ===============================================
[2024-09-05T03:03:44.362Z] renaissance-movie-lens_0 Start Time: Thu Sep 5 03:03:43 2024 Epoch Time (ms): 1725505423805
[2024-09-05T03:03:44.362Z] variation: NoOptions
[2024-09-05T03:03:44.362Z] JVM_OPTIONS:
[2024-09-05T03:03:44.362Z] { \
[2024-09-05T03:03:44.362Z] echo ""; echo "TEST SETUP:"; \
[2024-09-05T03:03:44.362Z] echo "Nothing to be done for setup."; \
[2024-09-05T03:03:44.362Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17255054234261/renaissance-movie-lens_0"; \
[2024-09-05T03:03:44.362Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17255054234261/renaissance-movie-lens_0"; \
[2024-09-05T03:03:44.362Z] echo ""; echo "TESTING:"; \
[2024-09-05T03:03:44.362Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/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_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17255054234261/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-05T03:03:44.362Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17255054234261/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-05T03:03:44.362Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-05T03:03:44.362Z] echo "Nothing to be done for teardown."; \
[2024-09-05T03:03:44.362Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17255054234261/TestTargetResult";
[2024-09-05T03:03:44.362Z]
[2024-09-05T03:03:44.362Z] TEST SETUP:
[2024-09-05T03:03:44.362Z] Nothing to be done for setup.
[2024-09-05T03:03:44.362Z]
[2024-09-05T03:03:44.362Z] TESTING:
[2024-09-05T03:03:48.114Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-05T03:03:50.123Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-09-05T03:03:53.918Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-05T03:03:54.563Z] Training: 60056, validation: 20285, test: 19854
[2024-09-05T03:03:54.563Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-05T03:03:54.563Z] GC before operation: completed in 123.688 ms, heap usage 99.571 MB -> 36.976 MB.
[2024-09-05T03:04:00.609Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:04:05.408Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:04:10.652Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:04:14.626Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:04:16.688Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:04:19.663Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:04:23.545Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:04:25.588Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:04:26.265Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:04:26.265Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:04:26.902Z] Movies recommended for you:
[2024-09-05T03:04:26.902Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:04:26.902Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:04:26.902Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (32079.005 ms) ======
[2024-09-05T03:04:26.902Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-05T03:04:26.902Z] GC before operation: completed in 215.370 ms, heap usage 206.533 MB -> 53.075 MB.
[2024-09-05T03:04:31.733Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:04:35.565Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:04:39.340Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:04:42.268Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:04:44.393Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:04:47.045Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:04:49.919Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:04:51.966Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:04:52.637Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:04:52.637Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:04:52.637Z] Movies recommended for you:
[2024-09-05T03:04:52.637Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:04:52.637Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:04:52.637Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (25717.012 ms) ======
[2024-09-05T03:04:52.637Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-05T03:04:52.637Z] GC before operation: completed in 125.816 ms, heap usage 123.708 MB -> 48.752 MB.
[2024-09-05T03:04:56.376Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:05:01.189Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:05:04.999Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:05:07.059Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:05:09.155Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:05:12.081Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:05:15.016Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:05:17.236Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:05:17.873Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:05:17.873Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:05:18.500Z] Movies recommended for you:
[2024-09-05T03:05:18.500Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:05:18.500Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:05:18.500Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (25701.677 ms) ======
[2024-09-05T03:05:18.500Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-05T03:05:18.500Z] GC before operation: completed in 126.227 ms, heap usage 75.754 MB -> 48.981 MB.
[2024-09-05T03:05:22.274Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:05:26.214Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:05:29.997Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:05:33.838Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:05:35.917Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:05:37.957Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:05:40.867Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:05:43.763Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:05:43.763Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:05:43.763Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:05:43.763Z] Movies recommended for you:
[2024-09-05T03:05:43.763Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:05:43.763Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:05:43.763Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (25571.073 ms) ======
[2024-09-05T03:05:43.763Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-05T03:05:44.414Z] GC before operation: completed in 155.144 ms, heap usage 322.875 MB -> 49.668 MB.
[2024-09-05T03:05:48.215Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:05:52.137Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:05:55.970Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:05:59.819Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:06:01.913Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:06:04.037Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:06:06.996Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:06:09.203Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:06:09.828Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:06:09.828Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:06:10.452Z] Movies recommended for you:
[2024-09-05T03:06:10.452Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:06:10.452Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:06:10.452Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (26115.330 ms) ======
[2024-09-05T03:06:10.452Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-05T03:06:10.452Z] GC before operation: completed in 155.787 ms, heap usage 90.864 MB -> 49.540 MB.
[2024-09-05T03:06:15.434Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:06:19.254Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:06:24.462Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:06:28.425Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:06:30.484Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:06:33.404Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:06:35.494Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:06:37.583Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:06:38.263Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:06:38.263Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:06:38.263Z] Movies recommended for you:
[2024-09-05T03:06:38.263Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:06:38.263Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:06:38.263Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (27819.198 ms) ======
[2024-09-05T03:06:38.263Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-05T03:06:38.263Z] GC before operation: completed in 178.290 ms, heap usage 201.804 MB -> 49.593 MB.
[2024-09-05T03:06:42.176Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:06:47.070Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:06:51.318Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:06:55.411Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:06:57.715Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:06:59.915Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:07:02.936Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:07:05.079Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:07:05.795Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:07:05.795Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:07:05.795Z] Movies recommended for you:
[2024-09-05T03:07:05.795Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:07:05.795Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:07:05.795Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (27312.470 ms) ======
[2024-09-05T03:07:05.795Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-05T03:07:05.795Z] GC before operation: completed in 189.634 ms, heap usage 255.632 MB -> 49.811 MB.
[2024-09-05T03:07:10.862Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:07:14.779Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:07:18.775Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:07:22.719Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:07:24.855Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:07:27.726Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:07:30.692Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:07:33.696Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:07:34.369Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:07:34.369Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:07:34.369Z] Movies recommended for you:
[2024-09-05T03:07:34.369Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:07:34.369Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:07:34.369Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (28458.945 ms) ======
[2024-09-05T03:07:34.369Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-05T03:07:34.369Z] GC before operation: completed in 158.834 ms, heap usage 112.192 MB -> 49.885 MB.
[2024-09-05T03:07:39.344Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:07:43.319Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:07:49.475Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:07:53.402Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:07:55.510Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:07:57.692Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:08:00.689Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:08:02.797Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:08:03.449Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:08:03.449Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:08:03.449Z] Movies recommended for you:
[2024-09-05T03:08:03.449Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:08:03.449Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:08:03.449Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (29056.643 ms) ======
[2024-09-05T03:08:03.449Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-05T03:08:04.109Z] GC before operation: completed in 125.827 ms, heap usage 148.402 MB -> 49.816 MB.
[2024-09-05T03:08:08.161Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:08:13.103Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:08:18.047Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:08:22.027Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:08:24.133Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:08:26.235Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:08:29.237Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:08:32.228Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:08:32.228Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:08:32.228Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:08:32.228Z] Movies recommended for you:
[2024-09-05T03:08:32.228Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:08:32.228Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:08:32.228Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (28731.650 ms) ======
[2024-09-05T03:08:32.228Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-05T03:08:32.868Z] GC before operation: completed in 180.789 ms, heap usage 255.867 MB -> 49.965 MB.
[2024-09-05T03:08:36.807Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:08:41.858Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:08:47.042Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:08:52.013Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:08:54.171Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:08:57.066Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:08:59.985Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:09:02.964Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:09:02.964Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:09:02.964Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:09:02.964Z] Movies recommended for you:
[2024-09-05T03:09:02.965Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:09:02.965Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:09:02.965Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (30563.226 ms) ======
[2024-09-05T03:09:02.965Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-05T03:09:03.595Z] GC before operation: completed in 178.776 ms, heap usage 228.391 MB -> 49.758 MB.
[2024-09-05T03:09:07.500Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:09:11.536Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:09:17.687Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:09:21.578Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:09:23.725Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:09:25.904Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:09:28.764Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:09:31.659Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:09:31.659Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:09:31.659Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:09:32.329Z] Movies recommended for you:
[2024-09-05T03:09:32.329Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:09:32.329Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:09:32.329Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (28639.076 ms) ======
[2024-09-05T03:09:32.329Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-05T03:09:32.329Z] GC before operation: completed in 158.367 ms, heap usage 266.814 MB -> 50.053 MB.
[2024-09-05T03:09:36.302Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:09:41.115Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:09:45.450Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:09:50.336Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:09:51.687Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:09:53.776Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:09:56.841Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:09:59.856Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:10:00.503Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:10:00.503Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:10:00.503Z] Movies recommended for you:
[2024-09-05T03:10:00.503Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:10:00.503Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:10:00.503Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (28350.823 ms) ======
[2024-09-05T03:10:00.503Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-05T03:10:01.195Z] GC before operation: completed in 237.662 ms, heap usage 170.356 MB -> 50.043 MB.
[2024-09-05T03:10:05.104Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:10:09.996Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:10:16.125Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:10:19.199Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:10:22.075Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:10:25.176Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:10:28.246Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:10:30.542Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:10:31.211Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:10:31.211Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:10:31.211Z] Movies recommended for you:
[2024-09-05T03:10:31.211Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:10:31.211Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:10:31.211Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (30295.775 ms) ======
[2024-09-05T03:10:31.211Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-05T03:10:31.211Z] GC before operation: completed in 177.163 ms, heap usage 129.270 MB -> 49.767 MB.
[2024-09-05T03:10:36.140Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:10:39.936Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:10:44.940Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:10:48.857Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:10:51.783Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:10:53.884Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:10:56.782Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:10:58.334Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:10:59.070Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:10:59.070Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:10:59.070Z] Movies recommended for you:
[2024-09-05T03:10:59.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:10:59.070Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:10:59.070Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (27653.755 ms) ======
[2024-09-05T03:10:59.070Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-05T03:10:59.070Z] GC before operation: completed in 102.973 ms, heap usage 248.531 MB -> 50.090 MB.
[2024-09-05T03:11:02.974Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:11:08.004Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:11:14.169Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:11:18.194Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:11:21.247Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:11:23.375Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:11:27.355Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:11:29.519Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:11:30.172Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:11:30.172Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:11:30.172Z] Movies recommended for you:
[2024-09-05T03:11:30.172Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:11:30.172Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:11:30.172Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (31314.373 ms) ======
[2024-09-05T03:11:30.172Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-05T03:11:30.830Z] GC before operation: completed in 180.339 ms, heap usage 108.081 MB -> 50.974 MB.
[2024-09-05T03:11:35.961Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:11:39.916Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:11:44.943Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:11:48.800Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:11:50.854Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:11:53.867Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:11:56.865Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:12:00.045Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:12:00.779Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:12:00.779Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:12:00.779Z] Movies recommended for you:
[2024-09-05T03:12:00.779Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:12:00.779Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:12:00.779Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (30086.461 ms) ======
[2024-09-05T03:12:00.779Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-05T03:12:00.779Z] GC before operation: completed in 168.086 ms, heap usage 62.070 MB -> 52.463 MB.
[2024-09-05T03:12:05.967Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:12:10.975Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:12:16.268Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:12:22.377Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:12:25.327Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:12:28.295Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:12:31.220Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:12:34.246Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:12:34.897Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:12:34.897Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:12:35.595Z] Movies recommended for you:
[2024-09-05T03:12:35.595Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:12:35.595Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:12:35.595Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (34464.259 ms) ======
[2024-09-05T03:12:35.595Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-05T03:12:35.595Z] GC before operation: completed in 148.369 ms, heap usage 252.304 MB -> 50.072 MB.
[2024-09-05T03:12:40.657Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:12:45.495Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:12:53.950Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:12:59.461Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:13:02.475Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:13:05.467Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:13:10.633Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:13:13.777Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:13:14.480Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:13:14.480Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:13:14.480Z] Movies recommended for you:
[2024-09-05T03:13:14.480Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:13:14.480Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:13:14.480Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (39114.783 ms) ======
[2024-09-05T03:13:14.480Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-05T03:13:14.480Z] GC before operation: completed in 241.162 ms, heap usage 256.994 MB -> 50.310 MB.
[2024-09-05T03:13:20.638Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T03:13:26.806Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T03:13:31.876Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T03:13:36.783Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T03:13:39.687Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T03:13:41.077Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T03:13:46.255Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T03:13:49.216Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T03:13:49.216Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-09-05T03:13:49.216Z] The best model improves the baseline by 14.34%.
[2024-09-05T03:13:49.216Z] Movies recommended for you:
[2024-09-05T03:13:49.216Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T03:13:49.216Z] There is no way to check that no silent failure occurred.
[2024-09-05T03:13:49.216Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (34665.174 ms) ======
[2024-09-05T03:13:49.851Z] -----------------------------------
[2024-09-05T03:13:49.851Z] renaissance-movie-lens_0_PASSED
[2024-09-05T03:13:49.851Z] -----------------------------------
[2024-09-05T03:13:49.851Z]
[2024-09-05T03:13:49.851Z] TEST TEARDOWN:
[2024-09-05T03:13:49.851Z] Nothing to be done for teardown.
[2024-09-05T03:13:49.851Z] renaissance-movie-lens_0 Finish Time: Thu Sep 5 03:13:49 2024 Epoch Time (ms): 1725506029681