renaissance-movie-lens_0
[2024-10-31T16:59:16.404Z] Running test renaissance-movie-lens_0 ...
[2024-10-31T16:59:16.404Z] ===============================================
[2024-10-31T16:59:16.404Z] renaissance-movie-lens_0 Start Time: Thu Oct 31 16:59:15 2024 Epoch Time (ms): 1730393955852
[2024-10-31T16:59:16.404Z] variation: NoOptions
[2024-10-31T16:59:16.404Z] JVM_OPTIONS:
[2024-10-31T16:59:16.404Z] { \
[2024-10-31T16:59:16.404Z] echo ""; echo "TEST SETUP:"; \
[2024-10-31T16:59:16.404Z] echo "Nothing to be done for setup."; \
[2024-10-31T16:59:16.404Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17303939554079/renaissance-movie-lens_0"; \
[2024-10-31T16:59:16.404Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17303939554079/renaissance-movie-lens_0"; \
[2024-10-31T16:59:16.404Z] echo ""; echo "TESTING:"; \
[2024-10-31T16:59:16.404Z] "/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_17303939554079/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-31T16:59:16.404Z] 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_17303939554079/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-31T16:59:16.404Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-31T16:59:16.404Z] echo "Nothing to be done for teardown."; \
[2024-10-31T16:59:16.404Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_s390x_linux_rerun/aqa-tests/TKG/../TKG/output_17303939554079/TestTargetResult";
[2024-10-31T16:59:16.405Z]
[2024-10-31T16:59:16.405Z] TEST SETUP:
[2024-10-31T16:59:16.405Z] Nothing to be done for setup.
[2024-10-31T16:59:16.405Z]
[2024-10-31T16:59:16.405Z] TESTING:
[2024-10-31T16:59:21.541Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-31T16:59:23.733Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-10-31T16:59:30.073Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-31T16:59:30.073Z] Training: 60056, validation: 20285, test: 19854
[2024-10-31T16:59:30.073Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-31T16:59:30.073Z] GC before operation: completed in 81.933 ms, heap usage 83.737 MB -> 37.026 MB.
[2024-10-31T16:59:43.600Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T16:59:51.322Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T16:59:58.891Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:00:05.417Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:00:09.616Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:00:13.281Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:00:16.424Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:00:20.501Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:00:21.228Z] 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.
[2024-10-31T17:00:21.947Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:00:21.947Z] Movies recommended for you:
[2024-10-31T17:00:21.947Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:00:21.947Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:00:21.947Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (51528.478 ms) ======
[2024-10-31T17:00:21.947Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-31T17:00:21.947Z] GC before operation: completed in 170.775 ms, heap usage 174.627 MB -> 54.617 MB.
[2024-10-31T17:00:26.973Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:00:33.475Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:00:39.817Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:00:45.143Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:00:49.225Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:00:51.377Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:00:55.529Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:00:59.025Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:00:59.801Z] 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.
[2024-10-31T17:00:59.801Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:00:59.801Z] Movies recommended for you:
[2024-10-31T17:00:59.801Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:00:59.801Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:00:59.801Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (38082.830 ms) ======
[2024-10-31T17:00:59.801Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-31T17:01:00.451Z] GC before operation: completed in 216.547 ms, heap usage 294.649 MB -> 49.187 MB.
[2024-10-31T17:01:05.667Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:01:10.834Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:01:18.770Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:01:23.591Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:01:26.663Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:01:29.718Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:01:31.933Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:01:35.001Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:01:35.722Z] 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.
[2024-10-31T17:01:35.722Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:01:35.722Z] Movies recommended for you:
[2024-10-31T17:01:35.722Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:01:35.722Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:01:35.722Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (35386.651 ms) ======
[2024-10-31T17:01:35.722Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-31T17:01:35.722Z] GC before operation: completed in 216.450 ms, heap usage 276.089 MB -> 49.588 MB.
[2024-10-31T17:01:40.718Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:01:45.805Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:01:50.922Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:01:55.927Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:01:59.045Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:02:02.162Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:02:06.428Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:02:08.659Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:02:09.381Z] 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.
[2024-10-31T17:02:09.381Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:02:09.381Z] Movies recommended for you:
[2024-10-31T17:02:09.381Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:02:09.382Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:02:09.382Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (33534.310 ms) ======
[2024-10-31T17:02:09.382Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-31T17:02:09.382Z] GC before operation: completed in 176.897 ms, heap usage 193.187 MB -> 49.712 MB.
[2024-10-31T17:02:14.455Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:02:19.135Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:02:23.546Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:02:28.877Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:02:32.013Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:02:36.197Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:02:39.402Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:02:41.659Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:02:42.365Z] 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.
[2024-10-31T17:02:42.365Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:02:42.365Z] Movies recommended for you:
[2024-10-31T17:02:42.365Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:02:42.365Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:02:42.365Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (33063.158 ms) ======
[2024-10-31T17:02:42.365Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-31T17:02:43.064Z] GC before operation: completed in 150.538 ms, heap usage 74.819 MB -> 51.779 MB.
[2024-10-31T17:02:48.166Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:02:54.853Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:03:01.614Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:03:06.909Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:03:09.592Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:03:11.894Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:03:15.033Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:03:18.282Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:03:18.282Z] 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.
[2024-10-31T17:03:18.282Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:03:18.282Z] Movies recommended for you:
[2024-10-31T17:03:18.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:03:18.282Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:03:18.282Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (35689.648 ms) ======
[2024-10-31T17:03:18.282Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-31T17:03:18.923Z] GC before operation: completed in 203.031 ms, heap usage 229.875 MB -> 49.895 MB.
[2024-10-31T17:03:23.524Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:03:29.851Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:03:34.982Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:03:40.240Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:03:42.599Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:03:45.794Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:03:49.851Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:03:52.210Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:03:53.012Z] 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.
[2024-10-31T17:03:53.012Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:03:53.686Z] Movies recommended for you:
[2024-10-31T17:03:53.686Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:03:53.686Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:03:53.686Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (34870.227 ms) ======
[2024-10-31T17:03:53.686Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-31T17:03:53.686Z] GC before operation: completed in 182.479 ms, heap usage 249.990 MB -> 50.062 MB.
[2024-10-31T17:03:57.749Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:04:04.492Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:04:10.024Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:04:14.043Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:04:17.098Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:04:19.477Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:04:22.639Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:04:26.437Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:04:26.437Z] 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.
[2024-10-31T17:04:26.437Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:04:26.437Z] Movies recommended for you:
[2024-10-31T17:04:26.437Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:04:26.437Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:04:26.437Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (32407.022 ms) ======
[2024-10-31T17:04:26.437Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-31T17:04:26.437Z] GC before operation: completed in 207.649 ms, heap usage 247.682 MB -> 50.287 MB.
[2024-10-31T17:04:30.480Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:04:35.849Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:04:40.942Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:04:44.939Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:04:47.247Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:04:50.443Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:04:53.656Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:04:56.723Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:04:56.723Z] 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.
[2024-10-31T17:04:56.723Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:04:56.723Z] Movies recommended for you:
[2024-10-31T17:04:56.723Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:04:56.723Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:04:56.723Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (30519.169 ms) ======
[2024-10-31T17:04:56.723Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-31T17:04:57.409Z] GC before operation: completed in 554.811 ms, heap usage 156.557 MB -> 50.187 MB.
[2024-10-31T17:05:02.532Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:05:06.642Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:05:11.963Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:05:14.953Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:05:18.183Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:05:20.320Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:05:23.782Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:05:26.013Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:05:26.923Z] 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.
[2024-10-31T17:05:26.923Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:05:26.923Z] Movies recommended for you:
[2024-10-31T17:05:26.923Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:05:26.923Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:05:26.923Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (29566.491 ms) ======
[2024-10-31T17:05:26.923Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-31T17:05:26.923Z] GC before operation: completed in 123.508 ms, heap usage 141.562 MB -> 50.185 MB.
[2024-10-31T17:05:32.257Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:05:37.543Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:05:42.779Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:05:47.019Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:05:50.191Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:05:53.293Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:05:56.389Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:05:59.611Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:05:59.611Z] 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.
[2024-10-31T17:05:59.611Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:06:00.325Z] Movies recommended for you:
[2024-10-31T17:06:00.325Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:06:00.325Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:06:00.325Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (32807.451 ms) ======
[2024-10-31T17:06:00.325Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-31T17:06:00.325Z] GC before operation: completed in 133.496 ms, heap usage 338.434 MB -> 53.380 MB.
[2024-10-31T17:06:04.589Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:06:08.882Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:06:12.846Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:06:18.460Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:06:21.684Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:06:25.935Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:06:29.494Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:06:31.698Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:06:32.437Z] 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.
[2024-10-31T17:06:32.438Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:06:33.135Z] Movies recommended for you:
[2024-10-31T17:06:33.135Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:06:33.135Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:06:33.135Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (32787.287 ms) ======
[2024-10-31T17:06:33.135Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-31T17:06:33.797Z] GC before operation: completed in 625.623 ms, heap usage 131.676 MB -> 50.128 MB.
[2024-10-31T17:06:38.905Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:06:43.023Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:06:48.424Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:06:53.577Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:06:56.741Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:06:59.042Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:07:03.263Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:07:05.652Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:07:06.315Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-31T17:07:06.315Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:07:06.315Z] Movies recommended for you:
[2024-10-31T17:07:06.315Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:07:06.315Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:07:06.315Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (32810.978 ms) ======
[2024-10-31T17:07:06.315Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-31T17:07:06.315Z] GC before operation: completed in 275.456 ms, heap usage 232.566 MB -> 50.466 MB.
[2024-10-31T17:07:11.431Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:07:16.543Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:07:21.723Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:07:25.833Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:07:28.103Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:07:30.255Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:07:32.989Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:07:35.153Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:07:35.810Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9082701964919572.
[2024-10-31T17:07:35.810Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:07:35.810Z] Movies recommended for you:
[2024-10-31T17:07:35.810Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:07:35.810Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:07:35.810Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (29445.202 ms) ======
[2024-10-31T17:07:35.810Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-31T17:07:36.522Z] GC before operation: completed in 304.980 ms, heap usage 255.555 MB -> 50.107 MB.
[2024-10-31T17:07:41.741Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:07:46.902Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:07:53.401Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:07:57.447Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:07:59.633Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:08:02.687Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:08:06.913Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:08:09.120Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:08:09.802Z] 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.
[2024-10-31T17:08:09.802Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:08:09.802Z] Movies recommended for you:
[2024-10-31T17:08:09.802Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:08:09.802Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:08:09.802Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (33402.126 ms) ======
[2024-10-31T17:08:09.802Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-31T17:08:09.802Z] GC before operation: completed in 124.465 ms, heap usage 136.355 MB -> 50.214 MB.
[2024-10-31T17:08:15.178Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:08:20.416Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:08:25.800Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:08:29.899Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:08:33.756Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:08:35.972Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:08:39.015Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:08:41.265Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:08:42.005Z] 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.
[2024-10-31T17:08:42.005Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:08:42.005Z] Movies recommended for you:
[2024-10-31T17:08:42.005Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:08:42.005Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:08:42.005Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (32155.921 ms) ======
[2024-10-31T17:08:42.005Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-31T17:08:42.005Z] GC before operation: completed in 145.136 ms, heap usage 114.079 MB -> 50.248 MB.
[2024-10-31T17:08:47.117Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:08:52.189Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:09:00.125Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:09:05.523Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:09:09.374Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:09:12.521Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:09:16.733Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:09:19.982Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:09:20.664Z] 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.
[2024-10-31T17:09:20.664Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:09:20.664Z] Movies recommended for you:
[2024-10-31T17:09:20.664Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:09:20.664Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:09:20.664Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (38600.135 ms) ======
[2024-10-31T17:09:20.664Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-31T17:09:21.380Z] GC before operation: completed in 407.950 ms, heap usage 88.607 MB -> 50.720 MB.
[2024-10-31T17:09:25.665Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:09:30.776Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:09:34.841Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:09:40.252Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:09:44.369Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:09:47.744Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:09:50.033Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:09:53.144Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:09:53.887Z] 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.
[2024-10-31T17:09:53.887Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:09:53.887Z] Movies recommended for you:
[2024-10-31T17:09:53.887Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:09:53.887Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:09:53.887Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (32865.794 ms) ======
[2024-10-31T17:09:53.887Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-31T17:09:53.887Z] GC before operation: completed in 143.495 ms, heap usage 168.466 MB -> 50.240 MB.
[2024-10-31T17:09:59.075Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:10:03.432Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:10:07.968Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:10:12.167Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:10:14.434Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:10:17.588Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:10:21.598Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:10:23.737Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:10:23.737Z] 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.
[2024-10-31T17:10:23.737Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:10:24.401Z] Movies recommended for you:
[2024-10-31T17:10:24.401Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:10:24.401Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:10:24.401Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (30008.105 ms) ======
[2024-10-31T17:10:24.401Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-31T17:10:24.401Z] GC before operation: completed in 171.413 ms, heap usage 343.131 MB -> 53.803 MB.
[2024-10-31T17:10:29.644Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T17:10:34.683Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T17:10:40.410Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T17:10:43.489Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T17:10:45.708Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T17:10:48.940Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T17:10:53.143Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T17:10:55.644Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T17:10:56.343Z] 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.
[2024-10-31T17:10:56.343Z] The best model improves the baseline by 14.34%.
[2024-10-31T17:10:56.343Z] Movies recommended for you:
[2024-10-31T17:10:56.343Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T17:10:56.343Z] There is no way to check that no silent failure occurred.
[2024-10-31T17:10:56.343Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (31938.411 ms) ======
[2024-10-31T17:10:57.857Z] -----------------------------------
[2024-10-31T17:10:57.857Z] renaissance-movie-lens_0_PASSED
[2024-10-31T17:10:57.857Z] -----------------------------------
[2024-10-31T17:10:57.857Z]
[2024-10-31T17:10:57.857Z] TEST TEARDOWN:
[2024-10-31T17:10:57.857Z] Nothing to be done for teardown.
[2024-10-31T17:10:57.857Z] renaissance-movie-lens_0 Finish Time: Thu Oct 31 17:10:57 2024 Epoch Time (ms): 1730394657292