renaissance-movie-lens_0
[2024-11-23T10:47:25.218Z] Running test renaissance-movie-lens_0 ...
[2024-11-23T10:47:25.218Z] ===============================================
[2024-11-23T10:47:25.218Z] renaissance-movie-lens_0 Start Time: Sat Nov 23 10:47:24 2024 Epoch Time (ms): 1732358844665
[2024-11-23T10:47:25.218Z] variation: NoOptions
[2024-11-23T10:47:25.218Z] JVM_OPTIONS:
[2024-11-23T10:47:25.218Z] { \
[2024-11-23T10:47:25.218Z] echo ""; echo "TEST SETUP:"; \
[2024-11-23T10:47:25.218Z] echo "Nothing to be done for setup."; \
[2024-11-23T10:47:25.218Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17323572807194/renaissance-movie-lens_0"; \
[2024-11-23T10:47:25.218Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17323572807194/renaissance-movie-lens_0"; \
[2024-11-23T10:47:25.218Z] echo ""; echo "TESTING:"; \
[2024-11-23T10:47:25.218Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/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_s390x_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17323572807194/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-23T10:47:25.218Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17323572807194/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-23T10:47:25.218Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-23T10:47:25.218Z] echo "Nothing to be done for teardown."; \
[2024-11-23T10:47:25.218Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_s390x_linux/aqa-tests/TKG/../TKG/output_17323572807194/TestTargetResult";
[2024-11-23T10:47:25.218Z]
[2024-11-23T10:47:25.218Z] TEST SETUP:
[2024-11-23T10:47:25.218Z] Nothing to be done for setup.
[2024-11-23T10:47:25.218Z]
[2024-11-23T10:47:25.218Z] TESTING:
[2024-11-23T10:47:29.070Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-23T10:47:32.033Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 2 (out of 2) threads.
[2024-11-23T10:47:36.995Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-23T10:47:36.995Z] Training: 60056, validation: 20285, test: 19854
[2024-11-23T10:47:36.995Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-23T10:47:36.995Z] GC before operation: completed in 172.671 ms, heap usage 43.066 MB -> 36.368 MB.
[2024-11-23T10:47:49.536Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:47:54.395Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:47:59.235Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:48:04.110Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:48:06.214Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:48:08.307Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:48:11.273Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:48:14.252Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:48:14.252Z] 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.
[2024-11-23T10:48:14.252Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:48:14.252Z] Movies recommended for you:
[2024-11-23T10:48:14.252Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:48:14.252Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:48:14.252Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (37256.425 ms) ======
[2024-11-23T10:48:14.252Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-23T10:48:14.894Z] GC before operation: completed in 183.864 ms, heap usage 218.215 MB -> 46.588 MB.
[2024-11-23T10:48:19.828Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:48:23.749Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:48:27.164Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:48:30.157Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:48:33.137Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:48:34.492Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:48:36.621Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:48:38.691Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:48:39.318Z] 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.
[2024-11-23T10:48:39.318Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:48:39.318Z] Movies recommended for you:
[2024-11-23T10:48:39.318Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:48:39.318Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:48:39.318Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (24857.862 ms) ======
[2024-11-23T10:48:39.318Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-23T10:48:39.969Z] GC before operation: completed in 251.385 ms, heap usage 239.616 MB -> 48.619 MB.
[2024-11-23T10:48:44.897Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:48:48.865Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:48:51.881Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:48:55.755Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:48:57.828Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:48:59.938Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:49:01.284Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:49:03.390Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:49:04.025Z] 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.
[2024-11-23T10:49:04.025Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:49:04.025Z] Movies recommended for you:
[2024-11-23T10:49:04.025Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:49:04.025Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:49:04.025Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (24166.434 ms) ======
[2024-11-23T10:49:04.025Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-23T10:49:04.025Z] GC before operation: completed in 188.705 ms, heap usage 138.608 MB -> 48.632 MB.
[2024-11-23T10:49:07.846Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:49:10.766Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:49:14.561Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:49:16.667Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:49:18.786Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:49:20.870Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:49:22.931Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:49:24.990Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:49:24.990Z] 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.
[2024-11-23T10:49:24.990Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:49:24.990Z] Movies recommended for you:
[2024-11-23T10:49:24.990Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:49:24.990Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:49:24.990Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20882.360 ms) ======
[2024-11-23T10:49:24.990Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-23T10:49:24.990Z] GC before operation: completed in 183.745 ms, heap usage 148.679 MB -> 49.025 MB.
[2024-11-23T10:49:28.826Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:49:31.741Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:49:35.573Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:49:38.481Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:49:40.579Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:49:42.697Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:49:44.813Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:49:46.156Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:49:46.814Z] 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.
[2024-11-23T10:49:46.814Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:49:46.814Z] Movies recommended for you:
[2024-11-23T10:49:46.814Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:49:46.814Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:49:46.814Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (21691.027 ms) ======
[2024-11-23T10:49:46.814Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-23T10:49:47.495Z] GC before operation: completed in 283.946 ms, heap usage 140.969 MB -> 49.163 MB.
[2024-11-23T10:49:51.457Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:49:55.516Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:49:58.818Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:50:01.766Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:50:03.890Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:50:06.074Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:50:08.179Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:50:09.527Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:50:10.181Z] 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.
[2024-11-23T10:50:10.181Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:50:10.181Z] Movies recommended for you:
[2024-11-23T10:50:10.181Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:50:10.181Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:50:10.181Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (22961.830 ms) ======
[2024-11-23T10:50:10.181Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-23T10:50:10.181Z] GC before operation: completed in 183.372 ms, heap usage 118.126 MB -> 49.087 MB.
[2024-11-23T10:50:14.015Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:50:17.011Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:50:19.972Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:50:22.865Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:50:24.948Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:50:26.289Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:50:28.403Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:50:29.733Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:50:30.383Z] 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.
[2024-11-23T10:50:30.383Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:50:30.383Z] Movies recommended for you:
[2024-11-23T10:50:30.383Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:50:30.383Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:50:30.383Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20067.734 ms) ======
[2024-11-23T10:50:30.383Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-23T10:50:30.383Z] GC before operation: completed in 177.989 ms, heap usage 183.243 MB -> 49.343 MB.
[2024-11-23T10:50:34.209Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:50:37.105Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:50:40.060Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:50:43.008Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:50:45.926Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:50:47.255Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:50:49.391Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:50:51.494Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:50:51.494Z] 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.
[2024-11-23T10:50:51.494Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:50:51.494Z] Movies recommended for you:
[2024-11-23T10:50:51.494Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:50:51.494Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:50:51.494Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20918.299 ms) ======
[2024-11-23T10:50:51.494Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-23T10:50:51.494Z] GC before operation: completed in 124.907 ms, heap usage 112.543 MB -> 49.514 MB.
[2024-11-23T10:50:55.369Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:50:59.241Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:51:02.139Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:51:05.034Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:51:07.085Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:51:09.192Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:51:11.264Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:51:13.340Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:51:13.340Z] 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.
[2024-11-23T10:51:13.340Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:51:13.976Z] Movies recommended for you:
[2024-11-23T10:51:13.976Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:51:13.976Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:51:13.976Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (22087.811 ms) ======
[2024-11-23T10:51:13.976Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-23T10:51:13.976Z] GC before operation: completed in 169.197 ms, heap usage 148.029 MB -> 49.414 MB.
[2024-11-23T10:51:17.804Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:51:20.670Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:51:24.578Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:51:26.661Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:51:29.277Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:51:30.641Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:51:32.713Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:51:34.796Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:51:34.796Z] 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.
[2024-11-23T10:51:34.796Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:51:34.796Z] Movies recommended for you:
[2024-11-23T10:51:34.796Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:51:34.796Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:51:34.796Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20980.047 ms) ======
[2024-11-23T10:51:34.796Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-23T10:51:34.796Z] GC before operation: completed in 156.967 ms, heap usage 137.725 MB -> 49.482 MB.
[2024-11-23T10:51:38.603Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:51:41.538Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:51:44.434Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:51:47.324Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:51:48.667Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:51:50.760Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:51:52.839Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:51:54.178Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:51:54.178Z] 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.
[2024-11-23T10:51:54.178Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:51:54.178Z] Movies recommended for you:
[2024-11-23T10:51:54.178Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:51:54.179Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:51:54.179Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19307.814 ms) ======
[2024-11-23T10:51:54.179Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-23T10:51:54.816Z] GC before operation: completed in 146.556 ms, heap usage 117.354 MB -> 49.186 MB.
[2024-11-23T10:51:57.743Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:52:00.654Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:52:03.606Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:52:06.505Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:52:08.584Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:52:09.928Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:52:12.008Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:52:14.079Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:52:14.079Z] 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.
[2024-11-23T10:52:14.079Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:52:14.079Z] Movies recommended for you:
[2024-11-23T10:52:14.079Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:52:14.079Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:52:14.079Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19716.931 ms) ======
[2024-11-23T10:52:14.079Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-23T10:52:14.079Z] GC before operation: completed in 168.469 ms, heap usage 152.123 MB -> 49.434 MB.
[2024-11-23T10:52:17.966Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:52:20.861Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:52:23.744Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:52:26.642Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:52:27.278Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:52:29.381Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:52:30.688Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:52:32.732Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:52:32.732Z] 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.
[2024-11-23T10:52:32.732Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:52:32.732Z] Movies recommended for you:
[2024-11-23T10:52:32.732Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:52:32.732Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:52:32.732Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (18499.944 ms) ======
[2024-11-23T10:52:32.732Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-23T10:52:32.732Z] GC before operation: completed in 149.449 ms, heap usage 127.890 MB -> 49.511 MB.
[2024-11-23T10:52:35.605Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:52:38.521Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:52:41.411Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:52:44.341Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:52:45.682Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:52:47.813Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:52:49.909Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:52:51.277Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:52:51.277Z] 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.
[2024-11-23T10:52:51.932Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:52:51.933Z] Movies recommended for you:
[2024-11-23T10:52:51.933Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:52:51.933Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:52:51.933Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (18695.197 ms) ======
[2024-11-23T10:52:51.933Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-23T10:52:51.933Z] GC before operation: completed in 200.169 ms, heap usage 149.631 MB -> 49.362 MB.
[2024-11-23T10:52:54.822Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:52:58.171Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:53:01.070Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:53:03.965Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:53:05.310Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:53:07.383Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:53:08.698Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:53:10.825Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:53:10.825Z] 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.
[2024-11-23T10:53:10.825Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:53:10.825Z] Movies recommended for you:
[2024-11-23T10:53:10.825Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:53:10.825Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:53:10.825Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19071.712 ms) ======
[2024-11-23T10:53:10.825Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-23T10:53:11.450Z] GC before operation: completed in 152.651 ms, heap usage 213.180 MB -> 49.621 MB.
[2024-11-23T10:53:14.286Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:53:17.155Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:53:20.074Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:53:22.138Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:53:24.186Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:53:25.488Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:53:27.540Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:53:28.915Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:53:29.584Z] 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.
[2024-11-23T10:53:29.584Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:53:29.584Z] Movies recommended for you:
[2024-11-23T10:53:29.584Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:53:29.584Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:53:29.584Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (18273.062 ms) ======
[2024-11-23T10:53:29.584Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-23T10:53:29.584Z] GC before operation: completed in 161.904 ms, heap usage 62.648 MB -> 49.485 MB.
[2024-11-23T10:53:32.476Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:53:35.403Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:53:39.247Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:53:42.279Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:53:44.384Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:53:46.456Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:53:48.563Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:53:50.678Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:53:50.678Z] 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.
[2024-11-23T10:53:50.678Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:53:50.678Z] Movies recommended for you:
[2024-11-23T10:53:50.678Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:53:50.678Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:53:50.678Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (21319.002 ms) ======
[2024-11-23T10:53:50.678Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-23T10:53:51.321Z] GC before operation: completed in 160.046 ms, heap usage 274.220 MB -> 49.599 MB.
[2024-11-23T10:53:54.258Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:53:58.090Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:54:01.947Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:54:04.897Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:54:07.470Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:54:08.836Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:54:10.927Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:54:13.066Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:54:13.695Z] 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.
[2024-11-23T10:54:13.695Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:54:13.695Z] Movies recommended for you:
[2024-11-23T10:54:13.695Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:54:13.695Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:54:13.695Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (22545.706 ms) ======
[2024-11-23T10:54:13.695Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-23T10:54:13.695Z] GC before operation: completed in 193.763 ms, heap usage 141.522 MB -> 49.556 MB.
[2024-11-23T10:54:16.647Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:54:19.583Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:54:22.496Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:54:26.348Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:54:27.676Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:54:29.016Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:54:31.117Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:54:33.246Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:54:33.246Z] 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.
[2024-11-23T10:54:33.246Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:54:33.246Z] Movies recommended for you:
[2024-11-23T10:54:33.246Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:54:33.246Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:54:33.246Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19518.833 ms) ======
[2024-11-23T10:54:33.246Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-23T10:54:33.877Z] GC before operation: completed in 257.905 ms, heap usage 273.790 MB -> 49.794 MB.
[2024-11-23T10:54:37.722Z] RMSE (validation) = 3.621968954548762 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-23T10:54:40.636Z] RMSE (validation) = 2.134092321459638 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-23T10:54:44.474Z] RMSE (validation) = 1.3105186097961345 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-23T10:54:47.799Z] RMSE (validation) = 1.0039112263869625 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-23T10:54:49.891Z] RMSE (validation) = 1.2279489569306759 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-23T10:54:51.233Z] RMSE (validation) = 1.1317743270958185 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-23T10:54:53.327Z] RMSE (validation) = 0.9270299248067019 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-23T10:54:54.711Z] RMSE (validation) = 0.8979370339906045 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-23T10:54:54.711Z] 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.
[2024-11-23T10:54:54.711Z] The best model improves the baseline by 14.34%.
[2024-11-23T10:54:55.347Z] Movies recommended for you:
[2024-11-23T10:54:55.347Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-23T10:54:55.347Z] There is no way to check that no silent failure occurred.
[2024-11-23T10:54:55.347Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (21567.211 ms) ======
[2024-11-23T10:54:55.992Z] -----------------------------------
[2024-11-23T10:54:55.992Z] renaissance-movie-lens_0_PASSED
[2024-11-23T10:54:55.992Z] -----------------------------------
[2024-11-23T10:54:55.992Z]
[2024-11-23T10:54:55.992Z] TEST TEARDOWN:
[2024-11-23T10:54:55.992Z] Nothing to be done for teardown.
[2024-11-23T10:54:55.992Z] renaissance-movie-lens_0 Finish Time: Sat Nov 23 10:54:55 2024 Epoch Time (ms): 1732359295307