[2024-08-29T04:55:49.409Z] Running test renaissance-movie-lens_0 ... [2024-08-29T04:55:49.409Z] =============================================== [2024-08-29T04:55:49.409Z] renaissance-movie-lens_0 Start Time: Thu Aug 29 04:55:48 2024 Epoch Time (ms): 1724907348616 [2024-08-29T04:55:49.409Z] variation: NoOptions [2024-08-29T04:55:49.409Z] JVM_OPTIONS: [2024-08-29T04:55:49.409Z] { \ [2024-08-29T04:55:49.409Z] echo ""; echo "TEST SETUP:"; \ [2024-08-29T04:55:49.409Z] echo "Nothing to be done for setup."; \ [2024-08-29T04:55:49.409Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17249060406468/renaissance-movie-lens_0"; \ [2024-08-29T04:55:49.409Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17249060406468/renaissance-movie-lens_0"; \ [2024-08-29T04:55:49.409Z] echo ""; echo "TESTING:"; \ [2024-08-29T04:55:49.409Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-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_x86-64_alpine-linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17249060406468/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \ [2024-08-29T04:55:49.409Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17249060406468/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \ [2024-08-29T04:55:49.409Z] echo ""; echo "TEST TEARDOWN:"; \ [2024-08-29T04:55:49.409Z] echo "Nothing to be done for teardown."; \ [2024-08-29T04:55:49.409Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_alpine-linux/aqa-tests/TKG/../TKG/output_17249060406468/TestTargetResult"; [2024-08-29T04:55:49.409Z] [2024-08-29T04:55:49.409Z] TEST SETUP: [2024-08-29T04:55:49.409Z] Nothing to be done for setup. [2024-08-29T04:55:49.409Z] [2024-08-29T04:55:49.409Z] TESTING: [2024-08-29T04:55:52.724Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties [2024-08-29T04:55:55.628Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads. [2024-08-29T04:56:02.333Z] Got 100004 ratings from 671 users on 9066 movies. [2024-08-29T04:56:03.079Z] Training: 60056, validation: 20285, test: 19854 [2024-08-29T04:56:03.079Z] ====== movie-lens (apache-spark) [default], iteration 0 started ====== [2024-08-29T04:56:03.079Z] GC before operation: completed in 144.452 ms, heap usage 80.129 MB -> 36.426 MB. [2024-08-29T04:56:16.651Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:56:23.371Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:56:31.509Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:56:38.228Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:56:41.572Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:56:44.909Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:56:49.269Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:56:53.225Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:56:54.040Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:56:54.040Z] The best model improves the baseline by 14.52%. [2024-08-29T04:56:54.040Z] Movies recommended for you: [2024-08-29T04:56:54.040Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:56:54.040Z] There is no way to check that no silent failure occurred. [2024-08-29T04:56:54.040Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (51149.352 ms) ====== [2024-08-29T04:56:54.040Z] ====== movie-lens (apache-spark) [default], iteration 1 started ====== [2024-08-29T04:56:54.040Z] GC before operation: completed in 245.851 ms, heap usage 237.308 MB -> 48.124 MB. [2024-08-29T04:57:00.764Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:57:07.471Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:57:14.209Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:57:19.717Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:57:24.176Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:57:25.732Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:57:28.125Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:57:30.515Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:57:30.515Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:57:30.516Z] The best model improves the baseline by 14.52%. [2024-08-29T04:57:30.516Z] Movies recommended for you: [2024-08-29T04:57:30.516Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:57:30.516Z] There is no way to check that no silent failure occurred. [2024-08-29T04:57:30.516Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (36231.581 ms) ====== [2024-08-29T04:57:30.516Z] ====== movie-lens (apache-spark) [default], iteration 2 started ====== [2024-08-29T04:57:30.516Z] GC before operation: completed in 100.323 ms, heap usage 222.780 MB -> 49.068 MB. [2024-08-29T04:57:34.866Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:57:38.200Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:57:42.561Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:57:45.888Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:57:48.281Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:57:50.670Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:57:53.985Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:57:55.524Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:57:56.268Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:57:56.268Z] The best model improves the baseline by 14.52%. [2024-08-29T04:57:56.268Z] Movies recommended for you: [2024-08-29T04:57:56.268Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:57:56.268Z] There is no way to check that no silent failure occurred. [2024-08-29T04:57:56.268Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (25425.005 ms) ====== [2024-08-29T04:57:56.268Z] ====== movie-lens (apache-spark) [default], iteration 3 started ====== [2024-08-29T04:57:56.268Z] GC before operation: completed in 97.705 ms, heap usage 225.450 MB -> 49.325 MB. [2024-08-29T04:57:58.650Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:58:01.953Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:58:04.339Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:58:08.670Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:58:11.082Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:58:12.617Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:58:14.160Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:58:15.694Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:58:15.694Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:58:15.694Z] The best model improves the baseline by 14.52%. [2024-08-29T04:58:15.694Z] Movies recommended for you: [2024-08-29T04:58:15.694Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:58:15.694Z] There is no way to check that no silent failure occurred. [2024-08-29T04:58:15.694Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (19512.379 ms) ====== [2024-08-29T04:58:15.694Z] ====== movie-lens (apache-spark) [default], iteration 4 started ====== [2024-08-29T04:58:15.694Z] GC before operation: completed in 82.450 ms, heap usage 231.308 MB -> 49.668 MB. [2024-08-29T04:58:19.010Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:58:21.407Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:58:23.933Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:58:26.321Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:58:28.356Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:58:29.895Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:58:31.433Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:58:32.971Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:58:33.714Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:58:33.714Z] The best model improves the baseline by 14.52%. [2024-08-29T04:58:33.714Z] Movies recommended for you: [2024-08-29T04:58:33.714Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:58:33.714Z] There is no way to check that no silent failure occurred. [2024-08-29T04:58:33.714Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17629.328 ms) ====== [2024-08-29T04:58:33.714Z] ====== movie-lens (apache-spark) [default], iteration 5 started ====== [2024-08-29T04:58:33.714Z] GC before operation: completed in 84.895 ms, heap usage 196.508 MB -> 49.833 MB. [2024-08-29T04:58:36.115Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:58:38.508Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:58:41.824Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:58:44.218Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:58:45.757Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:58:47.294Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:58:48.838Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:58:50.374Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:58:50.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:58:50.374Z] The best model improves the baseline by 14.52%. [2024-08-29T04:58:50.374Z] Movies recommended for you: [2024-08-29T04:58:50.374Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:58:50.374Z] There is no way to check that no silent failure occurred. [2024-08-29T04:58:50.374Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16873.919 ms) ====== [2024-08-29T04:58:50.374Z] ====== movie-lens (apache-spark) [default], iteration 6 started ====== [2024-08-29T04:58:50.374Z] GC before operation: completed in 82.999 ms, heap usage 196.963 MB -> 49.817 MB. [2024-08-29T04:58:52.763Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:58:56.076Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:58:58.461Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:59:00.853Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:59:01.596Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:59:03.253Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:59:04.789Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:59:06.323Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:59:06.323Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:59:06.323Z] The best model improves the baseline by 14.52%. [2024-08-29T04:59:06.323Z] Movies recommended for you: [2024-08-29T04:59:06.323Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:59:06.323Z] There is no way to check that no silent failure occurred. [2024-08-29T04:59:06.323Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16092.454 ms) ====== [2024-08-29T04:59:06.323Z] ====== movie-lens (apache-spark) [default], iteration 7 started ====== [2024-08-29T04:59:06.323Z] GC before operation: completed in 87.483 ms, heap usage 127.699 MB -> 49.880 MB. [2024-08-29T04:59:09.638Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:59:12.025Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:59:14.419Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:59:17.361Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:59:18.104Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:59:19.643Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:59:21.180Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:59:22.725Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:59:23.497Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:59:23.497Z] The best model improves the baseline by 14.52%. [2024-08-29T04:59:23.497Z] Movies recommended for you: [2024-08-29T04:59:23.497Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:59:23.497Z] There is no way to check that no silent failure occurred. [2024-08-29T04:59:23.497Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16589.769 ms) ====== [2024-08-29T04:59:23.497Z] ====== movie-lens (apache-spark) [default], iteration 8 started ====== [2024-08-29T04:59:23.497Z] GC before operation: completed in 82.911 ms, heap usage 194.091 MB -> 50.196 MB. [2024-08-29T04:59:25.893Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:59:28.295Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:59:31.611Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:59:33.145Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:59:34.680Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:59:36.219Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:59:37.754Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:59:39.287Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:59:39.287Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:59:39.287Z] The best model improves the baseline by 14.52%. [2024-08-29T04:59:39.287Z] Movies recommended for you: [2024-08-29T04:59:39.287Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:59:39.287Z] There is no way to check that no silent failure occurred. [2024-08-29T04:59:39.287Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16157.607 ms) ====== [2024-08-29T04:59:39.287Z] ====== movie-lens (apache-spark) [default], iteration 9 started ====== [2024-08-29T04:59:39.287Z] GC before operation: completed in 80.842 ms, heap usage 142.811 MB -> 50.016 MB. [2024-08-29T04:59:42.601Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T04:59:44.987Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T04:59:47.393Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T04:59:49.875Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T04:59:51.411Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T04:59:53.151Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T04:59:53.898Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T04:59:55.440Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T04:59:56.186Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T04:59:56.186Z] The best model improves the baseline by 14.52%. [2024-08-29T04:59:56.186Z] Movies recommended for you: [2024-08-29T04:59:56.186Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T04:59:56.186Z] There is no way to check that no silent failure occurred. [2024-08-29T04:59:56.186Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16334.142 ms) ====== [2024-08-29T04:59:56.186Z] ====== movie-lens (apache-spark) [default], iteration 10 started ====== [2024-08-29T04:59:56.186Z] GC before operation: completed in 81.179 ms, heap usage 209.113 MB -> 50.158 MB. [2024-08-29T04:59:58.586Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:00:00.987Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:00:03.385Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:00:05.781Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:00:07.323Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:00:08.863Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:00:10.405Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:00:11.946Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:00:11.946Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:00:11.946Z] The best model improves the baseline by 14.52%. [2024-08-29T05:00:11.946Z] Movies recommended for you: [2024-08-29T05:00:11.946Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:00:11.946Z] There is no way to check that no silent failure occurred. [2024-08-29T05:00:11.946Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16066.366 ms) ====== [2024-08-29T05:00:11.946Z] ====== movie-lens (apache-spark) [default], iteration 11 started ====== [2024-08-29T05:00:11.946Z] GC before operation: completed in 79.665 ms, heap usage 125.473 MB -> 49.864 MB. [2024-08-29T05:00:15.279Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:00:17.679Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:00:20.075Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:00:21.620Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:00:23.191Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:00:24.733Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:00:26.273Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:00:27.814Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:00:27.814Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:00:27.814Z] The best model improves the baseline by 14.52%. [2024-08-29T05:00:28.564Z] Movies recommended for you: [2024-08-29T05:00:28.564Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:00:28.564Z] There is no way to check that no silent failure occurred. [2024-08-29T05:00:28.564Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16063.751 ms) ====== [2024-08-29T05:00:28.564Z] ====== movie-lens (apache-spark) [default], iteration 12 started ====== [2024-08-29T05:00:28.565Z] GC before operation: completed in 80.325 ms, heap usage 208.196 MB -> 50.095 MB. [2024-08-29T05:00:30.960Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:00:33.355Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:00:35.756Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:00:38.158Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:00:39.700Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:00:40.957Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:00:42.500Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:00:44.044Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:00:44.790Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:00:44.790Z] The best model improves the baseline by 14.52%. [2024-08-29T05:00:44.790Z] Movies recommended for you: [2024-08-29T05:00:44.790Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:00:44.790Z] There is no way to check that no silent failure occurred. [2024-08-29T05:00:44.790Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16214.631 ms) ====== [2024-08-29T05:00:44.790Z] ====== movie-lens (apache-spark) [default], iteration 13 started ====== [2024-08-29T05:00:44.790Z] GC before operation: completed in 82.587 ms, heap usage 203.343 MB -> 50.233 MB. [2024-08-29T05:00:47.191Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:00:49.589Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:00:51.990Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:00:54.387Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:00:55.927Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:00:57.466Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:00:59.005Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:01:00.546Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:01:00.546Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:01:00.546Z] The best model improves the baseline by 14.52%. [2024-08-29T05:01:00.546Z] Movies recommended for you: [2024-08-29T05:01:00.546Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:01:00.546Z] There is no way to check that no silent failure occurred. [2024-08-29T05:01:00.546Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16106.105 ms) ====== [2024-08-29T05:01:00.546Z] ====== movie-lens (apache-spark) [default], iteration 14 started ====== [2024-08-29T05:01:00.546Z] GC before operation: completed in 80.656 ms, heap usage 134.771 MB -> 49.954 MB. [2024-08-29T05:01:03.867Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:01:06.273Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:01:08.672Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:01:11.074Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:01:11.822Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:01:13.364Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:01:14.907Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:01:16.448Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:01:16.448Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:01:16.448Z] The best model improves the baseline by 14.52%. [2024-08-29T05:01:16.448Z] Movies recommended for you: [2024-08-29T05:01:16.448Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:01:16.448Z] There is no way to check that no silent failure occurred. [2024-08-29T05:01:16.448Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (15990.580 ms) ====== [2024-08-29T05:01:16.448Z] ====== movie-lens (apache-spark) [default], iteration 15 started ====== [2024-08-29T05:01:17.192Z] GC before operation: completed in 83.462 ms, heap usage 203.064 MB -> 50.173 MB. [2024-08-29T05:01:19.591Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:01:21.988Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:01:25.041Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:01:27.441Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:01:28.188Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:01:29.732Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:01:31.275Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:01:32.818Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:01:33.568Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:01:33.568Z] The best model improves the baseline by 14.52%. [2024-08-29T05:01:33.568Z] Movies recommended for you: [2024-08-29T05:01:33.568Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:01:33.568Z] There is no way to check that no silent failure occurred. [2024-08-29T05:01:33.568Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16531.062 ms) ====== [2024-08-29T05:01:33.568Z] ====== movie-lens (apache-spark) [default], iteration 16 started ====== [2024-08-29T05:01:33.568Z] GC before operation: completed in 84.964 ms, heap usage 141.985 MB -> 50.163 MB. [2024-08-29T05:01:35.965Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:01:38.367Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:01:40.766Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:01:43.168Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:01:44.714Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:01:45.460Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:01:47.014Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:01:48.555Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:01:48.555Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:01:48.555Z] The best model improves the baseline by 14.52%. [2024-08-29T05:01:49.321Z] Movies recommended for you: [2024-08-29T05:01:49.321Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:01:49.321Z] There is no way to check that no silent failure occurred. [2024-08-29T05:01:49.321Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (15496.025 ms) ====== [2024-08-29T05:01:49.321Z] ====== movie-lens (apache-spark) [default], iteration 17 started ====== [2024-08-29T05:01:49.321Z] GC before operation: completed in 82.991 ms, heap usage 234.000 MB -> 50.121 MB. [2024-08-29T05:01:51.723Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:01:54.121Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:01:56.524Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:01:58.925Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:02:00.473Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:02:02.064Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:02:03.605Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:02:04.350Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:02:05.095Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:02:05.095Z] The best model improves the baseline by 14.52%. [2024-08-29T05:02:05.095Z] Movies recommended for you: [2024-08-29T05:02:05.095Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:02:05.095Z] There is no way to check that no silent failure occurred. [2024-08-29T05:02:05.095Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (15941.579 ms) ====== [2024-08-29T05:02:05.095Z] ====== movie-lens (apache-spark) [default], iteration 18 started ====== [2024-08-29T05:02:05.095Z] GC before operation: completed in 80.723 ms, heap usage 142.201 MB -> 50.082 MB. [2024-08-29T05:02:07.506Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:02:09.909Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:02:12.800Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:02:16.126Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:02:17.680Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:02:19.220Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:02:20.759Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:02:22.297Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:02:22.297Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:02:22.297Z] The best model improves the baseline by 14.52%. [2024-08-29T05:02:22.297Z] Movies recommended for you: [2024-08-29T05:02:22.297Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:02:22.297Z] There is no way to check that no silent failure occurred. [2024-08-29T05:02:22.297Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (17104.468 ms) ====== [2024-08-29T05:02:22.297Z] ====== movie-lens (apache-spark) [default], iteration 19 started ====== [2024-08-29T05:02:22.297Z] GC before operation: completed in 80.371 ms, heap usage 216.692 MB -> 50.377 MB. [2024-08-29T05:02:24.778Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20. [2024-08-29T05:02:28.166Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20. [2024-08-29T05:02:32.593Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20. [2024-08-29T05:02:35.938Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20. [2024-08-29T05:02:38.350Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10. [2024-08-29T05:02:40.749Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10. [2024-08-29T05:02:42.288Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10. [2024-08-29T05:02:43.835Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10. [2024-08-29T05:02:44.705Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611. [2024-08-29T05:02:44.705Z] The best model improves the baseline by 14.52%. [2024-08-29T05:02:44.705Z] Movies recommended for you: [2024-08-29T05:02:44.705Z] WARNING: This benchmark provides no result that can be validated. [2024-08-29T05:02:44.705Z] There is no way to check that no silent failure occurred. [2024-08-29T05:02:44.705Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (22252.531 ms) ====== [2024-08-29T05:02:44.705Z] ----------------------------------- [2024-08-29T05:02:44.705Z] renaissance-movie-lens_0_PASSED [2024-08-29T05:02:44.705Z] ----------------------------------- [2024-08-29T05:02:44.705Z] [2024-08-29T05:02:44.705Z] TEST TEARDOWN: [2024-08-29T05:02:44.705Z] Nothing to be done for teardown. [2024-08-29T05:02:44.705Z] renaissance-movie-lens_0 Finish Time: Thu Aug 29 05:02:44 2024 Epoch Time (ms): 1724907764508