renaissance-movie-lens_0
[2024-10-30T21:26:00.112Z] Running test renaissance-movie-lens_0 ...
[2024-10-30T21:26:00.112Z] ===============================================
[2024-10-30T21:26:00.112Z] renaissance-movie-lens_0 Start Time: Wed Oct 30 21:25:58 2024 Epoch Time (ms): 1730323558975
[2024-10-30T21:26:00.112Z] variation: NoOptions
[2024-10-30T21:26:00.112Z] JVM_OPTIONS:
[2024-10-30T21:26:00.112Z] { \
[2024-10-30T21:26:00.112Z] echo ""; echo "TEST SETUP:"; \
[2024-10-30T21:26:00.112Z] echo "Nothing to be done for setup."; \
[2024-10-30T21:26:00.112Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17303227144960/renaissance-movie-lens_0"; \
[2024-10-30T21:26:00.112Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17303227144960/renaissance-movie-lens_0"; \
[2024-10-30T21:26:00.112Z] echo ""; echo "TESTING:"; \
[2024-10-30T21:26:00.112Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_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_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17303227144960/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-30T21:26:00.112Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17303227144960/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-30T21:26:00.112Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-30T21:26:00.112Z] echo "Nothing to be done for teardown."; \
[2024-10-30T21:26:00.112Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17303227144960/TestTargetResult";
[2024-10-30T21:26:00.112Z]
[2024-10-30T21:26:00.112Z] TEST SETUP:
[2024-10-30T21:26:00.112Z] Nothing to be done for setup.
[2024-10-30T21:26:00.112Z]
[2024-10-30T21:26:00.112Z] TESTING:
[2024-10-30T21:26:02.027Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-30T21:26:03.959Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-10-30T21:26:07.076Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-30T21:26:07.076Z] Training: 60056, validation: 20285, test: 19854
[2024-10-30T21:26:07.076Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-30T21:26:07.076Z] GC before operation: completed in 53.789 ms, heap usage 112.230 MB -> 37.225 MB.
[2024-10-30T21:26:12.389Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:26:15.356Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:26:18.332Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:26:20.249Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:26:22.169Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:26:24.089Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:26:25.024Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:26:26.940Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:26:26.940Z] 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.
[2024-10-30T21:26:26.940Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:26:27.906Z] Movies recommended for you:
[2024-10-30T21:26:27.906Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:26:27.906Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:26:27.906Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20256.648 ms) ======
[2024-10-30T21:26:27.906Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-30T21:26:27.906Z] GC before operation: completed in 72.838 ms, heap usage 256.896 MB -> 54.399 MB.
[2024-10-30T21:26:31.028Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:26:31.961Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:26:34.929Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:26:36.854Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:26:38.774Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:26:39.712Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:26:41.642Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:26:42.577Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:26:42.577Z] 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.
[2024-10-30T21:26:42.577Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:26:42.577Z] Movies recommended for you:
[2024-10-30T21:26:42.577Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:26:42.577Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:26:42.577Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (15385.774 ms) ======
[2024-10-30T21:26:42.577Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-30T21:26:42.577Z] GC before operation: completed in 67.610 ms, heap usage 70.520 MB -> 52.379 MB.
[2024-10-30T21:26:45.539Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:26:47.459Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:26:49.380Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:26:51.308Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:26:53.227Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:26:54.162Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:26:56.102Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:26:57.223Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:26:57.223Z] 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.
[2024-10-30T21:26:57.223Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:26:57.223Z] Movies recommended for you:
[2024-10-30T21:26:57.223Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:26:57.223Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:26:57.223Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (14433.328 ms) ======
[2024-10-30T21:26:57.223Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-30T21:26:57.223Z] GC before operation: completed in 67.243 ms, heap usage 390.712 MB -> 50.305 MB.
[2024-10-30T21:26:59.152Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:27:01.080Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:27:04.041Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:27:06.111Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:27:07.052Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:27:08.979Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:27:09.912Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:27:10.847Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:27:11.780Z] 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.
[2024-10-30T21:27:11.780Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:27:11.780Z] Movies recommended for you:
[2024-10-30T21:27:11.780Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:27:11.780Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:27:11.780Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (13989.302 ms) ======
[2024-10-30T21:27:11.780Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-30T21:27:11.780Z] GC before operation: completed in 67.987 ms, heap usage 271.010 MB -> 50.505 MB.
[2024-10-30T21:27:13.711Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:27:15.640Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:27:17.558Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:27:20.531Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:27:21.466Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:27:22.400Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:27:24.318Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:27:25.250Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:27:25.250Z] 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.
[2024-10-30T21:27:25.250Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:27:25.250Z] Movies recommended for you:
[2024-10-30T21:27:25.250Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:27:25.250Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:27:25.250Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13939.964 ms) ======
[2024-10-30T21:27:25.250Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-30T21:27:25.250Z] GC before operation: completed in 64.549 ms, heap usage 103.814 MB -> 50.586 MB.
[2024-10-30T21:27:27.181Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:27:29.102Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:27:33.081Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:27:34.253Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:27:35.187Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:27:36.120Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:27:37.068Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:27:38.985Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:27:38.985Z] 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.
[2024-10-30T21:27:38.985Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:27:38.985Z] Movies recommended for you:
[2024-10-30T21:27:38.985Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:27:38.985Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:27:38.985Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13515.359 ms) ======
[2024-10-30T21:27:38.985Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-30T21:27:38.985Z] GC before operation: completed in 66.222 ms, heap usage 89.293 MB -> 50.424 MB.
[2024-10-30T21:27:40.903Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:27:42.818Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:27:44.757Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:27:46.676Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:27:48.594Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:27:49.527Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:27:50.460Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:27:51.393Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:27:52.326Z] 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.
[2024-10-30T21:27:52.326Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:27:52.326Z] Movies recommended for you:
[2024-10-30T21:27:52.326Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:27:52.326Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:27:52.326Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (12951.662 ms) ======
[2024-10-30T21:27:52.326Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-30T21:27:52.326Z] GC before operation: completed in 60.932 ms, heap usage 122.484 MB -> 50.704 MB.
[2024-10-30T21:27:54.242Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:27:56.164Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:27:58.081Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:28:00.008Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:28:00.952Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:28:01.890Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:28:03.810Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:28:04.759Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:28:04.759Z] 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.
[2024-10-30T21:28:04.759Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:28:04.759Z] Movies recommended for you:
[2024-10-30T21:28:04.759Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:28:04.759Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:28:04.759Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (12688.444 ms) ======
[2024-10-30T21:28:04.759Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-30T21:28:04.759Z] GC before operation: completed in 70.621 ms, heap usage 441.505 MB -> 54.606 MB.
[2024-10-30T21:28:06.677Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:28:08.592Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:28:10.511Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:28:12.429Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:28:13.364Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:28:14.298Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:28:16.215Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:28:17.147Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:28:17.147Z] 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.
[2024-10-30T21:28:17.147Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:28:17.147Z] Movies recommended for you:
[2024-10-30T21:28:17.147Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:28:17.147Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:28:17.147Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12479.147 ms) ======
[2024-10-30T21:28:17.147Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-30T21:28:17.147Z] GC before operation: completed in 69.408 ms, heap usage 136.404 MB -> 50.828 MB.
[2024-10-30T21:28:19.063Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:28:20.982Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:28:22.908Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:28:24.828Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:28:25.760Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:28:27.678Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:28:28.610Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:28:29.544Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:28:29.544Z] 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.
[2024-10-30T21:28:29.544Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:28:29.544Z] Movies recommended for you:
[2024-10-30T21:28:29.544Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:28:29.544Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:28:29.544Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12442.302 ms) ======
[2024-10-30T21:28:29.544Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-30T21:28:29.544Z] GC before operation: completed in 74.676 ms, heap usage 435.330 MB -> 54.529 MB.
[2024-10-30T21:28:31.465Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:28:34.354Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:28:35.288Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:28:37.211Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:28:38.150Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:28:40.068Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:28:41.001Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:28:41.933Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:28:41.933Z] 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.
[2024-10-30T21:28:41.933Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:28:41.933Z] Movies recommended for you:
[2024-10-30T21:28:41.934Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:28:41.934Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:28:41.934Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12292.683 ms) ======
[2024-10-30T21:28:41.934Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-30T21:28:41.934Z] GC before operation: completed in 69.422 ms, heap usage 199.874 MB -> 50.716 MB.
[2024-10-30T21:28:43.861Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:28:45.774Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:28:47.696Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:28:49.612Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:28:50.544Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:28:51.478Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:28:53.418Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:28:54.372Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:28:54.372Z] 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.
[2024-10-30T21:28:54.372Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:28:54.372Z] Movies recommended for you:
[2024-10-30T21:28:54.372Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:28:54.372Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:28:54.372Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12210.640 ms) ======
[2024-10-30T21:28:54.372Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-30T21:28:54.372Z] GC before operation: completed in 63.423 ms, heap usage 97.381 MB -> 50.890 MB.
[2024-10-30T21:28:56.303Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:28:58.222Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:29:00.153Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:29:02.074Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:29:03.011Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:29:05.144Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:29:06.075Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:29:07.010Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:29:07.010Z] 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.
[2024-10-30T21:29:07.010Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:29:07.945Z] Movies recommended for you:
[2024-10-30T21:29:07.945Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:29:07.945Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:29:07.945Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (12922.881 ms) ======
[2024-10-30T21:29:07.945Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-30T21:29:07.945Z] GC before operation: completed in 62.018 ms, heap usage 85.183 MB -> 50.876 MB.
[2024-10-30T21:29:09.856Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:29:11.768Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:29:13.682Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:29:14.613Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:29:16.530Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:29:17.463Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:29:18.394Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:29:19.329Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:29:20.264Z] 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.
[2024-10-30T21:29:20.264Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:29:20.264Z] Movies recommended for you:
[2024-10-30T21:29:20.264Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:29:20.264Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:29:20.264Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12432.866 ms) ======
[2024-10-30T21:29:20.264Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-30T21:29:20.264Z] GC before operation: completed in 67.020 ms, heap usage 117.013 MB -> 50.764 MB.
[2024-10-30T21:29:22.176Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:29:24.151Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:29:26.082Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:29:27.014Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:29:28.929Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:29:29.862Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:29:30.796Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:29:31.730Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:29:33.717Z] 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.
[2024-10-30T21:29:33.717Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:29:33.717Z] Movies recommended for you:
[2024-10-30T21:29:33.717Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:29:33.717Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:29:33.717Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12357.210 ms) ======
[2024-10-30T21:29:33.717Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-30T21:29:33.717Z] GC before operation: completed in 60.265 ms, heap usage 116.729 MB -> 50.948 MB.
[2024-10-30T21:29:34.650Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:29:36.593Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:29:38.516Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:29:40.440Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:29:41.376Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:29:42.308Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:29:43.251Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:29:45.176Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:29:45.176Z] 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.
[2024-10-30T21:29:45.176Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:29:45.176Z] Movies recommended for you:
[2024-10-30T21:29:45.176Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:29:45.176Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:29:45.176Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12579.113 ms) ======
[2024-10-30T21:29:45.176Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-30T21:29:45.176Z] GC before operation: completed in 59.705 ms, heap usage 84.753 MB -> 50.956 MB.
[2024-10-30T21:29:47.089Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:29:49.039Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:29:51.001Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:29:52.927Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:29:53.858Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:29:54.791Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:29:55.725Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:29:56.676Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:29:57.610Z] 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.
[2024-10-30T21:29:57.610Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:29:57.610Z] Movies recommended for you:
[2024-10-30T21:29:57.610Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:29:57.610Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:29:57.610Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12232.088 ms) ======
[2024-10-30T21:29:57.610Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-30T21:29:57.610Z] GC before operation: completed in 60.342 ms, heap usage 84.891 MB -> 50.754 MB.
[2024-10-30T21:29:59.529Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:30:01.449Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:30:03.363Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:30:05.296Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:30:06.237Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:30:07.174Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:30:09.113Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:30:10.044Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:30:10.044Z] 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.
[2024-10-30T21:30:10.044Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:30:10.044Z] Movies recommended for you:
[2024-10-30T21:30:10.044Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:30:10.044Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:30:10.044Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12611.985 ms) ======
[2024-10-30T21:30:10.044Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-30T21:30:10.044Z] GC before operation: completed in 70.683 ms, heap usage 115.219 MB -> 50.962 MB.
[2024-10-30T21:30:11.955Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:30:13.881Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:30:15.799Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:30:17.720Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:30:18.653Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:30:19.588Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:30:21.503Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:30:22.434Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:30:22.434Z] 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.
[2024-10-30T21:30:22.434Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:30:22.434Z] Movies recommended for you:
[2024-10-30T21:30:22.434Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:30:22.434Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:30:22.434Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12615.531 ms) ======
[2024-10-30T21:30:22.434Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-30T21:30:22.434Z] GC before operation: completed in 68.058 ms, heap usage 84.734 MB -> 51.009 MB.
[2024-10-30T21:30:24.348Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-30T21:30:26.263Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-30T21:30:28.185Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-30T21:30:30.099Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-30T21:30:32.020Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-30T21:30:33.903Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-30T21:30:33.903Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-30T21:30:34.836Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-30T21:30:34.836Z] 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.
[2024-10-30T21:30:34.836Z] The best model improves the baseline by 14.52%.
[2024-10-30T21:30:35.780Z] Movies recommended for you:
[2024-10-30T21:30:35.780Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-30T21:30:35.780Z] There is no way to check that no silent failure occurred.
[2024-10-30T21:30:35.780Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12572.408 ms) ======
[2024-10-30T21:30:35.780Z] -----------------------------------
[2024-10-30T21:30:35.780Z] renaissance-movie-lens_0_PASSED
[2024-10-30T21:30:35.780Z] -----------------------------------
[2024-10-30T21:30:35.780Z]
[2024-10-30T21:30:35.780Z] TEST TEARDOWN:
[2024-10-30T21:30:35.780Z] Nothing to be done for teardown.
[2024-10-30T21:30:35.780Z] renaissance-movie-lens_0 Finish Time: Wed Oct 30 21:30:35 2024 Epoch Time (ms): 1730323835435