renaissance-movie-lens_0
[2024-12-06T21:53:14.647Z] Running test renaissance-movie-lens_0 ...
[2024-12-06T21:53:14.647Z] ===============================================
[2024-12-06T21:53:14.647Z] renaissance-movie-lens_0 Start Time: Fri Dec 6 21:53:13 2024 Epoch Time (ms): 1733521993400
[2024-12-06T21:53:14.647Z] variation: NoOptions
[2024-12-06T21:53:14.647Z] JVM_OPTIONS:
[2024-12-06T21:53:14.647Z] { \
[2024-12-06T21:53:14.647Z] echo ""; echo "TEST SETUP:"; \
[2024-12-06T21:53:14.647Z] echo "Nothing to be done for setup."; \
[2024-12-06T21:53:14.647Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17335211171672/renaissance-movie-lens_0"; \
[2024-12-06T21:53:14.647Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17335211171672/renaissance-movie-lens_0"; \
[2024-12-06T21:53:14.647Z] echo ""; echo "TESTING:"; \
[2024-12-06T21:53:14.647Z] "/home/jenkins/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17335211171672/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-06T21:53:14.647Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17335211171672/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-06T21:53:14.647Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-06T21:53:14.647Z] echo "Nothing to be done for teardown."; \
[2024-12-06T21:53:14.647Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64le_linux/aqa-tests/TKG/../TKG/output_17335211171672/TestTargetResult";
[2024-12-06T21:53:14.647Z]
[2024-12-06T21:53:14.647Z] TEST SETUP:
[2024-12-06T21:53:14.647Z] Nothing to be done for setup.
[2024-12-06T21:53:14.647Z]
[2024-12-06T21:53:14.647Z] TESTING:
[2024-12-06T21:53:17.652Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-06T21:53:19.608Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2024-12-06T21:53:22.614Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-06T21:53:22.614Z] Training: 60056, validation: 20285, test: 19854
[2024-12-06T21:53:22.614Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-06T21:53:22.614Z] GC before operation: completed in 65.209 ms, heap usage 85.632 MB -> 36.419 MB.
[2024-12-06T21:53:27.999Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:53:32.142Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:53:36.025Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:53:36.972Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:53:38.931Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:53:41.053Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:53:42.000Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:53:43.958Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:53:43.958Z] 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.
[2024-12-06T21:53:43.958Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:53:43.958Z] Movies recommended for you:
[2024-12-06T21:53:43.958Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:53:43.958Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:53:43.958Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (21640.097 ms) ======
[2024-12-06T21:53:43.959Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-06T21:53:44.907Z] GC before operation: completed in 101.997 ms, heap usage 134.066 MB -> 49.028 MB.
[2024-12-06T21:53:46.855Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:53:49.858Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:53:51.806Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:53:54.851Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:53:55.799Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:53:57.753Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:53:58.709Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:54:00.655Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:54:00.655Z] 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.
[2024-12-06T21:54:00.655Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:54:00.655Z] Movies recommended for you:
[2024-12-06T21:54:00.655Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:54:00.655Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:54:00.655Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16581.772 ms) ======
[2024-12-06T21:54:00.655Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-06T21:54:01.603Z] GC before operation: completed in 105.907 ms, heap usage 325.490 MB -> 49.198 MB.
[2024-12-06T21:54:03.564Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:54:06.588Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:54:08.537Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:54:10.499Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:54:12.445Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:54:14.390Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:54:15.340Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:54:17.286Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:54:17.286Z] 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.
[2024-12-06T21:54:17.286Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:54:17.286Z] Movies recommended for you:
[2024-12-06T21:54:17.286Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:54:17.286Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:54:17.286Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16171.063 ms) ======
[2024-12-06T21:54:17.286Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-06T21:54:17.286Z] GC before operation: completed in 87.489 ms, heap usage 114.842 MB -> 49.322 MB.
[2024-12-06T21:54:20.032Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:54:21.992Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:54:23.942Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:54:26.955Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:54:27.904Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:54:29.856Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:54:30.805Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:54:31.753Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:54:32.702Z] 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.
[2024-12-06T21:54:32.702Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:54:32.702Z] Movies recommended for you:
[2024-12-06T21:54:32.702Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:54:32.702Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:54:32.702Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (15196.968 ms) ======
[2024-12-06T21:54:32.702Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-06T21:54:32.702Z] GC before operation: completed in 94.979 ms, heap usage 313.772 MB -> 49.824 MB.
[2024-12-06T21:54:34.649Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:54:37.678Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:54:39.626Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:54:42.674Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:54:43.620Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:54:44.572Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:54:46.522Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:54:47.474Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:54:48.421Z] 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.
[2024-12-06T21:54:48.421Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:54:48.421Z] Movies recommended for you:
[2024-12-06T21:54:48.421Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:54:48.421Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:54:48.421Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (15549.789 ms) ======
[2024-12-06T21:54:48.421Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-06T21:54:48.421Z] GC before operation: completed in 88.439 ms, heap usage 177.912 MB -> 49.846 MB.
[2024-12-06T21:54:50.386Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:54:52.357Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:54:55.399Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:54:57.499Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:54:58.447Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:54:59.395Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:55:01.347Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:55:02.294Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:55:02.294Z] 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.
[2024-12-06T21:55:02.294Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:55:03.241Z] Movies recommended for you:
[2024-12-06T21:55:03.241Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:55:03.241Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:55:03.241Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (14498.213 ms) ======
[2024-12-06T21:55:03.241Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-06T21:55:03.241Z] GC before operation: completed in 99.193 ms, heap usage 201.449 MB -> 49.818 MB.
[2024-12-06T21:55:05.199Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:55:07.143Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:55:10.146Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:55:12.091Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:55:13.041Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:55:14.003Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:55:17.002Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:55:17.002Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:55:17.002Z] 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.
[2024-12-06T21:55:17.002Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:55:17.002Z] Movies recommended for you:
[2024-12-06T21:55:17.002Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:55:17.002Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:55:17.002Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (14322.749 ms) ======
[2024-12-06T21:55:17.002Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-06T21:55:17.002Z] GC before operation: completed in 78.804 ms, heap usage 115.504 MB -> 49.946 MB.
[2024-12-06T21:55:20.053Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:55:22.000Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:55:23.943Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:55:25.890Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:55:26.847Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:55:27.833Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:55:29.784Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:55:30.732Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:55:30.732Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2024-12-06T21:55:30.732Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:55:30.732Z] Movies recommended for you:
[2024-12-06T21:55:30.732Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:55:30.732Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:55:30.732Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (13853.955 ms) ======
[2024-12-06T21:55:30.732Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-06T21:55:31.680Z] GC before operation: completed in 79.924 ms, heap usage 170.084 MB -> 50.240 MB.
[2024-12-06T21:55:33.628Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:55:35.580Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:55:37.546Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:55:39.493Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:55:41.438Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:55:42.388Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:55:43.337Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:55:45.284Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:55:45.284Z] 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.
[2024-12-06T21:55:45.284Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:55:45.284Z] Movies recommended for you:
[2024-12-06T21:55:45.284Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:55:45.284Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:55:45.284Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (13948.887 ms) ======
[2024-12-06T21:55:45.284Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-06T21:55:45.284Z] GC before operation: completed in 79.811 ms, heap usage 90.454 MB -> 50.020 MB.
[2024-12-06T21:55:47.235Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:55:50.242Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:55:52.191Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:55:54.138Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:55:55.092Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:55:56.041Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:56:06.921Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:56:06.921Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:56:06.921Z] 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.
[2024-12-06T21:56:06.921Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:56:06.921Z] Movies recommended for you:
[2024-12-06T21:56:06.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:56:06.921Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:56:06.921Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (13912.700 ms) ======
[2024-12-06T21:56:06.921Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-06T21:56:06.921Z] GC before operation: completed in 87.303 ms, heap usage 351.133 MB -> 50.311 MB.
[2024-12-06T21:56:06.921Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:56:06.921Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:56:06.921Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:56:07.876Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:56:08.824Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:56:09.789Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:56:11.734Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:56:12.684Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:56:12.684Z] 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.
[2024-12-06T21:56:12.684Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:56:12.684Z] Movies recommended for you:
[2024-12-06T21:56:12.684Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:56:12.684Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:56:12.684Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (13588.211 ms) ======
[2024-12-06T21:56:12.684Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-06T21:56:12.684Z] GC before operation: completed in 94.248 ms, heap usage 245.077 MB -> 49.997 MB.
[2024-12-06T21:56:14.642Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:56:16.592Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:56:19.410Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:56:21.358Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:56:22.305Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:56:23.261Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:56:25.210Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:56:26.158Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:56:26.158Z] 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.
[2024-12-06T21:56:26.158Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:56:26.158Z] Movies recommended for you:
[2024-12-06T21:56:26.158Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:56:26.158Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:56:26.158Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (13427.633 ms) ======
[2024-12-06T21:56:26.158Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-06T21:56:26.158Z] GC before operation: completed in 78.525 ms, heap usage 190.658 MB -> 50.146 MB.
[2024-12-06T21:56:29.169Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:56:31.119Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:56:33.160Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:56:35.112Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:56:36.059Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:56:37.010Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:56:38.968Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:56:39.921Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:56:40.904Z] 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.
[2024-12-06T21:56:40.904Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:56:40.904Z] Movies recommended for you:
[2024-12-06T21:56:40.904Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:56:40.904Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:56:40.904Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (14147.697 ms) ======
[2024-12-06T21:56:40.904Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-06T21:56:40.904Z] GC before operation: completed in 79.598 ms, heap usage 129.981 MB -> 50.176 MB.
[2024-12-06T21:56:42.930Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:56:44.884Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:56:46.872Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:56:48.846Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:56:49.796Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:56:51.777Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:56:52.725Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:56:53.675Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:56:54.629Z] 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.
[2024-12-06T21:56:54.629Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:56:54.629Z] Movies recommended for you:
[2024-12-06T21:56:54.629Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:56:54.629Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:56:54.629Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (13678.212 ms) ======
[2024-12-06T21:56:54.629Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-06T21:56:54.629Z] GC before operation: completed in 82.634 ms, heap usage 262.143 MB -> 50.099 MB.
[2024-12-06T21:56:56.399Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:56:58.351Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:57:00.295Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:57:02.240Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:57:04.188Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:57:05.138Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:57:06.087Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:57:08.044Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:57:08.044Z] 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.
[2024-12-06T21:57:08.044Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:57:08.044Z] Movies recommended for you:
[2024-12-06T21:57:08.044Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:57:08.044Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:57:08.044Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (13634.874 ms) ======
[2024-12-06T21:57:08.044Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-06T21:57:08.044Z] GC before operation: completed in 80.376 ms, heap usage 124.229 MB -> 50.073 MB.
[2024-12-06T21:57:09.998Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:57:11.964Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:57:13.919Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:57:15.864Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:57:17.811Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:57:18.758Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:57:19.704Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:57:21.653Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:57:21.653Z] 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.
[2024-12-06T21:57:21.653Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:57:21.653Z] Movies recommended for you:
[2024-12-06T21:57:21.653Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:57:21.653Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:57:21.653Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (13526.403 ms) ======
[2024-12-06T21:57:21.653Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-06T21:57:21.653Z] GC before operation: completed in 83.728 ms, heap usage 105.106 MB -> 50.175 MB.
[2024-12-06T21:57:23.605Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:57:25.550Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:57:27.495Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:57:29.439Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:57:31.509Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:57:32.455Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:57:33.440Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:57:35.403Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:57:35.403Z] 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.
[2024-12-06T21:57:35.403Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:57:35.403Z] Movies recommended for you:
[2024-12-06T21:57:35.403Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:57:35.403Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:57:35.403Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (13529.222 ms) ======
[2024-12-06T21:57:35.403Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-06T21:57:35.403Z] GC before operation: completed in 89.099 ms, heap usage 121.869 MB -> 50.056 MB.
[2024-12-06T21:57:37.350Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:57:40.525Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:57:41.477Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:57:43.421Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:57:44.371Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:57:46.316Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:57:47.264Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:57:48.210Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:57:49.162Z] 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.
[2024-12-06T21:57:49.162Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:57:49.162Z] Movies recommended for you:
[2024-12-06T21:57:49.162Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:57:49.162Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:57:49.162Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (13599.550 ms) ======
[2024-12-06T21:57:49.162Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-06T21:57:49.162Z] GC before operation: completed in 82.280 ms, heap usage 253.553 MB -> 50.210 MB.
[2024-12-06T21:57:51.137Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:57:53.084Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:57:55.033Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:57:57.137Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:57:59.091Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:58:00.039Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:58:00.992Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:58:02.937Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:58:02.937Z] 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.
[2024-12-06T21:58:02.937Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:58:02.937Z] Movies recommended for you:
[2024-12-06T21:58:02.937Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:58:02.937Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:58:02.937Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (13939.121 ms) ======
[2024-12-06T21:58:02.938Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-06T21:58:02.938Z] GC before operation: completed in 87.830 ms, heap usage 180.933 MB -> 50.357 MB.
[2024-12-06T21:58:05.940Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-06T21:58:07.888Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-06T21:58:09.833Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-06T21:58:11.779Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-06T21:58:12.726Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-06T21:58:14.674Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-06T21:58:15.622Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-06T21:58:16.575Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-06T21:58:17.523Z] 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.
[2024-12-06T21:58:17.523Z] The best model improves the baseline by 14.52%.
[2024-12-06T21:58:17.523Z] Movies recommended for you:
[2024-12-06T21:58:17.523Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-06T21:58:17.523Z] There is no way to check that no silent failure occurred.
[2024-12-06T21:58:17.523Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (14186.689 ms) ======
[2024-12-06T21:58:17.523Z] -----------------------------------
[2024-12-06T21:58:17.523Z] renaissance-movie-lens_0_PASSED
[2024-12-06T21:58:17.523Z] -----------------------------------
[2024-12-06T21:58:17.523Z]
[2024-12-06T21:58:17.523Z] TEST TEARDOWN:
[2024-12-06T21:58:17.523Z] Nothing to be done for teardown.
[2024-12-06T21:58:17.523Z] renaissance-movie-lens_0 Finish Time: Fri Dec 6 21:58:17 2024 Epoch Time (ms): 1733522297305